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Determinants of Online Customer Satisfaction in an Emerging Market–a Mediator Role of Trust

International Journal of Contemporary Management, 13(1), 830
Nguyen Thi Tuyet Mai*, Takahashi Yoshi**, Nham Phong Tuan***
Background. Customer satisfaction, in many cases affected by trust, is critical to the post-
consumption intention and is regarded as the key success factor of sales in general and elec-
tronic commerce websites in particular. However few studies indicate clearly the determi-
nants and especially their influential strengths on online customer satisfaction in emerging
Research aims. This study investigates what factors determine customer satisfaction.
Methods. Conducted research is using data collected from 758 online customers in Vietnam,
mostly young people.
Key findings. The particular contribution of these results shows that distributive fairness,
customer interface quality, perceived security, perceived usefulness and trust are significant
predictors of customer satisfaction; especially, the mediator role of trust is proved.
Keywords: Young online customer satisfaction, Trust, E-commerce, Mediator role
The appearance of the Internet has paved the way for the rapid growth of
electronic commerce (e-commerce). The economy and transaction meth-
ods have turned a new page since high-technology systems were exploit-
ed by applications. Finding partners and customers is not limited by state
borders and therefore the choice of products/services has increased due
to more suppliers from all over the world, available on the Internet. Be-
sides more opportunities, the competition among electric vendors (e-
vendors) has grown, especially for emerging markets where many interna-
tional giants operate. Hence marketers have tried to keep customer inten-
tion by raising customer satisfaction mainly through improving trust.
* Nguyen Thi Tuyet Mai, Graduate School for International Development and Coopera-
tion, Hiroshima University.
** Takahashi Yoshi, Graduate School for International Development and Cooperation,
Hiroshima University,
*** Nham Phong Tuan, University of Economics and Business, Vietnam National Uni-
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
One approach online companies can adopt is ensuring distributive
fairness and procedural fairness. Distributive and procedural fairness will
trigger the feelings of equity of outputs (what is received), departed from
inputs (what is invested) (Adams, 1963, p. 347, 1965) and of outcome-
determining procedures (Folger & Greenberg, 1985). From then, trust and
customer satisfaction will be maintained (Chiu, Lin, Sun, & Hsu, 2009).
Other aspects include customer interface quality, perceived security
and perceived usefulness. In offline commerce, face-to-face interaction
may directly satisfy buyers through supporting services. In e-commerce,
salespeople interact via website interfaces. The challenges facing online
sellers are to alleviate the uncertainty of incomplete or distorted infor-
mation (Ba & Pavlou, 2002) as well as ensure the security for sensitive
contents and transactions. Moreover, in emerging markets, customers trust
in virtual transactions is not strong. Therefore, the mission of web design-
ers is to create an attractive interface, updating latest information, and
security systems, thus enhancing the perception of usefulness among cus-
tomers. However, few studies investigate the above mentioned cognition
related to determinants of trust and satisfaction in online contexts in
emerging markets. Furthermore, trust is definitely one of the important
factors that have an impact on customer satisfaction (Chiou, 2003; Singh &
Sirdeshmukh, 2000) but few efforts are made to estimate trust as the key
mediator for paths to satisfaction in post-consumption intention. The above
reasons motivate our work to profoundly understand the impacting factors
on trust and satisfaction along with the mediator role of trust.
Literature Review
Trust. Trust has been conceptualized by previous scholars in a variety of
ways, both theoretically and empirically. Gefen, Karahanna, and Straub
(2003) summarize prior conceptualizations into four main categories: trust
is viewed as (a) a set of specific beliefs relying on the integrity, benevo-
lence and ability of an exchange partner in order to achieve a desired but
uncertain objective in a risky situation (Doney, Cannon, & Mullen, 1998;
Ganesan, 1994; Giffin, 1967), (b) a general belief that people are trustwor-
thy (Gefen, 2000; Hosmer, 1995; Moorman, Zaltman, & Deshpande, 1992),
sometimes measured as trusting intentions (McKnight, Cummings, & Cher-
vany, 1998) or! "the! willingness! to! b e! vulnerable#! (Schoorman, Mayer, &
Davis, 2007, p. 347), (c)!"feelings!of! confidence! and!security! in! the! caring!
response#! (Rempel, Holmes, & Zanna, 1985, p. 96), (d) a combination of
these elements. For example, Doney and Cannon (1997) combine the first
two conceptualizations into one.
In online shopping, trust is also conceptualized in diversified ways,
based on the four above categories, but more specifically in terms of ob-
jectives or contexts. For example, trust in e-commerce is a belief in com-
International Journal of Contemporary Management, 13(1), 830
petence, benevolence, and integrity (McKnight, Choudhury, & Kacmar,
2002; Pavlou & Fygenson, 2006) or expectations that others will do as ex-
pected (Jarvenpaa, Knoll, & Leidner, 1998), therefore these definitions be-
long to the first category. Other examples include trust in e-commerce as
being conceptualized as a general belief in an e-vendor that leads to be-
havioral intentions (Gefen, 2000) or a consumer%s willingness to become
vulnerable to the seller of an Internet store (Jarvenpaa, Tractinsky, & Saa-
rinen, 1999), so these conceptions belong to the second category. Our defi-
nition agrees with and relies on the concept of McKnight et al. (2002) and
Pavlou and Fygenson (2006) in the first category because identically to
them, in our study trust is seen from the aspect of customers% beliefs
about the quality of e-vendors, not about their willingness to be vulnera-
ble or security. Thus, trust is defined in this study as specific beliefs in the
competence, benevolence, integrity and trustworthiness of an e-vendor.
Trust is vital in many business relationships (Kumar, Scheer, &
Steenkamp, 1995; Moorman et al., 1992), especially in online shopping and
in emerging markets because here transactions contain an element of risk
and vulnerability (Reichheld & Schefter, 2000). Trust is also a critical as-
pect of e-commerce because the lack of assured guarantees and the indi-
rect character of transactions may result in unfair pricing, privacy viola-
tions, or unauthorized tracking (Gefen, 2000). Actually, some suggestions
point out that online customers generally avoid distrusted e-companies
(Jarvenpaa et al., 1999; Reichheld & Schefter, 2000). Since trust is the cen-
tral aspect in many e-transactions but few studies research its role as
a mediator between cognition during online shopping and post-consumption
intention including customer satisfaction.
