Tourism Management 40 (2014) 372e381

Contents lists available at ScienceDirect

Tourism Management
journal homepage: www.elsevier.com/locate/tourman

Social interactions and intentions to revisit for agritourism service
encounters
Hyungsuk Choo a, *, James F. Petrick b,1
a
b

School of Human Movement, Sports, and Leisure Studies, Bowling Green State University, Bowling Green, OH 43402, USA
Department of Recreation, Park, and Tourism Sciences, Texas A&M University, 2261 TAMU College Station, TX 77843, USA

h i g h l i g h t s
 The study suggests a model integrating agritourists’ interactions with service providers, companions, and other customers.
 Interactions with service providers and those with companions positively affected satisfaction with the farm visit.
 Interactions with companions influenced satisfaction more than those with other customers.

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 2 March 2012
Accepted 18 July 2013

This study addresses how agritourists’ social interactions affect their satisfaction and, in turn, revisit
intentions. Adopting social exchange theory and resource theory, the study proposes that social interactions with service providers, local residents, companion tourists, and other customers influence

satisfaction, which in turn affects revisit intentions. For this, an onsite survey was conducted to examine
the proposed model and test the hypotheses. Subjects (N ¼ 266) were tourists who visited farms. All, but
one of the hypotheses were supported or partially supported by the data and the proposed model also
had an acceptable fit. Results provide direction for the development of a theoretical framework to understand revisit intentions by seeking to improve the social exchange relationships with agritourists. In
addition, the results call for the incorporation of social interactions as a component of the agritourism
servicescape.
Ó 2013 Elsevier Ltd. All rights reserved.

Keywords:
Social interactions
Satisfaction
Revisit intentions
Agritourism

1. Introduction
Agritourism has long been a phenomenon in many countries,
but its popularity has only recently increased for farmers, tourists
and consumers of agricultural products and services (Sharpley &
Vass, 2006). On the supply side, as traditional methods of agriculture production system are becoming less viable, farming communities have experienced economic and social challenges,
including decreased farm incomes (Busby & Rendle, 2000). Thus,
farmers have looked for alternatives to help diversify traditional
farm operations, hoping to reverse the steady erosion of net farm
incomes (Fleischer & Pizam, 1997). Farm diversification into
tourism, in general, presents a potential to generate additional income, diversify the farming economy, lower risks and uncertainties

* Corresponding author. Tel.: þ1 419 372 7862; fax: þ1 419 372 0383.
E-mail addresses: , (H. Choo),
(J.F. Petrick).
1
Tel.: þ1 979 845 8806; fax: þ1 979 845 0446.

0261-5177/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved.
/>
and form a symbiotic relationship with agriculture for farming
communities (Clarke, 1999). Agritourism also provides benefits to
tourists and consumers. Since the majority of the general population may have little or no contact with agriculture, agritourism
could also be a mechanism by which urbanites can enjoy nature
and culture, learn about agriculture and purchase locally grown
farm products (Sonnino, 2004). In sum, agritourism has been
commonly guided and motivated by a vision for a thriving, diverse,
small-scale farm that remains profitable, enhances the environment, enriches the indigenous culture, and improves the quality of
life for farmers and consumers.
While a growing body of literature related to agritourism exists,
the vast majority has dealt with tourism from the supply side (Jolly
& Reynolds, 2005; McIntosh & Bonnemann, 2006). To date, little
attention has been given to farm tourists and their relationships
with farmers even though the recent growth in agritourism has
been driven by both demand and supply (Tew & Barbieri, 2012).
There are considerable opportunities for growth of the demand for
agritourism as an increasing number of farmers are diversifying
into tourism businesses (Lobo et al., 1999). Therefore, it is believed


H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381

that research should be conducted to understand the factors
affecting consumers’ perspectives for agritourism activities in order
to fill this gap.
Like other forms of tourism, agritourism involves much service.
This creates a need to focus on service encounters in which a
customer interacts with staff and/or other customers (Bitner,

Booms, & Tetreault, 1990). Service encounters often occur in the
presence of multiple customers and service providers who share
the servicescape with each other, involving a series of interactions
and/or relationships. In this sense, it would appear to be important
to integrate the types of interactions at service encounters to understand how those influence customers’ service experiences. In
the service marketing literature, service encounters have typically
represented social encounters in which employees’ interpersonal
skills affect customer satisfaction and behavior (Bitner, Booms, &
Mohr, 1994; Bowers, Martin, & Luker, 1990) and where customers
influence one another indirectly as a part of the environment or
directly through interpersonal encounters (Bitner et al., 1994;
Martin, 1996). Similarly, tourism scholars have examined the
dyadic interface between tourists and employees (Solnet, 2007)
and customer-to-customer interactions (Huang & Hsu, 2010; Wu,
2007). Additionally, interactions with tourists’ companions and
with local residents might also be critical parts of tourists’ tourism
experiences.
This study therefore sets out to examine an integrated social
interaction in agritourism service encounters including four
distinctive relationships including: 1) agritourist-to-service provider, 2) agritourist-to-local resident, 3) agritourist-to-companion
tourist, and 4) agritourist-to-other customer. Taking findings
related to social exchange theory (Homans, 1958) and affect theory
of social exchange (Lawler, 2001), this study will examine the link
between agritourists’ social interactions and satisfaction with their
service experience. Moreover, these four types of social interactions
will be compared to see how these interactions can individually or
in combination, positively influence post-purchase behaviors. It is
hoped that this study will provide marketing implications for
developing tourism businesses on farms by seeking to understand
the social exchange relationships that agritourists have.


