How does mobile service bundling moderate the impact of switching
barriers on consumers’ switching intentions?
M.P. Bras
S2012332
Master Thesis
MSc BA Strategic Innovation Management
University of Groningen
Faculty of Economics and Business
Supervisor: T.L.J. Broekhuizen
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Abstract
Policy makers warn for the possible lock-in of consumers when they bundle their mobile services. The purpose of this study is to determine the extent of influence of perceived switching barriers, and how service bundling moderates the impact of switching barriers on consumer switching intentions in the dynamic Dutch mobile service industry. Using data among 392 consumers living in the Netherlands, this study finds, through principal component analysis and hierarchical regression analysis, that service bundling does not moderate any of the perceived switching barriers. Consumer switching intentions are determined by the relationship investment, the availability of alternatives and switching costs barriers. Managerial and theoretical implications are discussed.
Key words: perceived switching barriers, consumer switching intentions, mobile service bundling, Dutch mobile industry
1. Introduction
For mobile service providers it becomes increasingly more difficult to retain their consumers due to increased competition from new virtual service providers (Calvo-Porral & Lévy-Mangin, 2015), as well as the increased diversification amongst providers in mobile service offerings (Ranganathan et al., 2006).
Virtual service providers have entered the market and rent network infrastructures from traditional service providers (Gerpott et al., 2005) and disrupt the mobile service industry by focusing on delivering mobile service at lower prices than their traditional counterparts (Calvo-Porral & Lévy-Mangin, 2015).
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Therefore, a large body of research exists on the drivers of customer retention in the service industry (Lee et al., 2001; Malhotra & Malhotra, 2013; Ranganathan et al., 2006) as well as its consequences (Fornell & Wernerfelt, 1987).
This literature directs towards consumer satisfaction and switching barriers to prevent consumers from leaving (Caruana, 2004; Colgate & Lang, 2001; Malhotra & Malhotra, 2013). Consumer satisfaction is the degree that the service fulfilment is pleasant (Oliver, 1999) and switching barriers are defined as factors that make it difficult, costly or unwanted for consumers to change providers (Jones et al., 2007). Perceived switching barriers are consumers perceptions of time, money and effort associated with changing service providers (Jones et al., 2000). They can be grouped in many different typologies (Caruana, 2004; Colgate & Lang, 2001; Malhotra & Malhotra, 2013), such as the broadly used distinction into relationship investment, switching costs, availability and attractiveness of alternatives, and service recovery. (Colgate & Lang, 2001).
Several changes in the mobile service industry necessitate an update of research on switching barriers. Firstly, the majority of service barrier research was conducted whilst mobile service providers overwhelmingly used contract-based mobile service plans. However, recently the major mobile service providers in the U.S. are providing a mix of contract and no-contract plans. Secondly, mobile service providers began offering to pay switchers as encouragement to switch between providers. Providers offered hefty discounts or signing bonusses to new consumers. Furthermore, most service providers lost their exclusive right to sell new mobile phones, and consumers are now able to purchase them separately such that consumers are only interested in the mobile service connection (Tesfom et al., 2016). On the other hand, mobile service companies have also started to expand their services by bundling the standard mobile phone services with other services such as television, internet or landline connection. Recent numbers show that 42.5% of households in the Netherlands bundle their mobile services (BroadbandTV, 2018), a percentage that has doubled within a year.
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Although switching intentions between service providers may be lowered due to a higher impact of perceived switching costs (Burnham et al., 2003), consumers (and service providers) may also benefit from service bundling. It is advantageous for the consumer as service bundling is cheaper and more adaptable to their needs (Gordijn et al., 2011), while it saves costs for the service provider and enables them to serve more complex consumer needs.
This study aims to build on existing research by investigating how service bundling (i.e., the decision of consumers to use a package from a mobile service provider) may impact previously identified perceived switching barriers between mobile service providers in the Dutch mobile service industry. It will contribute to the understanding of consumer switching intentions and switching barriers in the Dutch mobile service industry. In order to do so, the perceived switching barriers should be determined. This study will therefore answer the following research question;
To what extent do perceived switching barriers determine switching intentions in the Dutch mobile service industry?
Subsequently the moderating impact of service bundling will be evaluated, which results in the sub question;
How does mobile service bundling moderate the relationship between perceived switching barriers and switching intentions in the Dutch mobile service industry?
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2. Background & hypotheses
2.1 Switching intentionWill I stay with my current mobile service provider or is this a suitable moment to switch between service providers? What do I desire to receive from this new mobile service provider and what will be the costs and benefits associated with switching? Many consumers have been in this cognitive process or will find themselves in this ‘switching dilemma’ in the nearby future (Colgate & Lang, 2001). Firms around the world have adopted customer satisfaction as the ‘de facto’ standard for monitoring progress, convinced by the belief that customer retention and profitability will follow (Burnham et al., 2003). Whilst customer satisfaction influences repeated purchasing behaviour, it typically explains only a quarter of the variance in behavioural switching intentions and decision making (Szymanski and Henard, 2001). Rather than using consumer satisfaction, Burnham et al. (2003) recommend to use the level and types of costs that consumers associate with switching, as they better explain consumer switching intentions. While this does not suggest that firms should abandon their pursuit of customer satisfaction, it does indicate the need to understand, measure and manage switching barriers perceptions (Jones et al., 2000).
