International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 [Vol. 3 No. 1 March 2021]
Journal website: http://myjms.mohe.gov.my/index.php/ijbec
FACTORS INFLUENCING THE INTENTION TO SWITCH PREPAID MOBILE PHONE SERVICES IN MALAYSIA
Wan Aliaa Wan Anis1* and Nor Azila Mohd Noor2
1 Othman Yeop Abdullah Graduate School of Business, University Utara Malaysia, Sintok, MALAYSIA
2 Institute for Business Competitiveness, Standards and Sustainability Initiative, School of Business Management, University Utara Malaysia, Sintok, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 5 March 2021 Revised date : 23 March 2021 Accepted date : 28 March 2021 Published date : 30 March 2021 To cite this document:
Wan Anis, W., & Mohd Noor, N.
(2021). FACTORS INFLUENCING THE INTENTION TO SWITCH PREPAID MOBILE PHONE SERVICES IN MALAYSIA.
International Journal Of Business And Economy, 3(1), 139-155.
Abstract: Telecommunication industry is one of the largest sub-sectors that contributes to the development of economic of Malaysia. The increasing number of mobile phone service provider has increased the competition among mobile phone companies. With this competition, various attractive packages or plan has been offered to attract the customers, and this scenario has encouraged subscribers to switch to other service provider. Since there is no contractual obligation between Prepaid subscribers and service provider, Prepaid subscribers are easier to switch to another service provider. This study concentrates on the factors influencing customer’s intention to switch in the Malaysian Prepaid mobile phone services. It examines the influence of price, satisfaction, alternative attractiveness, and MNP-induced self-efficacy on intention to switch. The study was conducted at six universities in the northern region of Peninsular Malaysia namely Universiti Utara Malaysia (UUM), Universiti Malaysia Perlis (UniMAP), Universiti Sains Malaysia (USM) and Universiti Teknologi MARA (UiTM) which is represented by three branches (i.e., Arau, Perlis, Sungai Petani, Kedah and Permatang Pauh, Pulau Pinang campus). A total of 339 respondents were involved in this study. This study used intercept survey method as a data collection method, in which a 28-item questionnaire was used to measure price (10
1. Introduction
The use of mobile phone has increased across the globe including Malaysia (Balakrishnan &
Gopal, 2012). As reported by Malaysian Communications and Multimedia Commission (MCMC) performance report, the number of mobile phone subscribers is on the increasing trend over the past ten years. It rose from 33,859 million subscribers in total, or 119.2 percent penetration rate to 44,600 million subscribers in total, or 135.4 percent penetration during the period 2010 to 2019. Despite the increasing trend of mobile phone subscriptions, the industry is facing with increased competition and high churn rate (Chuah, Rauschnabel, Marimuthu, Ramayah & Nguyen, 2017). To make it worse, mobile phone subscribers are allowed to retain their current subscribers’ phone number when changing the mobile subscription with mobile number portability (MNP). This scenario has encouraged mobile phone subscribers to switch to other service providers. In addition to that, Prepaid mobile phone subscribers are now exposed to great pricing deals and bundle of packages by a wider choice of mobile phone service providers (Ong, Tan, & Andrews, 2012). Customers tend to switch their mobile service provider if they have better alternative (Lim, Yeo & Ling, 2018). Besides that, customers are more concern about price and which mobile service provider offering better services (Lim, Yeo
& Ling, 2018). Therefore, it is critically important for service provider to understand the factors that influence customer switching especially among Prepaid mobile phone subscribers.
Furthermore, consumer switching behavior has become the main issue in marketing study since the past decade due to the effect of consumer switching behavior to the survival, profitability and growth of the companies (Nimako, 2012). In telecommunication services, the annual churn rate of 15 – 30% can cost mobile service providers up to a $10 billion in revenue (Chuah, Rauschnabel, Marimuthu, Ramayah & Nguyen, 2017). Considering on the above discussion, this study is conducted to examine the factors that influence customer’s intention to switch in the Malaysian Prepaid mobile phone services by focusing on the influences of price, satisfaction, alternative attractiveness, and MNP-induced self-efficacy.
analysis undertaken, it was found that price, alternative attractiveness, and MNP-induced self-efficacy positively and significantly influence intention to switch, while satisfaction negatively and significantly influences intention to switch.