2. Literature review
2.1. Agritourism and service encounters
The primary reason for the recent emergence of tourism as an
important rural economic activity can be found from the supply
side. Farm-based tourism has increasingly given farmers an opportunity to generate additional income (Knowd, 2006), to be an
avenue for direct marketing to consumers (Sonnino, 2004; Tew &
Barbieri, 2012; Veeck, Che, & Veeck, 2006) and as a way to counteract social and economic problemsdloss of income, increased
expenses, globalization, and othersdassociated with the decline of
traditional agriculture industries (McGehee, 2007). In general,
farmers diversify into tourism services for significant and steady
retail sales of farming products, but opportunities for educating
agritourists and consumers about the farming and farming resources and offering entertainment/recreation services are useful
side benefits of these activities. While these potential benefits have
attracted many farmers into agritourism, farmers should keep in
mind that this activity requires them to have extended marketing
practices. Compared to long supply chains of traditional agricultural systems as a part of the production system, agritourism involves much service, including direct interactions with agritourists
and consumers. This suggests attention needs to be paid to service
encounters in both agritourism research and practice.

373

In the service marketing literature, service encounters are
defined as any period of time during which a customer interacts
with a service (Bitner, 1990; Shostack, 1985). This definition includes discrete, separate, and distinct events and behaviors, as well
as customers’ interactions with all the dimensions of a service.
However, a majority of service encounter scholars believe that
interpersonal interactions between customers and service providers are typically important because it is during this time when
customers judge the services provided to them and most services
involve at least one human being interacting with another (Czepiel,

1990; Shostack, 1985). Hence, such an encounter has been the focus
of service marketing research.
While different scholars have paid attention to specific types of
interactions during service encounters, an integrated model
explaining three discrete relationships has been identified in general service environments: customer-to-organization, customer-toservice provider, and customer-to-customer interactions (Yi &
Gong, 2009). All of these interactions seem relevant to general
tourism service encounters, but they are not necessarily the same
for small-scale operations which predominate in agritourism.
Agritourists seem not to distinguish their interactions with organizations or employees because farm owners themselves are service providers in many cases (Wilson, 2007). Therefore, out of the
three types of interactions, this study will not consider customerto-organization interactions.
Agritourists do encounter local residents, although not on a
regular basis. Local residents’ behavior toward tourists can influence whether the experience of agritourists is pleasant. Tourist-toother customer interactions have received scholarly attention in
that the presence of other customers can affect the nature of the
service outcome and process. Lastly, as the indigenous presence of
social groups has been recognized in the tourism literature
(Crompton, 1981), travel companions might also influence the
tourism experience. A vast majority of leisure tourists do not travel
solo and most tourism statistics indicate an average travel party
over two. Although the phenomenon of tourists’ interaction with
their companions has not been identified well in the tourism
literature, this specific interaction, afforded by families and friends
in shared leisure activities, has been explored through the concept
of leisure companionship in other fields (Iso-Ahola & Park, 1996;
McCormick, 1999). In sum, this study suggests that at least four
types of social interactions exist in agritourismdwith service providers, companion tourists, other customers, and local residents.
This study will further examine how these interactions influence
revisit intentions through satisfaction.
2.2. Satisfaction and social exchange theory
Satisfaction is one of the most heavily researched topics in
consumer behavior and marketing. The importance of understanding satisfaction is primarily based on its potential outcomes,

such as: loyalty and commitment (Cronin & Taylor, 1992), word-ofmouth (Huia, Wan, & Ho, 2006), complaining behavior (Landon,
1977), and repurchase intentions (Hu, 2003; Petrick, 2004;
Petrick & Backman, 2001, 2002; Petrick, Morais, & Norman, 2001;
Petrick, Tonner, & Quinn, 2006).
Customer satisfaction has generally been conceptualized as a
post-purchase evaluative judgment concerning a specific purchase
choice (Westbrook & Oliver, 1999). Satisfaction is created more
from feelings-based criteria than cognitive criteria, yet it tends to
relate as much to perceptions of the intermediate steps of personal
exchange during the process of service delivery as to its actual
outputs (Nowak & Washburn, 1998). Satisfaction is further
complicated by the influence of personal and social variables such
as needs, disposition, traveling companions and previous


374

H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381

experience (Crompton & Love, 1995; Kozak, 2001). This suggests
that the importance of examining various antecedents of satisfaction (Cronin & Taylor, 1992).
According to social exchange theory, interpersonal interaction
includes exchanges of resources and satisfaction is primarily
influenced by the social and economic outcomes of those exchanges (Homans, 1958). On the contrary, the expectancydisconfirmation paradigm, which is arguably a dominant satisfaction framework, focuses on internal processing which involves
comparison of the actual and expected performance of a product or
service (Oliver, 1977). Therefore, a key advantage of social exchange
theory is that it considers the interpersonal variables influencing
satisfaction. Successful relationships are characterized by reciprocity (Gouldner, 1960), and it is likely that they are the keys to
positive feelings about sustained social relationships. Social exchange theory was originally built upon rational choice assumption
of human behavior, but Lawler and his colleagues connected rates