Customer satisfaction. There are many definitions of customer satisfac-
tion in the literature. However, these definitions can be categorized into
two main groups: (a) a cognitive process of comparing what a customer
receives (rewards) against what they achieve with a service (costs); and
(b) an emotional feeling departing from an evaluative process. An example
of the first group: customer satisfaction is defined by Oliver (1997, p. 14) as
fulfillment, and hence a satisfaction judgment, which involves at the min-
imum two stimuli an outcome and a comparison referent•, used by Igle-
sias and Guillén (2004) as a complete evaluation of the accumulation pur-
chase and consumption experience, from which a comparison between
the sacrifice experienced and the perceived rewards is reflected; by
Churchill and Surprenant (1982) as an outcome of purchase and use result-
ing from buyers% comparison of the rewards and costs of a purchase in
relation to the anticipated consequences. An example of the second group:
customer satisfaction is defined by Tse and Wilton (1988, p. 204) as an
evaluation of the perceived discrepancy between prior expectations
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
()and the actual performance of the product•; by Oliver (1997, p. 13) as
•consumer•s fulfillment response•; by Howard and Seth (1969) as a cogni-
tive state about the appropriateness or inappropriateness of the reward
received in exchange for the service experienced by a buyer; or by West-
brook (1981) as an emotional state that occurs in response to the evalua-
tion of a service.
Recently, along with the boom in the Internet and e-commerce, many
studies are conducted with an aim to extend our understanding of satisfac-
tion in the virtual environment. In e-commerce, customer satisfaction is
also conceptualized according to the two main groups mentioned above.
For the first group, both Szymanski and Hise (2000) and Evanschitzky,
Iyer, Hesse, and Ahlert (2004) define customer satisfaction as consumers•
judgment of how the Internet retail experience and traditional retail stores
compare. For the second group, customer satisfaction is defined as a cus-
tomer•s contentment with a given e-commerce store (Anderson & Sriniva-
san, 2003). In this study, we conceptualized in unison with Anderson and
Srinivasan (2003) in the second group because similarly we care about
contentment of customers rather than cognitive processes. Therefore cus-
tomer satisfaction in online shopping is defined as the contentment of cus-
tomers after shopping in a given virtual store.
Customer satisfaction is very important in online shopping where hu-
man-to-human interactions is replaced by human-to-machine interactions
(Evanschitzky et al., 2004). Moreover, due to strong competition in e-
commerce and easily introducible changes in other stores, dissatisfied
customers are more likely to yield to the overtures of other competitors
(Anderson & Srinivasan, 2003). However, few studies have comprehensive-
ly covered the determinants of customer satisfaction in the post-con-
sumption intention in emerging markets, although the role of trust as the
key to customer satisfaction is well discussed in those studies.
Research Model and Hypotheses Development
The following sub-section will discuss the relationships concerned. Some
of our hypotheses aim to investigate the direct effects of cognitive deter-
minants on customer satisfaction after excluding the indirect ones through
the mediation of trust. However, we have to rely on the literature on the
total (direct + indirect) effects for developing those hypotheses due to
a lack of precise discussion on the issue.
The proposed model is shown in Figure 1.
International Journal of Contemporary Management, 13(1), 830
Figure 1. Research model
Source: own elaboration.
Distributive fairness. Distributive fairness, also known as perceived
fairness of outcomes, was introduced by Adams (1963). Adams emphasized
that there are correlations between inputs and expected outcomes. Expec-
tation departs from contributions to an exchange, for which a fair return
will be hopefully gained.
Interestingly, many previous studies mention the relationship between
distributive fairness and trust. Pilai, Williams, and Tan (2001) had a strong
argument on high levels of trust ensuing fair outcomes distributions. Be-
sides, equity theory is developed to confirm that individuals will be en-
couraged to trust if they can receive fairly distributive satisfaction (Adams,
1965; Blodgett, Hill, & Tax, 1997). Particularly in the case of e-commerce,
Chiu et al. (2009) added that when customers find products equal to their
expectations, the level of their trust in the vendor raises.
On the other hand, distributive fairness is also found to be correlated
with customer satisfaction. Distributive fairness is traditionally explored as
a predictor for customer satisfaction (Huppertz, Arenson, & Evans, 1978).
According to the research of Kumar, Scheer, and Steenkamp (1995) and
Oliver and Desarbo (1988), in marketing channels, distributive fairness will
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
build good customer satisfaction among buyers when the inputs and out-
puts of an exchange are considered in purchase transactions. Oliver and
Swan (1989) posited that distributive fairness influences outcomes includ-
ing customer satisfaction about products/services and from then it will
spill over onto a larger question of customer satisfaction with sellers. Con-
sistent with the theoretical discussion in psychology, other studies have
supported the positive effects of distributive fairness on customers satis-
faction (del Río-Lanza, Váquez-Casielles, & Díaz-Martín, 2009; Homburg &
Frst, 2005; Maxham & Netemeyer, 2003; Sinha & Batra, 1999; Smith, Bolton,
& Wagner, 1999; Tax, Brown, & Chandrashekaran, 1998; Vaidyanathan &
Aggarwal, 2003). In an e-commerce context, Chiu et al. (2009) also tested
successfully the impacts of distributive fairness on customer satisfaction.
Thus, based on the above discussion, we propose the following hy-
Hypothesis 1 (H1): Distributive fairness positively influences trust in
online shopping.
Hypothesis 2 (H2): Distributive fairness positively influences customer
satisfaction in online shopping.
Procedural fairness. Another stream of fairness is procedural fairness
which refers to the equity of the process of determining outcomes (Folger
& Greenberg, 1985). Procedural fairness is utilized to ensure provision of
accurate, unbiased, correctable, representative information and conform-
ance with standards of ethics or morality (Leventhal, 1980).
The relationship between procedural fairness and trust is found in
many studies. According to Pearce, Bigley, and Branyiczki (1998), trust as
well as organizational commitment results from procedural fairness in co-
workers. The same idea of a relationship between procedural fairness and
trust was also supported by the research of Cohen-Charash and Spector
(2001) and Aryee, Budhwar, and Chen (2002). In an online shopping con-
text particularly, Chiu et al. (2009) argued that the perceived fairness of
policies and procedures of shopping in the virtual markets has an influ-
ence on trust.