Keywords: intention to switch, price, satisfaction, alternative attractiveness, MNP-induced self-efficacy, telecommunication services; prepaid segment.
2. Literature Review
The relationship between price and intention to switch
In most literatures, price has been shown to influence switching behavior (Narteh, 2013; Vyas
& Raitani, 2014). Price has also been confirmed significantly and positively influence switching intention in the studies of child-care services (Grace & O’Cass, 2001) energy services (Vigolo & Cassia, 2014), banking services (Clemes et al., 2010; Puspitaningtyas, 2014) and airline services (Jung et al., 2017). Particularly, studies in telecommunication services context have also verified that price significantly and positively influences switching intention and switching behavior (Kaur & Sambyal, 2016; Nimako & Ntim, 2015; Sahi, Sambyal, & Sekhon, 2016). In view of previous studies, this present study also hypothesizes that price will influence switching intention. Hence, the following hypothesis is formulated:
H1: Price positively influences intention to switch
The relationship between satisfaction and intention to switch
Satisfaction has been recognized to play a significant role in switching decision (Bansal &
Taylor, 1999). Customers who experience dissatisfaction probably switch to another service provider (Keaveney, 1995). This view is supported by Cho and Song (2012) who stated that negative sides of satisfaction will encourage customers to switch. In a study by Chuang & Tai (2016), it has been declared that satisfaction is the most frequently used predictor variables to explain switching intention. This is supported by Ye and Potter (2011) who stated that user who have low satisfaction toward the incumbent probably have higher intention to switch.
Bhattacherjee et al., (2012) in their study also revealed that user tends to switch service provider when there are dissatisfied. In addition, a study by Jung et al., (2017) concluded that unsatisfied consumer tends to show high switching intention and search for an alternative than satisfied consumer.
Particularly in telecommunication service context, Nimako and Ntim (2015) and Shin and Kim (2008) have proved that satisfaction significantly influence switching intention. Customers who are not satisfied with their current network operator is believed to switch the operator (Kaur & Sambyal, 2016). This view is supported by Sahi et al., (2016) who stated that the possibility of customer to leave the current service provider is higher if they are not satisfied with the service. In a study by Calvo-Porral & Lévy-Mangin (2015), it has been declared that customer satisfaction has a strong negative relationship with switching intention. A study by Chuang (2011) have confirmed the significant and negative effect of satisfaction on switching intention. Other related studies of switching behavior also revealed the significant and negative effects of satisfaction on switching intention (Mannan et al., 2017; Nikbin, Ismail &
Marimuthu, 2012). Since previous studies have proposed that satisfaction negatively influences switching intention, the present study comes up with the second hypothesis:
H2: Satisfaction negatively influences intention to switch
The relationship between alternative attractiveness and intention to switch
Alternative attractiveness is defined as positive features of the competing service provider that influence subscribers to switch (Chuang, 2011). Lai et al., (2012) in their study has verified that consumers are strongly influenced by alternative attractiveness. In addition, alternative attractiveness has also been recognized to play significant roles in consumer switching intention (Bansal et al., 2005). Specifically, in telecommunication service context, several studies have been conducted and have revealed the significant and positive relationship between alternative attractiveness switching intention (Chuang, 2011). A study by Leng (2014) concluded that the users might decide to change to another service provider if the attractiveness of another service provider is higher than their level of satisfaction at the current service provider.