of social exchanges and positive emotions (Lawler, Thye, & Yoon,
2000; Lawler & Yoon, 1993). The theory takes its specific form as
an affect theory of social exchange, which conceives of the importance of emotion as an outcome of social exchanges for relational
commitment (Lawler, 2001; Lawler & Thye, 2006). This theory
accordingly supports the affective outcome (i.e., satisfaction)
resulted from social interactions that this study asserts.
Social exchange relationships evolve when an individual who
supplies rewarding services to another obligates them. To discharge
this obligation, the second must in turn furnish benefits to the first
(Blau, 1964). To the extent that both parties apply the reciprocity
norm to their relationships, favorable treatment by either party is
reciprocated, leading to mutually beneficial outcomes (Rhoades &
Eisenberger, 2002). Similar to above reasoning, the four types of
interactions suggested in this study have been recognized. Of these,
the interactions with service providers (Morais, Dorsch, &
Backman, 2004; Sierra & McQuitty, 2005) or other customers
(Huang & Hsu, 2010; Rosenbaum & Massiah, 2007; Wu, 2007) have
been identified both in the general service and tourism literatures
with respect to positive post-purchase behaviors. In addition to
these two parities, some tourism scholars have suggested that
direct and indirect interactions with local residents can play a role
in creating positive tourist experiences (Carmichael, 2006; Fick &
Ritchie, 1991). Within a travel group, Kozak and Duman (2012)
recently investigated the role of other members in a family affect
a spouse’ (or partner’)s vacation satisfaction, concluding the postpurchase evaluation is part of a joint-decision making process.
Combining these prior evidences, the following four hypotheses
were derived.
Hypothesis 1. Interactions with service providers will have a
positive effect on satisfaction.
Hypothesis 2. Interactions with local residents will have a positive effect on satisfaction.

Hypothesis 3. Interactions with companion tourists will have a
positive effect on satisfaction.
Hypothesis 4. Interactions with other customers will have a
positive effect on satisfaction.

2.3. Satisfaction and resource theory
Resource theory is a social psychological framework for understanding social interactions and relationships. It is closely related to
social exchange theory, which very broadly refers to any conceptual
model or theoretical approach that focuses on the exchange of resources between or among people. In this way, social interactions

are seen as providing the means by which persons can obtain
needed resources from others and, thus, gain satisfaction as a result
of the effect these transactions have on them (Rettig & Bubolz,
1983). Consequently, resource theory represents a broad conceptual framework that helps us to understand interpersonal behavior
and the relationships between individuals in everyday life. In
particular, this theory posits that the resources exchanged by those
having relationships are expected to be qualitatively different as
well as engaging in a greater quantity of exchanges (Foa & Foa,
1976).
Within the resource theory framework, resources can be broadly
defined as “any item, concrete or symbolic, which can become the
object of exchange among people” (Foa & Foa 1980, p. 78). Resources
can be classified into six classes: (1) Lovedan expression of affectionate regard, warmth, or comfort; (2) Statusdan evaluative
judgment conveying high or low prestige, regard, or esteem; (3)
Information eany advice, knowledge, opinions, or suggestions; (4)
Moneydany coin or token that has some standard of exchange
value; (5) Goodsdany tangible items that are exchanged; and (6)
Servicesdactivities provided to or by an individual.
According to Foa and Foa (1980), individuals satisfy personal
needs through resource exchanges with others. Incorporating the

material and nonmaterial needs of an individual with another,
resource theory has the potential to assist in understanding satisfaction in an agritourism context where both material and nonmaterial exchanges are necessarily common. According to some
relationship scholars (Buunk & Verhoeven, 1991; Miller & Berg,
1982), the type of relationship is an influential factor in social exchanges, as previous research in social psychology has indicated
that different types of social interaction have distinct effects on life
satisfaction. Among them, Rook (1987a, 1987b) compared the role
of companionship and other social relationships on life satisfaction,
emphasizing the important nature of shared experiences and activities associated with companionships. Accordingly, when the
tourist-to-companion tourist interaction is compared with the
tourist-to-other customers interaction on satisfaction judgment,
the effect of the former may be more significant than the latter in
agritourism encounters. In a similar vein, how tourists interact with
service providers is hypothesized to be more prominent in their
satisfaction judgment than their interaction with other local residents (i.e., other local farmers). This does not mean that interactions with local residents are not important, but rather to
understand how tourists’ interactions with service providers and
local residents both influence agritourism encounters. Therefore,
the specific hypotheses regarding the type of relationship are:
Hypothesis 5. The effect of agritourists’ interactions with their
own companions on satisfaction will be stronger than the effect of
agritourists’ interactions with other customers on satisfaction.
Hypothesis 6. The effect of agritourists’ interactions with service
providers on satisfaction will be stronger than the effect of agritourists’ interactions with local residents on satisfaction.

2.4. Revisit intentions
Many tourism scholars have increasingly discussed the concept
of revisit intentions and its antecedents by examining their beneficial rewards; creating positive word-of-mouth, achieving better
cost-effectiveness by repeat visitors, and increasing economic
profit (Shoemaker & Lewis, 1999). In agritourism, as seasonal
changes are part of the farming environment, this also creates the
importance of attracting a high portion of repeat tourists.

