Cross-national investigation of shopping satisfaction antecedents

Introduction

According to 2014 Euromonitor statistics (http://www.euromonitor.com), Antalya is at the eleventh position on the most popular destinations list in terms of tourist arrivals in the world. Approximately 40% of the foreign tourists visiting Antalya prefer to accommodate in the Side region that is home to many historical places like the antique city of Seleukeia, Aspendos Amphitheatre, Temple of Apollo and the historic town of Side. In the Side region, more than 500 accommodation facilities (including 109 five-star hotels) and 45 Blue Flag beaches are located (Turkey Environmental Education Foundation, 2015). Similar to many other destinations, tourism income is the major contributor to Side’s economy. In addition, tourist shopping has a considerable share in the total tourism revenues. Especially, the historic town of Side, as a shopping habitat that hosts more than 600 retail stores, offers a traditional shopping experience to the tourists visiting the site. Therefore, understanding tourist shopping behaviour is important for the destination authorities.

Despite tourist shopping’s importance, there is inadequate knowledge about tourist shopping behaviour (Lloyd et al., 2011). Notably, there is a need to understand tourist shopping satisfaction and how it is determined by shopping attributes because satisfaction is an important antecedent of both repurchase intentions and recommendations, which in turn increase total tourism revenues. Although some research has been done to investigate the relationship between shopping attributes and tourist shopping satisfaction in different destinations (e.g. Hong Kong, Dubai and Taipei), to the best of the authors’ knowledge, no studies have been carried out in a historic town.

The focus of this study is to examine tourist shopping behaviour in a sample from the historic town of Side. More specifically, the aims of this research are (1) to identify the main shopping attributes; (2) to compare the perceptions of German, Dutch and British tourists on shopping attributes, which are the top three markets of the Side destination and (3) to investigate the effects of shopping attributes on shopping satisfaction for German, Dutch and British tourists by using asymmetric impact performance analysis (AIPA) (Caber et al., 2013). AIPA was selected by the authors since it is the most appropriate and visually appealing method. AIPA considers the asymmetric relationships between attributes and customer satisfaction, which were revealed by most of the previous research (e.g. Fuchs and Weiermair, 2004; Matzler et al., 2004).

Tourist shopping

Butler (1991) notes that the relationship between shopping and tourism can be divided into two categories: (1) shopping tourism and (2) tourist shopping. These are two concepts that are related but different from each other. In the case of shopping tourism, shopping is the main motivator for the tourists’ journey (Rabbiosi, 2011). Nowadays, tour operators organize shopping tours towards the destinations that are known as shopping cities like Paris, Hong Kong and Dubai. Shopping is one of the main motivations for the tourists who are keen to participate in such shopping tours (Jansen-Verbeke, 1991; Moscardo, 2004). In contrast, tourist shopping, which often occurs as an unplanned travel experience (Wong and Wan, 2013), refers to going shopping during a trip. For tourist shopping, the main travel motivation may be different from shopping, such as relaxation or escaping. While shopping is not the main motivation, tourists are motivated to shop for other reasons such as collecting tangible evidence of their experiences (Tosun et al., 2007) or obtaining unique products that are unavailable in their homelands (LeHew and Wesley, 2007).

Moreover, tourist shopping is an unplanned travel experience that can be influenced by some impulsive factors such as sales staff and uniqueness of products (Wong and Wan, 2013). Furthermore, shopping is a hedonistic activity for tourists (Timothy & Butler, 1995) since shopping provides an opportunity to interact with local people and experience the local lifestyle of the host country (Choi et al., 2016; Hsieh and Chang, 2006). In addition, a good shopping experience is assumed to build a favourable destination image (Tosun et al., 2007) and provide a competitive advantage against other destinations (Timothy, 2005). Thus, it is important to establish distinctive shopping experiences for tourists.

For most of the researchers, tourism and shopping are inseparable (e.g. Hsieh and Chang, 2006). Similarly, for many tourists, no tourism or travel experience is complete without having spent time on shopping (Lloyd et al., 2011). Moreover, the shopping expenditures of tourists might be higher than other activities like lodging, dining or entertainment (Ryan, 1995; Turner and Reisinger, 2001), which shows the economic importance of tourist shopping for the destinations. Thus, recently, some destinations (e.g. Dubai, Singapore, Istanbul) have started to organize shopping fests, which also contribute to increase the occupancy rates of the accommodation facilities.