2.2 Switching barriers
In the mobile service industry, which is well known for its subscriber loss, managing switching barriers to retain consumers is of major importance for several reasons (Colgate & Lang, 2001; Lee et al., 2001). Firstly, acquiring a new customer can cost up to five times more than retaining a current customer. Secondly, the value of a customer, also known as Customer Lifetime Value, increases when consumers stay longer with the same mobile service provider (McDougall, 2001). Finally, long-term customers are more likely to generate a long-run positive reputation effect (attracting non-customers) and decrease consumer price sensitivity (opportunities for higher margins) according to Lee et al. (2001).
Research further emphasizes the value of switching barriers by acknowledging that even dissatisfied consumers could remain loyal when confronted with high perceived switching barriers (Burnham et al., 2003; Szymanski & Henard, 2001). Similarly, Colgate and Lang (2003), and Malhotra and Malhotra (2013) indicate that mobile service providers discourage their customers from leaving by instituting various switching barriers.
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Colgate and Lang (2001) classified switching barriers into relationship investment, switching costs, availability and attractiveness of alternatives, and service recovery. An important distinction in this typology compared to others (Lee et al., 2001; Malhotra & Malhotra, 2013) is that Colgate & Lang (2001) define the costs of changing services in terms of time, monetary and psychological costs as switching costs, in contrast to most other researchers (Burnham et al., 2003; Lee et al., 2001) who include all switching barriers in their definition of switching costs.
More recent research into switching barriers by Malhotra & Malhotra (2013) distinguishes hard- and soft lock ins. Hard lock-ins are financial barriers, which seem effective, but simultaniously could create ‘false’ loyalty. In the long run this could negatively impact the mobile service provider due to negative word-of-mouth. Soft lock-ins create or increase consumer loyalty by high service quality and innovative performance, and therefore increase the intention of consumers to stay with the current service provider.
Lee et al. (2001) distinguish three types of switching barriers: transactional costs, learning costs and contractual costs. Caruana (2003) utilizes the same three type of costs, although their definitions of these type of costs differ significantly. For example, Lee et al. (2001) define transactional costs as the costs reflecting the long-term relationship between customer and service provider, whereas Caruana (2003) defines transactional costs as the costs that occur when starting a new relationship with a provider. Caruana (2003) also includes the cost necessary to terminate an existing relationship as transactional costs. These costs of terminating a relationship are defined by Lee et al. (2001) as contractual costs.
Likewise, Burnham et al. (2003) distinguish procedural switching costs, financial switching costs and relational switching costs. These three groups are further segmented into eight sub-categories. They define economic risk-, evaluation, set-up and learning costs as forms of procedural costs, benefit loss and monetary loss as forms of financial switching costs, and relational switching costs consist of personal-, and brand relationship costs.
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This study chooses to adapt Colgate & Lang’s (2001) typology of switching barriers, as it has recently been tested in the mobile service industry by Tesfom et al. (2016).
2.3 Conceptual framework
Figure I depicts the conceptual framework and the perceived switching barriers as proposed by Colgate and Lang (2001) and their relationships with the intention of consumers to switch to another mobile service provider. Relational investment (H1), switching costs (H2) and service recovery (H4) are expected to have a negative relationship with switching intentions, and thus decrease the intention of consumers to switch to another mobile service provider. Availability and attractiveness of alternatives (H3) is expected to have a positive relationship with consumers’ intention to switch, as it is more likely Table I: An overview of Switching Barrier Typologies
Colgate & Lang, 2001 Relational Benefits Investment into a consumer-provider relationship. Switching Costs Costs of changing services in terms of time,
monetary and
psychological costs.
A&A of alternatives Number of alternative providers, which seem at least as good as the current provider.
Service Recovery All activities employed to rectify, and restore losses following a service failure. Malhotra & Malhotra, 2013 Soft Lock-In
Relational benefits enjoyed by the consumer by a continued relationship with the provider.
Hard Lock-in
Negative financial switching barriers.
Lee et al., 2001 Transactional Costs Reflects the long-term relationship with customers.
Contractual Costs Fixed costs to abandon a contract.
Learning Costs Personalised strategy of promotional tools.
Caruana, 2003 Transactional Costs Occurring costs when starting a new relationship with a provider. Contractual Costs Firm-induced costs to penalise switching by customers. Learning Costs Effort required to reach the same level of comfort, which may not be transferred to another brand. Burnham et al., 2003 Relational Costs Psychological discomfort due to the loss of identity and personal relationship loss.
Procedural Costs Expenditure of time and effort.
Procedural Costs Learning and set-up costs of establishing a new relationship with another provider. Financial Costs
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to increase the intention of the customers to switch to another mobile service provider when the attractiveness increases.
The positive and negative relationships of the perceived switching barriers are moderated by the effect of bundling mobile services (H5). This study introduces mobile service bundling as a moderator of the relationship between switching barriers and consumers’ intention to switch. This moderation effect has not yet been verified in an empirical setting, and this is – to the author’s best knowledge – the first study to study its impact. How the bundling of services can impact the perceived switching barriers is explained in subchapter 2.3.5.
2.3.1 Relationship investment
A body of literature has emerged indicating that investments into a relationship are one reason why consumers stay loyal to their service provider (Colgate & Lang, 2001).
Consumers receive many benefits from developing a relationship with their mobile service provider. These benefits can be classified into confidence, social and special treatment benefits (Meldrum & Kaczynski, 2007). These benefits can vary from faster device upgrades, discounted or free services, instalment plans to pay for device costs, to free call time to friends in the same network. By switching to a competitive provider, existent consumers would lose the benefits from their relationship-specific investment.
