Internet service provider switching intention: The case of Hanoi consumers
Pham Thi Thuy Van, Nguyen Thi Anh Tram, Ngo Anh Cuong/ MICA 2018 Proceedings
International Conference on Marketing in the Connected Age (MICA-2018), October 6th, 2018
Danang City, Vietnam
Internet Service Provider Switching Intention: The Case of
Hanoi Consumers
Pham Thi Thuy Vana*, Nguyen Thi Anh Trama, Ngo Anh Cuonga
aUniversity of Labor and Social Affairs, 43 Tran Duy Hung Street, Hanoi, Vietnam
A B S T R A C T
Research on service provider switching behaviour of consumers is a new research topic in Vietnam.
According to Push-Pull-Mooring (PPM) Migration Model of Switching Service Provider, this research
proposes Internet Service Provider Switching Intention of Hanoi consumers Model with three factors
originating from present service provider and one from alternative service provider. The research makes use
of both qualititative method and quantitative method. The research model is tested with the survey data of 260
Hanoi consumers. Results show that the model is suitable for research into Internet service provider switching
intention of Hanoi consumers. This research also considered a suggestion for the scholars and businesses to
broaden knowledge of stimulating customer’s relationship.
Keywords: Service provider switching intention; PPM Migration Model of Switching Service Provider.
1. Introduction
Over the globe, the topic of switching behavior is receiving great attention from scholars, who have carried
out researches both theoretically and practically to find effective ways to maintain and cultivate relationship with
consumers. In order to understand, analyse and predict switching behavior, many a researcher gives special
consideration to switching process approach.
Specifically, Service Provider Switching Behavior Model (PPM) of Bansal Irving, P. G., and Taylor, S. F.,
(2005) has been developed by researchers over the world to explain and predict service provider switching
behaviors in different service sectors such as Information Technology (Lui, S. M , 2005), IT products (Chen Ye,
Previous researches have presented that during the switching process, switching intention is a central factor,
which decides the service provider switching behavior of consumers. However, in Vietnam, there has yet to be
research into the topic of service provider switching behavior of consumers.
This study makes use of PPM Model by choosing suitable factors as to the Internet service context, building
research model to explain Internet provider switching intention of consumers. The result contributes to the
increase in awareness of service provider switching behavior, which is more and more ubiquitous in Vietnam,
from the perspective of PPM Model.
* Corresponding author. E-mail address: phamvan0279@gmail.com
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2. Theorectical background and Hypotheses Development
2.1. The Push – Pull – Moor Framework
Switching behavior is a concept that has interested numerous scholars, especially in a competitive market
condition. The first study was by S.M. Keaveney (1995) and there have also been numerous studies in recent
years. The majority of researches show that switching behavior of consumer is the decision to switch from using
the service of present provider to buying from its competitors. Studies on switching behavior have 3 approaches,
including: reasons leading to switching (S.M. Keaveney,1995; Colgate and M., Hedge, 2001; Gerrard, P. and
Cunningham, J. B., 2004…), switching process (Bansal, H. S., and Taylor, S. F. 1999, 2002; H.S. Bansal, S.F.
Taylor, Y.St. James, 2005; Zhang, K. Z. K., Cheung., C. M. K., Lee, M. K. O, 2012; Roos,1999, 2004; Inger
Roos, Margareta Friman, 2008) and consumer commitment (Bansal, H. S., Irving, P. G., and Taylor, S. F., 2004).
According to Push-Pull-Mooring theory in geographical documents, the immigrants from one region to
another are pushed away by pushing or negative factors namely natural calamity or are attracted by pulling
factors such as better job opportunities. In addition, there also are personal and social factors, specifically
transportation expenses as a mooring factor which is either advantageous or disadvantageous for migration
(Moon, 1995).
In order to implement migration strategies, 3 aspects have to be examined. Firstly, perception of the present
accomodation of immigrants can push them to switch to another place (pushing factor). Secondly, perception of
the destination of immigrants can acttract migration activity (pulling factor). Thirdly, perception of personal and
social aspects of immigrants can stimulate or hinder the migration process (mooring factor).