The concept of revisit intentions is adopted and modified from
both social psychology and marketing perspectives. In social


H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381

psychology, the intention to continue/to stay in a relationship is
referred to as relationship maintenance by social exchange theory
(Thibaut & Kelley, 1959). Consistent with this conceptualization of
revisit intentions as an extension of the relationship framework,
this study examines the relationship between agritourists’ interactions at agritourism experiences and their revisit intentions,
mediated by satisfaction. A number of tourism researchers in this
domain have suggested several other key antecedents of revisit
intentions, though theoretical and empirical findings are quite
consistent in suggesting satisfaction positively related to revisit
intentions (Chen & Tsai, 2007; Kozak, 2001; Petrick, 2004; Yuksel,
2001). This study thus postulates that satisfaction will ultimately
influence agritourists’ intention to revisit the farm in the following:
Hypothesis 7. There will be a positive relationship between
satisfaction and revisit intentions.

3. Methodology
3.1. Survey development
Following resource theory’s suggestion that social interactions
include as many as six different resources, a preliminary 18 items
(Table 2) were included to measure the concept of interaction with
service providers. In addition to 14 items suggested in the previous
literature (Morais, Backman, & Dorsch, 2003), 4 additional items
Table 1
Demographic characteristics of the sample.

Demographic characteristics
Gender
Male
Female
Age
18e39
40e59
60þ
Income (Average)
Less than 19,999
$20,000 to less than $40,000
$40,000 to less than $60,000
$60,000 to less than $80,000
$80,000 to less than $100,000
$100,000þ
Marital status
Single
Married
Single parent w/child(ren)
Married w/child(ren)
Other
Employment status
Employed full-time
Employed part-time
Self-employed
Full-time homemaker
Student
Retired
Not currently employed
Education background

Less than high school
Completed high school
Some college, not completed
Completed college
Post graduate work started/completed
Ethnic background
Caucasian
Hispanic or Mexican American
African American
Asian
Native American
Other

Percentage (N ¼ 266)
40.6%
59.4%
63.1%
27.6%
9.2%
$69,000
6.4%
12.7%
16.8%
31.9%
15.0%
17.3%
30.7%
28.1%
19.7%
19.9%

1.5%
35.2%
23.1%
5.3%
11.5%
13.02%
7.4%
4.4%
.3%
12.7%
4.3%
50.6%
32.2%
79.8%
5.5%
1.0%
10.3%
.5%
2.8%

375

relevant to agritourism context were included. For agritourists’ interactions with local residents, companions, and other customers,
the same items were used excluding six irrelevant items (three items
each of interactions through product and money exchange. e.g., local
residents/companions/other customers offered discounts). All variables were measured on five-point Likert-type scales ranging from
1 (strongly disagree) to 5 (Strongly agree). In addition, four sets of
polar items on a five-point modified semantic differential summation scale for satisfaction and the two items on a 5-point scale for
revisit intentions were adopted from Baker and Crompton (2000)
and Grewal, Monroe, & Krishnan (1998) respectively.

3.2. Data collection
Texas was selected for the study site due to its significant
contribution of agriculture to the whole country. Although data is
unavailable for the total number of farms involved in tourism in
Texas, according to the National Agriculture Statistics Service
(NASS) in 2008, Texas led the nation in number of farms (229,000),
total land in farms (129 million acres), and livestock and product
commodity sales ($9.3 billion) in 2008.
Word-of-mouth recommendations obtained from agricultural
professionals in practice, academics, and the government resulted
in the identification of 19 Texas farms engaged with tourism activity. Among those, five relevant agritourism farms located in
central Texas, were chosen based on the distance and year-round
availability. They were contacted for possible participation and
three farms agreed to participate in study. The data were collected
from February to March 2009 via onsite surveys. Every 5th visitor
was systematically approached (Dillman, 2000) and informed
about the purpose of the survey in advance before they were given
the questionnaire. During an 8-week period, a total of 307 surveys
were returned. Of those, 21 incomplete or duplicate responses were
identified and removed. In addition, responses from those who
stated that they routinely visited the farm almost every week (20
responses) were also removed as they were identified as local
customers who purchase farm products. Thus, 266 were kept in the
final sample for analysis, and the response rate was 82.6%.
As shown in Table 1, the majority of respondents were women
(59.4%) and in the 18 to 39 age cohort (63.1%). Agritourists tended
to be highly educated with 82.8 percent having completed college
and the average income being $69,000. Among the respondents,
58.3 percent were employed either full-time or part-time. Among
them, 29.5% were repeaters and the average number of visits

among the repeaters was 2.9 times. Of the respondents, 255 (95.9%)
accompanied companions. The average party size was 2.6 ranging
from 2 to 19, and their visit/s was/were mainly with their families
(60.8%) or friends (34.0%).
4. Results
4.1. Measures
The measurement models of all constructs (i.e., social interactions with service providers, companions, and other customers,
satisfaction, and revisit intentions) except social interactions with
local residents were identified. In this step, social interactions with
local residents was dropped from the final structural model due to its
low reliability (Hair, Anderson, Tatham, & Black, 1998) (Cronbach’s
alpha ¼ .402) and too many missing values (35.1% nonresponse rate
for this construct) (Raymond & Roberts, 1987).
Factor analyses were preliminarily conducted (Mulaik, 2004) in
order to reduce the number of variables for the three social interactions scales and unidimensionality of satisfaction and revisit
intentions scales. The social interaction scale had not been


376

H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381

Table 2
Result of exploratory factor analyses.
Factors & items
Love_S: Interactions with service providers through love
Fondness_S: Service providers were very fond of me.
Importance_S: Service providers treated me important.
Personal_S: Service providers treated me personally.
Care_S: Service providers cared about me.