To better understand the tourists’ shopping behaviour, a number of studies were conducted by academics in different settings, such as night markets (e.g. Hsieh and Chang, 2006), regional shopping malls (e.g. LeHew and Wesley, 2007) and popular tourist shopping destinations (e.g. Hong Kong, Taipei). However, Choi et al.’s (2016) literature review showed that most of the studies were performed in the United States (41.3%) and Asian countries (30.7%). Thus, a greater number of studies are needed about tourist shopping in different destinations, enabling the academicians to determine the main shopping attributes and their effects on satisfaction.

Shopping attributes and their effects on tourist shopping satisfaction

Early research investigating the antecedents of tourist shopping satisfaction was focused on the product value (Wong and Wan, 2013), since it fulfils the customer needs (Zeithaml, 1988). However, shopping experience is an amalgam of perceptions of both products and services (Wong and Law, 2003). In other words, it is a combined satisfaction, which is formed by all elements of the shopping experience, such as product, staff, store and even the shopping habitat.

Researchers have identified a number of shopping attributes in previous studies. For example, Heung and Cheng (2000), who compared tourist expectations and perceptions about shopping in Hong Kong, identified four shopping attributes: namely; ‘tangible quality’, ‘staff service quality’, ‘product value’ and ‘product reliability’. Staff service quality was the most important determinant of tourist shopping satisfaction, followed by product value and product reliability. Another study, which was also conducted in Hong Kong, identified that ‘service quality’, ‘quality of goods’, ‘variety of goods’ and ‘price of goods’ were the main shopping attributes (Wong and Law, 2003). The authors of that study revealed that expectation and perceived satisfaction from shopping attributes differed according to country of residence. Lin and Lin (2006) performed a survey in Taiwan to investigate Chinese tourists’ shopping perceptions by using expectancy disconfirmation theory. They identified ‘physical attraction’, staff service quality, ‘product features’, ‘unique and convenience’ and ‘discount and display’ as the underlying shopping attributes. Recently, Wong and Wan (2013) developed a scale consisting of four dimensions (‘service product and environment’, staff service quality, ‘merchandise value’ and ‘service differentiation’) for measuring tourist shopping satisfaction. Although, all shopping attributes were positive predictors of tourist shopping satisfaction, the effect of service product and environment was higher than the other attributes. Contrary to previous research, they noted that service differentiation was important for shopping satisfaction. More recently, Albayrak et al. (2016), who investigated the effect of shopping attributes on shopping value and behavioural intentions, identified ‘staff’, ‘products’ and ‘tangibles’ as the main determinants of shopping value.

To conclude, ‘product’, ‘staff’ and ‘tangibles’ were identified as the common shopping attributes by most of the researchers. While product, as a shopping attribute, consists of features related to price, quality and availability; language ability, attitude and product knowledge are the components of staff attribute. Tangibles, the third of the shopping attributes, include both store and location-related features. In addition, tourist shopping satisfaction has been found to be influenced by shopping attributes. In these studies, researchers mostly followed expectancy disconfirmation theory by assuming symmetric relationships between shopping attributes and shopping satisfaction when identifying the common features of shopping and their relationships with shopping satisfaction. Despite the aforementioned literature, as noted by Wong and Wan (2013), the antecedents of tourist shopping satisfaction have received inadequate research attention from the academics.

Three-factor theory of customer satisfaction and AIPA

According to the three-factor theory of customer satisfaction, which was developed by Kano et al. (1984), product attributes are categorized into three groups as basic, performance and excitement factors, depending on their varying effects on customer satisfaction (Busacca and Padula, 2005; Füller and Matzler, 2008; Kano et al., 1984). The effects of basic factors on customer satisfaction are high when their performance is low. However, high performance of these factors has a lower impact on customer satisfaction. In contrast, excitement factors are attributes that contribute to customer satisfaction when their performance is high, but they do not lead customer dissatisfaction when their performance is low. Thus, the effects of basic and excitement factors on customer satisfaction are asymmetric rather than symmetric. In summary, basic factors are prerequisites of the products, while excitement factors surprise the customers and lead to “delight” (Matzler and Renzl, 2007). In contrast to basic and excitement factors, performance factors have a symmetric effect on customer satisfaction. In other words, when their performance increases, customers will be satisfied or vice versa. This triple distinction is known as the three-factor theory of customer satisfaction (Figure 1).