H.S. Bansal, S.F. Taylor, Y.St. James (2005) are the first authors to study and apply the push-pull-moor theory
to the marketing field. According to H.S. Bansal, S.F. Taylor, Y.St. James, there are similarities between
migration and service provider switching behavior, therefore, pushing, pulling and mooring factors have their
roles in explaining switching process of consumers. Service Provider Switching Model (PPM) of H.S. Bansal,
S.F. Taylor, Y.St. James (2005) is a comprehensive theoretical framework and model, which identifies particular
variables and explains switching behavior of consumers in the service field. In PPM model, switching intention
plays a central role and has decisive impact on switching behavior. Individual variables fall into 3 categories
influencing switching intention of consumers, namely: pushing factor group, pulling factor group and mooring
factor group.
Pushing factors (Push) are factors which originate from present service provider and stimulate consumers’
switching, including: low quality, low satisfaction, low value, low trust, low commitment and high price
perceptions.
Pulling factors (Pull), factors originating from alternative service provider (destination), are identified
attractiveness of alternative service provider (Alternative attractiveness).
Mooring factors are personal, social, atmospheric factors can promote or hinder switching intention,
including: high switching costs, unfavorable subjective norms, infrequent prior switching behavior, low variety
seeking. The PPM model puts an emphasis on the importance of mooring variables as control variables as to
service provider switching intention of consumers.
2.2. Hypotheses Development
Switching intention describes consumers’ ability to switch from present provider to a different provider
(Chuang, 2011). Service provider switching intention is one of the bases for providers to predict consumers’
behavior, whether they plan to stay or leave.
Satisfaction
Satisfaction is an important concept in researches into consumer relationship and it has attracted a great deal
of interest from scholars in the previous decades. According to Oliver (1980), satisfaction is the response of
consumers to the fulfillment of their demands. As reported by Kotler (2001), satisfaction is the level of emotional
state rooting from comparing the result of the product/service with consumers’ expectation. In studies concerning
consumer relationship, satisfaction plays an important part in distinguishing 2 consumer’s groups: those who are
loyal and those who have had service provider switching behavior. Between two groups, there is significant
difference in their satisfaction with present provider (Ganesh, J., Arnold, M. J., and Reynolds, K. E, 2000).
Researchers have identified that consumers’ satisfaction have a reverse effect on switching behavior as
consumers have a tendency to buy from a different service provider if they are not satisfied during experimental
stage (Bansal et al, 2005; Lui, S. M , 2005; Yi-Fei Chuang et al, 2008; Chen Ye, 2009; Zhang et al, 2012).
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In studies which apply PPM model to research on consumers’ switching intention, satisfaction is one of the
pulling factors. Consumers will have switcing intention when their satisfaction is low and vice versa (Bansal et
al, 2005; Lui, S. M , 2005). In order to study the relationship between satisfaction and Internet service provider
switching intention, this study proposes a research hypothesis:
H1: Consumers’ satisfaction has a negative effect on Internet service provider switching intention.
Service price fairness perception
Price is an exchange correlation in the market. With regards to consumers, the price of a product or service is
the amount of money they have to pay to gain ownership and usership of a product or service. Price is usually the
primary factor deciding consumers’ choice (Engel et al, 1995). Perceptions of one price tend to vary among
consumers. According to studies on switching behavior, price is an essential factor in models of service provider
switching behavior (Keaveney, 1995; H.S. Bansal, S.F. Taylor, Y.St. James, 2005; Zhang, K. Z. K., Cheung., C.
M. K., Lee, M. K. O, 2012; Chen Ye, 2009; Lui. S.M, 2005; Yi-Fei Chuang, Yang-Fei Tai, 2016…)
As reported by the majority of studies on service provider switching behavior, consumer’s price perception is
measured by consumer’s perception process of present provider’s price compared with alternative provider’s
price (Lui, 2005).