9 2.3.2 Switching Costs
Perceived switching costs can be measured in time, monetary and psychological costs (Burnham et al. 2003; Malhotra & Malhotra, 2013), and may also impact whether consumers stay loyal to their current service provider.
Financial switching costs, such as a breach of contract-fee, are often perceived as negative costs (Jones et al., 2007; Malhotra & Malhotra, 2013) that increase the perceived switching barrier. Consumers may solely stay with their current provider based on the feeling of being trapped- or locked in because of the financial consequences of switching. Furthermore, constraints, such as time and effort to find a new service provider, are included in the switching barrier (Jones et al., 2007). Consumer inertia (Colgate & Lang, 2001) occurs when consumers do not switch between providers because they perceive switching is too much bother in terms of time and effort.
The psychological dimension of switching costs is represented by the perceived risk, which represents the uncertainty of switching to another service provider (Colgate & Lang, 2001). Consumers may refrain from switching to other mobile service providers, since they do not want to risk facing negative consequences (e.g., will the new provider be as good as my existent provider, will the transfer of my mobile number go smoothly?). Based on the above the following hypothesis regarding time, monetary and psychological switching costs is made:
Hypothesis 2: Higher perceived switching costs decrease consumers’ intention to switch to another mobile service provider.
2.3.3 Availability and attractiveness of alternatives
The availability and attractiveness of alternative providers (Colgate & Lang, 2001) is hypothesized to increase consumer switching intentions.
Without competing service providers, switching barriers will not be necessary to make consumers stay, since there are no alternatives to switch to (Lee et al., 2001). Colgate and Lang (2001) acknowledge that in some markets there are no alternatives, or no perceived alternatives that are at least as good as the current service provider. In these markets, consumer behaviour is not a case of loyalty and intention, yet rather a lack of good alternatives.
10 2.3.4 Service recovery
Service recovery offered by a mobile service provider is the last perceived switching barrier (Colgate & Lang, 2001).
It includes all activities and efforts employed by a service organization to rectify, amend, and restore the loss experienced by the consumer following a service failure (Grönroos, 1988). Consumers who have experienced a problem will usually experience dissatisfaction. Successful service recovery can reverse this dissatisfaction and can lead to the consumer being more satisfied and more loyal than prior to the problem; this is called the ‘service recovery paradox’ (Smith & Bolton, 1998). Hence, Hypothesis 4: Higher perceived service recovery decrease consumers’ intention to switch to another mobile service provider.
2.3.5 Bundling services
This study argues that the offering of bundling packages, as an external factor, moderates the relationship between perceived switching barriers and consumer switching intention in the Dutch mobile service market. The following section hypothesizes the direction of the moderation effect. Bundling services in the Dutch mobile service market has recently become a trend, especially since the introduction of quadplay in January 2013 (ACM, 2017). A quadplay offer is the bundling of mobile phone service, landline phone service, internet- and television connection. KPN, a major Dutch telecommunication company, started offering bundling packages in 2013. In 2017 VodafoneZiggo, a merger between two major telecommunication companies, started offering bundling packages as well, followed by T-Mobile. This emerging trend fits with the desire of mobile service providers to seek new ways to differentiate their offerings in order to become more attractive and increase profits; this is due to the fact that mobile service consumers have different preferences (Ferrer et al., 2010). According to Ranganathan et al. (2006), the relationship investment barrier is strengthened by service bundling. Service bundling is the aggregation of multiple services as a single package (Stremersch & Tellis, 2002). In other words, it is the quantity of different services from the same mobile service provider. Service bundling will strengthen the relationship between consumer and mobile service provider and therefore the relationship investment will have a stronger impact on the intentions of consumers switching to another provider.
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consumers consider switching to another mobile service provider, the perceived complexity will be increased, since they have to switch from multiple services at once, or lose their bundling advantages. As complexity rises, it becomes less likely that at similar levels of barriers consumers will switch. Thus, the impact of the barriers become more visible and hence have a stronger impact on customer switching intention in the case of bundling, as compared to the situation of unbundled services. On the basis of an extensive list of literature (ACM, 2017; Burnham et al., 2003; Jones et al., 2000; Lee et al., 2001; Malhotra & Malhotra, 2013) this research therefore argues that the relationships between the perceived switching barriers and consumer switching intention are strengthened due to the service bundling moderator. This means that the negative proposed relationships between relational investment, switching costs, and service recovery with consumers switching intention become more salient and have a stronger influence on consumers. The positive proposed relationship between availability and attractiveness of alternatives with consumer switching intention will have a stronger positive effect due to higher levels of service bundling.
Hypothesis 5: Bundling mobile services will strengthen the effects of the perceived switching barriers on consumers’ intention to switch.
3. Methodology
3.1 Sample and Questionnaire DesignData was collected between March 7 and May 9 of 2018, from students of the University of Groningen via an online questionnaire. To increase the response rate, students received a bonus on their exam when participating. In order to increase external validity and make the research more generalizable beyond the age group of students, the questionnaire was further distributed to a more diverse demographic sample group. The questionnaire targeted everyone who currently makes use of a mobile phone service provider, and above 18 years. In total 392 respondents completed the questionnaire, of which 343 eventually were usable. The respondents were nearly equally distributed on gender (male 53%, female 46%), varied in household- sizes and income, and the majority (87%) holds a university degree.
