Cultural Characteristics and Tourist Shopping Spending

Abstract

Although the cultural impact of tourism shopping has been extensively studied, prior research rarely related tourism shopping to specific cultural dimensions and distance, and tested the relationships statistically. This article fills this gap by investigating the comprehensive effects of Hofstede’s four cultural dimensions (power distance, individualism, masculinity, uncertainty avoidance) and cultural distance on shopping. In an analysis of Hong Kong’s inbound tourism, panel regression tests support power distance and masculinity as two key cultural values in determining a country’s tourist shopping spending in a destination, while the effects of individualism and uncertainty avoidance are marginally significant. A U-shaped curvilinear relationship is found for cultural distance and shopping spending ratio, suggesting that tourists’ allocation of monetary resource on shopping decreases with cultural distance first and increases later after an optimal point. A discussion of contributions and limitations is included.

Keywords

cultural dimensions cultural distance tourism shopping shopping spending

Introduction

In past decades, tourism has become one of the largest and fastest growing economic sectors in the world, with many emerging destinations and demand markets. Among multiple tourism activities, shopping is viewed as the most important driver to future tourism growth, an essential segment to the destination mix, and a crucial motivation of tourists to visit a destination (Lehto et al., 2004).

The economic impact of shopping has been eminent in many destinations. For example, half of the total tourism earnings in Hong Kong were generated by tourist shopping, with an average 9.6% growth rate in retail sales in the past 10 years (Census and Statistics Department of Hong Kong, 2015). Understanding such benefits, many other destinations, such as Paris and Dubai, have followed suit to position themselves as international shopping centers (Timothy, 2005).

The shopping behavior of tourists is dependent on many factors which relate to both destination attributes (e.g., price and shopping environment) and tourist characteristics (e.g., gender, age, and traveling purpose; McKercher, Mei, & Tse, 2006). Shopping is also a culturally specific activity, varying by the cultural values to which a tourist adheres (Wong & Law, 2003). As an example, Asian tourists spend more on shopping and buying luxury goods when on vacation than Western tourists (Suh & McAvoy, 2005).

Although the cultural differences of tourist shopping have been widely noted, the comprehensive effect of key cultural dimensions on shopping expenditure has rarely been discussed. As Sivakumar and Nakata (2001) posit, previous studies often fail to control for possible confounding effects of other cultural values. That is, prior studies investigating the cultural influence on shopping often simply compared shopping behaviors across different regions or countries, without testing the statistical relationships. This is problematic because it not only considers geographical areas (e.g., Asian and Western) as proxies to cultural values but also falsely assumes that an individual country represents only one dimension of culture (Li, 2014). The best way to understand how tourists from a specific culture behave while shopping is to detect the individual effect of each cultural value, so as to figure out which dimensions play the most important roles in influencing tourist shopping.

In addition to the cultural values, the cultural distance between two countries also potentially influences a tourist’s shopping behavior. Cultural distance has been researched in a number of tourism contexts, such as destination choice, travel motivation, and behavior patterns, but very few studies have related cultural distance to tourist shopping (Ahn & McKercher, 2015). Thus, how cultural distance influences tourism shopping is still elusive. In order to fill those gaps, this study aims to address two major questions related to culture and shopping: (a) How do the cultural dimensions of tourists’ home countries influence their shopping expenditure? b) What is the effect of cultural distance on tourists’ shopping expenditures? To this end, Hong Kong is the subject of the study, as it is an international destination receiving tourists from multiple cultural backgrounds and is also a worldwide shopping center, providing a good case for this research.

Literature Review

Tourist Shopping and Its Determinants

As shopping is one of the tourists’ favorite leisure activities on vacation, it has received increasing attention from both tourism practitioners and researchers. A wide range of studies has found that shopping by international tourists is influenced by various factors such as demographic characteristics, travel purposes, and product features (Park & Reisinger, 2009; H. Yu & Littrell, 2003). However, seemingly the most influential factor is culture. For example, general product features such as uniqueness and authenticity are found to be significant determinants to shopping, but the cultural linkage is a particularly critical factor that boosts international tourist shopping (H. Yu & Littrell, 2003). Suh and McAvoy (2005) demonstrated the impact of cultural similarity on shopping, showing that Japanese tourists visiting Korea tended to give the most consideration to shopping, unlike the travelers from Europe and North America who perceive local culture as the most important factor.

Hofstede’s Cultural Dimensions and Shopping Expenditures

Shopping behaviors are related to cultural values of tourists. According to Hofstede’s cultural theory, culture refers to “the collective programming of the mind which distinguishes the members of one group or category of people from another.” Culture manifests itself in the forms of value and practice—beliefs, rituals, heroes, symbols, language, and collective activities, which unconsciously influence and shape individual behaviors (Luna & Gupta, 2000). It is believed that people from the same cultural background share some behavioral similarity (Hofstede, Hofstede, & Minkov, 1997). Four cultural dimensions were identified by Hofstede (1997) to define countries.

Power distance is “the extent to which the less powerful members of organizations and institutions accept and expect that power is distributed unequally” (Hofstede, 1997). It means people from a society with a large power distance tend to perceive those who are above them in status as having more power. Given that fact, people from a hierarchical structured society may feel less powerful than those from a flatly structured culture since the former associates less influence on governmental policies and other activities.