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Figure 1.

The three-factor theory of customer satisfaction.

Recently, some analytical frameworks that visually reflect managerial recommendations by taking into account the asymmetric relationships between product attributes and customer satisfaction were proposed. One of them is AIPA, which categorizes attributes by both their asymmetric effects on customer satisfaction and their performance levels (Caber et al., 2013). Thus, AIPA visually reflects which attributes should be improved first to increase customer satisfaction. AIPA, which is a simplified version of impact asymmetry analysis (Mikulić and Prebezac, 2008), was already used in different research areas, such as the measurement of extranet quality and prioritization of hotel attributes. More recently, Albayrak and Caber (2015) compared AIPA and importance performance analysis (IPA) and stated that AIPA outperformed IPA as a result.

Methodology

The questionnaire includes 17 items that measure the respondents’ satisfaction with shopping attributes and 1 item about their overall satisfaction from shopping. Items were adapted from the works of Tosun et al. (2007) and Heung and Cheng (2000). Items were rated on a 5-point scale ranging from 1 = Strongly disagree to 5 = Strongly agree. In addition, nine questions about the demographic and trip-related characteristics of the respondents were included in the survey. Target samples of the survey were German, British and Dutch tourists visiting the historic town of Side, since they are the top three markets for this destination. Thus, the questionnaire, which was prepared in English, was translated into German and Dutch by a professional translation company and was controlled by one native speaker for each. A pilot test was performed to verify the comprehensibility and translation equivalence of the questionnaires using the participation of 60 tourists. The pilot test showed that all items were easily understood by the participants.

In the final survey, a desk was set up at the centre of the city. The survey was conducted on various days of the week, between 1 July and 30 August 2012. Two university students gave information about the survey and its aims to the volunteer tourists who already shopped and were members of the targeted nationalities. At the end of this period, a total of 614 fully answered questionnaires were collected.

Results

The demographic characteristics of the German, Dutch and the British participants are shown in Table 1. Overall, the sample participants were middle aged. While 49.6% of the German respondents were aged between 31 and 50 years, this percentage was 46.7 and 44.2% for Dutch and the British respondents, respectively. Regarding occupation, half of the German participants were company employees. This percentage was 38.3 and 35.4% for the Dutch and British respondents, respectively.

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Table 1.

Demographic characteristics of the respondents by their nationalities.

Variables		German		Dutch		British
		N	%	N	%	N	%
Gender	Male	105	43.4	79	47.3	86	44.8
	Female	137	56.6	88	52.7	106	55.2
Marital status	Single	141	58.3	96	57.5	113	58.9
	Married	101	41.7	71	42.5	79	41.1
Age (years)	Below 21	41	16.9	23	13.8	37	19.3
	Between 21 and 30	34	14.0	32	19.2	38	19.8
	Between 31 and 40	53	21.9	42	25.1	31	16.1
	Between 41 and 50	67	27.7	36	21.6	54	28.1
	Between 51 and 60	36	14.9	19	11.4	14	7.3
	>61	11	4.5	15	9.0	18	9.4
Occupation	Retired	12	5	9	5.4	16	8.3
	Government sector	26	10.7	24	14.4	17	8.9
	Student	41	16.9	26	15.6	39	20.3
	Company employee	121	50	64	38.3	68	35.4
	Housewife	10	4.1	9	5.4	9	4.7
	Business owner	10	4.1	15	9	15	7.8
	Other	22	9.1	20	12	28	14.5
Education level	Secondary school	19	7.9	7	4.2	3	1.6
	High school	77	31.8	61	36.5	79	41.1
	University	88	36.4	81	48.5	81	42.2
	Master degree	4	1.7	11	6.6	15	7.8
	Other	53	21.9	7	4.2	10	5.2
Number of children	Without children	117	48.3	63	37.7	94	49.0
	With children	125	51.7	104	62.3	98	51.0
Duration of holiday (days)	Between 1 and 7	73	30.2	66	40.0	70	36.5
	Between 8 and 13	77	31.8	50	30.3	40	20.8
	≥14	92	38.0	49	29.7	82	42.7
Times of visit to historic town of Side	First time	120	49.6	105	62.9	102	53.1
	Second or more times	122	50.4	62	37.1	90	46.9

In general, participants were highly educated. Approximately, 36, 48 and 42% of respondents had a university level of education in the German, Dutch and the British samples, respectively. Although half of the German participants were visiting the historic town of Side for the second or more times, this percentage was 46.9% for the British respondents and only 37.1% for Dutch respondents. Regarding the duration of their holiday, 42.7% of the British respondents preferred 14 days and more, while this ratio was 38% for German and 29.7% for Dutch respondents, respectively.