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All questions (except demographic questions) were measured using a 7-point Likert-scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Consumer switching intention, was measured for three moments in time; 6-, 12- and 24 months.
3.2 Construct measurement
Table II presents the order of analysis conducted in this study. Table II: Order of Analysis
Phase Analysis Purpose
1 Principal Component
Analysis
• Reduce the number of variables/items
• Detect structures in the relationships between variables • Exploration of factor loadings
• Removal of variables with low factor loadings • Constructing one factor for each switching barrier
2 Hierarchical Regression
Analysis
• Determining the effect for each perceived switching barrier on consumer switching intention
• Determining the effect of service bundling on consumer switching intention
• Determine the interaction effect between the perceived switching barriers and service bundling
3 Presentation of results • Discussion of findings
• Theoretical and managerial findings
3.2.1 Principal Component Analysis
In order check the validity of the measured variables, a principal component analysis (e.g. exploratory factor analysis) per independent variable is conducted. The main goals of principal component analysis (PCA) are to reduce the number of variables, and to detect structure in the relationships between variables. As the variables should be highly correlated, removing any variable should not alter the meaning of each construct (Jarvis, Mackenzie and Podsakoff, 2003).
The sample size of this study (N=343) is considered sufficient for PCA (Comrey, 1973). The analysis is executed separately per independent variable1. For each analysis the Kaiser-Meyer-Olkin value is at
least .5, which makes it suitable for PCA (Hair et al., 1994; Tabachnick, 2007). Another prerequisite for PCA is that Bartlett's Test of Sphericity should be significant (p ≤ .05) (Tabachnick, 2007). In this study all PCA are highly significant (p < 0.01) and therefore the chosen method is allowed. To extract the factors Orthogonal Varimax rotation was used, using a cut-off point per factor loading of ≥.5. By following these statistic rules of principal components analysis, the measured construct for each switching barrier will be valid. In the end a reliability analysis is performed, determining the Cronbach’s
1Through the high complexity of the model, extracting all variables at once did not yield the expected dimensional structure.
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α for each independent variable. The recommended reliability coefficient should be at least .70 (Santos, 1999), although at times a lower coefficient is used.
Independent variable – Relationship Investment
Relationship investment is measured by the following variables: (1) preferential treatment, (2) special benefit loss, (3) services that are not available from other providers, (4) mobile phone upgrades, (5) technology pioneering, (6) loyalty towards provider. The Cronbach’s α for this combined measurement is .77, which indicates sufficient internal reliability.
Through a principal component analysis, presented in table III, a new variable as is constructed out of the unweighted means of the six variables mentioned above. This factor of relationship investment, has an Eigenvalue of 2.78 and explains 46.23% of the total variance.
Table III: Principal Component Analysis - Relationship investment
Factor 1
I receive preferential treatment from my mobile phone service plan/provider. .70
If I change my mobile phone service plan/provider, I will lose my special benefits. .71
My mobile service provider offers new services that are not available from others .71
My mobile service plan/provider provides the best mobile phone upgrades. .69
My mobile phone service provider is a pioneer in new technology. .64
I feel a sense of loyalty to my mobile phone service provider. .64
Notes: N=343; Rotation: Varimax; KMO Value; 0.79; Bartlett’s test of sphericity: (p=.00)
Independent variable – Switching Costs
Switching costs are measured using the following variables: (1) amount of effort, (2) amount of time, (3) switching hassle, (4) procedural difficulties. The scale is reliable with a Cronbach’s α of .75. The results of the PCA are presented in table IV. The PCA showed confirmed the one-factor solution, which explained 58.39% of the variance, with an Eigenvalue of 2.33.
Table IV: Principal Component Analysis – Switching costs
Factor 1
Changing mobile phone service plans/providers requires a lot of effort. .87
Changing mobile phone service plans/providers requires a lot of time. .88
I might change my mobile phone service plan/provider if I could do so without a hassle. (r) .54
There are technical (procedural) difficulties in changing my mobile phone service plan/provider.
.71
Notes: N=343; Rotation: Varimax; KMO Value; 0.66; Bartlett’s test of sphericity: (p=.00)
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This study uses three (reversed) items to measure the availability and attractiveness of alternatives: (1) provider homogeneity, (2) doubt about better service via competitors, (3) lack of better service via competitors. These items are reversed items since they represent ‘unattractiveness of alternatives’. This scale has a Cronbach’s α of .51, which is lower than the preferred reliability coefficient of .70. The PCA of the availability and attractiveness of alternatives results in one factor explaining 50.39% of the variance with an Eigenvalue of 1.51.
Table V; Principal Component Analysis – Availability and attractiveness of alternatives Factor 1
All mobile phone service providers are the same. .76
I am not sure if other mobile phone service providers offer better service. .77
Other mobile phone service providers do not offer better service. .59 Notes: N=343; Rotation: Varimax KMO Value; 0.58; Bartlett’s test of sphericity: (p=.00)
Independent Variable – Service Recovery
To measure the switching barrier of service recovery, two items are used: (1) resolved complaint(s), and (2) satisfaction with mobile service provider’s response. A PCA was conducted and found only one factor. This factor had an Eigenvalue of 1.77 and explained 88.79% of the total variance. The corresponding Cronbach’s α is .87, which is well above the recommended level of reliability.