Power, defined as “the capacity to control one’s own and others’ resources and outcomes” can influence many aspects of our behavior, including propensity to take action, risk-taking, information processing, the level of abstract thinking, as well as, conspicuous consumption (Rucker & Galinsky, 2009). According to consumption theories, feeling powerless is an important driver to shopping for conspicuous products (Rucker & Galinsky, 2009; Rucker, Galinsky, & Dubois, 2012). A study conducted by Rucker and Galinsky (2009) supported that power can lead to rational consumption, whereas, powerlessness can foster the desire of consumers to get visible and conspicuous products. Low-power individuals attempt to restore their lack of power by consuming products associated with power (e.g., luxurious handbags) and, in fact, report an increased level of perceived power by doing so. Such a subconscious level of effort to compensate for the low level of power has been used as a critical factor explaining the consumption styles among different social groups. For example, Charles et al. (2009) argued the reason that Blacks and Hispanics spend a larger part of their incomes on visible consumption items such as clothing, jewelry, and cars than Whites may lie in the fact that they perceive to have less power than Whites in society.

As power state influences daily shopping expenditures, it may also influence tourist shopping expenditures. Compared with other tourism activities such as recreation, entertainment, and accommodation, shopping not only increased tourists’ internal pleasant feelings but also satisfied their needs of conspicuous consumption. In a society with a high power distance, possessing visible and tangible products associated with status may be used to boost the sense of power. Likewise, when people from a high power distance society travel, they may spend more on tangible souvenirs or other luxury products from duty-free shops rather than on tourism activities, which are invisible to others once they go back to their countries. The following is expected:

Hypothesis 1₀: Power distance has no effect on the ratio of shopping expenditure to overall domestic spending.

Hypothesis 1_a: Power distance has a positive effect on the ratio of shopping expenditure to overall domestic spending.

The Individualism–collectivism dimension is traditionally considered as the critical value distinguishing Western and Asian societies. This dimension reflects how people view their relation to others—family, coworkers, and other social members (Hofstede, 2010). Individualists see themselves as autonomous and independent. They are motivated by their preferences, needs, and rights and give priority to their personal goals, feelings, and emotions. Conversely, collectivists see themselves as an integral part of one or more collectives or in-groups. They are more often motivated by norms and duties of the in-group and give priority to such goals and try to emphasize their connectedness (Kacen & Lee, 2002).

Individualists value pleasure seeking and emotional independence, whereas, collectivists strive for group solidarity, collective identity, and sharing (C. Kim & Lee, 2000). Lehto et al. (2004) observed that exchanging gifts is an important tradition in the collectivism society, while it is rare in the society characterized by individualism. Thus, tourists from collectivism may give more consideration to the activities that can benefit their social networks than people from individualistic societies. In comparison with entertainment, sightseeing, and accommodation that primarily elevate personal feelings, shopping can particularly benefit tourists’ social networks by bringing back tangible gifts. The following hypothesis is put forth:

Hypothesis 2_0: Individualism has no effect on the ratio of shopping expenditure to overall domestic spending.

Hypothesis 2_a: Individualism has a negative effect on the ratio of shopping expenditure to overall domestic spending.

Masculinity refers to the equality of roles between genders (Hofstede, 1997). Masculinity represents a preference for achievement, heroism, assertiveness, and material rewards for success. Femininity is more consensus-oriented and appreciates the cooperation, modesty, and caring for the weak. Thus, a masculine society emphasizes much higher competition than a feminine society (Hofstede, 2010). It has been confirmed that masculinity has a positive relationship with people’s possessions of luxury products such as watches, and jewelry, along with suits and dresses, as people from a masculine society are associated with a higher need of signaling their social status (de Mooij & Hofstede, 2011). Many studies suggest that the Japanese attribute more importance to shopping during tourism in comparison with tourists from the United States and other Western countries (Özdemir & Yolal, 2017; Reisinger & Turner, 2002).

It is also evidenced that people from a masculine society value material rewards for performance more than nonfinancial rewards, whereas, employees with a low masculinity culture background evaluate nonfinancial rewards as more important (Newman & Nollen, 1996). Since consumers from a high masculine society prefer material rewards and associate more need to show success, it is expected that tourists from high masculinity cultures tend to allocate more monetary resources on shopping for tangible goods than those from a low masculinity society. The study hypothesized:

Hypothesis 3₀: Masculinity has no effect on the ratio of shopping expenditure to overall domestic spending.

Hypothesis 3_a: Masculinity has a positive effect on the ratio of shopping expenditure to overall domestic spending.

Uncertainty avoidance refers to the degree to which the members of society feel uncomfortable with uncertainty and ambiguity (Hofstede, 2010). People from a high uncertainty avoidance culture have a low tolerance of uncertainty and risk and prefer structured situations (Litvin et al., 2004). Uncertainty avoidance can essentially influence consumers’ preferences of product types. Results suggested that Indian people prefer Western luxury brands to local brands because people from high uncertainty avoidance societies tend to follow widely accepted norms and reference groups to reduce the potential risks (Eng & Bogaert, 2010). In addition, people from a high uncertainty avoidance culture would like to spend more on products such as clothing and footwear to reduce their social risk, and more medical and health products to reduce their physical risk, whereas, low uncertainty avoidance cultures tend to adopt more active health care approaches such as fitness and sports (de Mooij & Hofstede, 2002).