Meanwhile, the study by Chuang and Tai (2016) has declared that attractiveness of alternatives has been largely used as the predictor variables to explain switching intention. In addition, attractiveness of alternatives has also been recognized to play a larger role and strong factors in influencing switching intention (Kim, Shin & Geun, 2006). Previous literatures have confirmed that attractiveness of alternatives significantly influence switching intention (Jung et al., 2017). In a research study done by Zhang et al., (2012) verified that attractive alternatives are the most important determinant of service switching behavior. Besides that, the study by Zhang et al., (2009) has also declared that customer’s intention to switch is strongly influence by attractive alternatives. In consideration of those findings, this leads to the following hypothesis:
H3: Alternative attractiveness positively influences intention to switch
The relationship between MNP-induced self-efficacy and intention to switch
MNP-induced self-efficacy is individual’s overall judgements of his or her ability to switch with the implementation of MNP (Nimako, Ntim & Mensah, 2014). Several studies in telecommunication service context have confirmed the relationship; MNP-induced self- efficacy has a significant and positive effect on switching intention (Nimako et al., 2014;
Nimako & Ntim, 2015). In addition, in a study by Nimako et al., (2014), it has declared that MNP-induced self-efficacy (switching efficacy) is a strong predictor of switching intention. In view of this relationship, it is anticipated that individual’s overall judgements of his or her ability to switch with the implementation of MNP are more likely to switch service provider.
Hence, the present study comes up with the following hypothesis:
H4: MNP-induced self-efficacy positively influences intention to switch
Based on the discussion, the framework for this study is shown as below.
Figure 1: Theoretical Framework
2.1 Problem Statement
As reported by The Edge Financial Daily (October 10, 2017), Malaysia telecommunication industry is still dominated by Prepaid subscribers even though the number of postpaid subscribers is increasing. The statement then is supported in a statistic provided by MCMC (2019) in which the report reveals that there are 31, 260 million Prepaid mobile phone subscribers, as compared to post-paid mobile phone subscribers which is only 13,340 million.
This implies a significant of Prepaid mobile phone subscribers in Malaysian telecommunication industry. However, the number of Prepaid mobile phone subscribers illustrates a gradually decreasing trend since 2014. It dropped from 36.8 million subscribers to 30,837 million subscribers during the period 2014 to 2018 (MCMC, 2019). This indicate that Prepaid mobile phone subscribers have switched their service provider. Nevertheless, research focusing on switching behavior in Malaysia context is very limited (Lim, Yeo & Ling, 2018).
There are few studies have been conducted on switching behavior in Malaysia telecommunication services, but the study focuses only on direct relationship between price, competition, customer service, word-of-mouth, core service failure and switching behavior in the mobile phone services in general (Lim, Yeo & Ling, 2018). According to Nikbin, Ismail, Marimuthu and Armesh (2012), prepaid mobile plans is different from postpaid mobile plans in terms of payment structure because prepaid plan does not have periodic payments. They further state that the findings of study in postpaid mobile services are not applicable to prepaid mobile services as prepaid subscribers are not under contract that make them remain with the service operator. Therefore, to fill this gap, this study is conducted to identify the factors that influence customer’s intention to switch in the Malaysian Prepaid mobile phone services.
Satisfaction
Intention to switch Price
Alternative Attractiveness
MNP-induced self- efficacy
3. Method
A quantitative research approach was applied in this study to test the hypotheses and validate the propose framework. The framework is to test the relationship between price, satisfaction, alternative attractiveness, MNP-induced self-efficacy and intention to switch. This study used cross-sectional method to answer the research questions in which all data regarding each study variable is collected once. Survey method was applied in this study due to the fact that survey research is best adopted to obtain personal and social facts, beliefs, and attitudes (Kerlinger, 1973).
3.1 Materials 3.1.1 Samples
The unit analysis for this study was individual students who are undergraduate university students, and the Prepaid mobile phone subscribers. Students were chosen as a sample because they can reduce the impact of non-controllable confounding variables as they are homogeneous, in addition to, their consumption behaviours and perceptions are similar with other user’s characteristics (Sahi et al., 2016). Moreover, university students across the world not only use smartphones and tablets for personal purposes but also during the university day (Yeap, Ramayah & Soto-acosta, 2016).
3.1.2 Site
The authors distributed 456 sets of questionnaires: Universiti Utara Malaysia (170 samples), Universiti Malaysia Perlis (58 samples), Universiti Sains Malaysia (118 samples) and UiTM Perlis (34 samples), UiTM Kedah (34 samples) and UiTM Pulau Pinang (42 samples).