Table VI: Principal Component Analysis – Service recovery
Factor 1
A complaint that I had with my mobile phone service provider was resolved. .94
I was happy with how my mobile phone service provider responded to my complaint. .94 Notes: N=343; Rotation: Varimax; KMO Value; 0.50; Bartlett’s test of sphericity: (p=.00)
3.2.2 Hierarchical regression analysis
With the newly constructed independent variables (composite scores for constructs were created using unweighted averages) a hierarchical regression was performed. Via this method the effect of independent variables (predictors) can be determined on the dependent variable (predicted variable) (Cohen et al., 2013). This contributes to understanding the effect of which factor significantly influences consumer switching intentions, and to what extent the moderator and the interaction effects have an effect. This hierarchical regression analysis was performed three times, a separate analysis for each timeframe (6-,12-, and 24 months).
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variables and moderator were standardized before introducing the interaction terms as moderator by multiplying the standardized effects of the variables; this was done to reduce multicollinearity (Hair et al., 1994).
This process of creating a dummy variable, the standardized interaction terms and the hierarchical regression analysis was done for the two different questions measuring service bundling in the questionnaire. The number of respondents indicating the use of service bundling varied amongst those two questions (N=54, and N=69). To assess the differences between the results, two tests were performed. The results were the same. In the rest of this report, we refer to the findings of the bundling confirmation by 69 respondents (N=69).
4. Results
The results of this research are presented in three parts. First the descriptive statistics and correlations are presented, followed by the hierarchical regression analysis (testing H1-H4) and the interaction effects(testing H5).
4.1 Descriptive statistics & Correlations
Table VII presents the descriptive statistics and correlations.
The correlations amongst the three dependent variables of switching intention in 6 months, 12 months and 24 months are all positive and highly significant (p’s < 0.01), which makes intuitive sense as it indicates consumers tendency to switch.
Relationship investment correlates negatively with switching intention for all timeframes (all p’s < 0.01), with their correlations increasing over time.
The correlation between switching costs and switching intention is significant, although at a 10% level (p < 0.1) for 6 and 12 months, but for 24 months it shows no longer a significant correlation. It is remarkable that the results show a positive significant relationship, where a negative relationship was expected.
The availability and attractiveness of alternatives correlates positively with switching intentions. For 6 months this effect is significant at the 10% level (p < 0.1), and for 12 and 24 months this correlation is highly significant (p < 0.01).
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switching intention, no significant correlations are found. This suggests that service recovery has limited effect on the switching intention of consumers.
17 Table VII: Descriptive statistics & Correlations
Correlations
Variables Mean SD Respondents
(N=) Switching intention within 6 months Switching intention within 12 months Switching intention within 24 months Relationship Investment Switching Costs A&A of alternatives Service Recovery Service Bundling Dependent variables
Switching intention within 6 months
2.38 1.83 343 1.00 Switching intention within
12 months
3.18 1.93 343 .73** 1.00 Switching intention within
24 months 4.39 1.71 343 .48** .63** 1.00 Independent Variables Relationship investment 3.73 .97 343 -.17** -.23** -.31** 1.00 Switching Costs 4.03 1.13 343 .11* .11* .07 .03 1.00 Availability of Alternatives 3.79 1.05 343 .10* .16** .16** .10* .14** 1.00 Service Recovery 4.54 1.19 343 -.09* -.05 -.07 .26** -.09* .07 1.00 Service Bundling2 0.20 0.40 343 -.06 -.09* -.15** .27* .03 .09* .10* 1.00 Notes: ** p < 0.01, * p < 0.1, SD = std. deviation
2 Service Bundling is added as a dummy variable with values 0 and 1.
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4.2 Hierarchical Regression Analysis: Hypotheses Testing
The result of the hierarchical regression analysis for the three timeframes are presented in table VIII. Each timeframe consists of 2 models. The first model (per timeframe) shows the regression analysis with only the independent variables. The second model (per timeframe) includes the moderation- and interaction effects of service bundling.
Independent variables – Model I.
The total amount of variance explained by the models, and therefore the relevance for indicating the consumers switching intentions, differs per timeframe. For the 6 months’ timeframe, the total variance explained is 5.40%. For 12 months switching intention the level of variance explained is 9.40%, and with 12.70% the largest amount of variance is explained for the 24 months’ timeframe.
In support of hypothesis 1, relationship investment shows a highly significant negative effect (B = -.28, p < 0.01) on the intention of consumers to switch between provider within 6 months in model I. This indicates that with a higher level of relationship investment, consumers have a lower intention to switch. When looking at the intention to switch in 12 months, we see that the absolute value of the beta (B = -.44, p < 0.01) has increased considerably, again on a highly significant level. For the longest timeframe of 24 months, the negative effect is on its highest with a value of B = -.50, on a highly significant (p < 0.01) level.
In contrast to hypothesis 2, increased perceived switching costs positively influence (B = .23, p < 0.1) intention to switch service provider within 6 months. This positive effect increases (B = .27, p < 0.01) for the intention to switch service providers within 12 months, and remains positive for the period of 24 months (B = .17, p < 0.1).
In support of hypothesis 3, availability and attractiveness of alternatives shows a significant positive effect (B = .183, p < .01) on the intention to switch service providers within 6 months. It indicates the increase of intention to switch for consumers when the availability and attractiveness of alternatives is higher. This effect is strongest for a 12 months’ timeframe with a coefficient of B = -.30, with an effect of B =-.23 for 24 months. Both timeframes are highly significant (p < .01).