In PPM Model – service provider switching behavior, price perception is a factor pushing consumers to
alternative service provider (H.S. Bansal, S.F. Taylor, Y.St. James, 2005). As far as IT is concerned, Lui. S.M
(2005), who studies IT service provider switching behavior (the surveyed IT service includes mobile phone and
Internet), supposes that the price factor is influenced by consumer’s conception, therefore, consumer’s
disapproval of the price will give rise to switching intention.
Consumer approves of a price when the price is considered fair (the price is equal to or less than that of other
service providers), the price is not extortionate, the consumer is contented to pay the price, the price is lower than
the consumer’s paying capacity. As a result, this study proposes a research hypothesis reflecting the relationship
between service price perception and Internet provider switching intention:
H2: Service price fairness perception has a negative effect on Internet provider switching intention
Subjective Norm
Subjective norm is regarded as the influence of social surroundings on individual’s behavior. This is a
personal belief of how others are going to perceive one’s behavior. If a person expects and believe that one’s
behavior leads to positive results and important people (those who have influence on a personal scale) encourage
and support that behavior, intention to implement the behavior will be formed. In other words, individual’s
behavior stems from one’s expectation about positive outcome and one’s belief of support from others.
Subjective norm has been proved to be influential towards intention whereby it affects behavior as reported in
Ajzen’s study (1991). Subjective norm is the pressure which society has on each individual when consideration is
made on whether to conduct a behavior or not. Subjective norm also reflects the belief that observation and
judgment towards one’s behavior can be made by close and important people.
In the field of research, service provider switching intention of consumers witnesses an upward trend if their
close ones expect them to conduct switching behavior (ex: Bansal, H. S., Irving, P. G., and Taylor, S. F., 2004;
Chen Ye, 2009). On the basis of the given arguments, this study proposes a research hypothesis:
H3: Subjective norm has a positive effect on Internet provider switching intention
Alternative attraction perception
Sheth and Parvatiyar (1995) assert that while making buying choice, consumers have the tendency to choose
merely several alternatives according to their subjectivism to reduce the complication of the buying process,
therefore information processing is facilitated. That being said, when using a product/service, consumers tend to
search for better alternatives, this pushes them to switch their provider. Alternative attractiveness is defined as
estimation of consumers about the satisfaction they could have in another relationship.
Previous studies on switching behavior assert that alternative attractiveness affects service provider switching
intention (Bansal, Taylor, & James 2005; Lui, S. M., 2005, Carmen, A., Carmen, C., Mirtha, 2007…). The great
attractiveness of alternatives can have a direct impact on switching behavior (Kim et al, 2006). Alternative
attractiveness perception is a pulling factor in the PPM Model (H.S. Bansal, S.F. Taylor, Y.St. James, 2005; Lui,
S. M., 2005; Chen Ye, 2009; Zhang, K. Z. K., Cheung., C. M. K., Lee, M. K. O, 2012). Consumers invariably
compare values among companies based on give information, they are pushed by perception of the benefits,
values and service quality which is promised by a company (Bansal, Taylor and James, 2005; Keaveney, 1995).
Therefore, consumers may not be willing to switch their service provider if conditions namely benefit, values and
service quality of their present provider have bee improved. In other words, if a company offers more attractive
solutions than its competitor, it pulled its consumers to stay. As a result, in terms of this factor, this study
proposes a research hypothesis:
H4. The alternative attraction perception has a positive effect on Internet provider switching intention
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Pham Thi Thuy Van, Nguyen Thi Anh Tram, Ngo Anh Cuong/ MICA 2018 Proceedings
Image I illustrates a proposed research model on factors effecting Internet service provider switching intention
of Hanoi consumers.
Fig. 1. Proposed research model
Cronbach's Alpha
(-)
Formal scale
(-)
Internet
service
provider
switching
intention
(Push Effects)
(+)
(+)
Group discussion
(n=29)
Alternativeattractionperception
(Pull Effects)
3.1. Qualitative study
Qualitative research is conducted with the intensive interview method with an aim to examining and filtering
individual variables, which effect Internet service provider switching intention in the tentative research model, as
well as identifying preliminarily the relationship between variables in the research model. In addition, intensive
interview method also allows our research group to carry out necessary alterations to the questionnaire.
