Content uploaded by Serene Tse
Author content
All content in this area was uploaded by Serene Tse on Aug 30, 2021
Content may be subject to copyright.
https://doi.org/10.1177/0047287521997576
Journal of Travel Research
1 –16
© The Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0047287521997576
journals.sagepub.com/home/jtr
Empirical Research Article
Introduction
Tourism can facilitate intergroup relations between residents
and tourists (Lin, Chen, and Filieri 2017), and intergroup
relations could be conceptualized as comprising of cognitive,
affective, and behavioral components. For instance, previous
studies have explored residents’ cognitive evaluations of
tourists and tourism in various contexts, such as stereotypes
(Tung, King, and Tse 2020), destination image (Stylidis,
Shani, and Belhassen 2017), tourist–resident conflicts (Tsaur,
Yen, and Teng 2018), and tourist discriminations (Tse and
Tung 2020a). Studies have also investigated residents’ affec-
tive reactions in terms of emotional relationships (Woosnam
and Norman 2010), and attachment with tourists (Ouyang,
Gursoy, and Sharma 2017).
Yet, research into residents’ behavioral responses to tour-
ists warrants more attention. Although studies have docu-
mented residents’ positive (e.g., interactions with tourists)
and negative actions (e.g., verbal and physical harassment),
they have largely remained within the confines of under-
standing the “valence” of intergroup behaviors, that is, the
“good” or “bad” of such actions (N. Chen, Hsu, and Li 2018;
Kozak 2007). What is neglected is the consideration of
“intensity” (i.e., activeness or passiveness) for such behav-
iors and the corresponding matrix that could be generated by
considering both the dimensions of valence and intensity in
the context of tourism research. Both dimensions are crucial
as the connection between valence and intensity can progress
the conceptualization of residents’ behaviors from a dichoto-
mous view (i.e., positive or negative) to a multifaceted per-
spective (i.e., 2 × 2 matrix).
To address this gap, the goal of the present research is to
offer a more holistic view of residents’ intergroup behaviors
for and against tourists by generating a 2 (valence) × 2
(intensity) matrix that constitutes the focal framework of the
Behaviours from Intergroup Affect and Stereotype (BIAS)
Map that has yet to be examined in the tourism context
(Cuddy, Fiske, and Glick 2007). Specifically, the present
research aims to develop a framework that maps the valence
(i.e., facilitative or harmful) against the intensity of behaviors
(i.e., active or passive) to reflect four distinctive quadrants
997576JTRXXX10.1177/0047287521997576Journal of Travel ResearchTse and Tung
research-article2021
1School of Hotel and Tourism Management, The Hong Kong Polytechnic
University, Kowloon, Hong Kong
Corresponding Author:
Serene Tse, School of Hotel and Tourism Management, The Hong Kong
Polytechnic University, 17 Science Museum Road, Tsim Sha Tsui East,
Kowloon, Hong Kong.
Email: serene-wai-tsz.tse@polyu.edu.hk
Measuring the Valence and Intensity of
Residents’ Behaviors in Host–Tourist
Interactions: Implications for Destination
Image and Destination Competitiveness
Serene Tse1 and Vincent Wing Sun Tung1
Abstract
While studies have documented the valence (e.g., facilitation and harm) of residents’ behaviors toward tourists, research
into the intensity (i.e., activeness or passiveness) for such behaviors and the corresponding matrix that could be generated
by considering both of these dimensions in the context of tourism remains unexplored. This research offers a more holistic
conceptualization of residents’ behaviors by generating a matrix that constitutes the framework of the Behaviours from
Intergroup Affect and Stereotype (BIAS) Map. Twelve behaviors were measured and cross-culturally validated via samples
from Hong Kong, Singapore, and the United States: Active Facilitation (i.e., socializing, interacting, and starting a conversation
with tourists); Passive Facilitation (i.e., tolerating, accepting, and enduring tourists’ behaviors); Active Harm (i.e., mocking,
threatening, and being unfriendly to tourists); and Passive Harm (i.e., resisting, refraining, and being reluctant to help tourists).
This research provides implications for tourism policy makers to manage host–guest relations that could influence destination
image and destination competitiveness.
Keywords
resident behaviors, destination image, host–guest relations, intergroup behaviors, destination competitiveness
2 Journal of Travel Research 00(0)
of residents’ engagement with tourists: Active Facilitation,
Passive Facilitation, Active Harm, and Passive Harm.
This goal is achieved through two related studies. To
begin, study 1 uses a sample of Hong Kong residents to
assess a range of active or passive, as well as facilitative or
harmful behaviors against tourists in order to develop a 2 ×
2 model. Next, study 2 provides cross-cultural validity to the
model by using two new samples of residents from Singapore
and the United States. It also provides a comparative analysis
to evaluate the results between Singaporeans and Americans
across each of the quadrants.
The present research contributes to the field by connect-
ing insights from the BIAS Map in social psychology with
the tourism literature. This line of work is crucial as
residents’ behaviors could influence host–guest relations
and tourists’ subsequent image of a destination (Gong,
Detchkhajornjaroensri, and Knight 2019; Kock et al. 2019;
Monterrubio 2016). From a practical perspective, the inter-
actions between residents and tourists could be leveraged by
tourism policy makers to improve the attractiveness and
competitiveness of the destination (Crouch 2011; Mariani
et al. 2014). There are also important implications on how
residents’ behaviors may be deployed (e.g., as ambassadors
of their place) by destination marketing organizations
(DMOs) to help destinations promote themselves and con-
tribute to improving tourists’ perceived destination image
(Styvén, Mariani, and Strandberg 2020).
Literature Review
Host–Guest Relations
Host–guest relations in tourism research refers to the study
of interactions between residents and tourists within a desti-
nation (Sharpley 2014). The quality and nature of interac-
tions can influence host and tourist perceptions and attitudes
as well as support for tourism development (Eusébio, Vieira,
and Lima 2018). Among the breadth of existing studies in
host–guest relations, a frequent topic is the examination of
residents’ perceptions towards tourism impacts, such as eco-
nomic, sociocultural and environmental impacts within a
destination (Nunkoo and Gursoy 2012). Additionally, posi-
tive host–guest relations could influence tourists’ image of a
destination (Tasci and Severt 2017).
Examining host–guest relations resolves around the study
of both the quantity and quality of exchanges between resi-
dents and tourists. Quality contact situations between resi-
dents and tourists could increase their sense of affection
toward each other, and the frequency of contact could reduce
intercultural differences (Bornstein 1989). However, resi-
dents’ emotions could be volatile with respect to the number
of inbound tourists (Doxey 1975). As suggested by Doxey’s
Irritation Index, residents could start with euphoria, passing
through apathy and annoyance, and eventually reach antago-
nism. These four levels of irritations reflect the destination
stages of exploration, development, consolidation, and decli-
nation. This framework offers insights into the negativity
that residents may attach to tourism development, and the
corresponding feelings and behaviors that they may exhibit
against tourists.
In many destinations, however, host–guest relations have
been under significant pressure due to issues such as anti-
and overtourism. For example, in Barcelona, residents have
demonstrated anti-tourism sentiments by protesting about
the high number of visitors (Hughes 2018). This has been
fueled in part by the impact of overtourism, which affects
residents’ everyday life and the makeup of their communities
(Gonzalez, Coromina, and Galí 2018). Consequently, residents
may oftentimes want to avoid interactions with tourists,
although interactions between them may form an important
element in a tourist’s experience (Sharpley 2014).
Intergroup Relations and Behaviors
The actions that a resident exhibit towards a tourist reflect a
form of intergroup behaviors. Intergroup behaviors refer to
the actions performed by an individual towards a member of
another social group based on perceived group identification
(Tajfel 1984). These actions could be categorized into two
types: approach and avoidance (Wyer 2010). Approach are
positive actions that reflect an individual “moving towards
another person” while avoidance are negative actions asso-
ciated with “moving away.” Individuals who engage in
approachable behaviors could promote intergroup relations
while those who avoid other individuals could erode inter-
group contact (A. J. Elliot 2006).
While the approach–avoidance spectrum represents
the positive and negative sides of one’s behaviors, it
does not indicate the intensity of the behaviors performed.
Furthermore, not all behaviors are direct interactions as some
could be indirect, such as ignoring others. Consequently, a
more comprehensive intergroup behavior model based on
two primary dimensions of valence and intensity was
extended from the approach–avoidance spectrum (Cuddy,
Fiske, and Glick 2007). Valence reflects facilitation (i.e.,
prosocial or approach) and harmful behaviors (i.e., antisocial
or avoidance), while intensity discerns the activeness or pas-
siveness of such behaviors. Activeness refers to actions that
are produced in maximal deliberative efforts, purposive
intention, direct and high risk. Passiveness refers to actions
that are produced with minimal deliberative efforts, possibly
unintended, and indirect. The corresponding 2 × 2 matrix
that could be generated by considering both the dimensions
of valence and intensity is referred to as the Behaviors
from Intergroup Affect and Stereotype (BIAS) Map. It has
four quadrants: Active Facilitation, Active Harm, Passive
Facilitation, and Passive Harm (see Table 1).