Money_S: Interactions with service providers through money
Discount_S: Service providers offered discounts.
Money_S: Service providers provided monetary benefits
Free_S: Service providers provided or share a free stuff.
Souvenirs_S: Service providers provided or shared souvenirs.
Service_S: Interactions with service providers through service
Equipment_S: Service providers provided or shared good quality
equipment to use in this visit (basket, bag, etc).
Advantage_S: I took advantage of service providers’ help.
Love_C: Interactions with companions through love
Fondness_C: My companions were very fond of me during the visit.
Importance_C: My companions treated me as an important person.
Personal _C: My companions treated me personally.
Care_C: My companions cared about me.
Information_C: Interactions with companions through information
Attraction_C: My companion(s) provided me with information on
attraction, lodging, or restaurant around the farm.
Problem_C: My companion(s) provided me with information about the
problems.
Education_C: My companion(s) educated me about a farm.
Advantage_C: I took advantage of my companion(s) ’s help.
Status_O: Interactions with other customers through status
Fondness_O: Other customers were very fond of me.
Personal_O: Other customers treated me personally.
Esteem_O: Other customers treated me with high esteem.
Care_O: Other customers cared about me.
Special_O: Other customers treated me special.
Information_O: Interactions with other customers through information
Attraction_O: Other customers provided me with information on attraction,
lodging, or restaurant around the farm.

Problem_O: Other customers provided me with information about the problems.
Satisfaction (SA)
I was satisfied with the farm and its experience.
I was pleased with the farm and its experience.
My experience at the farm was favorable.
My overall feeling about the farm was positive.
Revisit Intentions (RI)
If I were to visit a farm again, the probability that it would be this farm
again.
The likelihood that I’d consider visiting this farm again is .

FC

EV

VE (%)

RC

7.26

42.17

.93

2.63

15.49

.70


1.02

6.00

.73

7.19

59.88

.93

1.07

8.88

.82

6.49

54.11

.93

1.02

8.46

.78


3.71

92.38

.92

1.83

91.54

.90

.86
.88
.87
.86
.81
.80
.80
.85
.68
.80
.84
.83
.84
.73
.64
.83
.76

.74
.82
.81
.80
.58

.85
.63
.96
.97
.95
.97
.96
.96

Note: FC: Factor loadings, EV: Eigen value, VE: Variance extracted, RC: Reliability Coefficient, S, C, and O indicate Service providers, Companion tourists, and Other customers
respectively.

sufficiently tested since its development in a tourism setting
(Morais et al., 2003). Hence, it was determined that it would be
more appropriate to conduct a multi-step process for examining
and refining each scale. Exploratory Factor Analysis (EFA) to
examine the dimensional structure and properties of the measure
relevant to the study context was chosen as suggested by Churchill
(1979). Kaiser-Meyer-Olkin (KMO) measure with .79e.91 (Kaiser,
1970) and Bartlett’s test of sphericity (Bartlett, 1950) of 10,211
(p < .001) found that the data were appropriate for factor analysis.
Various cutoff criteria were used to determine the number of factors derived, such as eigenvalues, scree plot, percentage of variance,
item communalities, and factor loadings (Hair et al., 1998). Items
with loadings lower than .4 and with loadings higher than .4 on

more than one factor were eliminated. For social interactions with
service providers, output of the EFA with a Varimax rotation using
SPSS 15.0 suggested three factors (social interactions through love,
money, and service), which explained 64.2% of the variance. Two
latent factors were identified for social interactions with companions (social interactions through love and information) and
explained 68.8% of the variance. Analysis of social interactions with
other customers revealed two factors (social interactions through

status and information), explaining 68.7% of the variance. In total,
17 items were removed in this preliminary step due to low factor
loadings or dual factor loadings and details of the results of the EFA
are shown in Table 2. The reliability coefficients of factors identified
for the three social interactions ranged from .70 to .93, which
exceeded the minimum standard for reliability of .70 recommended by Nunnally and Bernstein (1994). For the satisfaction and
revisit intentions constructs, factor analyses confirmed one factor
each, accounting for 92.4% and 91.5% of the total variance, respectively. Reliability coefficients of .92 and .90 respectively, indicated
acceptable reliability (Nunnally & Bernstein, 1994).

4.2. The hypothesized structural models
AMOS 17.0 was employed to examine the structural models. For
this, Skewness and Kurtosis tests were preliminary performed to
evaluate normality of the data. The absolute value for univariate
skewness and kurtosis ranged from .03 to 2.09 and from .01 to 3.79
respectively and fell within conventional criteria of normality
(Kline, 2005).


H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381
Table 3
Comparison of overall fit indices for the hypothesized and alternative models

(N ¼ 266).
Model

c2 (df)

RMSEA

CFI

GFI

NNFI

IFI

RFI

Hypothesized
structural
model
Alternative
model

426.01
(162)

.07

.91


.90

.90

.94

.93

413.21
(155)

.07

.90

.90

.89

.93

.93

Dc2

12.8*

Note: RMSEA: Root mean square error of approximation, CFI: Comparative Fit Index,
GFI: goodness-of-fit index, NNFI: Non-normed Fit Index, IFI: Incremental Fit Index,
RFI: Relative Fit Index.

*p < .05.