Our research groups have conducted intensive interviews with 10 Hanoi inhabitants including: 06 Internet
service consumers, 02 experts of the marketing field, 02 managers working in Internet service enterprises.
Interviews took place in private homes, offices, cafes… in order to ensure that interviews will not be interrupted
and create a friendly atmosphere. Each interview lasts from 30 to 45 minutes with pre-prepared content.
Qualitative research results in general support the proposed model of this study.
3.2. Consumer survey
3.2.1. Scale
The concepts used in this study include: service provider switching intention, satisfaction, Service price
fairness perception, subjective norm, alternative attractiveness perception. Each and every scale is succeeded
from previous studies and altered as necessary based on the suggestion of qualitative research result. A 5-point
Likert scale is used in all scales, in which 1 means strongly disagree and 5 is strongly agree.
Service provider switching intention scale is inherited from Arvind Malhotra’s scale (2013), consisting of 5
observation variables (example: I don’t have any intention to use Internet service of my present provider
permanently).
Satisfaction scale is inherited from Wenhua Shi, Jianmei Ma and Chen Ji’s scale (2015), including 4
observation variables (example: I am satisfied my my present Internet provider).
Service price fairness perception scale is inherited from Lui (2005), comprised of 4 observation variables
(example: The service price is reasonable regarding its quality; The service price is equal to that of similar
service provider)
Subjective norm scale is inherited from Bansal, H. S., Irving, P. G., and Taylor, S. F. (2004), consisting of 3
observation variables ( example: The most important people in my life consider that I should switch to new
Internet service provider).
Alternative attraction scale is inherited from Lui (2005), including 4 observation variables (example: All
things considered, alternative Internet provider is better than my present provider).
The questionnaire is constructed based on observation variables used to measure studied concepts in the
model. Questions in the questionnaire are examined thoroughly according to a strict procedure to ensure
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Pham Thi Thuy Van, Nguyen Thi Anh Tram, Ngo Anh Cuong/ MICA 2018 Proceedings
consistency of meaning between the original and the translation. After that, the questionnaire is perfected using
the survey result on small and convenient sample.
3.2.2. Sample and data collection
This study is officially conducted with the qualitative method and via direct interview technique. A
convenient sample includes 260 consumers currently using Internet service over 10 Hanoi districts.
This study makes use of the method of choosing convenient sample based on particular criteria such as:
gender, education level, age… in order to make sure the data has the highest representativeness.
The sample consists of consumers from 18 years old, in which those between 26 and 54 account for 82%. In
the sample, female and male makes up for 34% and 67%, respectively. In terms of education level, roughly 50%
respondents receive university education or higher, over 10% surveyed consumers have graduated high school.
With regards to household’s average income, the sample includes object groups with more than 10 million dong:
households with income of 15 to 25 million dong per month comprise 50% and about 9% of the total sample
belongs to household with over 25 million dong per month.
4. Results
4.1. Properties of measures
This study utilizes Cronbach alpha coefficient to rate the reliability of each scale and exploratory factor
analysis (EFA) is conducted to rate the convergence value and differentiation of scales. Analysis result presents
that the reliability of every scale is qualified (Hair et al, 1998).
Specifics: Cronbach alpha coefficient of every variable shows high reliability (> 0.7). EFA is conducted
individually for dependent variable (switching intention) and simultaneously for 20 observation variables. EFA
result shows that the scales are qualified in terms of KMO, with total variance explained >50% and factor
loading >0.5.
4.2 Structural Model
This study uses correlation coefficients to reflect the relationship of variables in each research hypothesis.
Correlation coefficient reflects the relationship between two quantitative variables. Correlation coefficient always
accepts value between -1,1.