Moreover, it has become a widely accepted assumption that intangible products associated more uncertainties than tangible products. Most tourism products such as accommodations, entertainment, and recreation involve a large intangible component (e.g., service) and associate with higher uncertainty in terms of product functionality and product quality (Lim et al., 2004). However, pleasantness of shopping is always based on tangible products that are visible and predictable. Thus, it is expected that high uncertainty avoidance tourists are more likely to allocate monetary resources on a predictable activity such as shopping and spend less on intangible products. The following is hypothesized:

Hypothesis 4₀: Uncertainty avoidance has no effect on the ratio of shopping expenditure to overall domestic spending.

Hypothesis 4_a: Uncertainty avoidance has a positive effect on the ratio of shopping expenditure to overall domestic spending.

Effects of Cultural Distance on Shopping Expenditure

Another cultural characteristic that may affect tourist shopping behavior is culture distance. Culture distance reflects the extent to which the tourists’ home cultures differ from that of the visited area; it determines the level of a tourist’s acceptance of visited countries’ values, norms, and behaviors (Kogut & Singh, 1988). This concept has been well studied in the context of tourism demand and destination choice (Ahn & McKercher, 2015). Mixed effects were suggested.

First, results suggest a decaying effect of cultural distance. Lepp and Gibson (2003) stated visiting culturally similar places can mitigate the effect of culture shock, leading to easier association with the host country, and, thus, leading to more positive experiences. Byrne and Nelson (1965) suggested that it is human nature to be attracted to persons or things that share similarities. People tend to visit culturally proximate destinations because the areas are more easily accessible in terms of language and behavioral habits.

Similarity also influences a tourist’s expenditure behavior and preference of local products. Suh and McAvoy (2005) observed that travelers from Japan to Korea tend to give more consideration to shopping, whereas, the travelers from Europe and North America to Korea rate the activity of exploring local culture and food more important. Moreover, the frequency of collateral trade between two culturally similar countries is much higher than that between culturally different countries, because people like consuming culturally compatible goods (C.M.J. Yu & Zietlow, 1995). It is hypothesized that

Hypothesis 5₀: Cultural distance has no effect on the ratio of shopping expenditure to overall domestic spending.

Hypothesis 5_a: Cultural distance negatively influences the ratio of shopping expenditure to overall domestic spending.

However, there exists a contradictory opinion in terms of the driving function of cultural distance. Kastenholz (2010) proposed that cultural distance is a limiting factor for some tourists who seek the feelings of strangeness, difference, and complexity caused by cultural distance. It is suggested that tourists tend to regard distant destinations as more exotic and products of them as more unique (Yoo et al., 2004). People are strongly motivated to explore and discover what lies beyond their known horizon (Anderson, 1970) and to learn about different cultures by visiting cultural tourism attractions, museums, art galleries, or performances (Mckercher, 2002). Uniqueness is one of the important reasons why tourists purchase souvenirs of visited countries. S. Kim and Littrell (1999) have evidenced a positive relationship between uniqueness and purchase intention with a sample of U.S. outbound tourists to Mexico. Thus, an opposite relation can be hypothesized:

Hypothesis 5_b: Cultural distance positively influences the ratio of shopping expenditure to overall domestic spending.

Combining the two findings above, cultural distance may influence tourists’ shopping behaviors through both similarity and difference mechanisms, which was also suggested by S. I. Ng, Lee, and Soutar (2007) regarding destination choice. Tourists from culturally similar countries may share similar needs and taste of products. That’s why cross-border shopping is prevalent in many areas of the world, such as the Chinese to Hong Kong and South Korea, and the Canadians to the United States (Timothy & Butler, 1995). However, cultural difference may increase the possibility of purchasing souvenirs which can reflect local uniqueness (H. Yu & Littrell, 2003). Özdemir and Yolal (2017) who studied the behavioral difference of international tourists to Istanbul, observed that Japanese and Americans gave more importance to shopping and buying souvenirs than tourists from Spain, France, Germany, and Italy, the countries close to Turkey. Based on this discussion, this study posits that the effect of cultural distance on shopping is curvilinear in nature:

Hypothesis 5_c: There exists a curvilinear relationship between cultural distance and the ratio of shopping expenditure to overall domestic spending.

Data and Variables

The study was conducted using a single destination—Hong Kong. Hong Kong is an international destination receiving tourists from multiple cultural backgrounds, and it is also a worldwide shopping center because of its zero customs policy, location, and urbanization. Thus, it provides a good base for researchers to study cultural effects and shopping behaviors of tourists.

The data used to test the hypotheses were primarily taken from the Visitor Profile Report annually released by the Hong Kong Tourism Board, drawing on the visitor survey conducted by the Immigration Department. The report is developed based on sampled tourists and contains aggregated trip information for each market country, including trip expenditure, travel motivations, length of stay, transportation, social–demographic characteristics, and other tourism statistics. More than 20 market countries are included in the report, but the specifically surveyed markets vary by years. For the purpose of this study, only the countries that could be found in the Hofstede’s culture index reported online were included.