Active Facilitation represents intentional behaviors that
aim to help, protect, and benefit others. Passive Facilitation
reflects cooperative or associative actions, and contact
Tse and Tung 3
between individuals are tolerated, but not necessarily inten-
tional. Active Harm reflects intentional behaviors that
produce negative outcomes for others, such as attacking,
fighting, or sabotaging. Passive Harm represents actions
where an individual distances or demeans other individuals
by devaluing their social worth through exclusion or neglect.
These four quadrants could be seen as act for, act with, act
against, or act without members of another social group
(Cuddy, Fiske, and Glick 2007).
The BIAS Map has been used in various empirical studies
to measure, predict, and comprehend intergroup behaviors
among social groups, especially in the area of majority-
minority interactions (Seate and Mastro 2017). For instance,
Cuddy, Fiske, and Glick (2008) examined attributional ante-
cedents of these four quadrants, and highlighted the impor-
tance of social judgment, context, and cultural differences in
affecting one’s behaviors. Becker and Asbrock (2012) sug-
gested that stereotypes and differences in socioeconomic sta-
tus between individuals could influence the extent to which
they exhibit harmful behaviors. It is important to clarify that
Cuddy, Fiske, and Glick (2007) also considered the connec-
tions among stereotypes, emotions, and behaviors, with emo-
tions mediating the relationship between stereotypes and
behavioral tendencies. However, the scope of the present
research focuses primarily on understanding intergroup
behaviors rather than the full conceptual links among tourist
stereotypes, and residents’ emotional reactions and behav-
ioral responses.
Adopting and Revising the BIAS Map for Tourism
Research
The BIAS Map has yet to be examined in the tourism con-
text. Existing studies have largely focused on reporting the
valence of residents’ behaviors (Carmichael 2000; N. Chen,
Hsu, and Li 2018; Tse and Tung 2020b) without considering
the intensity or the corresponding matrix that could be gener-
ated by considering both of these dimensions in a multifac-
eted perspective (i.e., 2 × 2 matrix). However, despite the
strengths of the BIAS Map, it would be inappropriate to
directly adopt the framework without considering the unique
context of tourism. For example, some of the attributes in
the BIAS Map, such as “attacking” and “fighting,” do not
represent typical resident–tourist interactions. The BIAS
Map measures intergroup behaviors with respect to the hier-
archical ranks of the social groups, such as locals versus
immigrants, and superiors versus subordinates; however, the
hierarchy between residents and tourists is less discrete.
An important step in revising the BIAS Map to suit tourism
research is to identify a range of positive and negative,
verbal and nonverbal behaviors that have been investigated
in the existing literature. Casual conversations, courtesy, and
politeness, for instance, could stimulate intergroup inter-
actions between residents and tourists (Nadeau et al. 2008;
Thiel, Foth, and Schroeter 2015; Tung 2019). Some scholars
also recognized that residents showing hospitality to tourists
are willing to interact and socialize with the tourists
(N. Chen, Hsu, and Li 2018; Saveriades 2000; Teng 2011).
Regrettably, with news of unpleasant tourist behaviors at
many destinations, residents’ attitudes have shifted from
positively and actively engaging with tourists to simply
“accepting,” “tolerating,” and “enduring” them (Pile 2017).
In contrast to the line of research on positive behaviors,
another area of focus has been residents’ negative behaviors
against tourists. This includes insulting (Kozak 2007), mock-
ing (Ambroz 2008), and staring at tourists to display a sense
of disagreement and dissatisfaction (Maoz 2006). More
aggressive behaviors from residents have also been docu-
mented, such as harassing or threatening tourists (Otoo,
Badu-Baiden, and Kim 2019).
It is important to note that a weakness of the BIAS Map is
the dichotomy between active and passive, which suggests
that intention is either present or absent in behaviors. There
could be situations, however, in which an individual may not
intend to act at the onset, but the real-life situation changes
and moves that individual to behave unintentionally. For
example, a resident may not intend to socialize with a tourist,
but when the tourist approaches the resident for assistance,
such as asking for directions or suggestions, the resident is
placed in a situation in which he or she interacts (i.e., Active
Facilitation), although that was not the initial intent. Despite
this limitation, the conceptualization of the BIAS Map still
adds considerable value to the literature by considering resi-
dents’ behaviors in terms of both valence and intensity in the
context of host–guest relations. Figure 1 summarizes a num-
ber of behaviors from residents against tourists within the
BIAS Map.
Residents’ Behaviors and Destination Image
Destination image is an important aspect of tourism research.
Tourists can form a perceived image through different mar-
keting and social media channels, as well as an actual image
of the destination through their firsthand tourism experiences
(Beerli and Martin 2004; Martín-Santana, Beerli-Palacio,
and Nazzareno 2017). These images reflect both cognitive
and affective components; cognitive image refers to the
tangible attributes of a destination while affective image
Table 1. BIAS Map and Associated Behaviors.
Active Facilitation (Act for)
Assist
Help
Protect
Active Harm (Act against)
Fight
Attack
Sabotage
Passive Facilitation (Act with)
Cooperate
Unite
Associate
Passive Harm (Act without)
Demean
Exclude
Neglect
Source: Cuddy, Fiske, and Glick (2007).
Note: BIAS = Behaviors from Intergroup Affect and Stereotype.
4 Journal of Travel Research 00(0)
represents the general feelings about the destination (Pike
and Ryan 2004).
Tourists’ perceived or actual images of a destination could
be influenced by their views and experiences with local resi-
dents; that is, residents’ behaviors—positive or negative—
could strongly influence tourists’ destination image. For
example, tourists’ perceptions of local residents as “friendly”
is a key attribute of cognitive image (Agapito, Oom do Valle,
and da Costa Mendes 2013; S. Elliot, Papadopoulos, and Kim
2011). Positive behaviors from residents, such as interacting
and socializing, may strengthen tourists’ view of locals as
hospitable and friendly whereas residents’ irresponsible
behaviors could have a highly negative impact on tourists’
image of the community and the destination (Kour, Jasrotia,
and Gupta 2020). Harmful behaviors from residents against
tourists (e.g., yelling at them) could also be reflected in tour-
ists’ views of the destination as unpleasant and distressing,
which are affective components of destination image.
While the literature above considered tourists’ destination
image, it is also important to consider destination image from
the residents’ perspective. Positive destination image per-
ceived by residents could improve their perceived economic,
sociocultural, and environment impacts from tourism, which
then enhances their support for tourism development (Stylidis
et al. 2014). In turn, this could affect the extent to which they
are willing to exhibit positive, facilitative behaviors—instead
of negative, harmful behaviors—to tourists.
Moving a step further, destination image studies have
been a precursor of destination competitiveness research
(Enright and Newton 2004). Tourism destinations that are
able to contribute to economic prosperity, maintain environ-
mental stewardship, and improve standard of living as well
as quality of life could improve residents’ image of tourism
and the competitiveness of the destination overall (Crouch
and Ritchie 1999). In this regard, DMOs could encourage
positive behaviors from residents to improve tourists’ per-
ceived destination image (Styvén, Mariani, and Strandberg
2020), which ultimately could improve the competitiveness
of a destination (Crouch 2011; Mariani et al. 2014).
Methods and Findings
After a review of the literature, the next step is to collect
empirical evidence for the model. There are two studies in the
present research. Study 1 develops a resident behavior model
from a sample of Hong Kong residents. Study 2 provides
cross-cultural validation from Singaporeans and Americans.
Study 1
Development of initial items. A focal tourist group is needed to
begin the classification of residents’ behaviors into the four
quadrants of the model. Mainland Chinese tourists were cho-
sen as the focal group because it is an influential source mar-
ket for many destinations. An initial pool of positive and
negative behaviors was generated from a review of the litera-
ture as per Figure 1. Next, a supplementary online free
response task was conducted with fifty-six Hong Kong resi-
dents based on the process suggested by Hall, Philips, and
Townsend (2015). Residents of Hong Kong were chosen as
the sample for several reasons. Hong Kong has been one of
the most visited destinations by Mainland Chinese tourists
owing to its close proximity. A series of policy relaxations
by the Chinese government has promoted Mainland Chinese
tourists’ tremendous growth in the city (Tourism Commis-
sion 2019). While this market has contributed significantly
Figure 1. Summary of residents’ behaviors from existing literature.
Tse and Tung 5
to the city’s economy, it has also stirred negative social ten-
sions, such as overcrowding and parallel trading. Conse-
quently, there have been reports of residents’ harmful
behaviors against them, such as verbal abuse and unfair
treatments (Qiu Zhang et al. 2017). Overall, the identifica-
tion of items from Hong Kong residents could assist the
city’s tourism authority in understanding residents’ behav-
iors that are necessary for fostering positive destination
image and a competitive destination.
Hong Kong residents were recruited through convenience
and snowball sampling. The research team invited respon-
dents through their contacts, and then asked them to share the
online questionnaire via their social networks. Respondents
were invited to list all positive and negative, verbal and non-
verbal, behaviors that they have performed toward Mainland
Chinese tourists. Behaviors that were mentioned by more
than one respondent were retained, and in the case of differ-
ent variations of the same behavior, only one version was
kept. This process produced seven positive and six negative
behaviors that were added to the pool of items from the lit-
erature. In total, the list consisted of 37 items (i.e., 18 posi-
tive and 19 negative) (Appendix 1). All these items were
presented to the calibration sample for the scale purification
process.