19 Moderation and interaction effect – Model II
The total amount of total variance explained increases per timeframe. The percentage of variance explained in consumer switching intention increases from 7.30% for 6 months, to 11.00% for 12 months, and to 13.80% for 24 months.
Model II presents the results of the proposed moderation effects. All moderation effects (as indicated by the interaction terms) are found to be insignificant for all switching barriers at any timeframe (all p’s > .10). Thereby rejecting hypothesis 5 for all switching barriers.
20 Table VIII: Hierarchical regression analysis results
6 Months intention 12 Months intention 24 months intention
Model I Model II Model I Model II Model I Model II
Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error
Independent Variables
Constant 2.38** .10 2.36** .10 3.18** .10 3.18** .10 4.39** .09 4.36** .09 Relationship investment -.28** .11 -.29* .11 -.44** .11 -.46** .11 -.50** .09 -.51** .10 Switching costs .23* .10 .25* .10 .27** .10 .28** .10 .17* .09 .17* .09 Availability and attractiveness of alternatives .18* .10 .19* .10 .30** .10 .31** .10 .23** .09 .23* .09 Service Recovery -.06 .10 -.04 .10 .07 .10 .10 .11 .05 .09 .05 .09
Moderators
Service Bundling -.06 .11 -.04 .12 -.15 .10
Relationship Investment x Service bundling .18 .11 .10 .11 .15 .10
Switching Costs x Service bundling -.11 .11 -.10 .11 -.03 .10
Availability of alternatives x Service bundling -.12 .11 -.17 .11 -.11 .10
Service Recovery x Service bundling -.11 .10 -.09 .10 -.10 .09
R² .05 .07 .09 .11 .13 .14
Adjusted R² .04 .05 .08 .09 .11 .12
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5. Discussion
The objective of this study was to determine to what extent perceived switching barriers influence the switching intention of consumers (within the next 6-, 12-, or 24 months) and to investigate if these service barriers were moderated by service bundling. Although evidence was found for some of the switching barriers, service bundling did not moderate any of the relationships between perceived switching barriers and consumer switching intentions.
The discussion will elaborate on the explained variance for each model in each timeframe and how the study findings relate to existing research.
Model’s Predictive Power - Explained variance
Overall the explained variance for each model, and every timeframe, is not that high (highest value about 14%). The results suggest that consumers’ intention to switch to another mobile service provider is dependent on more variables than the four perceived switching barriers investigated in this study. While Burnham et al. (2003) suggest that switching barriers explain more variance than consumer satisfaction, our study compares unfavourably to, for example, Szymanski and Henard (2001) who explained a higher percentage (25% of variance) in their meta-analysis to the effect of consumer satisfaction on switching intentions. It is however important to realize that Szymanski and Henard (2001) measured consumers satisfaction, and not perceived switching barriers, which could be an explanation for the lower percentage of variance explained.
The findings suggest that the variance explained increase over time for both model I and model II, suggesting that consumers’ switching intention can be better predicted for longer time horizons. This can be explained, as temporary barriers (e.g., contracts) may disappear for such horizons, making the four barriers to have more explanatory power.
Extent of influence of the perceived switching barriers
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As suggested by Colgate and Lang (2001), this may very well be caused by the nature of the banking industry, who might not be willing or able to develop meaningful relationship with their consumers. Furthermore the extent of influence was found to be increasing over time. Indicating that investments in the relationship with a consumer would yield loyalty advantages in the long-term. In a similar fashion, Lee et al. (2007) found that consumers are less likely to switch due to their established long-term relationship with their current service provider. This is explained by Malhotra & Malhotra (2013) as a reinforcing circle, where more loyal (long-term) consumers receive more relational advantages, in return for their loyalty. These additional relational advantages result again in creating even more loyalty and decreasing the intention to switch to another service provider.
In contrary to this study’s expectations a positive relation was found for consumers’ intention to switch when perceived switching costs increase. This may be explained by the dependent variable of this study. The measured dependent variable of this study was the intention to switch to another provider, and not the actual consumer retention for mobile service providers. Malhotra & Malhotra (2013) describe this phenomenon as ‘false’ loyalty, and found a similar effect for hard lock-ins. So where increased switching cost (hard lock-ins) might increase consumer retention, it does not necessarily decrease consumers intention to switch.
When consumers intention to switch increases, but they remain at their current service provider, it will result in spuriously loyal consumers (Kim & Yoon, 2004). These consumers remain solely due to high perceived switching costs, but will switch towards another provider as soon as they get the opportunity (e.g. expiration of current mobile contract). This next opportunity differs for consumers, and therefore the positive effect remains more or less on the same level for each timeframe.
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The expected positive relationship between availability and attractiveness, and consumers intention to switch was supported by the data of this study. However, the extent of influence compared unfavourably against the influence in the banking- and insurance industry (Colgate & Lang, 2001), and the influence of procedural learning and set-up costs found by Burnham et al. (2003) in the credit card industry.
What is striking about the results is that the influences of alternative attractiveness on switching intentions increases over time. This is remarkable since both the availability and the attractiveness of these alternatives can change drastically in the given timeframes due to continuously technological developments in the dynamic mobile service industry. Again, this may be explained by the awareness of consumers of the next switching opportunity. This could also explain the beforementioned differences with other research (Burnham et al., 2003; Colgate & Lang, 2001). In the mobile service industry there is a higher long-term contract density (86.38% in this study), compared to the banking industry or credit card industry, and therefore consumers do not consider switching to alternative mobile service providers until their contract expires.