The nine constructs were incorporated into the structural model
to examine the hypothesized relationships among the latent factors.
Since some of the factors were measured by more than four items, a
parceling procedure (Bagozzi & Heatherton, 1994) was adopted. This
procedure, by combining items randomly into composites, can help
reduce random errors, increase the stability of the parameter estimates, improve the variable to sample size ratio, remedy small
sample sizes, and simultaneously maintain the properties of multiple indicators (Bagozzi & Edward, 1998; Hallak, Brown, & Lindsay,
2012). In addition, a structural model that is based on parceled items
is more “parsimonious” than a model with individual items (Little,
Cunningham, Shahar, & Widaman, 2002), and parameter estimates
calculated when item parcels are used are more stable and therefore, more generalizable (Cunningham, 2007). Researchers often
recommend the use of item parceling strategies, particularly when
the underlying research questions involve relationships between
the constructs rather than the functioning of individual items
(Labouvie & Ruetsch, 1995; Rocha & Chelladurai, 2012). For this
study, a total of ten parcels were created for the five social interaction dimensions having more than four items.
Maximum likelihood model estimation was used to test the fit
the hypothesized structural model. In the hypothesized structural
model, the seven social interaction constructs were exogenous, and
predicted satisfaction, which in turn predicted revisit intentions.
The results of the SEM showed that the proposed model provided a
good fit to the data (Table 3) (c2 (162) ¼ 426.01 p < .001, CFI ¼ .91;
NNFI ¼ .90; GFI ¼ .90; IFI ¼ .94; RFI ¼ .93; and RMSEA ¼ .07). These
fit indices appropriately met the cutoff requirements of suggested
model fit indices by Kline (2005) and Bollen (1989). The hypothesized structural model indicated that all standard factor loadings
were greater than .50 (Kline, 2005) and no variable has modification indices (MI) scores greater than 100. Moreover, the present MI
results were fairly complex, and did not present a theoretically
meaningful solution to improve the model fit further.

In the hypothesized model, all the indicators loaded significantly
and substantively on their factors (p < .05), suggesting convergent

377

validity (Bagozzi & Yi,1988). As shown in Table 4, the average variance
extracted (AVE) exceeded .5, further supporting convergent validity
(Fornell & Larcker, 1981). The correlations among factors were not
higher than .85 (Kenny, 2012). In addition, the most conservative
method using AVE also confirmed the discriminant validity because
the AVE for each construct was greater than the squared correlation
coefficients for the corresponding inter-constructs and this confirms
discriminant validity (Fornell & Larcker, 1981) (Table 4). The items
included in the hypothesized model are identified in Fig.1, which also
shows the standard path coefficients and standard deviations.
4.3. Alternative model
In order to validate the hypothesized model and the mediating
role of satisfaction, an alternative model which included direct
paths between the social interaction constructs and revisit intentions was also examined. The c2 difference test examined the
null hypotheses of no significant difference with a nested structured model. If the null hypothesis is sustained, the more constrained model would be tentatively accepted.
A set of fit statistics indicated that the alternative model moderately fit the data (c2 (155) ¼ 413.21, p < .001, CFI ¼ .90; NNFI ¼ .89;
GFI ¼ .90; IFI ¼ .93; RFI ¼ .93; and RMSEA ¼ .07). The change in chisquare indicated that the fit of the alternative model did not perform
better than the hypothesized structural model (Dc2 ¼ 12.8, df ¼ 7,
p < .01). Examination of the individual path coefficients indicated
that three paths were found to be positive and statistically significant
(baMoneyeService Providers ¼ .08, p < .05, baServiceeService Providers ¼ .12,
p < .01, baInformationeCompanion ¼ .09, p < .05). Although three of the
seven direct paths from the seven social interaction constructs to
revisit intentions were statistically significant (p < .05), all three path
coefficients indicated only weak direct relationships, whereas the

paths between the six social interaction constructs and satisfaction
and those between satisfaction and revisit intentions remained
relatively strong and significant (Fig. 2). Although these results
technically indicated partial mediation (Baron & Kenny, 1986), given
the fact that the more parsimonious hypothesized model fit the data
better and only three paths from social interaction to revisit intentions were statistically significant with the relatively weaker path
loadings, it was concluded that the hypothesized structural model fit
the data better than the alternative model.
4.4. Hypotheses testing
Path coefficients estimated by SEM and the results of hypotheses 1
to 6 are presented in Fig. 1. The path coefficient from social interactions with service providers to satisfaction was significant at the
.01 level, indicating a positive relationship (bLoveeService Providers ¼ .31,
p < .01, bServiceeService Providers ¼ .25, p < .01, bMoneyeService Providers ¼ .11,

Table 4
Correlation (squared correlation), average variance extracted (AVE), and mean of the hypothesized model.
1
1. Love_S
2. Money_S
3. Service_S
4. Love_C
5. Information_C
6. Status_O
7. Information_O
8. SA
9. RI
AVE
Standard deviation
Mean


1
.172
.762
.597
.549
.012
.103
.568
.447
.793
1.34
3.95

2
(.030)
(.566)
(.356)
(.301)
(.000)
(.011)
(.323)
(.200)

All correlations are significant at p < .05.