To identify the underlying dimensions of shopping attributes, an exploratory factor analysis with varimax rotation was used, which resulted in three factors explaining 64.20% of the total variance. Two items (‘variety of products’ and ‘authenticity’) having low communalities (below 0.50) were excluded from the analysis. The extracted factors were named according to the items that they captured as tangibles, staff and products. The internal consistencies of the factors were high, as Cronbach’s α scores ranged from 0.833 to 0.881 (Table 2).

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Table 2.

Dimensions of the shopping attributes.

		Factor loadings	Means	Variance explained (%)	Cronbach’s α
Tangibles			4.08	22.76	0.845
T1	Opening hours	0.807	4.26
T2	Neatness and cleanliness	0.647	3.94
T3	Lighting and ambiance	0.662	4.05
T4	Accessibility	0.774	4.05
T5	Choice of payment methods	0.681	3.99
T6	Location	0.702	4.18
Staff			3.63	18.22	0.833
S1	Language ability of sales staff	0.715	3.81
S2	Attitude of sales staff	0.796	3.49
S3	Efficiency of sales staff	0.726	3.63
S4	Product knowledge of sales staff	0.691	3.55
Products			3.43	23.20	0.881
P1	Price of product	0.685	3.34
P2	Value for the money	0.682	3.46
P3	Availability of product	0.782	3.59
P4	Product reliability	0.861	3.36
P5	Product quality	0.828	3.36

Note: Total variance explained 64.20%; KMO = 0.908; Bartlett’s test of sphericity = 4885.9 (p = .000). KMO: The Kaiser–Meyer–Olkin measure of sampling adequacy.

Next, an analysis of variance was conducted to identify the differences among nationalities, considering their levels of satisfaction for each of the shopping attributes. As seen in Table 3, there were significant differences in the perceptions of shopping attributes among nationalities, except for the tangibles attribute. Tukey’s post hoc test was used to assess which nationality showed the significant difference. The results revealed that the British respondents had the highest mean score and higher satisfaction level for the staff attribute compared to German and Dutch respondents. Regarding the products attribute, German participants’ satisfaction was lower than Dutch and the British participants.

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Table 3.

Comparison of the shopping attributes’ perceptions.

	German (G)	Dutch (D)	British (B)	p	Differences
	Mean (SD)	Mean (SD)	Mean (SD)
Tangibles	4.10 (0.69)	4.05 (0.62)	4.08 (0.70)	0.786
Products	3.28 (0.78)	3.49 (0.77)	3.56 (0.79)	0.001*	G < D; G < B
Staff	3.58 (0.84)	3.46 (0.89)	3.86 (0.84)	0.000*	B > G; B > D

SD: standard deviation.

*p < 0.01.

Before conducting the AIPA, separate regression analyses for each nationality were performed to determine the relationships between shopping attributes and shopping satisfaction. Results of the analyses revealed that shopping satisfaction was significantly related to shopping attributes (Table 4). For the British respondents, 48.1% of the variance in shopping satisfaction was explained by shopping attributes, while this percentage was 33.9 and 29.5 for German and Dutch respondents, respectively. This suggests that shopping attributes have a strong explanatory power over shopping satisfaction. The most important determinant of shopping satisfaction for the British and German respondents was the products attribute, while it was staff for Dutch respondents.

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Table 4.

Comparison of the shopping attributes’ effects on shopping satisfaction.

	German	Dutch	British
Constant	3.797	3.899	3.807
Tangibles	0.204	0.166	0.271
Products	0.354	0.236	0.375
Staff	0.330	0.308	0.331
F	42.210	23.584	59.365
R 2	0.339	0.295	0.481

Note: All regression coefficients are significant at 0.01 level.