3.1.3 Procedures
Sampling Design & Data Collection
The questionnaire was personally administered to the respondents using systematic sampling technique in which every 10th leaving customer from the mall or cafeteria in the selected universities were intercepted to answer the questionnaire (Hair et al., 2008; Sudman, 1980). In addition, as suggested by Sudman (1980), the questionnaire was distributed in morning, noon, and evening from 10am to 3pm (i.e., first half) and 3pm to 8pm (second half), on different days on weekdays and weekends.
Variables
Looking closely at the research model as illustrates in Figures 1, there are four direct factors that influence intention to switch namely price, satisfaction, alternative attractiveness, and MNP-induced self-efficacy. Price, satisfaction, alternative attractiveness, and MNP-induced self-efficacy are expected to have direct relationship with intention to switch.
Sample Size
Based on the number of students enrolled in year 2019 obtained from the student affair department from each university, there were 81,875 number of students. Therefore, following the suggestions of Cohen, this study employed a sample of 228. Furthermore, a minimum sample size determination using G*Power 3 software indicate a minimum of 92, as the model had a maximum of 5 predictors (for the outcome variable intention to switch), the study set the alpha at 0.05 and power needed at 0.80 which is based on Cohen’s guidelines for behavioural sciences with the effect size as medium (0.15). Taking into account the possibility of in- complete questionnaire, a total of 456 questionnaires were distributed.
3.2 Measurement
Five items of intention to switch were adapted from Hino (2017). Price was measure by ten items adapted from Kaur and Sambyal (2016). Five items were used to measure satisfaction were adapted from Xu et al., (2017). Alternative attractiveness was measure by five items adapted from Bansal et al., (2005). Three items of MNP-induced self-efficacy were taken from Nimako, Ntim, & Mensah (2014). All the variables were assessed using 5-point Likert-type scales where 1 indicates strongly disagree, 2 is disagree, 3 marks as neither agree nor disagree, 4 is agree and 5 is strongly agree.
3.3 Data Analysis
Data were analyzed using SPSS version 26.0 and Smart-PLS version 3.3.2. This includes the descriptive analysis for profiling purposes, the measurement and structural models for factors loading, reliability and validity, path coefficients and the t-values after bootstrapping for hypotheses testing.
Demographic Profile
A total of 456 questionnaires were distributed, but only 408 were returned and finally, 339 were valid and usable, giving a response rate of 74.3%. In this study, majority of the respondents were doing Bachelor’s degree representing 57.2%, while the remaining 42.8%
were doing Diploma. In terms of mobile phone service provider that respondents subscribe, 34.5% were using Celcom, 23.6% uses TM, 21.2% uses Maxis, 18% were using Digi and about 5% were using services provided by XOX Mobile. The demographic distribution of gender in this shows that majority of the respondents are female (72%). The remaining respondents are male, which is about 28%. In terms of ethnicity, majority of the respondents are Malay (94.1%), followed by Chinese (2.9%), other ethnic groups (2.4%) and Indian (0.6%). 94.1 percent were Malays, 2.9 percent Chinese, 2.4 percent from other ethnic groups and 0.6 percent were Indian.
The demographic profile of respondents is shown in Table 1 below.
Table 1: Demographic Profile of Respondents (N = 339)
Variable Category Frequency %
Study Programme Diploma 145 42.8
Bachelor’s Degree
194 57.2
Service Provider Celcom 117 34.5
Digi 61 18.0
Maxis 72 21.2
TM 80 23.6
XOX Mobile 9 2.7
Gender Male 95 28.0
Female 244 72.0
Age 18 – 25 years 321 94.7
26 – 35 years 16 4.7
36 & above 2 0.6
Ethnicity Malay 319 94.1
Chinese 10 2.9
Indian 2 0.6
Others 8 2.4
3.3.1 Validity and Reliability Measurement Model (Outer Model)
This study follows a two steps approach which are measurement model (outer model) and structural model (inner model). The first step is to evaluate the measurement model (outer model) in which construct’s reliability, convergent validity and discriminant validity were analysed as suggested by Hair et al., (2014). Composite reliability (CR) was calculated to assess the construct reliability, whereas, for convergent validity, average variance extracted (AVE) values were calculated. According to Hair et al., (2014), the minimum required value for composite reliability is greater than 0.7 and it is considered as acceptable when the loading for each item is 0.70 and above; and average variance extracted (AVE) is 0.50 and above. In this study, the composite reliability for all the variables were above 0.7 and all the average variance extracted (AVE) were above 0.5. However, four items (PR3, PR4, PR7 and PR8) were deleted due to the loading for each item were below 0.7. The measurement model is illustrated in Figure 2 and the result of measurement model is presented in Table 2.