On the other side, the correlation between procedural fairness and
customer satisfaction has been estimated. Previously, scholars emphasized
the importance of procedural processes in which receivers do not feel
satisfied even though they obtain favorable returns. In contrast, they are
happy with fair procedures even if the outcomes are not proportional
(Lind & Tyler, 1988). Besides, Teo and Lim (2001) and Maxham and
Netemeyer (2002) indicated the positive effect of procedural on customer
satisfaction. Many researchers also find positive influences of procedure
on customer satisfaction in service encounters (Bolton, 1998; Hui &
Bateson, 1991; Smith et al., 1999), in complaint handling (Goodwin & Ross,
International Journal of Contemporary Management, 13(1), 830
1989; Homburg & Frst, 2005; Tax et al., 1998), in organization (Brockner &
Siegel, 1995), in service quality (Seiders & Berry, 1998; Smith et al., 1999;
Tax et al., 1998) and also in online shopping (Chiu et al., 2009).
Hypothesis 3 (H3): Procedural fairness positively influences trust in
online shopping.
Hypothesis 4 (H4): Procedural fairness positively influences customer
satisfaction in online shopping.
Customer interface quality. Customer interface quality is a concept
involving many aspects and is measured in different ways. Negash, Ryan,
and Igbaria (2003) mentioned three facets: information quality (information
and entertainment), system quality (interactivity and access) and service
quality (tangibles, reliability, assurance, responsiveness, and empathy).
Parasuraman, Zeithaml, and Malhotra (2005) utilized four dimensions: effi-
ciency of the website, system availability, privacy, and the post-transaction
experience whereas five transaction process-based (eTransQual) measures
including functionality, design, enjoyment, process, reliability and respon-
siveness were developed by Bauer, Falk, and Hammerschmidt (2006).
Convenience, interactivity, customization, and character are four dimen-
sions of the research of Chang and Chen (2009). In order to avoid overlap-
ping with other factors (distributive fairness and procedural fairness), this
study just wants to focus on text and picture displays, because for online
shoppers, friendly and effective user interfaces with an appropriate mode
of information presentation are very important. When purchasing a famil-
iar item online, pictures seem more efficient and effective than text, how-
ever with unfamiliar products items, that advantage diminishes (Chau, Au,
& Tam, 2000). Based on prior research (Chang & Chen, 2009; Chau et al.,
2000; Thakur & Summey, 2007), our study is composed of information and
character of websites. Information is the overall content display on a web-
site. It is always updated by adding the latest information and new prod-
ucts/services and consistently stimulates customers with a wider choice by
tailoring to their needs. Character is the overall image that the visual con-
tent impresses and which creates a friendly atmosphere to users. It is
composed of fonts, graphics, colors, background patterns, etc.
The most important determinant of e-trust is information presentation
on the website (Thakur & Summey, 2007). Chau, et al (2000) emphasized
that the key to acceptance and usage of a website is a user- friendly envi-
ronment with a suitable taste of the information presented on its interface.
According to Hoffman, Novak, and Peralta (1999), customers may not trust
website providers due to their suspicious entity data. The impact of cus-
tomer interface quality on trust is also shown in the study of Szymanski
and Hise (2000). Besides, customer interface quality has also proved to be
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
influential on customer satisfaction. An online stores !front design actually
improves store traffic and sales, and thus customer satisfaction (Lohse &
Spiller, 1999). The more extensive and higher quality information that is
available online may result in higher levels of e-satisfaction (Park & Kim,
2003; Peterson, Balasubramanian, & Bronnenberg, 1997; Szymanski & Hise,
2000). Eighmey and McCord (1998) concluded that considerations of design
efficiency will lead to good satisfaction, thus attracting repeat visits. Mon-
toya-Weis and Voss (2003) recognized that information content, navigation
structure, and graphic style are three website design factors impacting cus-
tomers !use!of!an!online!channel!and!their overall satisfaction. Therefore:
Hypothesis 5 (H5): Customer interface quality positively influences
trust in online shopping.
Hypothesis 6 (H6): Customer interface quality positively influences
customer satisfaction in online shopping.
Perceived security. Perceived security refers to customers !belief in the
safety of transmitting sensitive information (Chang & Chen, 2009). Hoffman
et al. (1999) proved that 69% of web users did not provide any data to any
websites out of sensitive data concerns. One report points that 86% of
commercial websites do not explain their purposes for using sensitive data
(Landesberg, Toby, Caro line, & Lev, 1998). The loss of customers !trust in
the protection of their privacy and the security of systems has proven to
be a main reason for a slow-down in the growth of the Internet and e-
commerce. The trustful relationship between customers and e-vendors is
built only by ensuring a major alliance of information technology, financial
control and audit functions (Keen, 2000). In line with the discussion above,
Jin and Park (2006), Szymanski and Hise (2000) and Park and Kim (2003)
proved that perceived security is a significant contributor to trust and
satisfaction. Therefore:
Hypothesis 7 (H7): Perceived security positively influences trust in
online shopping.
Hypothesis 8 (H8): Perceived security positively influences customer
satisfaction in online shopping.
Perceived usefulness. Perceived usefulness is the belief of customers in
enhancing online transaction performances (Chiu et al., 2009; Davis, 1989).
Whenever customers have perceived usefulness, they tend to trust a giv-
en e-vendor (Babin & Babin, 2001; Davis, 1989; Davis, Bagozzi, & War-
shaw, 1989; Mathieson, 1991; Pavlou & Fygenson, 2006; Taylor & Todd,
1995). Perceived usefulness is essential in shaping consumer attitudes and
satisfaction with the e-commerce channel (Devaraj, Fan, & Kohli, 2002).
The usage of Internet-based learning systems relies on the extended ver-
sion of the technology acceptance model (TAM) and is perceived to be
International Journal of Contemporary Management, 13(1), 830
useful in helping increase learners% satisfaction (Bhattacherjee & Premku-
mar, 2004; Saade & Bahli, 2004). Therefore:
Hypothesis 9 (H9): Perceived usefulness positively influences trust in
online shopping.
Hypothesis 10 (H10): Perceived usefulness positively influences cus-
tomer satisfaction in online shopping.