1
.476
.172
.485
.032

.160
.315
.251
.611
1.38
2.75

3

(.228)
(.030)
(.235)
(.001)
(.026)
(.099)
(.063)

1
.631
.643
.018
.158
.665
.451
.569
1.37
3.55

4


(.398)
(.413)
(.000)
(.025)
(.442)
(.203)

1
.702
.075
.175
.614
.487
.782
1.49
3.86

5

(.491)
(.006)
(.031)
(.377)
(.237)

1
.051
.223
.642
.458

.923
1.34
3.02

(.003)
(.050)
(.412)
(.210)

6

7

8

1
.482 (.232)
.080 (.006)
.240 (.058)
.566
1.28
3.00

1
.254 (.065)
.160 (.026)
.490
1.28
3.34


1
.756 (.572)
.899
1.30
4.22

9

1
.911
1.40
3.94


378

H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381

Fig. 1. Estimation of the hypothesized structural model.

p < .05). The path coefficients from the two factors of social interactions with companions to satisfaction were also significant,
indicating a positive relationship (bLoveeCompanions ¼ .28, p < .01,
bInformationeCompanions ¼ .15, p < .05). Yet, for the path coefficients
between the two factors of social interactions with other
customers and satisfaction, only the path coefficient from the Status_O factor to satisfaction was positive and statistically significant

(bStatuseOther customers ¼ .12, p < .05). The path coefficient from the
Information_O factor to satisfaction was negative, yet statistically
insignificant (bInformationeOther Customers ¼ À.09, p > .10). The relationship between satisfaction and revisit intentions showed a positive relationship, significant at the .01 level (bSatisfaction ¼ .68, p < .01).
Therefore, while hypothesis 1 and 3 were supported, hypothesis 4 was

only partially supported.

Fig. 2. Estimation of the alternative model.


H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381

Hypothesis 5 was not able to be examined due to removal of social
interaction with local residents. But, the Love_S factor for social
interaction with service providers had the highest explanatory power for satisfaction among all types of social interactions, based on
the relative values of the path coefficients shown in Fig. 1. For hypothesis 6, the effect of social interactions with companions on
satisfaction was higher than that of social interactions with other
customers (bLoveeCompanions ¼ .28, p < .01, bInformationeCompanions ¼ .15,
p < .05); bStatuseOther customers ¼ .12, p < .05, bInformationeOther
Customers ¼ À.09, p > .05). Therefore, hypothesis 6 was supported.
5. Discussion and implications
The purposes of this study were to: (1) integrate observable
interpersonal interactions between service providers, local residents, companions, and other customers in small-scale farms
involved in tourism; and (2) examine the relationships between the
interactions and revisit intentions mediated by satisfaction.
The proposed model examined integrated social interactions
that have been observed in tourism contexts particularly for smallscale tourism operations on farms, and extends Yi and Gong’s work
regarding service encounters as an exchange process (2009). By
examining agritourism service encounters from a social exchange
perspective, this study suggests that agritourism operators need to
consider a tourist’s interpersonal interactions and how those interactions influence his/her tourism experiences. Providing an opportunity for positive and supportive interactions using
agritourism programs and services could help improve tourists’
satisfaction with their tourism experience. As important as a person
perceives the process and outcome of the relationship, he/she will
most likely devote him/herself to it. Thus, it is believed to be an

important part of functional social exchange because it ensures that
partners will put forth the effort necessary to produce mutually
desirable outcomes. However, it should be noted that all social
interactions make important, but complementary contributions to
tourists’ satisfaction judgment.
The results of this study supported Hypothesis 1 to 6, except
hypotheses 2 and 5, which included social interactions with local
residents. The integrated model indicated that social interactions
with service providers through love, money, and service exchange
and those with companions through love and information exchange positively affected satisfaction with the farm visit. For the
effect of interactions with other customers, exchange of status resources was positive, but the link between interactions through
exchange of information resources was neither positive nor statistically significant. Additionally, this study demonstrated that the
types of relationships were also important indicators in comparing
the effects of interactions on satisfaction, as interactions with
companions influenced satisfaction more than those with other
customers. Although the tourism literature has not paid attention
to the relationships between tourists’ and their companions associated with service experiences, this study revealed an important
role of travel companions on agitourists’ overall experience.
5.1. Theoretical implications
This study contributed to the repeat visit and satisfaction literatures because it examined an alternative theoretical explanation
focusing on social interactions. To the best of the authors’ knowledge, this is the first study in tourism to examine social interactions
with service providers, companions, and other customers simultaneously in the visitors’ domain. Although there are different types of
social interactions that can play critical roles in tourism service
encounters, previous research has focused mainly on those interactions respectively with service providers and customers. By

379

integrating observable social interactions at agritourism encounters, this study provides a framework for understanding the contributions of different types of social interactions to satisfaction and
revisit intention that are grounded in social exchange theory and
resource theory. In general, relationships between customers and

tourism operations are based on repetitive interactions over time,
which provide opportunities for customers to develop an enduring,
positive relationship with service providers, companions, and other
customers. This implies the importance of examining the role of
social interactions from a customer perspective drawn from social
exchange theory, which has only been applied to local residents in
the tourism literature (Gursoy, Chi, & Dyer, 2009; Perdue, Long, &
Kang, 1999). Additionally, the study provided empirical support to
the hypothesized influence of social interactions on satisfaction and
to the usefulness of resource theory as an alternative theoretical
framework to explain satisfaction and revisit intentions. Different
from previous studies on customers’ social interactions with service
providers (Solnet, 2007) and other customers (Huang & Hsu, 2010),
this study adopted resource theory, which suggests multidimensional constructs of social interactions. The usefulness of resource
theory in measuring customers’ social interactions is manifest in
important contributions to the research (Berg, Piner, & Frank, 1993).
This study could also contribute to developing a servicescape
framework specific to agritourism or possibly relevant to general
tourism. The servicescape concept builds upon well-established
research traditions in environmental psychology and marketing
that the design of the physical environment can be an extremely
important element in influencing consumption patterns and practices by emphasizing the co-creation of experience between service
providers and customers. The servicescape is typically comprised of
three dimensions: ambient conditions, spatial layout and signs/
symbols/artifacts and the concept (Bitner, 1992). It has been argued
that these dimensions remain invaluable to tourism marketing
(Abubakar, 2002). However, many servicescape researchers have
increasingly moved beyond a consumption setting’s physical
dimension to less palpable dimensions, including social dimensions
which are also housed within the servicescape (Hightower, 2010;