As a result, 20 countries or areas were targeted by the study. They were Mainland China, Taiwan, the United States, the United Kingdom, Canada, Netherlands, Germany, France, Italy, South Africa, Australia, New Zealand, Japan, South Korea, Indonesia, Malaysia, Philippines, Singapore, Thailand, and India. Moreover, there were 12 years of visitor profile reports from 2002 to 2013. Accordingly, 240 yearly sample observations (20 countries multiplied by 12 years) were available for this study’s main analysis.

Measures of Dependent and Independent Variables

This study used the ratio of shopping spending to overall expenditure (SS) as the dependent variable. The ratio of shopping expenditure can tell the importance of shopping activity relative to others for a market country. The data of each market country’s shopping spending ratio in Hong Kong were directly derived from the visitor profile report of Hong Kong. Independent variables include Hofstede’s four national cultural dimensions: power distance (PD), individualism (IND), uncertainty avoidance (UA), masculinity (MAS), and cultural distance between Hong Kong and tourists’ home countries. The value of each country’s cultural dimensions was retrieved from the online resource: geert-hofstede.com .

The cultural distance between Hong Kong and the tourist’s home country (CD) was measured as (Kogut & Singh, 1988):

C D_{j} = \sum_{i = 1}^{6} [{(I_{i j} - I_{i h})}^{2} / V_{i}],

where I_ij stands for the index of the ith cultural dimension of the jth country, I_ih refers to the index for the ith cultural dimension of Hong Kong, V_i is the variance of the index of the ith dimension, and CD_j is a cultural difference of the jth country from Hong Kong. All culture indexes were obtained online (geert-hofstede.com).

Control Variables

As mentioned above, tourist shopping depends on many factors that relate to either destination attributions or tourist characteristics. Specific to the article, the following control variables were considered. Average age (Age) of tourists was controlled for each market country; the percentage of male tourists (Male) was used to control for the variance caused by different gender structure of the market country because shopping has been considered as a thoroughly “gendered” activity linked to the female role for a long time (Campbell, 1997).

The percentage of business travelers (Business) and the percentage of leisure travelers (Leisure) were controlled to account for travel purposes of tourists, as travelers with different purposes associate different travel characteristics: Business travelers are more job-oriented and have less time for shopping than leisure travelers. The average stay (Nights) of tourists was controlled because the length of stay determines one’s budget allocation to other activities (such as accommodations, dining, and tours) and then influences one’s spending on shopping.

The average spending per person (ASPP) of the market country was controlled to account for the country’s spending power, as it is inferred that tourists will turn to meet secondary needs and may increase spending on shopping after satisfying basic traveling needs such as sightseeing, accommodation, and dining. The percentage of first-time visitors (First Time) was also controlled since the first-time visitors associate different interests from those who visit a destination two or more times. Above-mentioned control variables of Age, Male, Business, Leisure, Nights, ASPP, and First Time were all derived from the visitor profile reports released by the Hong Kong Tourism Board.

In addition, the study also took into account price distance, geographical distance, and economic distance between tourists’ home countries and Hong Kong. Price distance (PRD) refers to the price difference of the same product between the two countries. Price difference is a critical driver of cross-border shopping behaviors (Timothy & Butler, 1995), and tariffs and customs is the most important reason why prices of the same product differ across countries. PRD is measured with the mathematical difference between the ratings of Hong Kong and the tourist’s home country in terms of their tariffs and customs:

P R D = T_{i} - H K,

where T_i and HK denote the ratings of i country and Hong Kong.

Geographical distance (GD), which refers to the physical distance between two countries, can influence shopping behaviors of tourists because it decides one’s expense on transportation and lodging, then ultimately decides their monetary allocation for shopping. Geographical distance between a tourist’s home country and Hong Kong was measured with Mahalanobis pooled index which has been recommended as a better indicator than direct physical distance (Berry, Guillen, & Zhou, 2010).

Economic distance (ED) is another type of distance that may influence one’s activity in another country, since the smaller the economic distance between two countries, the closer the countries are in terms of their income, product diversity, government policies, and regulations (Campbell et al., 2012). ED between tourists’ home countries and Hong Kong was defined as

E D = G D P_{i} / G D P_{H K},

where GDP_i and GDP_HK denote gross domestic product per capita of i country and Hong Kong.

Last, to control the effect of random events occurring in a given year, the study also considered the year dummy variables from 2003 (Year03) to 2013 (Year13), using 2002 as the baseline. Table 1 presents the descriptive statistics of variables over the entire sample period. Before running the panel regression tests, the study also checked correlations among variables of cultural characteristics and control variables. As shown in Table 1, given that there are high correlations among variables of cultural characteristics, the study performed several separate regression tests to avoid the possibility of multicollinearity.