Calibration sample. An online questionnaire using Qualtrics
was distributed to Hong Kong residents in May 2019. Qual-
trics is an online survey company based in the United States
that recruits respondents internationally. Qualtrics has been
employed in recent tourism studies for data collection (e.g.,
Campbell and Kubickova 2020; Suess, Woosnam, and Erul
2020). In the questionnaire, respondents were required to
indicate “How often do you perform the following behaviors
towards Mainland Chinese tourists? (from 1 = never to 7 =
often)” for each of the 37 items. In total, 178 respondents
were recruited (see Table 2).
Purification of the scale. Since the initial pool of items con-
sisted of both positive and negative behaviors, the scale puri-
fication was conducted separately prior to a full model
assessment. This separation could decrease the possibility of
misrepresentations in the results owing to the opposite direc-
tions of signs and inconsistencies in meanings of the mea-
sured items (Kim et al. 2015). This approach was used in
previous tourism studies (Chan, Hsu, and Baum 2015; Lyons
et al. 2016; Tung, King, and Tse 2020). Item-to-total correla-
tions were examined, and items that were correlated at less
than 0.4 with the total score were removed (Choi and Sir-
akaya 2005). After the removal of two items from both posi-
tive and negative behaviors, Cronbach’s alpha was 0.916 and
0.926, respectively. Both values were greater than the thresh-
old value of 0.7, which represented good internal consistency
of the items in each subscale (Nunnally 1978).
Exploratory factor analysis (EFA) using principal compo-
nents analysis (PCA) and varimax rotation was conducted to
assess the dimensionality of each subscale (see Tables 3 and
4). For positive behaviors, Bartlett’s test of sphericity was
1278.677 (p < 0.0001), which indicated that the items were
appropriate for factor analysis. The Kaiser–Meyer–Olkin
(KMO) measure of sampling adequacy was 0.905, which
was considered a respectable representation of the propor-
tion of variance among the measured items (Kaiser 1974).
Items with communality and factor loading less than 0.5, as
well as factors with eigenvalues less than one were removed
(Kaiser 1960). Two factors of positive behaviors were
extracted and each contained three items that accounted for
65.7% of the total variance. Factor 1 contained active and
facilitative behaviors (i.e., “starting a conversation,” “social-
izing,” and “interacting with tourists”). Factor 2 contained
passive and facilitative items that represented accommoda-
tive behaviors (i.e., “tolerating,” “accepting,” and “enduring
tourists’ behaviors”). Cronbach’s alpha for both factors were
0.819 and 0.775, respectively.
For negative behaviors, the Bartlett’s test of sphericity
was 1679.540 (p < 0.0001) and KMO measure of sampling
adequacy was 0.867. KMO between 0.8 and 0.9 are regarded
as meritorious (Kaiser 1974). Items with communality and
factor loading less than 0.5, as well as factors with eigenval-
ues less than one, were removed (Kaiser 1960). Two factors
for negative behaviors were extracted and each contained
three items that accounted for 70.8% of the total variance.
Factor 1 involved passive but potentially harmful behaviors
(i.e., “resisting,” “refraining,” and “being reluctant to help
tourists in need”). Factor 2 involved active and harmful
Table 2. Respondent Characteristic (Study 1—Calibration and
Validation Sample).
Calibration
Sample (n = 178)
Validation
Sample (n = 381)
Variables Distribution (%) Distribution (%)
Gender
Female 135 (75.8) 208 (54.6)
Male 43 (24.2) 173 (45.4)
Age, years
18–24 118 (66.3) 154 (40.4)
25–34 30 (16.9) 111 (29.1)
35–44 13 (7.3) 81 (21.3)
45–54 12 (6.7) 22 (5.8)
≥55 5 (2.8) 13 (3.4)
Education
Up to secondary school 13 (7.3) 30 (7.9)
Postsecondary 30 (16.9) 66 (17.3)
Bachelor’s 125 (70.2) 241 (63.3)
Master’s 8 (4.5) 39 (10.2)
Doctorate 2 (1.1) 5 (1.3)
Resident District
Hong Kong Island 38 (21.4) 124 (32.6)
Kowloon Peninsula 67 (37.6) 127 (33.3)
New Territories 73 (41.0) 130 (34.1)
6 Journal of Travel Research 00(0)
behaviors (i.e., “mocking,” “threatening,” and “being
unfriendly to tourists”). Both factors achieved Cronbach’s
alpha of 0.720 and 0.843, respectively.
Validation of the scale. Confirmatory factor analysis was used
to evaluate the measurement model. The cutoff criteria for
the fit indices were as follows: 3–1 for the ratio of χ2 to the
degrees of freedom (χ2/df) (Bollen 1989); values greater than
0.9 for the comparative fit index (CFI) and goodness of fit
index (GFI) (Blunch 2008; Kline 2011); and values less than
0.08 for root mean square error of approximation (RMSEA)
(Hair et al. 1998; Hu and Bentler 1999).
For convergent validity, the average variance extracted
(AVE) should be greater than 0.5, or the value of Cronbach’s
alpha for the composite reliability of the dimension should
be greater than 0.6 (Fornell and Larcker 1981; Huang et al.
2013). For discriminant validity, the squared root of AVE
should be higher than the interdimension correlation coeffi-
cient (Hair et al. 2010), and the correlation among the vari-
ables should not be greater than 0.85 (Kline 2005).
Validation sample. An online questionnaire through Qual-
trics software was distributed to a new sample of Hong
Kong residents in June 2019. The questionnaire consists of
the 12 items from the calibration sample, and items were
measured using the same 7-point Likert scale of 1 = never
to 7 = often. Gender quota sampling was adopted as it is
crucial to consider input from both females and males in
today’s research. Three hundred eighty-one valid question-
naires were collected (i.e., 54.6% female and 45.4% male).
According to the Hong Kong Census and Statistics
Department (2018), the percentage of female and male
Hong Kong residents is 54.1% and 45.9%, respectively. In
addition, 69.5% of the respondents were aged 35 years old
and below; 74.8% received at least undergraduate-level
education; and 32.6% were from Hong Kong Island,
33.3% from Kowloon Peninsula, and 34.1% from New
Territories.
The results of the validation sample presented good model
fit. Maximum degrees of freedom χ2/df was within the
acceptable range (χ2/df = 119.324/46 = 2.594). CFI (0.971),
GFI (0.952), and nonnormed fit index (NNFI), also known as
the Tucker–Lewis Index (TLI; 0.958) were greater than 0.90,
and the RMSEA (0.065) was less than 0.08. The composite
reliability for each factor was between 0.745 and 0.885,
which suggested reliable internal consistency of the mea-
sured variables in their respective constructs. Three factors
had an AVE value of 0.5 and above except for Passive
Facilitation (AVE = 0.437), which was slightly lower than
the ideal value but achieved a composite reliability of 0.745
(Fornell and Larcker 1981; Huang et al. 2013) (see Table 5).
Discriminant validity was achieved as all factors had a
squared root of AVE higher than their interdimension
Table 3. Results of Exploratory Factor Analysis (Study 1—Calibration Sample).
Variables Eigenvalues
Cumulative
Variance (%) Communalities
Standardized Factor
Loading
Composite
Reliability AVE
Facilitation
Factor 1: Active Facilitation 1.554 41.110 0.819 0.638
Starting a conversation with tourists 0.692 0.814
Socializing with tourists 0.736 0.805
Interacting with tourists 0.735 0.776
Factor 2: Passive Facilitation 1.081 24.590 0.775 0.614
Accepting tourists’ behaviors 0.608 0.684
Tolerating tourists’ behaviors 0.728 0.830
Enduring tourists’ behaviors 0.792 0.827
Harm
Factor 3: Passive Harm 5.913 38.830 0.720 0.561
Being reluctant to help tourists 0.762 0.856
Resisting from helping tourists 0.634 0.706
Refraining from helping tourists 0.585 0.628
Factor 4: Active Harm 1.036 31.970 0.843 0.542
Being unfriendly to tourists 0.739 0.797
Mocking tourists 0.682 0.714
Threatening tourists 0.639 0.733
Note: AVE represents the average variance extracted of each behavioral quadrant
Table 4. Construct Intercorrelation (Study 1—Calibration Sample).
Variables AF PF PH AH
Active Facilitation (AF) 1.000
Passive Facilitation (PF) 0.336 1.000
Passive Harm (PH) 0.233 0.149 1.000
Active Harm (AH) 0.238 0.107 0.605 1.000
Tse and Tung 7
correlation coefficient with no correlation among variables
exceeding 0.85 (see Table 6).
Brief discussion of study 1. Study 1 identified a pool of posi-
tive and negative behaviors from residents that reflected the
four quadrants of the BIAS Map (see Figure 2). The findings
highlight the importance of considering both the valence
(i.e., facilitation or harm) and intensity (i.e., active or pas-
sive) of behaviors.