Table 1. Correlations of Constructs
Switching
intention
Alternative
attraction
perception
Satisfaction
Service price
perception
Subjective
norm
Switching
intention
Pearson
Correlation
Sig. (2-tailed)
1
-.642**
-.718**
.622**
.537**
.000
260
.000
260
.000
260
.000
260
N
260
Satisfaction
Pearson
Correlation
Sig. (2-tailed)
1
.461**
.528**
.350**
.000
260
.000
260
.000
260
N
260
Service
price
Pearson
Correlation
1
.524**
.202**
fairness
perception
Sig. (2-tailed)
.000
260
.001
260
N
260
Alternative
attraction
perception
Pearson
Correlation
1
.311**
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Pham Thi Thuy Van, Nguyen Thi Anh Tram, Ngo Anh Cuong/ MICA 2018 Proceedings
Sig. (2-tailed)
.000
260
N
260
Subjective
norm
Pearson
Correlation
1
Sig. (2-tailed)
N
260
**. Correlation is significant at the 0.01 level (2-tailed).
The positive correlation coefficient reflects that the two variables have proportional relationship. The negative
correlation coefficient shows that two variables have inverse relationship. The upward correlation coefficient of 0
reflects that two variables are not closely related. Research hypotheses are accepted when the significance level
of the correlation coefficient is less than 0.05.
The correlation coefficient (Table 1) shows that most of the correlation coefficients between the independent
variable and the dependent variable have a significance level of 99%. Thus, the hypotheses given by the research
team in this case are accepted.
The research team utilizes multiple-variable regression to analyze factors affecting Internet service provider
switching intention. Results illustrate that, all factors influence Internet service providers. The adjustment factor
of the regression model is 0.746, reflecting the independent variables are 74.6% explained for Internet service
provider switching intention.
The significance level of the regression coefficients for independent variables was all less than 0.05, reflecting
the regression coefficients of the independent variables are statistically significant in the model. The VIF values
of the independent variables in the model are less than 10, reflecting the model is with multi-collinearity but still
acceptable.
Table 2. Results of Hypotheses Testing
Unstandardized
Coefficients
-1.014
-.306
-.462
Standardized
Coefficients
Sig.
Collinearity
Statistics VIF
Constant
.000
.000
.000
Satisfaction
Service price
fairness
-.235
-.464
1.475
1.170
perception
Subjective norm
Alternative
attraction
.340
.209
.312
.157
.000
.000
1.562
1.647
perception
Dependent: Internet service provider switching intention
Adjusted R Square: 0.746
Research results show that, satisfaction and Service price fairness perception have opposite effect to service
provider switching intention because regression coefficients are negative (-), subjective norm and alternative
attraction perception have same effect service provider switching intention. Among factors affecting service
provider switching intention, price perception is the most influential factor because the regression coefficients are
0.464. Level of impact of the following factors respectively are subjective norm (0.312), satisfaction (0.235) and
alternative attraction perception (0.157).
5. Discussion and conclusion
This study is based on PPM Theoretical Model to examine the impact particular factors have on Internet
service provider switching intention of Hanoi consumers. Specifically, this study proposes a model with 4
hypotheses researching the effect of 4 factors (satisfaction, Service price fairness perception, subjective norm,
alternative attraction perception). Theses factors are found in order to verify pushing, pulling, mooring factors
concerning Internet service provider switching intention of Hanoi consumers. The analysis result has pointed out
4 research hypotheses. Similar to previous studies, the result has proven the positive effect of ‘Subjective norm’
and ‘Attractive alternativeness’ factors on ‘Internet service provider switching intention’ as well as the negative
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Pham Thi Thuy Van, Nguyen Thi Anh Tram, Ngo Anh Cuong/ MICA 2018 Proceedings
of ‘Satisfaction’ and ‘Service price fairness perception’ factors on ‘switching intention’. This study has suggested
decisive factors concerning Internet service provider switching intention of Hanoi consumers and proposes that
enterprises currently or wanting to be involved in the Internet service market need to construct strategies building
and maintaining relationship with consumers.
Future studies can expand research sample beyond Hanoi, to cities such as Hồ Chí Minh city and Đà Nẵng.
This research focuses on factors affecting Internet service provider switching intention. In the future, choosing
different service fields in order to compare the effects of factors on switching intention can give rise to intriguing
results.
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