Rosenbaum & Massiah, 2011; Tombs & McColl-Kennedy, 2003).
The importance of social dimensions is particularly evident in
tourism as tourists fulfill not only their utilitarian needs but also
their social and psychological needs. Therefore, an integrated
model of the three interactions during agritourism encounters can
serve as a basis for the social elements framework that are
encapsulated in the tourism servicescape. A servicescape framework embracing three types of interactions into three dimensions
of the physical elements advocates that the service setting is not
only physically appealing and symbolically welcoming, but also
socially supportive and engaging (Rosenbaum & Massiash, 2011).
5.2. Managerial implications
Regarding social interactions with service providers, steps should
be taken to encourage customereservice provider interactions, as
the current research suggested that these benefit customers. In
particular, in terms of the dimensions of social interactions with
service providers, an important tenet can be suggested. As the
theoretical framework suggested that particularistic resources
exchanged may help increase customers’ satisfaction with their
experience, this study provides evidence that providers who wish for
their customers to return should exchange love and services. For
example, providers could create personalized interactions to let their
customers know how they are cared for and how important they are.
On the other hand, monetary benefits such as price discounts did not
sustain customer satisfaction as highly as care and personal relationships did. The value associated with a price discount can be


380

H. Choo, J.F. Petrick / Tourism Management 40 (2014) 372e381


perceived as just a cheaper price that is applicable to all customers,
which might be why offering a price discount was the least valuable
resource that customers were looking for. In order to provide universal resources more effectively, results of this study suggest they
need to be designed to convey personal care and attention towards
individual tourists rather than monetary benefits.
Regarding interactions between unacquainted customers, status
exchanges were found to be important aspects. Thus, educating
customers on the types of behavior expected of them might be
important. As in some other service contexts, sharing the environment with unacquainted people and standing in line at the
farmer’s market, which are common aspects of agritourism, are
possible serious challenges. However, agritourism environments
that convey high prestige and regard among unacquainted customers could be managed as a satisfying experience through proper
customer education.
For farm tourists, it was found that families, friends, and relatives play important roles as travel companions who exchange care
and warmth through shared experience as well as being a source of
information related to farm visits. Accordingly, when developing
marketing programs, operators should emphasize the wants and
needs of travel groups as well as those of individual tourists.
Although interaction with companions is not directly controllable,
agritourism services could provide a context for mutual enjoyment
and shared experience, leading to couple-, family-, and groupfriendly environments. As a vast majority of respondents were
accompanied by their family to the farms, service providers should
emphasize in their advertising family-friendly environmental
characteristics that could enhance satisfaction for the travel party
with whom agritourists will travel.
5.3. Limitations and further research
Additional efforts in scale development need to be done to ensure
the validity and reliability of the social interactions scales used as the
process of developing the social interactions scale adopting resource
theory in the tourism field is fairly new. In particular, due to dual

factor loading and insufficient factor loading scores, all items
belonging to the product dimension of social interactions with service providers were not included in the final model. Therefore, the
influence of product exchange could not be tested in this study,
although at face value it appears to be theoretically and practically
important. Subsequent efforts in scale formation addressing this
dimension should be made for more theoretical completeness.
Additionally, a high item nonresponse rate of social interactions
with local residents resulted in the deletion of this concept from the
final model for this study. The characteristics of study farms (i.e.,
standalone farms without near farms or many neighbors) and the
lack of a concrete definition of “local residents” from the various
agritourists’ perspectives (i.e., local visitors, out-of-state visitors)
are potential reasons for the high item nonresponse rate. A more
specific definition of “local resident” relevant to the various types of
agritourists needs to be determined for future research. In addition,
the data collection for this study relied on survey informant gathered at only three farms in Texas, so the result of the study likely
should not be generalized beyond the study population.
Finally, this study suggests that the success of tourism business on
farms can be derived from the integration of social encounters into a
meaningful experience developing trust and attachment to current
visitors. Understanding agritourists behavior on small-scale tourism
operations, might not only broaden the horizons of theoretical
advancement for agritourist behavior, but also help small-scale
tourism operations develop marketing strategies and define their
own markets specific to them for a more successful business.

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Hyungsuk Choo is an Assistant Professor at Bowling Green
State University. Her research interest focuses on exploring
the applicability of service marketing principles and
social-psychology in the context of agritourism and festivals/events identifying the sustainability theory and practice from consumers’ perspective.

Jim Petrick is a Full Professor and Research fellow at Texas
A&M University. His research explores the determinants of
tourists’ purchase behaviors. In the past eleven years, he
has been awarded more than $2 million in research grants
and has been recognized for his research abilities with the
following awards: Emerging Scholar of Distinction – from
the International Academy for the Study of Tourism
(2009), Agri-life Research Fellow (2008), Most Outstanding
Conference Paper (TTRA National Conference, 2001),
Holland America Line Westours Research Award (2004 &
2000), American Society of Travel Agents Future Tourism
Leader Award (1999), and the Excellence in Research Award
from RCRA (1998).