Trust. Based on the social exchange theory (Blau, 1964), some scholars
theorized that trust will create the strong impacts on customer satisfaction
(Chiou, 2003; Singh & Sirdeshmukh, 2000). Morgan and Hunt (1994) indi-
cated the key role of trust in shaping customer satisfaction. Singh and
Sirdeshmukh (2000) specified trust mechanisms in cooperating and com-
peting with agency mechanisms to know the effect on satisfaction in indi-
vidual encounters. They proved that trust will have a direct effect on post-
purchase satisfaction. Chiou (2003) and Lin and Wang (2006) argued that
accumulated trust will have an impact on overall satisfaction. In terms of
e-commerce, Chiu et al. (2009) proved that trust is the strongest variable
that has an impact on customer satisfaction in online shopping. Therefore:
Hypothesis 11 (H11): Trust positively influences customer satisfaction
in online shopping.
Data Collection
The data was collected in October 2011 in Vietnam via an online survey
because of the advantages of cost and speed. This online data collection
method was also consistent with the research subjects of the study, online
buyers. We distributed the link through a survey website
The survey lasted two months. The participants were volunteers interest-
ed in such a research topic and had prior shopping experiences.
A total of 1,025 responses were received. 267 out of 1,025 responses
were invalid, incomplete or gave the same rating to all items; the remain-
ing 758 questionnaires were used for the analysis. The demographic pro-
file of the questionnaires was summarized in Appendix A. It can be ob-
served that most of the participants are young customers, even students
who are usually early adopters.
The questionnaire was designed to measure research constructs using
multiple-items scales adapted from previous studies that reported high
statistical reliability and validity. Each item was evaluated on the five-
point Likert scale ranging from (1) Strongly disagree to (5) Strongly agree.
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
Distributive fairness and procedural fairness were measured using scales
adapted from Folger and Konovsky (1989), Moorman (1991), and Maxham
and Netemeyer (2002). Items for measuring customer interface quality
were based on Srinivasan, Anderson, and Ponnavolu (2002) and Thakur
and Summey (2007). The items of perceived security were derived from
Salisbury and Allison (2001). The questionnaire contained the standard
TAM scales of perceived usefulness adopted from Davis (1989) and Gefen
et al. (2003). Trust measures were based on Gefen et al. (2003) and Pavlou
and Fygenson (2006) while the items to assess customer satisfaction were
adapted from Anderson and Srinivasan (2003).
By using t-test or ANOVA, all items among the constructs were tested
against demographic controls (gender, age, education, job, years of experi-
ence with the Internet, number of online shopping occurrences in the past
six months, websites). All insignificant mean scores of the items showed
that analyzing the data as a single group is valid.
Analysis of the Measurement Model
In accordance with a two-step methodology of Anderson and Gerbing
(1988), a confirmatory factor analysis (CFA) was developed for measuring
the model in order to establish unidimensionality, reliability, convergent
validity and discriminant validity. Then structural equation modeling
(SEM) was estimated to test the hypotheses. Two steps were carried out
by the maximum likelihood method using AMOS software (version 20). In
order to check the fit of the models, some indices needed to be satisfied
above the recommended values: a chi-square with degrees of freedom
(2/df) was less than 3; goodness-of-fit index (GFI), comparable fit index
(CFI); tucker lewis index (TLI), normed fit index (NFI) were greater than
0.9; adjusted goodness of fit index (AGFI) was greater than 0.8; root mean
square error of approximation (RMSEA) was less than 0.08.
The good-of-fit indices satisfied the suggested value ( 2/df = 2.759; GFI
= 0.94; CFI = 0.97; TLI = 0.967; NFI = 0.956; AGFI = 0.919; RMSEA = 0.048),
therefore there was a reasonable overall fit between the model and ob-
served data. The reliability assessment was based on the comparable fit
index (CR). As shown in Table 2, all CR indices of constructs were over
the recommended cut-off level of 0.7 (Fornell & Larcker, 1981). In terms of
convergent validity, Table 2 suggests that all standardized regression
weights are higher than 0.60 and the critical ratios are significant at p =
0.001. In addition, two criteria, CR and average variance extracted (AVE),
were above the suggested levels, 0.7 and 0.5 respectively, by Fornell and
Larcker (1981). Finally, discriminant validity was examined using the
guideline in the research of Fornell and Larcker (1981).
International Journal of Contemporary Management, 13(1), 830
able 2. CFA Results for Measurement Model.
Distributive fairness (DF)
I think what I got is fair compared with
the price I paid
0.84 24.695*
I think the value of the products that I
received from the online store is
proportional to the price I paid
0.83 -
I think the products that I purchased at
the online store are considered to be a
good buy
0.75 21.781*
Procedural fairness (PF)
I think the procedures used by the online
store for handling problems occurring in
the shopping process are fair
0.93 36.119*
I think the policies of the online store are
applied consistently across all affected
0.87 32.319*
I think the online store would clarify
decisions about any change in the Website
and provide additional information when
requested by customers
0.87 -
Customer interface quality (CI)
The Website s design is attractive to me
The website keeps me well informed
about the current information
0.84 25.34*
The Website keeps me well informed
about new products/services
0.84 -
Perceived security (PS)
The Website is a safe site for sensitive
information transfers
0.81 25.272*
I would feel totally safe providing
sensitive information about myself to the
0.90 27.985*
Overall, the Website is a safe place to
transmit sensitive information
0.84 -
Perceived usefulness (PU)
The Website enables me to search and
buy goods faster
0.86 29.016*
The Website enhances my effectiveness to
search and buy goods
0.85 30.585*
The Website makes it easier to search for
goods and purchase them
0.87 25.271*
The Website increases my productivity in
searching and purchasing goods
0.77 -
Trust (TR)
Based on my experience with the online
store in the past, I know it is honest
0.84 -
Based on my experience with the online
store in the past, I know it keeps its
promises to its customers
0.85 28.216*
Based on my experience with the online
store in the past, I know it is trustworthy
0.86 28.476*
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
Customer satisfaction (CS)
I am satisfied with my decision to
purchase from the Website
0.89 27.701*
I think I did the right thing by buying
from the Website
0.88 27.116*
If I had to purchase again, I would feel
differently about buying from the Website
0.79 -
Overall goodness-of-fit indices
2 = 518.658 (p = 0.000); df = 188; 2/df = 2.759
GFI = 0.94; CFI = 0.97; TLI = 0.967; NFI = 0.956; AGFI = 0.919; RMSEA = 0.048
Note: 2, chi-square; df, degrees of freedom; CR, composite reliability; AVE, average variance extracted;
GFI, goodness-of-fit index; CFI, comparable fit index; TLI, tucker lewis index; NFI, normed fit index; AGFI,
adjusted goodness of fit index; RMSEA, root mean square error of approximation; *p <0.001
Source: author
In Table 3, the correlations among constructs were listed with the AVE on
the diagonal (in bold). All diagonal elements were larger than inter-
construct correlations; hence discriminant validity was proved.