Interestingly, some of the items, such as “answering
tourists’ questions” and “helping tourists,” did not load into
any quadrants of the model. A possible explanation is that
technology has changed the nature of prosocial, helping
behaviors between residents and tourists. The advancement
of information technology and mobile applications that
provide itinerary suggestions, navigation, ratings, and
reviews have decreased the opportunities for tourists to
seek assistance from residents.
While study 1 focused on a sample of Hong Kong residents,
study 2 aims to provide further cross-cultural validation for
Table 5. Results of Confirmatory Factor Analysis (Study 1—Validation Sample).
Variables Standardized Factor Loading Composite Reliability AVE
Factor 1: Active Facilitation 0.885 0.724
Starting a conversation with tourists 0.849
Socializing with tourists 0.878
Interacting with tourists 0.825
Factor 2: Passive Facilitation 0.745 0.437
Accepting tourists’ behaviors 0.924
Tolerating tourists’ behaviors 0.471
Enduring tourists’ behaviors 0.484
Factor 3: Passive Harm 0.659 0.659
Being reluctant to help tourists 0.793
Resisting from helping tourists 0.785
Refraining from helping tourists 0.856
Factor 4: Active Harm 0.620 0.620
Being unfriendly to tourists 0.836
Mocking tourists 0.756
Threatening tourists 0.768
Figure 2. Residents’ behaviors in the four quadrants of the BIAS Map.
Table 6. Construct Intercorrelations (Study 1—Validation Sample).
Variables AF PF PH AH
Active Facilitation (AF) 0.851
Passive Facilitation (PF) 0.549 0.661
Passive Harm (PH) −0.148 −0.159 0.787
Active Harm (AH) 0.058 −0.001 0.754 0.812
Note: Bold value is the squared root of AVE.
8 Journal of Travel Research 00(0)
the model with two new samples of residents in major desti-
nations: Singapore and the United States. Singapore relies
heavily on tourism for its economy, and the government pro-
actively develops and manages an “East meets West” urban
destination experience. Similar to Hong Kong, the Mainland
Chinese market is one of the largest markets for Singapore
(Tay 2019). Additionally, there have been discussion on
social media about the increasing tensions between
Singaporeans and Mainland Chinese tourists. For example,
Singaporeans complain that Mainland Chinese tourists have
low English capabilities and violate local values. On the
other hand, Mainland Chinese tourists have shared their
extremely negative experiences with Singaporeans’ aggres-
sive behaviors against them. These incidents have been pub-
lished on social media forums and have fostered resounding
discussions between Singaporeans and Mainland Chinese
(Moon 2018). Furthermore, Singapore recorded an unprece-
dented influx of Mainland Chinese tourist in the first half of
2019, and such disturbances could increase and potentially
deteriorate host–guest relations. Singapore can serve to vali-
date the model while the United States can provide insights
beyond the Asian context.
Study 2
Participants and procedure. A questionnaire with the 12 resi-
dents’ behaviors, measured with 1 = never to 7 = often,
were distributed to Singaporeans and Americans via Qual-
trics, an online survey platform. Using a gender quota sam-
pling approach, 235 and 203 completed questionnaires were
collected from Singapore in June 2019 and the United States
in February 2020, respectively. The Singaporean sample
consisted of 50.6% female and 49.4% male, while American
sample consisted of 49.8% female and 50.3% male. The Sin-
gaporeans were mainly aged 34 and below (57.3%) while the
majority of the Americans were aged 25–34 years (33.5%)
and at least 55 years old (33.5%) (see Table 7).
Assessment of the model. The overall model fit was evaluated
by various goodness-of-fit indices without applying any
modifications (see Table 8). For the Singaporean sample, the
ratio of χ2 to the degrees of freedom (1.998) was less than
three; CFI (0.914), GFI (0.966), and NNFI (0.952) were
greater than 0.90; and the RMSEA (0.065) was less than
0.08. For the American sample, the ratio of χ2 to the degrees
of freedom (2.193) was less than three; CFI (0.986), GFI
(0.942), and NNFI (0.980) were greater than 0.90; and the
RMSEA (0.05) was less than 0.08. The findings showed
good model fit for both samples.
The standardized factor loadings for the 12 items ranged
from 0.557 to 0.917, and the composite reliability (CR)
scores for each quadrants were between 0.697 and 0.926 in
both samples, suggesting good internal consistency of the
measured variables (Nunnally 1978). Convergent validity
was achieved as the factor loadings of all measured variables
were higher than 0.4 and the total average variance extracted
(AVE) were higher than 0.50 (Fornell and Larcker 1981).
Discriminant validity was supported, as the square root of
the AVE of each quadrant exceeded the coefficient of inter-
correlations between any two quadrants (Fornell and Larcker
1981) (see Table 9).
Comparative analysis was conducted to evaluate the
results between Singaporeans (denoted with subscript S) and
Americans (denoted with subscript A) in the model. Both
samples rated facilitative (MA = 3.9926, SDA = 1.590; MS
= 3.822, SDS = 1.048) higher than harmful behaviors
against tourists (MA = 2.178, SDA = 1.416; MS = 2.5447,
SDS = 1.238). Furthermore, they rated passive (MA = 2.943,
SDA = 1.187; MS = 2.8950, SDS = 1.119) higher than active
behaviors (MA = 3.2274, SDA = 1.150; MS = 3.4716, SDS
= 0.935). Overall, both Americans and Singaporeans exhib-
ited higher extents of facilitative and passive behaviors.
With respect to each of the four quadrants, the results for
both Singaporeans and Americans exhibited similar patterns;
that is, both samples reported highest ratings for Passive
Facilitation followed by Active Facilitation, Passive Harm,
and Active Harm. However, the results between Singaporeans
and Americans began to diverge when independent samples
t tests were performed between the samples across each
dimension.
The results showed significant differences in three of the
four quadrants (see Figure 3). On average, Americans
reported a significant higher extent of Active Facilitation
(e.g., conversing and socializing) than Singaporeans (MA =
3.899, SDA = 1.711; MS = 3.481, SDS = 1.419), t(393) =
−2.764, p = 0.006, while Singaporeans indicated higher
ratings for Active Harm (e.g., mocking and being
unfriendly) than Americans against Mainland Chinese tour-
ists (MA = 1.987, SDA = 1.440; MS = 2.309, SDS = 1.283),
t(436) = 2.477, p = 0.014. Furthermore, Singaporeans
reported significantly higher ratings for Passive Harm (e.g.,
resisting and being reluctant to help tourists) than Americans
(MA = 2.369, SDA = 1.567; MS = 2.780, SDS = 1.395),
t(436) = 2.878, p = 0.004. Passive Facilitation (e.g., toler-
ating, accepting, and enduring tourists’ behaviors) was the
Table 7. Respondent Characteristic (Study 2—Validation Sample).
Variables
Singapore (n = 235),
Distribution (%)
United States (n = 203),
Distribution (%)
Gender
Female 119 (50.6) 101 (49.8)
Male 116 (49.4) 102 (50.3)
Age, years
18–24 50 (21.3) 0 (0.0)
25–34 87 (36.0) 68 (33.5)
35–44 64 (27.2) 53 (26.1)
45–54 21 (9.0) 34 (16.8)
≥55 13 (5.5) 68 (33.5)
Tse and Tung 9
only dimension without significant differences between the
two samples (MA = 4.085, SDA = 1.724; MS = 4.163, SDS
= 1.185), t(350) = 0.541, p = 0.589. Collectively, the
results showed that Singaporeans reported higher extents of
harmful behaviors than Americans against Mainland
Chinese tourists.
Brief discussion of study 2. Study 2 provided cross-cultural
validation for the model with residents from Singapore and
the United States. The results of the comparative analysis
showed that both Americans and Singaporeans generally
exhibited higher extents of facilitative and passive behaviors
to Mainland Chinese tourists. However, when each of the
four quadrants were compared between Singaporeans and
Americans, Americans indicated higher extents of Active
Facilitation (e.g., conversing and socializing) while Singa-
poreans reported higher ratings of Active and Passive Harm
(e.g., intimidating and neglecting). A possible reason is that
prior to COVID-19, there have been increasing reports on
cultural violations among Mainland Chinese tourists when
they visited Singapore that have sparked disputes among
Singaporeans (S. Chen 2017). For Singapore, a city-state of
5.69 million, there were more than 3.6 million visitors from
Mainland China compared to around 3 million Mainland
Chinese tourists who visited the United States, a country
with a much larger population and geographical area. Given
the current health situation with COVID-19 as well as the
political tensions between the United States and China, it
would be interesting to see how Americans’ behaviors may
change as international travel and tourism hopefully resumes
when borders reopen in 2021.
General Discussion
This research consisted of two studies that highlighted the
intergroup behaviors between residents and tourists. Study 1
developed a scale to measure the valence (i.e., facilitation vs.
harm) and intensity (i.e., active vs. passive) of residents’
behavior by drawing from the BIAS Map. Twelve types of
behaviors were identified and categorized into four quadrants:
Active Facilitation, Passive Facilitation, Active Harm, and
Passive Harm. These four quadrants could be regarded as
Table 8. Results of Confirmatory Factor Analysis (Study 2—Cross-Cultural Validation Samples).