Impact asymmetry (IA) and the performance scores of attributes should be used for constructing the AIPA matrix. While performance scores are customer perceptions on subject attributes, the following steps were used to obtain IA scores as Mikulić and Prebezac (2008) suggested. First, a penalty reward contrast analysis was conducted. For this, the shopping attributes factor scores, which were obtained by factor analysis, were recoded as follows: the lowest and highest tertiles were defined to form two sets of dummy variables to quantify the effects of low and high performances. By using overall satisfaction as the dependent variables and the dummy variables as independent variables, a multiple regression analysis was performed to obtain penalty and reward indices. Second, attributes’ ranges of impact on customer satisfaction (RIOCS) were calculated by summing the absolute values of these penalty and reward indices. Third, reward and penalty indices were separately divided by RIOCS to obtain the satisfaction-generating potential (SGP) and dissatisfaction-generating potential (DGP) of each attribute. Finally, the IA value for each attribute was obtained by subtracting the absolute value of DGP from the absolute value of SGP. Table 5 shows the penalty and reward indices and IA of each attribute belonging to German, Dutch and the British respondents.

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Table 5.

The asymmetric effects of destination attributes on shopping satisfaction.

	Dummy variable regression coefficients
	Reward indices	Penalty indices	RIOCS	SGP	DGP	IA
German
Products	0.466*	−0.333*	0.799	0.583	−0.417	0.166
Tangibles	0.069	−0.438*	0.507	0.136	−0.864	−0.728
Staff	0.360*	−0.302**	0.662	0.544	−0.456	0.088
Dutch
Products	0.278**	−0.232	0.510	0.545	−0.455	0.090
Tangibles	0.286**	−0.156	0.442	0.647	−0.353	0.294
Staff	0.058	−0.497*	0.555	0.105	−0.895	−0.791
British
Products	0.346*	−0.463*	0.809	0.428	−0.572	−0.145
Tangibles	0.309**	−0.356*	0.665	0.465	−0.535	−0.071
Staff	0.259**	−0.425*	0.684	0.379	−0.621	−0.243

RIOCS: ranges of impact on customer satisfaction; SGP: satisfaction-generating potential; DGP: dissatisfaction-generating potential; IA: impact asymmetry.

Note: R ² = 0.321, 0.238 and 0.462 for German, Dutch and British participants, respectively. Note that regression coefficients are unstandardized.

*p < 0.01; **p < 0.05.

By using the attribute performance value as the x coordinate and IA value as the y coordinate, each attribute was positioned on the AIPA matrix. As shown in Figure 2, the AIPA matrix was divided into two parts by using the grand mean of all attribute performances. Moreover, the −0.1 and +0.1 points of the IA were used as the cutoff points to divide the matrix into three parts.

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Figure 2.

AIPA matrix for German, Dutch and the British respondents.

The AIPA results (Figure 2) indicate that the role of shopping attributes for shopping satisfaction considerably differs according to nationality. For example, the products attribute is an excitement factor with high performance for German respondents. This means that the products attribute will ensure high shopping satisfaction for German respondents. In contrast, it is a basic factor with high performance for the British respondents. Thus, any improvement in this attribute’s performance does not lead to increased shopping satisfaction, since it is a prerequisite for the British respondents. For Dutch respondents, the products attribute is a performance factor with high performance. In other words, with this performance level, it will generate high shopping satisfaction.

While staff is a low-performing basic factor for Dutch respondents, it is a high-performing basic factor for the British respondents. This suggests that staff will not lead to high shopping satisfaction for both groups. However, it causes shopping dissatisfaction for Dutch respondents since their performance perceptions about staff attribute are low. For German respondents, since the staff attribute is a low-performing performance factor, it will cause shopping dissatisfaction.

While the tangibles attribute is an excitement factor for Dutch respondents, it is a performance factor for the British respondents and a basic factor for German respondents. Meanwhile, performance perceptions of the subject nationalities about this attribute are low. It means that the tangibles attribute is the reason for shopping dissatisfaction for the British and German respondents. However, it will not create shopping dissatisfaction for Dutch respondents. In contrast, performance improvement of the tangibles attribute is expected to create high shopping satisfaction for these respondents.

Discussion and conclusion

One of the primary concerns of the researchers and practitioners who focus on tourist shopping is to understand how tourist shopping satisfaction can be improved. Therefore, the current study investigates tourist shopping satisfaction and its antecedents in the sample from the historic town of Side. The results of the study have important contributions to the literature. First, it is an exceptional study that was carried out in a historic town although a number of studies already investigated the antecedents of tourist shopping satisfaction in other settings such as night markets (Hsieh and Chang, 2006) and regional shopping malls (LeHew and Wesley, 2007). Tourists’ shopping behaviour in a historic town may be different from other settings since historic town offers a traditional shopping experience to the tourists. As Wong and Wan (2013) noted, tourists prefer a large-scale shopping area where they can find various kinds of items, leisure and local dining options. It is a fact that the historic town of Side is a unique touristic shopping habitat with more than 600 retail stores offering a wide range of shopping options for tourists.