Table 1

Descriptive Statistics and Correlation

	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Dependent variable
1. SS	38.49	13.37
Cultural dimensions
2. PD	50.30	22.97	.56**
3. IND	54.55	33.41	−.75**	−.76**
4. MAS	55.45	17.99	.03	−.08	.18**
5. UA	43.85	19.51	.15*	−.22**	.07	.08
Cultural distance
6. CD	2.08	1.17	−.54**	−.74**	.80**	.00	.16*
Control variables
7. Male	58.56	11.19	−.60**	−.33**	.54**	.14*	.16*	.19**
8. Age	40.61	2.73	−.63**	−.74**	.81**	.17**	−.03	.76**	.23**
9. Leisure	52.58	14.06	.49**	.50**	−.68**	−.14*	.02	−.30**	−.80**	−.40**
10. Business	30.44	12.92	−.49**	−.27**	.42**	.16*	.19**	.04	.90**	.13*	−.82**
11. Nights	3.25	0.63	−.30**	−.17**	.47**	−.05	−.44**	.20**	.10	.33**	−.33**	−.04
12. First time	40.68	11.20	−.38**	−.29**	.39**	.00	.37**	.28**	.25**	.15*	−.12	.12	.04
13. ASPP	5377.93	1149.38	−.11	−.20**	.22**	.11	−.17**	.08	−.14*	.35**	−.09	−.07	.39**	−.19**
14. PRD	26.37	20.69	.47**	.43**	−.52**	−.21**	.06	−.56**	−.17**	−.68**	.27**	−.13*	−.07	.12	−.10
15. GD	58.49	40.54	−.73**	−.70**	.89**	.08	.04	.64**	.55**	.75**	−.72**	.48**	.41**	.39**	.24**	−.52**
16. ED	0.80	0.56	−.68**	−.70**	.70**	.10	.17**	.68**	.33**	.70**	−.39**	.25**	.15*	.17**	.14*	−.70**	.60**

Note: SS = ratio of shopping spending to overall expenditure; PD = power distance; IND = individualism; MAS = masculinity; UA = uncertainty avoidance; CD = cultural distance between Hong Kong and the tourist’s home country; ASPP = average spending per person; PRD = price distance; GD = geographical distance; ED = economic distance.

Significance at the 5% level. **Significance at 1% level.

Panel Regression Models

The Effects of Cultural Characteristics on Shopping Expenditure: A Linear Relationship

This study used a panel regression test to examine the influences of cultural characteristics (cultural dimensions and cultural distance) on shopping expenditure. Panel analysis is a statistical method used to deal with two-dimensional data that are usually collected over time and from the same individuals (Maddala & Lahiri, 1992). Specific to this study, the data are organized within two dimensions: years and countries. Chen (2010, 2011) noted that panel analysis can control for individual heterogeneity, reduce multicollinearity problems, alleviate estimation biases, and specify the time-varying effects of explanatory variables.

The panel regression test equation is given as

S S = c_{1} + a_{1} I D V + \sum_{j = 1}^{21} b_{1 j} C V_{j} + ε_{1},

where IDV denotes independent variables (PD, IND, MAS, UA, or CD) and CV_i denotes the control variable i (i = 1, . . ., 21). According to Equation (4), there is a linear relationship between SS and dependent variables if $a_{1 i}$ (i = 1, . . ., 5) are significantly different from zero, that is, $a_{1 i} = 0$ can be rejected ( $a_{1 i} \neq 0$ ).

Note that this study considered three estimation methods when performing panel regression tests. Panel regressions based on Equation (4) can be estimated using pooled ordinary least squares (OLS), the fixed effects method, and the random effects method. Assuming there are no differences between the estimated cross-sections, the pooled OLS method estimates the common constant for all cross-sections. The constant is treated as section-specific in the fixed effects method. The difference between the fixed effects method and random effects method is that the constants of the random effects method for each section are random parameters (Dimitrios, 2005).

The study used an F test to determine whether the pooled OLS was more appropriate than the fixed effects method. The null hypothesis was that all the constants are the same and the common constant (pooled OLS) method was applicable. If the F-statistic is larger than the F-test critical value, the null hypothesis can be rejected. This suggests that the fixed effects method was more appropriate than the pooled OLS method. Otherwise, the pooled OLS method was more appropriate than the fixed effects. Furthermore, if the fixed effects method was more appropriate than the pooled OLS method, the Hausman (1978) test was used to check whether panel regressions based on Equation (4) should be estimated using a fixed or random effects method. The F-test result indicated that panel regressions based on Equation (4) were not significant at the 5% level, implying that panel regressions based on Equation (4) should be estimated using the pooled OLS method.

Table 2 reports panel regression test results of cultural characteristics on shopping expenditure. As the results shown in Table 2, PD (β = .058, p ≤ .01) and MAS (β = .055, p ≤ .01) significantly influenced the shopping spending ratio of a market country, suggesting that the higher power distance or masculinity value with which a country associates, the more monetary resources people would like to allocate from it on shopping during tourism. It supports Hypotheses 1_a and 3_a that power distance and masculinity are two important cultural values in driving tourism shopping behavior; thus, the null hypotheses of Hypotheses 1₀ and 3₀ were rejected. In addition, researchers found UA (β = .33, p ≤ .10) and IND (β = .043, p ≤ .10) were only associated with a marginally significant effect on shopping. Thus, Hypotheses _2a and _4a were only partially supported; Hypotheses 2₀ and 4₀ were not fully rejected.