Table 3. Correlation of Latent Variables
Note: Diagonal elements (in bold) are the square root of the average variance extracted (AVE). Off-
diagonal elements are the correlations among constructs; CI, customer interface quality; PS, perceived
security; CS, customer satisfaction; TR, trust; PU, perceived usefulness; PF, procedural fairness; DF, di s-
tributive fairness.
Source: author
Analysis of the SEM
Figure 2 and Table 4 shows the result of the SEM. Referred to the corre-
sponding recommended values all fit indices achieved a good model fit (2
= 479.036 (p = 0.000); df = 168; 2/df = 2.851; GFI = 0.942; CFI = 0.973; TLI =
0.967; NFI = 0.96; AGFI = 0.92; RMSEA = 0.049). The explanatory power of
the research model was shown in Figure 2 in which the model accounts
for 71 and 72% of variance (R2 score).
International Journal of Contemporary Management, 13(1), 830
Figure 2. SEM Analysis of the Search Model
Note: *p<0.001, **p<0.01; R2, square multiple correlations; the solid lines means reaching the significance at
p-value of 0.01, the dashed line means an insignificant path level of p-value of 0.01
Source: own elaboration
Ten out of eleven paths were significant. Among them, nine exhibited
a p-value of 0.001. H1, H2 were supported by the significant coefficient
paths from distributive fairness to trust and customer satisfaction of 0.232
and 0.145. Procedural fairness was associated with trust and with an insig-
nificant coefficient path with customer satisfaction, therefore H3 was sup-
ported but H4 was not supported. H5, H6, H7, H8 proposed that customer
interface quality and perceived security would positively impact on trust
and customer satisfaction, and the results were strongly supported (31=
0.285; !32= 0.165; 41=0.161; !42= 0.099). H9 and H10 posited that perceived
usefulness would have a positive effect on trust and customer satisfaction,
the results were significant, and therefore H9 and H10 were supported.
H11 was supported because trust had a positive influence on customer
satisfaction ("62= 0.32).
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
Table 4. The result of the SEM
Hypothesized relationship
Critical ratio
Distributive fairness Trust
Distributive fairness Customer satisfaction
Procedural fairness Trust
Procedural fairness Customer satisfaction
Customer interface quality Trust
Customer interface quality Customer satisfaction
Perceived security Trust
Perceived security Customer satisfaction
Perceived usefulness Trust
Perceived usefulness Customer satisfaction
Trust Customer satisfaction
Not supported
Overall goodness-of-fit indices
2 = 479.036 (p = 0.000); df = 168; "2/df = 2.851
GFI = 0.942; CFI = 0.973; TLI = 0.967; NFI = 0.96; AGFI = 0.92; RMSEA = 0.049
Note: "2, chi-square; df, degrees of freedom; GFI, goodness-of-fit index; CFI, comparable fit index; TLI,
tucker lewis index; NFI, normed fit index; AGFI, adjusted goodness of fit index; RMSEA, root mean square
error of approximation; *p< 0.001, **p<0.01
Source: author
Table 5. Direct and indirect influences on customer satisfaction
Distributive fairness
Procedural fairness
Customer interface quality
Perceived security
Perceived usefulness
Source: author
In general, our study supports the theoretical model and the hypotheses
among constructs. There are several findings.
First, among the expected determinants of trust, distributive fairness
and procedural fairness are positive predictors. The results have the con-
sensus with the antecedents (Kumar et al., 1995; Tyler & Lind, 1992). Cus-
tomer interface quality, perceived security and perceived usefulness are
also significant predictors of trust. It is appropriate to suggest that receiv-
ing authentic and updated information, insurance of safety as well as en-
hancing the belief that using a given website can improve transaction
performance which will trigger positive trust responses from customers.
Besides, the R2 value of predicting trust is 71%. It means that distributive
fairness, procedural fairness, customer interface quality, perceived security
International Journal of Contemporary Management, 13(1), 830
and perceived usefulness all together are important in building trust;
moreover, it is the background for e-satisfaction.
Second, the expected determinants of customer satisfaction, distribu-
tive fairness, customer interface quality, perceived security and perceived
usefulness as well as trust proved to be positive predictors. Since custom-
ers feel the outcomes are proportional with inputs, good environments,
safety, perception of usefulness, they feel satisfied and willing to repeat
their actions. Additionally, the fact that 75% of variance in satisfaction is
explained by those six factors shows the importance of creating the indi-
vidual s! perception! of! distributive! fairness,! customer! interface! quality,!
perceived usefulness and trust to enhance customer satisfaction. On the
other hand, inconsistent with Folger and Greenberg (1985), the study had
an insignificant result regarding the relationship between procedural fair-
ness and customer satisfaction. It may be due to unperfected implementa-
tion in procedure-problem-solving systems. It is possible that in proce-
dure-problem-solving systems, procedural fairness is not carried out in
every transaction but customers still feel satisfied to some extent, or vice
versa, although procedural fairness is executed, customers feel uncomfort-
able and less satisfied. For example, due to some reasons the products
were delivered late, despite receiving products late without any excuses
from the e-companies, customers still remain satisfied because they finally
received their products within the expected time; or vice versa despite
many excuses from e-companies by telephone calls or in emails, custom-
ers still felt angry because they had to wait. Thus it leads to insignificant
coefficients with customer satisfaction for the overall sample. It is clear
that trust involves all processes beginning!with!customers !previous!expe-
rience until service after shopping, whereas customer satisfaction is the
contentment of customers after shopping in a given virtual store as stipu-
lated by our definition above. That is the difference between trust and
customer satisfaction, which presents in procedural fairness having a signifi-
cant co-efficient with trust but an insignificant one with customer satisfaction.
Third, on the other side, while we propose the above 11 hypotheses,
we also make sure of the mediator role of trust between affecting factors
(distributive fairness, procedural fairness, customer interface quality, per-
ceived security, and perceived usefulness) and the targeted factor (cus-
tomer satisfaction). Several previous studies suggest an invisible relation-
ship in which trust appears as a mediator between five determinants and
customer satisfaction when those affecting factors have positive influences
on both trust and customer satisfaction and then trust have the positive
influence to customer satisfaction (Chiu et al., 2009; Huang, Chiu, & Kuo,
2006; Maxham & Netemeyer, 2002). It is understandable that customers
trust a website, because it enjoys a good reputation by providing fairness,
security, usefulness and interface quality.