Service recovery was not an important factor in consumers’ intention to switch to another mobile service provider, similarly to the research of Colgate & Lang (2001). This can be explained by three reasons according to Colgate & Lang (2001). Firstly, there is a flawless relationship with the service provider, and therefore no reason to complain. Secondly, not every consumer complains when a service failure occurs and finally consumers do not necessarily receive a satisfactory resolution when they do complain. For those reasons, most research (Caruana, 2003; Lee et al., 2013; Malhotra & Malhotra, 2013) does not acknowledge service recovery as a switching barrier. A decision which is once again confirmed by the results of this study.
24 Moderating effect of service bundling
Now this study has established the extent of influence of the four perceived service barriers, the proposed moderation effect can be evaluated. As described in the results, the expected moderation was not found on a significant level for any of the perceived switching barriers. The following section will continue with a short explanation why this effect was not found.
The relationship between relationship investment and switching intention was expected to be increasingly negative due to service bundling, based on Ranganathan et al. (2006), and Burnham et al. (2003). Ranganathan et al. (2006) found that mobile users would be less likely to switch to another provider through an increased quantity of different services with the same mobile service provider. The different results of this study can be explained by the contractual obligations in the Dutch mobile service industry (86.38% in this study), where the respondents in the study of Ranganathan et al. (2006) did not have any contractual obligations. Therefore other switching barriers, such as service bundling, may have a greater influence on the intention to switch to another mobile service provider.
Likewise, the expected increasingly negative relationship between switching costs and consumer switching intention through the moderation of service bundling was not found. This indicates that consumers do not perceive switching of multiple services at once, as more complex or more demanding in time and effort than switching from only their mobile service. In practice, this can be explained by the assistance in switching of mobile service providers. Not only do service providers pay off you breach of contract fee, they can also take care of all the hassles associated with switching, since they know how valuable a new consumer is to them. These assistance is offered for consumers who switch only their mobile service provider, but also for consumers who have bundled their services. The effect of availability and attractiveness on switching intention was expected to be increasingly positive due to the moderation of service bundling, which was not supported by the results of this study. Indicating that, although a service bundle can ensure exclusive content, consumers do not see other service providers as less attractive. This contradicts Jones et al. (2000) who found that offering appropriate content decreases the attractiveness of alternatives. An explanation can be found in the digital era where we live in. Consumers may get access to exclusive content via other media channels, and do not depend solely on their service provider bundle, therefore there are no perceived exclusive content benefits via service bundling.
25 Theoretical implications
From an academic perspective this study contributes to a more comprehensive understanding regarding consumer switching intentions. This study provides new insights.
Firstly, the emerging trend of bundling multiple services into one package is not of significant influence on the perceived switching barriers in the Dutch mobile service industry. Although previous literature would suggest otherwise, as does the Authority of Consumers and Markets (ACM, 2017), this moderation effect of mobile service bundling is not found in this study.
Secondly, the positive effect of switching costs on consumers switching intention hints at the false loyalty effect found by Malhotra & Malhotra (2013). Otherwise it could be explained by the earlier mentioned reverse causality. Either way, it is very important not to confound consumers switching intentions with consumer retention. The positive findings in this study indicate that switching costs may have an opposite effect on switching intentions in comparison with consumer retention.
Managerial implications
Managers at mobile service providers face the difficult task to create effective switching barriers and stimulate loyalty in the dynamic environment of the mobile service industry. The strategy should focus on retaining consumers and preventing consumer from switching to other providers. To aid these managers in their decisions four recommendations will be made based upon the findings of this study. Firstly, the most important result for managers is that the relationship investment barrier has the strongest impact on the switching intention of consumers. This accounts for all three timeframes, so it should be included in both short-, as long-term switching barrier strategies. Managers of mobile service providers should put their focus on maintaining and investing in the relationships with their consumers.
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Thirdly, the perceived impact of switching barriers holds influence over both the short-term and the long-term intentions of consumers. An explanation for this finding is the importance of the next switching opportunity. Since mobile service consumer are more impressionable when their current contract is about to end. To keep consumers connected to the mobile service providers, special attention should be given to consumers with expiring contracts. For example, a new contract should be offered, including a price discount and a new mobile device. This will strengthen the relationship, and decrease consumers intention to switch to another provider in the moment they are the most impressionable.
Finally, mobile service providers who offer bundling packages, should not expect huge differences in the way they manage their current switching barrier strategy since there are no significant interaction effects. To see if there are any differences in switching intentions between consumers who bundle their services and consumers who do not bundle their services, an exploratory independent-samples t-test is performed per timeframe. This resulted in one significant difference for the 24 months’ timeframe, with an t-value of 1.791 (p < 0.01). Another implication of service bundling for managers at mobile service providers is that competing mobile services are less easy to compare through service bundling. Since different bundling packages (including exclusive content) are not just expressible in monetary value. Therefore consumers might be less price sensitive.
Research Limitations & Directions for Future research
There are limitations to this study, which provide interesting directions for future researchers to build upon this study. Firstly, this study uses cross-sectional data and uses the responses of a homogenous group of students. This homogenous group of students is plausible more price-conscious. Furthermore students predominantly live in shared housing, where telecommunication services (e.g. television, internet) are shared, and therefore cannot be bundled with mobile services. This may strongly limit the generalisability of the results. Hence, this study could be replicated throughout a more diversified sample.