Table 2

Panel Regression Test Results: Linear Relationship

	Regression I	Regression II	Regression III	Regression IV	Regression V
Constant	140.342**	148.394**	159.990**	156.255**	153.168**
Cultural dimensions
PD	.058**
IND		−.043^†
MAS			.053**
UA				.033^†
Cultural distance
CD					−1.125**
Control variables
Male	−.289**	−.253**	−.278**	−.264**	−.264**
Age	−.778**	−.815**	−1.141**	−1.027**	−.807**
Nights	−7.044**	−6.070**	−6.346**	−5.936**	−6.733**
Leisure	−.625**	−.586**	−.559**	−.578**	−.587**
Business	−.730**	−.702**	−.710**	−.711**	−.748**
First time	−.007	.014	−.033	−.027	−.005
ASPP	.005**	.005**	.005**	.005**	.005**
PRD	.012	−.007	.009	−.017	−.006
ED	−6.731**	−7.321**	−7.381**	−8.168**	−7.058**
GD	−.108**	−0.104**	−.096**	−.113**	−.103**
Year03	5.251**	5.444**	5.515**	5.347**	5.618**
Year04	6.058**	6.251**	6.150**	6.217**	6.238**
Year05	2.522*	2.659*	2.600*	2.659*	2.584*
Year06	1.124	1.216	1.294	1.294	1.167
Year07	−1.643	−1.132	−.982	−.846	−1.509
Year08	−2.361	−1.846	−1.540	−1.507	−2.191
Year09	−1.855	−1.273	−1.333	−1.139	−1.876
Year10	−4.706**	−4.380**	−4.166**	−4.363**	−4.726**
Year11	−10.077**	−9.816**	−9.318**	−9.730**	−10.064**
Year12	−4.620**	−14.368**	−13.739**	−14.285**	−14.580**
Year13	−16.547**	−16.297**	−16.017**	−16.572**	−16.532**
F-statistics	119.583	117.351	122.782	117.67	120.026
Adjusted R²	.916	.915	.918	.915	.916

†

Significance at the 10% level. *Significance at the 5% level. **Significance at 1% level.

The study also tested the linear effect of cultural distance on shopping expenditure. As shown in Table 2, the results supported a negative linear relationship between CD and SS (β = −1.125, p ≤ .01), and statistically significant at the 1% level. However, the relationship, linear or nonlinear, needed to be further confirmed by testing the quadratic part, which was conducted in the next analysis.

The Effects of Cultural Characteristics on Shopping Expenditure: A Curvilinear Function

The following panel regression test equations, including both the linear and quadratic terms of dependent variables, were performed to examine the potential curvilinear relationship between SS and cultural characteristics:

S S = c_{2} + a_{21} I D V + a_{22} I D V^{2} + \sum_{i = 1}^{21} b_{2 i} C V_{i} + ε_{2},

where IDV denotes dependent variable (PD, IND, MAS, UA, or CD). According to the panel regression test equation given above, the curvilinear relationship between SS and IDV (PD, IND, MAS, UA, or CD) exists if a₂₂ is significantly different from zero. Specifically, there is a U-shaped or an inverted U-shaped effect of IDV on SS if both a₂₁ and a₂₂ are significantly different from zero. For example, there is an inverted U-shaped (a U-shaped) relationship between SS and IDV if a₂₁ = 0 and a₂₂ = 0 are rejected, and the coefficient of IDV on SS is significantly positive (negative) and the coefficient of IDV²on SS is significantly negative (positive).

According to the results of the F-test and Hausman (1978) test, panel regressions based on Equation (5) would be estimated using the pooled OLS method. The results shown in Table 3 indicated that there is no curvilinear relationship found between SS and the cultural dimensions of PD, IND, MAS, and UA, which further confirmed effects of PD, IND, MAS, and UA on SS were linear not curvilinear. The test results suggested a curvilinear relationship between SS and CD, as both coefficients of CD and CD² were statistically significant. Specifically, a U-shaped relationship between CD and SS was found as a₂₁ is −3.672 and a₂₂ is .407, which means a market country’s shopping spending ratio decreased first as its cultural distance with Hong Kong increased, but after a critical point, it started to increase along with cultural distance. Figure 1 plots the curvilinear relation between SS and CD. The results supported Hypothesis 5_c that both similarity and difference mechanisms of cultural distance play roles in driving shopping behavior. Hypotheses 5_a, 5_b, and the null hypothesis of Hypothesis 5₀ were rejected.