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
Based on Mackinnon and Warsi (1995) and Hair, Black, Babin, and
Anderson (2010) that showed the method of identifying this mediation
more specifically, we will review all the results from H1 to H11. Firstly,
we investigated the relationship between the independent variables (dis-
tributive fairness, procedural fairness, customer interface quality, per-
ceived security, and perceived usefulness) and the mediator (trust). Sec-
ondly, a relationship between the mediator and the dependent variable
(customer satisfaction) was investigated. Thirdly, we estimated the rela-
tionship between the independent variables (distributive fairness, proce-
dural fairness, customer interface quality, perceived security, and per-
ceived usefulness) and the dependent variable (customer satisfaction). We
can recognize that trust partially mediated for positive impacts of distribu-
tive fairness, customer interface quality, perceived security, perceived
usefulness to customer satisfaction and fully mediated for procedural fair-
ness. Table 5 can be summarized indirectly through trust, and directly by
the total effects of independents on customer satisfaction. The greatest
total influences derived from trust, showed the same results with the study
of Chiu et al. (2009). The second rank in the total effects belonged to cus-
tomer interface quality while procedural fairness ranked the lowest. It is
not exaggerated to say that trust is a guarded signal and the most domi-
nant predictor of customer satisfaction (62=0.32). If sellers can apply all
good factors to enhance trust, the probability of having a significantly
positive influence on customer satisfaction is high, especially customer
interface quality followed by distributive fairness.
Limitation and Future Research
Despite contributing to the literature and finding out some interesting
points, the current study also has some limitations that opens avenues for
future researches.
First, certain aspects of sample collection could be improved. It was
recognized that the majority of respondents were students. It was reasona-
ble since online customers are young and higher-educated but it would
have been better if the sample had been collected from non-students who
are busy and have no time for conventional shopping methods. In addi-
tion, female respondents outnumbered male ones. Besides, the question-
naire was designed to force the respondents to answer all the questions.
Respondents may prefer giving no answer than providing a wrong one.
The online survey could have included some points in which the re-
spondents can choose not to answer. Another point was that although we
took care to translate the questionnaire into Vietnamese, it still could have
influenced the results of factor structures.
Second, customer interface quality is a multi-faceted concept, but we
could not include its every component, and just focused on information
International Journal of Contemporary Management, 13(1), 830
and character that were most related to the online context. The results
yielded could differ should different components be applied.
Third, regarding post-consumption intention, we just stopped at trust
and customer satisfaction. It would be more comprehensive if the study
addressed loyalty and word-of-mouth, as they too are major drivers of
success in e-commerce (Aderson & Mittal, 2000; Reichheld, Markey & Hop-
ton, 2000).
Final Remarks
Trust and customer satisfaction are very important to e-companies in
post-consumption intention. Our study empirically examined the significant
influence of distributive fairness, procedural fairness, customer interface
quality, perceived security and perceived usefulness on trust as well as on
customer satisfaction. The mediator role of trust was also successfully
proved. Practitioners can consider our study as a reference to establish
trust and satisfaction in e-commerce, in order to raise post-consumption
intention more attention needs be paid to distributive fairness, procedural
fairness, customer interface quality, perceived security, perceived useful-
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< 20
> 25
Education background
Junior high school
High school
Vocational school
Technical college
Master or higher
Full-time student
Non-full-time student*
Years of experience with the Internet
1 year
2-5 years
N.T. Tuyet Mai, Y. Takahashi, N.P. Tuan, Determinants
5-10 years
10 years+
Number of visits over last six months
< 1 time
1 time
2 times
3-5 times
10 times +
The website in which the replier use online
shopping experience for the questionnaire
(see Appendix 1 for the ranking of these
*Despite working with permanent full-time jobs, they are enrolling on some course to have higher degrees
Source: own elaboration.
Traffic rank in
Alexa traffic
Traffic rank in
B2C in Vietnam
Source: in March 13, 2012
International Journal of Contemporary Management, 13(1), 830
T³o badañ. Satysfakcja klienta, w wielu przypadkach zale¿na od zaufania, jest kluczowa dla
pokonsumpcyjnych zachowañ kupuj¹cych i jest ogólnie postrzegana jako kluczowy czynnik
sukcesu w sprzeda¿y, w szczególno!ci w sprzeda¿y internetowej. Jednak nieliczne badania
wyra"nie okre!laj¹ determinanty satysfakcji klientów (w szczególno!ci si³ê wp³ywu poszcze-
gólnych czynników), w handlu elektronicznym na wschodz¹cych rynkach.
Cele badañ. Celem przeprowadzonych badañ jest identyfikacja czynników determinuj¹cych
satysfakcjê klienta.
Metodyka. Przeprowadzone badania oparte s¹ na analizie danych uzyskanych od 758 klien-
tów sklepów internetowych w Wietnamie.
Kluczowe wnioski. Uzyskane rezultaty badañ pokazuj¹, ¿e sprawiedliwo!æ dystrybutywna,
jako!æ interfejsu klienta, poczucie bezpieczeñstwa, postrzegana u¿yteczno!æ oraz zaufanie
istotnymi predyktorami satysfakcji klienta. W szczególno!ci wykazana zosta³a mediacyjna rola
S³owa kluczowe: satysfakcja m³odego klienta w Internecie, zaufanie, handel elektroniczny,
mediacyjna rola
... Consumer satisfaction is extremely critical in e-commerce where human-to-human interaction has been substituted by human-to-machine interaction (Mai, Takahashi & Tuan 2014). Such satisfaction is the factor that drives revenues because it dictates the formation of long-term relationships between consumers and businesses that lead to repeat purchase behaviour. ...
... Trust is one of the most significant influences on, and predictors of, consumer satisfaction; however, few efforts have been made to determine the extent of the mediator role it plays regarding online consumer satisfaction, which is critical to post-consumption intention (Mai, Takahashi & Tuan 2014). Trust is described by various scholars in diverse ways. ...