Second, in accordance with Heung and Cheng’s (2000) and Lin and Lin’s (2006) studies, tangibles, staff and products emerged as the underlying shopping attributes. In addition, the study findings revealed that the perception of tourists about shopping attributes differed depending on nationality. For example, on the one hand, the British tourists’ satisfaction from staff attribute was higher than that of German and Dutch tourists. On the other hand, the German tourists’ satisfaction level from the products attribute was lower than that of the British and Dutch tourists. Thus, the findings supported previous research showing cross-national differences in tourist shopping (e.g. Wong and Law, 2003).

Third, the results of the regression analyses revealed that shopping attributes were crucial antecedents of tourist shopping satisfaction and their importance varied according to respondents’ nationalities. Although the results showed that tangibles, staff and products affected shopping satisfaction, it is important to realize that other factors, such as mood and personality, may also play an important role in shopping satisfaction.

Fourth, previous research that investigated the relationships between shopping attributes and shopping satisfaction assumed that a symmetric relationship exists among these concepts. In other words, if an attribute’s performance increases, shopping satisfaction will also increase or vice versa. However, starting with Kano et al. (1984), a number of studies showed that this relationship might be asymmetric rather than symmetric. This study, therefore, extends the tourist shopping literature by examining asymmetric effects of shopping attributes on shopping satisfaction. The AIPA results revealed that shopping attributes could be separated as the basic requirements and delighting attributes. For example, staff is a high-performing basic factor for the British tourists. Even though it avoids dissatisfaction, it does not lead to satisfaction as well. In contrast, staff is a low-performing performance factor for German tourists. Thus, with this performance level, it causes dissatisfaction, but any performance increase will generate satisfaction.

The results of this study should be interpreted under some limitations. For example, concentration on the historic town of Side may create limitations with regard to the generalizability of the findings. In addition, using an adapted scale, which consists of predetermined items for identifying the main shopping attributes, may limit the exploration of some other attributes specific to this historic town. Thus, in future studies, scale items can be generated by semi-structured interviews with the tourists so that important features of shopping can be exposed.

Managerial implications

The results of the current research have some important managerial implications. By knowing that the categorization of shopping attributes differs according to nationality, retailers should adopt individual strategies for each market segment. For example, the tangibles attribute is an excitement factor for Dutch tourists. In the meantime, it is a basic factor for German tourists and a performance factor for British tourists. Since the staff attribute is a performance factor with low performance for German tourists, its performance level should be improved with the aim of achieving high tourist satisfaction. In the area, companies may organize training programs for improving staff skills. Moreover, customer–staff interactions should be encouraged. For many of the tourists, contacting with staff is an opportunity to learn about the local lifestyle and culture. Such interactions can be reinforced if staff can express the symbolic meanings and stories of the local products.

The results of the study also reveal that the tangibles attribute is a low-performing excitement factor for Dutch tourists. This shows that its performance should be improved in order to achieve high tourist shopping satisfaction. Taking into consideration that most of the tourist shopping is an unplanned behaviour, tangibles can be used as a driver for shopping if these attributes are designed to create an authentic atmosphere.

Since the products attribute is a high-performing excitement factor for German tourists, it will lead to high shopping satisfaction. Products are generally preferred by tourist as tangible evidence of their experience. Hence, new authentic products that are unique to this destination should be designed.

As mentioned earlier, findings of this study indicate that different marketing strategies should be followed for three market segments (i.e. German, Dutch and British) in terms of three shopping attributes (i.e. tangibles, staff and products). However, majority of the companies in the area are small and medium sized. This limit their abilities to perform customized marketing strategies for different market segments and to cover the cost of these attempts. For overcoming these difficulties, companies may cooperate with each other and get the financial supports from the trade union of Side.

To sum up, achieving high shopping satisfaction over competing shopping regions is a crucial task for each shopping habitat. For this purpose, it is important for company managers and destination authorities to understand the characteristics of shopping attributes and their effects on tourist shopping satisfaction.