Table 3

Panel Regression Test Results: Nonlinear Relationship

	Regression I	Regression II	Regression III	Regression IV	Regression V	Regression VI	Regression VII	Regression VIII	Regression IX	Regression X
Constant	142.544**	142.014**	159.757**	152.388**	153.618**	17.455**	58.517**	30.975**	45.384**	57.423**
Cultural dimensions
PD	.203**					.536**
PD²	−.001					−.002
IND		−.166**					−.523**
IND²		.001 × 10⁻¹					.002
MAS			.062					.136
MAS²			−.809 × 10⁻⁴					−.001
UA				−.087					−.231
UA²				.001					.001
Cultural distance
CD					−3.672**					−13.932**
CD²					.407*					1.771**
Control variables
Male	−.266**	−.310**	−.275**	−.234**	−.274**
Age	−.641**	−.663**	−1.143**	−.899**	−.838**
Nights	−6.953**	−6.044**	−6.367**	−5.769**	−6.739**
Leisure	−.644**	−.590**	−.560**	−.583**	−.567**
Business	−.778**	−.714**	−.712**	−.752**	−.725**
First time	.014	−.011	−.032	−.036	.004
ASPP	.005**	.005**	.005**	.005**	.005**
PRD	.001	.020	.009	−.006	−.008
ED	−7.193**	−6.131**	−7.345**	−9.058**	−7.067**
GD	−.109**	−.099**	−.097**	−.103**	−.099**
Year03	5.437**	5.277**	5.513**	5.421**	5.701**
Year04	6.236**	6.008**	6.146**	6.379**	6.257**
Year05	2.649*	2.574*	2.585*	2.754*	2.621*
Year06	1.231	1.149	1.273	1.340	1.217
Year07	−1.498	−1.928	−.991	−.695	−1.525
Year08	−2.172	−2.778*	−1.550	−1.382	−2.211
Year09	−1.934	−2.261^†	−1.337	−1.210	−1.848
Year10	−4.572	−5.725**	−4.171**	−4.539**	−4.709**
Year11	−9.837**	−11.215**	−9.331**	−9.899**	−10.049**
Year12	−14.398**	−15.849**	−13.751**	−14.530**	−14.545**
Year13	−16.231**	−17.933**	−16.021**	−16.894**	−16.509**
F-statistics	116.063	120.247	116.917	114.343	114.511	54.683	59.634	2.527	3.239	54.206
Adjusted R²	.917	.920	.918	.916	.916	.310	.370	.013	.019	.308

†

Significance at the 10% level. *Significance at the 5% level. **Significance at 1% level.

Figure 1

Curvilinear Relationship Between Cultural Distance and Shopping Spending Ratio ( $S S = 57.42 - 13.93 C D + 1.77 C D^{2}$ )

Discussion and Implications

This study contributes to the body of knowledge in several ways. First, it clarifies individual effect of each cultural dimension, identifying the crucial values influencing tourism shopping spending. Second, it is one of the few studies that discussed effect of cultural distance on shopping spending, and statistically confirmed the nonlinear relationship. With the merits presented above, the study provides significant theoretical and practical implications.

Theoretical Implications

The article made several theoretical contributions. First, the study fills the gap of unknown individual effect of cultural values on tourism shopping spending. In the previous literature, most of the cultural studies were conducted by comparing regions concerned, rarely relating observed differences to specific cultural values, and thus, the cultural value that leads to the difference was normally unknown. The approach of regional proxies in which regions or countries are used to represent certain cultural values, was also widely used (Li, 2014).

Despite the easy accessibility of the approach, it is facing significant disadvantages. First, it only allows operating one dimension with a group of countries, which increases the difficulty of studying all cultural dimensions in one single study. On the other hand, conclusions drawn on this approach might be misleading as a country is a collective reflection of multiple cultural values, not just one of them. Researchers need to control other cultural dimensions before drawing a conclusion on the tested one. Most of the studies that used the regional proxies approach failed to justify the countries they selected to measure a cultural value. If these proxies were similar on the dimensions beyond the study’s purpose, alternative explanations were excluded.

To avoid these disadvantages, the current article used direct values to assess the cultural dimensions, which enabled the exploration of individual effects of cultural values. Results suggest that power distance and masculinity are two crucial dimensions that motivate tourists to spend more on shopping, while uncertainty avoidance and individualism only associate marginal effects. It indicates that tourists are primarily motivated to shop for themselves for the purpose of lifting their social status and self-esteem, while strengthening social networks and coping with uncertainty are not significant motivators. The findings coincide with Lau, Sin, and Chan’s (2005) statement that status and authority tourists experienced outside the home is one of the major social motives for a tourist’s shopping.

Another contribution made by the study is that it used shopping spending ratio as the dependent variable. This enabled researchers to study the status of shopping relative to other tourism activities and the tendency of tourists to allocate monetary resources among different activities. In the previous literature, tourism shopping was primarily explored as an independent behavior with the focus on the exploration of its motivational and environmental factors; researchers normally used shopping preference as the dependent variable (e.g., Choi et al., 2016).

The study also contributes to the tourism shopping literature by confirming a U-shape nonlinear relationship between cultural distance and tourist shopping expenditure. Although it has been observed for a long time, cultural distance is a potential factor determining tourists’ behaviors in a destination; however, very few studies have explored its statistical effect. Results of this study indicate that the spending ratio on shopping decreases with cultural distance at first and increases, later, after an optimal point. This suggests that both similar and different mechanisms of cultural distance affect tourists’ shopping spending.

Cross-border shopping can be a persuasive evidence for the driving effect of cultural similarity. Cross-border shopping has taken place between many countries, such as between the Republic of Ireland and Northern Ireland, across the Danish–German border, and between Canada and the United States (Timothy & Butler, 1995). Cultural similarity driven by either historical or geographical closeness influences the residents of the two countries sharing similar tastes and high acceptance of each other’s goods; they are motivated to shop across borders to seek brand diversity, price advantage, and/or unique items.