... Trust is described by various scholars in diverse ways. As an example, Mai, Takahashi and Tuan (2014), summarise past conceptualisations of trust into four key classes: (1) a set of definite beliefs depending on the benevolence, ability and integrity of an exchange partner with the intention of achieving a desired but uncertain goal in a risky context; 2) a general belief that individuals are trustworthy, often gauged by trusting intentions or the willingness to be susceptible; 3) feelings of security and confidence in the caring response; and 4) a combination of different elements. In this context, it is possible to combine two of these descriptions into a single conceptualisation when describing trust. ...
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Consumer satisfaction has been cited to be an important determinant of repeat business. Therefore, it is important for businesses and other stakeholders to have insights about the factors influencing consumer satisfaction. The aim of this research is to explore these factors in the social commerce (SC) context. The research identifies the key predecessors of customer satisfaction, and gaining insights about the nature of their relationship with each other and with consumer satisfaction. Using a positivist paradigm and employing a quantitative approach, this study uses paper- and web-based surveys to collect research data. The study sample consist of Saudi Arabian students who have experience with SC websites and are studying either in Saudi Arabia or Australia. In total, 372 responses are collected. The collected data are analysed using the partial least squares (PLS) regression method to address the research questions and test the research hypotheses. Overall, the results support the theoretical model proposed in this study; that is, all factors affecting consumer satisfaction in SC are found to have a significant impact on consumer satisfaction. Specifically, it is found that word-of-mouth (WOM) is the factor affecting both trust and social influence most strongly, and trust is the factor affecting consumer satisfaction most strongly. Since trust is most strongly affected by WOM, it seems imperative that SC enterprises pay special attention to consumer feedback and responses. These findings are consistent with those of the current literature. This study provides insights into aspects of SC that have not previously been fully investigated. The SC phenomenon has been studied from the standpoint of consumer intention and behaviour but not in terms of consumer satisfaction. This study attempts to xiii fill this gap, along with the gap that exists in terms of defining the concept of consumer satisfaction in SC settings. It is expected that the proposed framework in this study would improve the understanding of the factors affecting consumer satisfaction and the structural relationships between these factors. In addition, this research is expected to act as a guide for SC businesses to help them better meet consumer requirements, which ultimately may lead to an increase in sales revenue. The results of the quantitative analysis need to be interpreted within the limitations of the study. The sample for this research only consisted of students. The researcher acknowledges the limitation that students comprise only a subset of all consumers, and the findings from this research may not be generalised to all SC users.
... Perceived ease of use refers to employee perceptions of the ease in terms of using online platforms (Hsu & Lin, 2008;Nguyen et al., 2013;Yu et al., 2010). Perceived ease of use influences behavioural intentions to use online platforms to participate in online knowledge sharing (Nguyen et al., 2015). If employees do not know how to use online platforms, their participation in online knowledge sharing becomes challenging (Nguyen, 2020b). ...
... Second, perceived ease of use of online platforms is important to young employees. A user-friendly and appealing interface should be designed for organizational online platforms to encourage young employees to share knowledge online (Nguyen et al., 2015). A technical support team may be helpful to reduce employees' concerns when sharing knowledge online (Davis, 1989). ...
Along with the development of information technology and artificial intelligence, online knowledge sharing has become an essential organizational resource. Online knowledge sharing can contribute to the success of organizations through effective knowledge management which is often enhanced by using artificial intelligence techniques. Young employees often make up the largest segment in organizations, but they tend to start their early career with temporary contracts which impact their likelihood to hide or hoard organizational knowledge. This study examines knowledge self-efficacy, perceived ease of use, organizational rewards, and top management support affecting the online knowledge sharing capability of young employees. A survey was conducted in Vietnam, targeting young employees aged 18–30 in three key industries. Results indicate that knowledge self-efficacy, perceived ease of use, and top management support significantly influence young employees’ online knowledge sharing. Interestingly, organizational rewards were found to only impact lurkers’ online knowledge sharing and work effectively if employees have either high perceived ease of use or top management support.
... Lack of trust is another reason for lurking (Yang and Chen, 2007). From the view of social exchange theory, if participants do not trust others, they are not willing to participate in social exchange (Gammelgaard, 2010;Nguyen et al., 2015;Zhao et al., 2013). In the studies by Ridings et al. (2006) and Rau et al. (2008), lurkers had less trust in others' benevolence and integrity than posters. ...
Purpose In the early days of online communities, researchers tended to view lurkers negatively and considered them illegitimate and peripheral members. However, the tide of opinion about lurkers has gradually become more positive. To take a broad view, lurkers should be included in the knowledge sharing context because while they may not share knowledge directly, they are still stakeholders in online communities who benefit from the knowledge shared. This study aims to review the literature from a knowledge sharing perspective to provide a comprehensive understanding of lurkers in online communities and identify additional reasons behind lurking behavior. Design/methodology/approach Previous studies that examined reasons behind lurking behavior in the literature were reviewed. Findings A four-dimensional model is provided, which categorizes the additional reasons for lurking into four domains: individual, social, organizational and technological. Originality/value The model serves as a roadmap for future researchers in examining lurkers and lurking behavior. Lurkers should be redefined. De-lurking strategies were suggested following the reasons for lurking behavior in the four-dimensional model, but de-lurking strategies were not recommended in all circumstances. An increase in active lurkers is another option to bring more value to online communities.
... Dang and Pham proposed that consumers' perception of web design is positively related to users' reliability, privacy, customer service and purchase intention [3]. Mai et al. believe that distribution fairness, customer interface quality, perceived security, perceived usefulness and trust are significant predictors of customer satisfaction, and customer satisfaction plays a crucial role in consumer spending intentions [4]. Reviewing the existing literature, the research on Vietnamese consumers' willingness to purchase online is mostly from one or two aspects of flow of information, flow of trading, flow of financing and flow of goods, but lacks the research from the whole four direction. ...
... It refers to "the subjective probability with which consumers believe that their personal information will not be viewed, stored, and manipulated during transit and storage by inappropriate parties in a manner consistent with their confident expectations" (Flavián & Guinalíu 2006). According to Mai, Yoshi, and Tuan (2014), in emerging markets, customers' trust in virtual transactions is not strong. Therefore, the Sonia San-Martín / Nadia Jiménez / Nuria Puente mission of web designers is to create an attractive interface, updating the latest information, and the security systems, thus enhancing the perception of usefulness among customers. ...