Singapore (n = 235) United States (n = 203)
Variables Standardized Factor Loading CR AVE Standardized Factor Loading CR AVE
Factor 1: Active Facilitation 0.879 0.665 0.926 0.807
Starting a conversation with tourists 0.861 0.883
Socializing with tourists 0.827 0.911
Interacting with tourists 0.794 0.900
Factor 2: Passive Facilitation 0.697 0.355 0.914 0.779
Accepting tourists’ behaviors 0.639 0.893
Tolerating tourists’ behaviors 0.588 0.894
Enduring tourists’ behaviors 0.557 0.861
Factor 3: Passive Harm 0.830 0.600 0.896 0.750
Being reluctant to help tourists 0.675 0.810
Resisting from helping tourists 0.820 0.670
Refraining from helping tourists 0.883 0.917
Factor 4: Active Harm 0.813 0.635 0.883 0.713
Being unfriendly to tourists 0.690 0.855
Mocking tourists 0.827 0.782
Threatening tourists 0.801 0.892
Table 9. Construct Intercorrelations (Study 2—Cross-Cultural Validation Sample).
Singapore (n = 235) United States (n = 203)
Variables AF PF PH AH AF PF PH AH
Active Facilitation (AF) 0.816 0.898
Passive Facilitation (PF) 0.291 0.596 0.715 0.883
Passive Harm (PH) 0.265 0.045 0.775 0.128 0.047 0.866
Active Harm (AH) 0.370 0.063 0.711 0.797 0.055 −0.026 0.774 0.844
Note: Bold values are the squared root of the average variance extracted values.
10 Journal of Travel Research 00(0)
residents’ initiatives that could benefit tourists; residents’
accommodative behaviors toward tourists; residents distanc-
ing away from tourists; and residents’ intimidating behaviors
against tourists, respectively. Study 2 examined the cross-
cultural validity of the model by using a new sample of
Singaporeans and Americans. The model fits of both samples
provided further support for the model. The results indicated
that Singaporeans exhibited higher extents of harmful behav-
iors, both actively and passively, while Americans reported
higher Active Facilitation towards Mainland Chinese tourists.
Theoretical Implications
This study connected the BIAS Map in the social psychology
literature with tourism research to develop a valid and reli-
able model to measure residents’ behaviors. While previous
studies identified residents’ attitudes and how these attitudes
could affect host–guest interactions (Ap and Crompton 1993;
Butler 1975; Carmichael 2000), existing studies have not
examined residents’ behaviors in terms of both valence and
intensity concurrently. Both considerations are critical as
valence provides important information about the attractive-
ness or averseness of the target while intensity informs about
the level of engagement of these behaviors.
It is important to note, however, that residents may behave
differently even within the same destination. Some residents
may be willing to interact and socialize, while others may
mock or be unfriendly to tourists. This study sought to iden-
tify the various types of behavioral reactions that residents
may perform, but residents may certainly perform them dif-
ferently and to various extents. Furthermore, for some desti-
nations, sociocultural differences among residents may be as
large as sociocultural differences between residents and
tourists. This would be relevant for multicultural nations
such as Canada and the United States. In contrast, Hong
Kong is comparatively homogenous as more than 95% of the
population are local Cantonese-speaking residents (Hong
Kong Census and Statistics Department 2020). Singapore,
on the other hand, also has a strong Singaporean identity;
hence, although the majority of residents are ethnically
Chinese, they are proud to be Singaporean first and
foremost.
Besides that, the dominance of the Mainland Chinese
market could have affected residents’ experiences with tour-
ism and influenced their behaviors. Owing to proximity,
Mainland Chinese tourists have been the top inbound tourist
market for Hong Kong (43 million in 2019) and Singapore
(3.6 million in 2019). However, record arrival numbers com-
bined with inappropriate tourist behaviors and overcrowding
have fostered detrimental host–tourist encounters as well as
negative sentiments, and harmful behavioral responses. On
the other hand, the United States received fewer Mainland
Chinese tourists (i.e., 2.9 million in 2019) compared with the
other two city destinations; hence, the problems related to
urban density and overcrowding from tourist arrivals in
Hong Kong and Singapore may not have been experienced
by Americans to the same extent.
There is theoretical merit for linking the BIAS Map in
tourism to the wider destination image literature. Positive
host–guest interactions through residents’ facilitative behav-
iors (e.g., interacting and socializing) could lead to tourists’
emotional attachment with residents and overall satisfaction;
in contrast, negative interactions could have the opposite
effect and damage tourists’ post-travel evaluations of desti-
nation image (Fan et al. 2017; Stylidis 2020; Woosnam,
Stylidis, and Ivkov 2020). For instance, Kour, Jasrotia, and
1
1.5
2
2.5
3
3.5
4
4.5
Composite Mean
Singaporeans
Americans
Figure 3. Ratings of behaviors from Americans and Singaporeans across the four quadrants.
Tse and Tung 11
Gupta (2020) analyzed the impact of the COVID-19 pan-
demic situation on host–guest relationships and its future
impact on travel intentions among tourists in India. Residents’
mistrust and irresponsible behaviors toward tourists has a
highly negative impact on the image of the community and
the destination.
This study contributes to the social psychology literature
by providing context to the BIAS Map, and by identifying
new behavioral attributes in an applied tourism perspective.
Many studies in psychology have employed the BIAS Map
without consideration of a prevalent societal context (i.e.,
tourism), which is a limitation as intergroup dynamics could
change according to the relationships between social groups
(i.e., in this case, residents and tourists) as well as the exam-
ined context (i.e., Hong Kong, Singapore, or the United
States). Tourism serves as a platform for social exchanges in
daily life, and thus, the behavioral attributes in this research
reflect real-life considerations between residents and tourists
that are beyond a controlled psychology setting.
The results of this research also shows that intergroup
behaviors in the tourism context could differ from general
intergroup behaviors identified in the BIAS Map from the
social psychology literature. For example, the behavioral
items from Passive Facilitation, Active Harm, and Passive
Harm were replaced with new items in the model that were
more relevant for tourism. In Passive Facilitation, the items
in this research reflected different levels of residents’ accom-
modative behaviors, ranging from accepting and tolerating to
enduring tourists’ behaviors. The behavioral items for resi-
dents’ harmful behaviors were also different from the BIAS
Map. While the original BIAS Map considered an item such
as “fighting” under Active Harm, this behavior may not be
particularly applicable in the tourism context as residents
typically do not “fight” tourists. Instead, this research recon-
ceptualized items for Active Harm to reflect residents’ who
may “threaten” or “mock” tourists instead. These are items
that are worthy of additional research attention.
Practical Implications
There are a number of ways for DMOs and tourism policy
makers to leverage positive interactions between residents
and tourists to improve the attractiveness and competitive-
ness of their destination. For example, residents’ behaviors
might be deployed by DMOs to help destinations promote
themselves and contribute to improve tourists’ perceived
destination image. This could be done in the form of enlist-
ing residents as “place ambassadors” (Styvén, Mariani, and
Strandberg 2020). Recent research by Styvén, Mariani, and
Strandberg (2020) suggests that local residents could act as
valuable ambassadors and co-creators of place-related brand
communication. DMOs could involve residents more proac-
tively in promoting their destinations, which could enhance
both destination competitiveness (Crouch and Ritchie 2012)
and advertising effectiveness, since embedding residents to
sustain tourism could be a more organic and cost-effective
approach (Uchinaka, Yoganathan, and Osburg 2019).
Positive destination image perceived by residents’ could
improve their perceived economic, sociocultural, and envi-
ronment impacts from tourism, which then enhances their
support for tourism developments (Stylidis et al. 2014). In
turn, this could affect the extent to which they are willing to
exhibit positive, facilitative behaviors. Tourism policy mak-
ers are encouraged to carefully assesses residents’ percep-
tions of the place before they develop their destination
marketing plans. This could involve evaluating and support-
ing residents’ sense of positivity so that they are inspired to
share organic communication material for the destination as
local place ambassadors.
In the digital age, destination could also promote them-
selves by encouraging residents and tourists to share interac-
tions between them. For example, DMOs could execute a
bottom–up approach by allowing the residents and tourists to
upload their positive interactions with each other through
online photos or videos on social media. Organic content
from residents and tourists could be viewed as more credible
than communication from official destination marketing
sources (Palmer, Koenig-Lewis, and Medi Jones 2013).
DMOs and tourism policy makers could also consider
more internal marketing to facilitate residents’ awareness of
Active Facilitative behaviors. Educational videos and poster
could be employed by DMOs and policy makers to deliver
prosocial norms and messages to residents. As per the results
of this research, content of the videos could be further
streamlined to show interactions, socialization, and conver-
sations between residents and tourists. For example, the
Hong Kong Tourism Board has videos that promoted posi-
tive host–guest interactions with residents showing hospital-
ity and smiling at tourists (Sun 2016). Indirectly, DMOs
could collaborate with other government agencies to stimu-
late prosocial behaviors among individuals within society.
For instance, the Equal Opportunity Commission (EOC) of
Hong Kong has created videos to encourage local residents
on facilitative behaviors, such as interacting and socializing
with other individuals. The aim is to encourage residents to
act positively to cultivate an inclusive society.