People also shop for cultural difference. Özdemir and Yolal (2017) explored behavioral difference of tourists from seven different nationalities in Istanbul, and they observed that the Japanese are the most distinct tourists in comparison with the United States, Spain, France, Germany, Britain, and Italy; Japanese and Americans bought more souvenirs than tourists from the countries of Europe. The results suggest that tourists from distant areas, especially culturally different countries, tend to buy more as souvenirs or gifts for bringing back to their home country, while tourists from nearby countries do less shopping for gifts.

Practical Implications

The study also provides significantly practical implications. First, knowing the individual effect of cultural dimensions on shopping spending ratio can help destinations evaluate how shopping is important to the tourists from a target country. Based on the country’s cultural characteristics, destinations can enact appropriate marketing strategies. Specifically, they can increase the promotion on shopping to tourists from high power distance and high masculinity culture. The findings will especially benefit the destinations planning on entering into new market countries for which there is little previous knowledge and the cultural values they demonstrate are the only clues for reference.

Destinations often rely on the region where a country is situated to roughly judge their cultural values (e.g., assuming Asian countries are high in collectivism, power distance, masculinity, and uncertainty avoidance, and European countries are high in individualism and low in power distance, masculinity, and uncertainty avoidance). However, in fact, this approach is not always effective, and countries from the same region can still associate different cultural values. For example, both Hungary and Denmark are European countries; however, Denmark is extremely low in power distance (Hofstede’s value = 18) and masculinity (Hofstede’s value = 16), and Hungary is much higher on the two dimensions of power distance (Hofstede’s value = 46) and masculinity (Hofstede’s value = 88). Destination managers should highlight tourism shopping when attracting Hungary’s tourists and weaken the focus on shopping when marketing to Denmark.

Moreover, knowing that both cultural similarity and difference can stimulate tourists’ intentions to shop, marketers should increase their promotion on shopping for either culturally distant or culturally close markets by providing tax return, discounts, or other incentives. With the rapid growth of its economy, China has become the greatest tourism market in the world. South Korea, which shares a similar culture with China, offered discounts to the Chinese tourists holding UnionPay cards to boost Chinese visitors. Based on the cultural similarity, tourism managers in Hong Kong should actively promote shopping to Chinese tourists. In 2015, 46 million Chinese visitors traveled to Hong Kong, accounting for 77% of all outbound tourists of China (Hong Kong Tourism Board, 2015).

Noticeably, the focus of shopping might be different between culturally similar and culturally different destinations. Tourists from culturally different destinations, as observed in Özdemir and Yolal’s (2017) study, tend to shop for souvenirs or gifts; those from the same culture, like Canadians shopping in the United States, shopped more for daily-use items such as clothes, foods, and groceries (Timothy & Butler, 1995). However, how cultural distance influences shopped-for items by tourists has not been well studied. Practitioners need further information based on on-site observations.

Limitations and Future Research Direction

This study is facing some limitations. First, only one case was involved in the study. Although Hong Kong provides a great case to research tourism shopping and cultural differences, more cases are needed to generalize the results. In the future, more destinations, such as Dubai and Paris, should be incorporated to confirm the findings. Studies on other types of destinations (those featuring sightseeing, cultural heritage, and vacationing) are also recommended to help researchers further understand tourism shopping in different settings.

Another limitation of the study refers to the methodology. The current article was built on secondary data. Details of the initial data were not well reported (e.g., number of respondents for each country or when/where the survey was conducted). Authenticity of the secondary data can affect reliability of the study; thus, use of primary data is suggested to further test the hypotheses. Additionally, although Hofstede’s culture index has been widely used to explain consumer behavior, it was developed in organizational settings. Future studies can use other culture theories such as Schwartz’s culture value orientation and GLOBE’s approach to understand consumer behaviors in relation to cultural differences. Finally, care must be exercised when interpreting the results. Although the effect of cultural characteristics is highlighted in this study, the researchers do not argue that tourists’ shopping expenditures are solely predicted by cultural characteristics. Tourists’ shopping behaviors, as any other consumer behavior, are influenced by various social, psychological, and economic factors. This study shows that cultural characteristics can be significant additional factors to help understand tourists’ shopping expenditures.

In summary, the study made a significant contribution to the knowledge of how Hofstede’s cultural dimensions and cultural distance influence tourists’ shopping behaviors. Based on the study, destinations can predict, to some extent, the importance of shopping to a given market according to its cultural values, so as to develop culturally specific marketing plans and provide corresponding shopping information and incentive policies for a specific market country.

Footnotes

Na Su, PhD (nsu@cnta.gov.cn), is a member of the research staff at China Tourism Academy, Beijing, People’s Republic of China. Hyounae Min, PhD (min@cpp.edu) is an assistant professor in the Collins College of Hospitality Management at California State Polytechnic University Pomona (Cal Poly Pomona), Pomona, California. Ming-Hsiang Chen, PhD (ming-hsiang.chen@wsu.edu), is an associate professor in the School of Hospitality Business Management, Carson College of Business at the Washington State University, Pullman, Washington. Nancy Swanger, PhD (swanger@wsu.edu), is associate dean and director in the School of Hospitality Business Management, Carson College of Business at the Washington State University, Pullman, Washington.

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