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Purpose – The purpose of this research is to study three clues (employee reputation, site design, and security) of the Customer Experience Management framework that can prompt mobile shopper satisfaction and repurchase intention. The moderator role of perceived distance of the retailer is explored. Design/methodology/approach – The Partial Least Squares approach was employed to analyze information gathered from 1053 mobile shoppers in a geographically extensive and emerging market (Mexico). Findings – The Customer Experience Management framework is helpful in explaining m-shopper satisfaction and intention to repurchase via mobile phone. Our findings show that reputation (particularly for consumers who perceive that retailers are near), site design (principally for consumers who perceive that retailers are far away), and security enhance mobile-shopper satisfaction. Satisfactory experiences increase repurchase intention, regardless of perceived distance of alternative retailers. Originality/value – This study contributes to understanding which factors mobile vendors (m-vendors) could manage in different ways to engender satisfaction and intention to repurchase via mobile, from the unexplored Customer Experience Management perspective and in a scarcely studied emerging market. Also, a key facet of this study is related to the moderating influence of perceived distance on the relationship between employee reputation, site design, and security, on one hand, and m-shopper satisfaction on the other.
... There are various factors seen in a first purchase: user interface, online help desk, ease of usability, variety of products, quality of products, timely delivery and after sales services. These factors all together if create a positive impact, leads to the customer becoming a permanent customer and builds a strong bond of trust (Mai, 2014). ...
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Conflicts are a routine phenomenon in every organizations. With the current workforce coming from diverse cultural and educational backgrounds along with demanding performance parameters necessitating cross functional teams, conflicts are an integral part of our work life and hence a critical area for ongoing research. A systematic review of literature methodology has been adopted in this article to identify the methodologies used and thrust areas on which substantial research in conflict management has been conducted in the past. In all 30 articles were reviewed and resulted in four major themes emerging namely -impact of conflict management on performance, achieving goals, styles of conflict management, effective management of conflicts. Methodological review brought to light the predominant qualitative research in this area along with other fragmented approaches. For contemporary understanding of conflict management, quantitative research and theory based approach are needed for expanding our knowledge and resolutions of conflict in our evolving workplaces.
The authors investigate whether it is necessary to include disconfirmation as an intervening variable affecting satisfaction as is commonly argued, or whether the effect of disconfirmation is adequately captured by expectation and perceived performance. Further, they model the process for two types of products, a durable and a nondurable good, using experimental procedures in which three levels of expectations and three levels of performance are manipulated for each product in a factorial design. Each subject's perceived expectations, performance evaluations, disconfirmation, and satisfaction are subsequently measured by using multiple measures for each construct. The results suggest the effects are different for the two products. For the nondurable good, the relationships are as typically hypothesized. The results for the durable good are different in important respects. First, neither the disconfirmation experience nor subjects’ initial expectations affected subjects’ satisfaction with it. Rather, their satisfaction was determined solely by the performance of the durable good. Expectations did combine with performance to affect disconfirmation, though the magnitude of the disconfirmation experience did not translate into an impact on satisfaction. Finally, the direct performance-satisfaction link accounts for most of the variation in satisfaction.
Equity theory was applied to retail exchange situations to test hypotheses about subjects’ perceptions of inequity and behaviors they would perform. Subjects in Group 1 made evaluative ratings of 16 hypothetical situations in which two sources of inequity, high price and poor service, were introduced, along with varying levels of shopping frequency and item cost. Subjects perceived high price inequity situations as less fair than low ones, and high service inequity situations as less fair than low ones when price inequity was low. When price inequity was high, subjects perceived high shopping frequency situations less fair than low ones. Subjects in Group 2 chose the behavior they would be most likely to perform in each situation. When inequity was present, most subjects chose leaving the store, although several chose complaining about price or service when shopping frequency was also high.
The authors extend consumer satisfaction literature by theoretically and empirically (1) examining the effect of perceived performance using a model first proposed by Churchill and Surprenant, (2) investigating how alternative conceptualizations of comparison standards and disconfirmation capture the satisfaction formation process, and (3) exploring possible multiple comparison processes in satisfaction formation. Results of a laboratory experiment suggest that perceived performance exerts direct significant influence on satisfaction in addition to those influences from expected performance and subjective disconfirmation. Expectation and subjective disconfirmation seem to be the best conceptualizations in capturing satisfaction formation. The results suggest multiple comparison processes in satisfaction formation.
Marketing managers must know the time orientation of a customer to select and use marketing tools that correspond to the time horizons of the customer. Insufficient understanding of a customer's time orientation can lead to problems, such as attempting a relationship marketing when transaction marketing is more appropriate. The author suggests that long-term orientation in a buyer/seller relationship is a function of two main factors: mutual dependence and the extent to which they trust one another. Dependence and trust are related to environmental uncertainty, transaction-specific investments, reputation, and satisfaction in a buyer/seller relationship. The framework presented here is tested with 124 retail buyers and 52 vendors supplying to those retailers. The results indicate that trust and dependence play key roles in determining the long-term orientation of both retail buyers and their vendors. The results also indicate that both similarities and differences exist across retailers and vendors with respect to the effects of several variables on long-term orientation, dependence, and trust.
Relationship marketing—establishing, developing, and maintaining successful relational exchanges—constitutes a major shift in marketing theory and practice. After conceptualizing relationship marketing and discussing its ten forms, the authors (1) theorize that successful relationship marketing requires relationship commitment and trust, (2) model relationship commitment and trust as key mediating variables, (3) test this key mediating variable model using data from automobile tire retailers, and (4) compare their model with a rival that does not allow relationship commitment and trust to function as mediating variables. Given the favorable test results for the key mediating variable model, suggestions for further explicating and testing it are offered.
Many companies consider investments in complaint handling as means of increasing customer commitment and building customer loyalty. Firms are not well informed, however, on how to deal successfully with service failures or the impact of complaint handling strategies. In this study, the authors find that a majority of complaining customers were dissatisfied with recent complaint handling experiences. Using justice theory, the authors also demonstrate that customers evaluate complaint incidents in terms of the outcomes they receive, the procedures used to arrive at the outcomes, and the nature of the interpersonal treatment during the process. In turn, the authors develop and test competing hypotheses regarding the interplay between satisfaction with complaint handling and prior experience in shaping customer trust and commitment. The results support a quasi “brand equity” perspective—whereas satisfaction with complaint handling has a direct impact on trust and commitment, prior positive experiences mitigate, to a limited extent, the effects of poor complaint handling. Implications for managers and scholars are discussed.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.