Internal marketing from DMOs and tourism policy mak-
ers could also address potential negative behaviors from resi-
dents. Social learning theory suggests that individuals could
acquire new behaviors by observing and imitating others in a
social context (Bandura 1971). Individuals who acknowl-
edge a shared identity (e.g., residents) may be encouraged to
mimic behaviors performed by other members of the same
social group towards outgroup members (e.g., tourists). For
example, if a resident performed a certain action (i.e., nega-
tive behavior) on a tourist and was observed by other resi-
dents, there is a possibility that a contagion effect of that
negative behavior could occur within the society (Tung
2021). In this view, DMOs and tourism policy makers are
recommended to address residents’ harmful behaviors
12 Journal of Travel Research 00(0)
immediately when they occur through internal marketing,
such as public announcements, to address possible negative
contagion effects among residents.
Finally, in addition to marketing communication and
internal marketing in the digital age, DMOs could also facili-
tate face-to-face opportunities for residents and tourists to
interact. These opportunities could include cultural events,
festivals, and activities. The purpose is to enable residents to
engage with and share their norms and values with tourists.
For DMOs, the goal is to showcase Active Facilitation from
residents to strengthen the positivity of the tourists’ per-
ceived destination image and enhance destination competi-
tiveness (Ritchie and Crouch 2003).
Limitations and Future Research
There are limitations in this study and opportunities for
future research. The two studies in this research were con-
ducted in Hong Kong and Singapore, and both destinations
represented a limited, urban tourism context. This study
focused solely on Mainland Chinese tourist and future
research could investigate residents’ behaviors toward tour-
ists from other source markets.
This research measured residents’ memories of behaviors
rather than actual behaviors. There could have been potential
capitalization of memories as they related to perceived behav-
iors, with subsequent implications on destination image (Tung,
Cheung, and Law 2018). Furthermore, although instructions
were given to respondents, some respondents may have evalu-
ated their perceived behavioral intentions rather than their
actual behaviors. Future studies could address this limitation
by observing residents’ actual behaviors instead.
Although this study mapped residents’ behaviors along
the dimensions of valence and intensity, the associations of
these two dimensions with other psychological constructs
such as stereotypes and emotions were excluded. Cuddy,
Fiske, and Glick (2007) suggested that positive stereotype
could elicit upward emotions and facilitative behaviors
while negative stereotypes could induce downward emo-
tions and harmful behaviors. Future studies could extend
these theoretical concepts with the adoption of the model in
this research to investigate the relationships among tourist
stereotypes, residents’ emotions and behaviors, thereby
enhancing knowledge of intergroup interactions in host–
guest relations.
Although the present research assessed the cross-cultural
validity of the measurement model, it did not investigate
nomological validity. Future studies could take inspiration
from Gatignon et al. (2002) and examine the nomological
validity of the present model by assessing its predictive pow-
ers on related constructs. For instance, Gatignon et al. (2002)
developed a measurement scale to assess innovation’s locus,
type, and characteristic through a structural approach. In
similar vein, future studies could explore the predictive
power of the present model on tourists’ perceived destination
image and destination competitiveness in a structural
approach (Kour, Jasrotia, and Gupta 2020).
Nomological validity could also be investigated by assess-
ing how the present model could predict future developments.
For instance, Govindarajan and Kopalle (2006) investigated
the predictive power of their measurement scale on innova-
tion disruptiveness to future market developments and profit
generation. Future research could evaluate the predictive
power of the model in this research on residents’ support for
future destination development and willingness to act as place
ambassadors (Styvén, Mariani, and Strandberg 2020).
There are limitations to viewing residents and tourists as
two groups with distinct social identities. There may be tour-
ists who had previously been residents in a specific area,
such as individuals going back for holidays to visit family
and friends. Residents should not always be viewed as a
homogenous social group because of individual-level differ-
ences as previous travel experience, openness to other cul-
tures, and other factors could affect their behaviors. In these
circumstances, stereotypes and affect are relevant at an indi-
vidual level, and there could be a limitation to applying the
BIAS Map to host–tourist relationships.
Future researcher may investigate the influence of
resident–tourist contacts in residents’ behaviors. Although
contact theory suggests that contact between individuals of
different social groups could increase individuals’ affections
toward each other and reduce categorization (Allport 1979),
“contact reduction” and “social distancing” have become the
norm in light of the current COVID-19 pandemic situation;
consequently, stereotyping and discrimination against
Mainland Chinese, in particular, have become ever more
salient. As the situation continues, it is possible that more
harmful behaviors could be directed toward Chinese visitors
when international tourism resumes. Future research could
longitudinally examine the changes in residents’ behaviors
toward Mainland Chinese tourists over time.
This study adopted convenience and snowball sampling in
recruiting Hong Kong residents for the free response task and
calibration sample, which may under-represent the city’s pop-
ulation as the sample was skewed toward young and educated
female residents. As a result, the items in the model are not
definitive for the population. Future studies could collect data
using probability sampling to strengthen representation.
Finally, it would be interesting for future research to
investigate the connection between residents’ behaviors
based on the four quadrants with the competitiveness of
a destination or tourism flows as per the Destination
Competitiveness and Sustainability (DCS) Model by Ritchie
and Crouch (2003). It would also be relevant for future
research to investigate the relationships between the model
in the present study with positive host–guest relationships
and tourists’ perceptions of destination brands, given the
importance of facilitating positive and memorable tourism
experiences for tourists (Stylidis, Belhassen, and Shani 2015;
Tan and Wu 2016).
Tse and Tung 13
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) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article:
The work described in this article was fully supported by a grant
from the Research Grants Council of the Hong Kong Special
Administrative Region, China (project no. PolyU255017/16B).
ORCID iDs
Serene Tse https://orcid.org/0000-0001-8508-0465
Vincent Wing Sun Tung https://orcid.org/0000-0001-9560-8761
References
Agapito, D., P. Oom do Valle, and J. da Costa Mendes. 2013. “The
Cognitive-Affective-Conative Model of Destination Image:
A Confirmatory Analysis.” Journal of Travel and Tourism
Marketing 30 (5): 471–81.
Allport, G. 1979. The Nature of Prejudice (unabridged, 25th anni-
versary ed.). Reading, MA: Addison-Wesley.
Ambroz, M. 2008. “Attitudes of Local Residents towards the
Development of Tourism in Slovenia: The Case of the
Primorska, Dolenjska, Gorenjska and Ljubljana Regions.”
Anthropological Notebooks 14 (1): 63–79.
Ap, J., and J. L. Crompton. 1993. “Residents’ Strategies for
Responding to Tourism Impacts.” Journal of Travel Research
32 (1): 47–50.
Bandura, A. 1971. Social Learning Theory. Morristown, NJ:
General Learning Press.
Becker, J. C., and F. Asbrock. 2012. “What Triggers Helping
versus Harming of Ambivalent Groups? Effects of the
Relative Salience of Warmth versus Competence.” Journal of
Experimental Social Psychology 48 (1): 19–27.
Beerli, A., and J. D. Martin. 2004. “Factors Influencing Destination
Image.” Annals of Tourism Research 31 (3): 657–81.
Blunch, N. J. 2008. Introduction to Structural Equation Modeling
Using SPSS and AMOS. London: Sage.
Bollen, K. A. 1989. “A New Incremental Fit Index for General
Structural Equation Models.” Sociological Methods and
Research 17 (3): 303–16.
Bornstein, R. 1989. “Exposure and Affect: Overview and Meta-
analysis of Research, 1968–1987.” Psychological Bulletin 106
(2): 265–89.
Butler, R. W. 1975. “Tourism as an Agent of Social Change.” Paper
presented at the International Geographical Union’s Working
Group on the Geography of Tourism and Recreation, Trent
University, Peterborough, Ontario.
Campbell, J. M., and M. Kubickova. 2020. “Agritourism
Microbusinesses within a Developing Country Economy: A
Resource-Based View.” Journal of Destination Marketing and
Management 17:100460.
Carmichael, B. A. 2000. “A Matrix Model for Resident Attitudes
and Behaviours in a Rapidly Changing Tourist Area.” Tourism
Management 21 (6): 601–11.
Chan, A., C. H. Hsu, and T. Baum. 2015. “The Impact of Tour
Service Performance on Tourist Satisfaction and Behavioral
Intentions: A Study of Chinese Tourists in Hong Kong.”
Journal of Travel and Tourism Marketing 32 (1/2): 18–33.
Chen, N., C. H. Hsu, and X. R. Li. 2018. “Feeling Superior or
Deprived? Attitudes and Underlying Mentalities of Residents
towards Mainland Chinese Tourists.” Tourism Management
66:94–107.
Chen, S. 2017. “Dos and Don’ts for Chinese Tourists Visiting
Singapore: No Whistling at the Theatre and Don’t Steal Stuff
from the Plane.” South China Morning Post, September 23.
https://www.scmp.com/news/china/society/article/21125
Appendix 1. Pool Items from Existing Literature and Free-Response Task.