Another limitation in this study is the measured dependent variable of consumers switching intention. As explained earlier, consumers intention to switch measures behavioural loyalty intentions. This may differ from affective loyalty, which is measured by actual consumer retention. This may impact the influence of the perceived switching barriers. Therefore, this research could be replicated, where the dependent variable is measured in effective loyalty (i.e. consumer retention).
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finding holds for consumers who do not have a contract with their mobile service provider. Since they do not have the wait for the next opportunity, they might be more aware of their possibility to switch to another mobile service provider. A comparison between contract and no-contract consumers, could provide valuable insights.
Another interesting avenue for future research would be to investigate if perceived switching barriers are different for virtual service providers, since their emergence recently disrupted the market. They offer more flexible contracts, and therefore different perspectives on switching barriers can be expected. As these consumers may be strongly driven by the offer (and less with the service provider relationship), the role of attractiveness of alternatives may be more pronounced in these settings.
6. Conclusion
This study investigated the impact of switching barriers on consumer switching intentions in the Dutch mobile service industry, this relationship was tested for the timeframes of 6-, 12-, and 24 months. Subsequently the moderation effect of service bundling on this relationship was examined.
The results showed that relationship investment is the most important switching barrier to decrease consumers intention to switch to another mobile service provider. Therefore the focus of mobile service providers should be on building and maintaining relationships with their current consumers. In contrary to expectations, increased switching costs showed a positive effect on consumers intention to switch to another provider. This effect is remarkable, but can be explained by the measurement of consumer intentions instead of consumer retention.
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31 Appendix I: All variables measured in the online questionnaire
Constructs & Variables. N= Mean Std. Deviation
Relationship Investment
(D) Ihave confidence that my mobile phone service plan provides the best deal. 343 4.49 1.15
I receive preferential treatment from my mobile phone service plan/provider. 343 3.93 1.60
If I change my mobile phone service plan/provider, I will lose my special benefits. 343 3.87 1.79
My mobile phone service provider offers new services that are not available from others. 343 3.27 1.70
My mobile phone service plan/provider provides the best mobile phone upgrades. 343 3.74 1.45 (D) My mobile phone service provider has mobile phones to choose from that are not
available from other providers.
343 2.70 1.43
(D) My mobile phone service provider has excellent connection quality everywhere. 343 4.86 1.55 (D) My mobile phone service provider has less dropped calls than other providers. 343 4.09 1.14 (D) My mobile phone service plan provides free or lowest international call and text
charges.
343 4.23 1.51
My mobile phone service provider is a pioneer in new technology. 343 3.66 1.44
(D) My mobile phone service plan/provider meets my expectations. 343 5.33 1.28
I feel a sense of loyalty to my mobile phone service provider. 343 3.91 1.79 (D) My mobile phone service provider has many service stores in my area. 343 4.22 1.80
(D) My mobile phone service provider staff is courteous to me. 343 4.57 1.29
Switching Costs
Changing mobile phone service plans/providers requires a lot of effort. 343 4.22 1.68
Changing mobile phone service plans/providers requires a lot of time. 343 4.27 1.60 (D) If I switch to another mobile phone service plan/provider I am concerned about
negative benefit outcomes.
343 4.14 1.59
I might change my mobile phone service plan/provider if I could do so without a hassle. 343 4.16 1.70
There are technical (procedural) difficulties in changing my mobile phone service plan/provider.
343 3.50 1.59
(D) I feel locked in because of the contract I have with my present mobile phone service
provider.
343 3.27 1.80
(D) I am reluctant to change my mobile phone service plan/provider when all my desired
services are in my present mobile phone service plan/provider.
343 4.65 1.58
Availability & Attractiveness of Alternatives ®
All mobile phone service providers are the same. 343 3.10 1.56
I am not sure if other mobile phone service providers offer better service. 343 4.30 1.60
Other mobile phone service providers do not offer better service. 343 3.97 1.25 (D) I know other mobile phone service providers offer better service, at the same or
lower cost, but I prefer to stay with my present main mobile phone service provider.
343 3.24 1.53
Service Recovery
A complaint that I had with my mobile phone service provider was resolved. 343 4.59 1.24
I was happy with how my mobile phone service provider responded to my complaint. 343 4.51 1.29 Service Bundling 3
Bundling services makes it easy for me to oversee my mobile service plans. 343 4.79 1.25
Bundling services makes me more dependent on the mobile service provider(s). 343 5.13 1.22
Bundling services increases the power of the mobile service provider(s). 343 5.36 1.09
I do not want to depend totally on one mobile service provider. 343 4.19 1.66
Bundling multiple services enhances the feeling of being connected with this mobile service provider(s).
343 4.63 1.30
Bundling multiple services ensures more special benefits from mobile service provider(s). 343 4.95 1.16
Bundling multiple services results in increased financial costs if I switch to another mobile service plan/provider.
343 4.55 1.29
Bundling multiple services results in increased time and effort when switching to another mobile service plan/provider.
343 4.72 1.33
A successfully solved complaint strengthens my trust in the mobile service provider(s). 343 5.37 1.17
I am not sure if other mobile service providers can offer the same bundling package as my current service provider(s).
343 4.37 1.38
Notes: (D) = dropped variable; ® = reversed variable.