Positive Behaviors Negative Behaviors
Existing Literature 1. Accept the tourist behaviors
2. Assist the tourist
3. Endure the tourist behaviors
4. Help the tourist
5. Interact with the tourist
6. Show courtesy to tourist
7. Show hospitality to tourist
8. Show politeness to tourist
9. Socialize with the tourist
10. Start a conversation with tourist
11. Tolerate the tourist
1. Act in a threatening manner toward tourist
2. Avoid going to spaces filled with tourist
3. Avoid interacting with tourist
4. Despise the tourist
5. Harass the tourist
6. Insult the tourist
7. Look down on tourist
8. Mock at the tourist
9. Refrain to help tourist
10. Reluctant to help tourist
11. Resist to help tourist
12. Stare at the tourist
13. Use offensive nicknames on tourist
Free-Response Task 12. Answer questions from tourist when they ask
13. Compliment the tourist
14. Going to spaces filled with tourist
15. Practice good manner on tourist
16. Provide recommendations to tourist
17. Respect the tourist
18. Volunteer to help tourist
14. Express unfriendliness to tourist
15. Ignore questions from tourist when they ask
16. Scold the tourist for their wrongdoings
17. Show hostility to tourist
18. Speak negatively about tourist
19. Use negative words on tourist
14 Journal of Travel Research 00(0)
64/dos-and-donts-chinese-tourists-visiting-singapore-no-
whistling.
Choi, H. S. C., and E. Sirakaya. 2005. “Measuring Residents’
Attitude toward Sustainable Tourism: Development of
Sustainable Tourism Attitude Scale.” Journal of Travel
Research 43 (4): 380–94.
Crouch, G. I. 2011. “Destination Competitiveness: An Analysis of
Determinant Attributes.” Journal of Travel Research 50 (1):
27–45.
Crouch, G. I., and J. B. Ritchie. 1999. “Tourism, Competitiveness,
and Societal Prosperity.” Journal of Business Research 44 (3):
137–52.
Crouch, G. I., and J. B. Ritchie. 2012. “Destination Competitiveness
and Its Implications for Host-Community QOL.” In Handbook
of Tourism and Quality-of-Life Research, edited by M. Uysal,
R. Perdue, and M. J. Sirgy, 491–513. Dordrecht, Netherlands:
Springer.
Cuddy, A. J., S. T. Fiske, and P. Glick. 2007. “The BIAS Map:
Behaviors from Intergroup Affect and Stereotypes.” Journal of
Personality and Social Psychology 92 (4): 631–48.
Cuddy, A. J., S. T. Fiske, and P. Glick. 2008. “Warmth and
Competence as Universal Dimensions of Social Perception:
The Stereotype Content Model and the BIAS Map.” Advances
in Experimental Social Psychology 40:61–149.
Doxey, G. V. 1975. “A Causation Theory of Visitor-Resident
Irritants: Methodology and Research Inferences.” Travel and
Tourism Research Association’s Sixth Annual Conference
Proceedings 195–98.
Elliot, A. J. 2006. “The Hierarchical Model of Approach-Avoidance
Motivation.” Motivation and Emotion 30 (2): 111–16.
Elliot, S., N. Papadopoulos, and S. S. Kim. 2011. “An Integrative
Model of Place Image: Exploring Relationships between
Destination, Product, and Country Images.” Journal of Travel
Research 50 (5): 520–34.
Enright, M. J., and J. Newton. 2004. “Tourism Destination
Competitiveness: A Quantitative Approach.” Tourism
Management 25 (6): 777–88.
Eusébio, C., A. L. Vieira, and S. Lima. 2018. “Place Attachment,
Host–Tourist Interactions, and Residents’ Attitudes towards
Tourism Development: The Case of Boa Vista Island in Cape
Verde.” Journal of Sustainable Tourism 26 (6): 890–909.
Fan, D. X., H. Q. Zhang, C. L. Jenkins, and P. Tavitiyaman. 2017.
“Tourist Typology in Social Contact: An Addition to Existing
Theories.” Tourism Management 60:357–66.
Fornell, C., and D. F. Larcker. 1981. “Structural Equation Models
with Unobservable Variables and Measurement Error: Algebra
and Statistics.” Journal of Marketing Research 18 (3): 382–88.
Gatignon, H., M. L. Tushman, W. Smith, and P. Anderson. 2002.
“A Structural Approach to Assessing Innovation: Construct
Development of Innovation Locus, Type, and Characteristics.”
Management Science 48 (9): 1103–22.
Gong, J., P. Detchkhajornjaroensri, and D. W. Knight. 2019.
“Responsible Tourism in Bangkok, Thailand: Resident
Perceptions of Chinese Tourist Behaviour.” International
Journal of Tourism Research 21 (2): 221–33.
Gonzalez, V. M., L. Coromina, and N. Galí. 2018. “Overtourism:
Residents’ Perceptions of Tourism Impact as an Indicator of
Resident Social Carrying Capacity-Case Study of a Spanish
Heritage Town.” Tourism Review 73 (3): 277–96.
Govindarajan, V., and P. K. Kopalle. 2006. “Disruptiveness of
Innovations: Measurement and an Assessment of Reliability
and Validity.” Strategic Management Journal 27 (2): 189–99.
Hair, J. F., R. E. Anderson, R. L. Tatham, and C. William. 1998.
Multivariate Data Analysis, 5th ed. New York: Prentice Hall.
Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2010.
Multivariate Data Analysis: A Global Perspective, 7th ed.
Upper Saddle River, NJ: Pearson Prentice Hall.
Hall, E. V., K. W. Phillips, and S. S. Townsend. 2015. “A Rose by
Any Other Name? The Consequences of Subtyping ‘African-
Americans’ from ‘Blacks.’” Journal of Experimental Social
Psychology 56:183–90.
Hong Kong Census and Statistics Department. 2018. “Women and
Men in Hong Kong Key Statistics.” https://www.statistics.gov.
hk/pub/B11303032018AN18B0100.pdf (accessed December
29, 2020).
Hong Kong Census and Statistics Department. 2020. “Hong
Kong Monthly Digest of Statistics—Use of Language in
Hong Kong.” https://www.statistics.gov.hk/pub/B72001FB20
20XXXXB0100.pdf (accessed June 14, 2020).
Hu, L. T., and P. M. Bentler. 1999. “Cutoff Criteria for Fit Indexes
in Covariance Structure Analysis: Conventional Criteria
versus New Alternatives.” Structural Equation Modeling: A
Multidisciplinary Journal 6 (1): 1–55.
Huang, C. C., Y. M. Wang, T. W. Wu, and P. A. Wang. 2013.
“An Empirical Analysis of the Antecedents and Performance
Consequences of Using the Moodle Platform.” International
Journal of Information and Education Technology 3 (2): 217–21.
Hughes, N. 2018. “‘Tourists Go Home’: Anti-tourism Industry
Protest in Barcelona.” Social Movement Studies 17 (4): 471–77.
Kaiser, H. F. 1960. “The Application of Electronic Computers to
Factor Analysis.” Educational and Psychological Measurement
20 (1): 141–51.
Kaiser, H. F. 1974. “An Index of Factorial Simplicity.”
Psychometrika 39 (1): 31–36.
Kim, W., H. M. Jun, M. Walker, and D. Drane. 2015. “Evaluating
the Perceived Social Impacts of Hosting Large-Scale Sport
Tourism Events: Scale Development and Validation.” Tourism
Management 48:21–32.
Kline, R. B. 2005. Principles and Practice of Structural Equation
Modelling, 2nd ed. New York: Guilford.
Kline, R. B. 2011. “Convergence of Structural Equation Modeling
and Multilevel Modeling.” In The SAGE Handbook of
Innovation in Social Research Methods, edited by M. Williams
and P. W. Vogt. London: Sage.
Kock, F., A. Josiassen, A. G. Assaf, I. Karpen, and F. Farrelly. 2019.
“Tourism Ethnocentrism and Its Effects on Tourist and Resident
Behavior.” Journal of Travel Research 58 (3): 427–39.
Kour, P., A. Jasrotia, and S. Gupta. 2020. “COVID-19: A Pandemic
to Tourism Guest-Host Relationship in India.” International
Journal of Tourism Cities. Published online October.
doi:10.1108/IJTC-06-2020-0131.
Kozak, M. 2007. “Tourist Harassment: A Marketing Perspective.”
Annals of Tourism Research 34 (2): 384–99.
Lin, Z., Chen, Y., & Filieri, R. 2017. “Resident-Tourist Value
Co-Creation: The Role of Residents’ Perceived Tourism Impacts
and Life Satisfaction.” Tourism Management 61: 436–442.
Lyons, K. D., T. Young, J. Hanley, and P. Stolk. 2016. “Professional
Development Barriers and Benefits in a Tourism Knowledge
Tse and Tung 15
Economy.” International Journal of Tourism Research 18 (4):
319–26.
Maoz, D. 2006. “The Mutual Gaze.” Annals of Tourism Research
33 (1): 221–39.
Mariani, M., D. Buhalis, C. Longhi, and O. Vitouladiti. 2014.
“Managing Change in Tourism Destinations: Key Issues
and Current Trends.” Journal of Destination Marketing and
Management 2:269–72.
Martín-Santana, J. D., A. Beerli-Palacio, and P. A. Nazzareno.
2017. “Antecedents and Consequences of Destination Image
Gap.” Annals of Tourism Research 62:13–25.
Monterrubio, C. 2016. “The Impact of Spring Break Behaviour: An
Integrated Threat Theory Analysis of Residents’ Prejudice.”
Tourism Management 54:418–27.
Moon, L. 2018. “Outburst at Singapore Food Court Stirs
Sympathy for Workers from China.” South China Morning
Post, June 21. https://www.scmp.com/news/china/society/
article/2151729/outburst-singapore-food-court-stirs-sympa-
thy-workers-china.
Nadeau, J., Heslop, L., O’Reilly, N., & Luk, P. 2008. “Destination
in a Country Image Context.” Annals of Tourism Research
35 (1): 84–106.
Nunkoo, R., and D. Gursoy. 2012. “Residents’ Support for Tourism:
An Identity Perspective.” Annals of Tourism Research 39 (1):
243–68.
Nunnally, J. C. 1978. Psychometric Theory. New York: McGraw-
Hill.
Otoo, F. E., F. Badu-Baiden, and S. S. Kim. 2019. “A Qualitative
Cognitive Appraisal of Tourist Harassment.” International
Journal of Tourism Research 21 (5): 575–89.
Ouyang, Z., D. Gursoy, and B. Sharma. 2017. “Role of Trust,
Emotions and Event Attachment on Residents’ Attitudes
toward Tourism.” Tourism Management 63:426–38.
Palmer, A., N. Koenig-Lewis, and L. E. M. Jones. 2013. “The
Effects of Residents’ Social Identity and Involvement on
Their Advocacy of Incoming Tourism.” Tourism Management
38:142–51.
Pike, S., and C. Ryan. 2004. “Destination Positioning Analysis
through a Comparison of Cognitive, Affective, and Conative
Perceptions.” Journal of Travel Research 42 (4): 333–42.
Pile, T. 2017. “Who Are the World’s Worst Tourists? Six Nations
That Stand Out—You May Be Surprised.” South China
Morning Post, July 14. https://www.scmp.com/magazines/
post-magazine/travel/article/2102308/who-are-worlds-worst-
tourists-six-nations-stand-out.
Qiu Zhang, H., D. X. Fan, T. S. Tse, and B. King. 2017. “Creating a
Scale for Assessing Socially Sustainable Tourism.” Journal of
Sustainable Tourism 25 (1): 61–78.
Ritchie, J. B., and G. I. Crouch. 2003. The Competitive Destination:
A Sustainable Tourism Perspective. Oxon: CABI.
Saveriades, A. 2000. “Establishing the Social Tourism Carrying
Capacity for the Tourist Resorts of the East Coast of the
Republic of Cyprus.” Tourism Management 21 (2): 147–156.
Seate, A. A., and D. Mastro. 2017. “Exposure to Immigration in
the News: The Impact of Group-Level Emotions on Intergroup
Behavior.” Communication Research 44 (6): 817–40.
Sharpley, R. 2014. “Host Perceptions of Tourism: A Review of the
Research.” Tourism Management 42:37–49.
Stylidis, D. 2020. “Exploring Resident–Tourist Interaction and Its
Impact on Tourists’ Destination Image.” Journal of Travel
Research. Published online November 16. doi:10.1177/0047
287520969861.
Stylidis, D., Y. Belhassen, and A. Shani. 2015. “Three Tales of a
City: Stakeholders’ Images of Eilat as a Tourist Destination.”
Journal of Travel Research 54 (6): 702–16.
Stylidis, D., A. Biran, J. Sit, and E. M. Szivas. 2014. “Residents’
Support for Tourism Development: The Role of Residents’
Place Image and Perceived Tourism Impacts.” Tourism
Management 45:260–74.
Stylidis, D., A. Shani, and Y. Belhassen. 2017. “Testing an
Integrated Destination Image Model across Residents and
Tourists.” Tourism Management 58:184–95.
Styvén, M. E., M. M. Mariani, and C. Strandberg. 2020. “This Is
My Hometown! The Role of Place Attachment, Congruity,
and Self-Expressiveness on Residents’ Intention to Share a
Place Brand Message Online.” Journal of Advertising 49 (5):
540–56.
Suess, C., K. M. Woosnam, and E. Erul. 2020. “Stranger-Danger?
Understanding the Moderating Effects of Children in the
Household on Non-hosting Residents’ Emotional Solidarity
with Airbnb Visitors, Feeling Safe, and Support for Airbnb.”
Tourism Management 77:103952.
Sun, N. 2016. “Hong Kong Finance Chief John Tsang Pokes Fun at
Himself in Tourism Promotion Video.” South China Morning
Post, April 23. https://www.scmp.com/news/hong-kong/econ-
omy/article/1937829/hong-kong-finance-chief-john-tsang-
pokes-fun-himself-tourism.
Tajfel, H. 1984. “Intergoup Relation, Social Myths and Social
Justice in Social Psychology.” In The Social Dimensions:
European Developments in Social Psychology, edited by H.
Tajfel, C. Fraser, and J. M. F. Jaspars, 695–715. Cambridge:
Cambridge University Press.
Tan, W. K., and C. E. Wu. 2016. “An Investigation of the
Relationships among Destination Familiarity, Destination
Image and Future Visit Intention.” Journal of Destination
Marketing & Management 5 (3): 214–26.
Tasci, A. D., and D. Severt. 2017. “A Triple Lens Measurement
of Host–Guest Perceptions for Sustainable Gaze in Tourism.”
Journal of Sustainable Tourism 25 (6): 711–31.
Tay, T. F. 2019. “Trump-Kim Summit and Crazy Rich Asian Film
Put Singapore in the Global Spotlight.” The Straits Time,
February 14. https://www.straitstimes.com/singapore/tourist-
arrivals-spending-at-record-highs.
Teng, C. C. 2011. “Commercial Hospitality in Restaurants and
Tourist Accommodation: Perspectives from International
Consumer Experience in Scotland.” International Journal of
Hospitality Management 30 (4): 866–874.
Thiel, S. K., M. Foth, and R. Schroeter. 2015. “Ad Hoc Communities
on the Road: Serendipitous Social Encounters to Enhance
Tourist Experiences.” Proceedings of the Annual Meeting of
the Australian Special Interest Group for Computer Human
Interaction 643–52.
Tourism Commission, The Government of Hong Kong Special
Administrative Region. 2019. “Tourism Fact Sheets.”
https://www.tourism.gov.hk/english/papers/papers_fact_
sheets_2018.html (accessed December 29, 2020).
Tsaur, S. H., C. H. Yen, and H. Y. Teng. 2018. “Tourist–
Resident Conflict: A Scale Development and Empirical
Study.” Journal of Destination Marketing and Management
10:152–63.
16 Journal of Travel Research 00(0)
Tse, S., and V. W. S. Tung. 2020a. “Residents’ Discrimination
against Tourists.” Annals of Tourism Research. Published
October 6. doi:10.1016/j.annals.2020.103060.
Tse, W. T. S., and V. W. S. Tung. 2020b. “Assessing Explicit and
Implicit Stereotypes in Tourism: Self-Reports and Implicit
Association Test.” Journal of Sustainable Tourism. Published
online December 18. doi:10.1080/09669582.2020.1860995.
Tung, V. W. S. 2019. “Helping a Lost Tourist: The Effects of
Metastereotypes on Resident Prosocial Behaviors.” Journal of
Travel Research 58 (5): 837–48.
Tung, V. W. S. 2021. “Reducing Tourist Stereotyping: Effectiveness
of Communication Messages.” Journal of Travel Research 60
(2): 281–92.
Tung, V. W. S., C. Cheung, and R. Law. 2018. “Does the Listener
Matter? The Effects of Capitalization on Storytellers’
Evaluations of Travel Memories.” Journal of Travel Research
57 (8): 1133–45.
Tung, V. W. S., B. E. M. King, and S. Tse. 2020. “The Tourist
Stereotype Model: Positive and Negative Dimensions.”
Journal of Travel Research 59 (1): 37–51.
Uchinaka, S., V. Yoganathan, and V. S. Osburg. 2019. “Classifying
Residents’ Roles as Online Place-Ambassadors.” Tourism
Management 71:137–50.
Woosnam, K. M., and W. C. Norman. 2010. “Measuring Residents’
Emotional Solidarity with Tourists: Scale Development
of Durkheim’s Theoretical Constructs.” Journal of Travel
Research 49 (3): 365–80.
Woosnam, K. M., D. Stylidis, and M. Ivkov. 2020. “Explaining
Conative Destination Image through Cognitive and Affective
Destination Image and Emotional Solidarity with Residents.”
Journal of Sustainable Tourism 28 (6): 917–35.
Wyer, N. A. 2010. “Salient Egalitarian Norms Moderate Activation
of Out-Group Approach and Avoidance.” Group Processes &
Intergroup Relations 13 (2): 151–65.
Author Biographies
Serene Tse is a postdoctoral fellow at the School of Hotel and
Tourism Management, The Hong Kong Polytechnic University.
Her research focuses on host-tourist relations, tourist stereotypes,
destination management, and China tourism.
Vincent Wing Sun Tung is an Associate Professor at the School
of Hotel and Tourism Management, The Hong Kong Polytechnic
University. His research focuses on host-tourist relations, social
issues, stereotypes, and tourism experiences.