Figure 2: Measurement Model
Table 2: Result of Measurement Model Construct Item Loading Composite
Reliability AVE Cronbach Alpha
Intention to Switch
INT1 INT2 INT3 INT4 INT5
0.90 0.92 0.91 0.93 0.89
0.96 0.83 0.92
Price
PR1 PR2 PR5 PR6 PR9 PR10
0.81 0.78 0.86 0.82 0.90 0.88
0.94 0.71 0.92
Satisfaction
SAT1 SAT2 SAT3 SAT4 SAT5
0.83 0.85 0.83 0.88 0.79
0.92 0.70 0.89
Alternative Attractiveness
AA1 AA2 AA3 AA4 AA5
0.83 0.87 0.88 0.88 0.86
0.94 0.75 0.92
MNP-Induced Self- Efficacy
MNP1 MNP2 MNP3
0.87 0.85 0.87
0.90 0.75 0.83
The next step was to retrieve the construct collinearity by evaluating the value of Variance Inflation Factors (VIF) (Hair et al., 2011). The result of Variance Inflation Factors (VIF) is shown in Table 3. All VIF values are below than 5, indicating that multicollinearity does not exist in this study.
Table 3: Variance Inflation Factors (VIF) Construct Collinearity
Constructs Intention to switch
Intention to switch
Price 2.78
Satisfaction 2.93
Alternative Attractiveness 1.22
MNP-Induced Self-Efficacy 1.36
After confirming the reliability of the measures, convergent validity and Variance Inflation Factors (VIF), the study applies Fornell and Larcker’s (1981) criterion to evaluate whether the discriminant validity among the constructs is acceptable. Under this criterion, discriminant validity among the constructs is achieved given that the square root of AVE value is higher than the coefficient of correlation values of corresponding row or column. Table 4 demonstrates that the diagonal values (square root of AVE) are considerably higher than the correlation coefficients between the constructs. Therefore, discriminant validity of the measurement model is confirmed.
Table 4: Discriminant Validity Analysis – Fornell – Larcker’s Criterion
Note: The off-diagonals represent the correlations, whereas, the diagonals indicate square root of AVE.
Meanwhile, Table 5 show the results of the result heteroit-monoteroit (HTMT) ratio.
Table 5: Result of HTMT Ratio
Structural Model (Inner Model)
Next, the structural model (inner model) was analysed by looking at the hypothesis’s relationships between the constructs, in addition to, measures of predictive capabilities. The path coefficients and R2 values were calculated by using Smart PLS 3.0. The path model in Figure 3 below indicates the positive relationship between price and intention to switch (path coefficient=0.28), alternative attractiveness and switching intention (path coefficient=0.20), as well as MNP-induced self-efficacy (path coefficient=0.20). In contrast, the value of the path coefficient between satisfaction and intention to switch (path coefficient=-0.27) revealed a negative relationship. Meanwhile, the R² value of intention to switch is 0.56. Therefore, it can be said that the R² value of intention to switch (56.0%) is moderate and the variance in the intention to switch can be described by price, satisfaction, alternative attractiveness, and MNP- induced self-efficacy.
Construct Alternative Attractiveness
Intention to switch
MNP-Induced
Self-Efficacy Price Satisfaction Alternative Attractiveness 0.87
Intention to switch 0.47 0.91
MNP-Induced Self-Efficacy 0.26 0.51 0.87
Price 0.38 0.66 0.47 0.84
Satisfaction -0.41 -0.67 -0.49 -0.79 0.84
Construct Alternative Attractiveness
Intention to switch
MNP-Induced
Self-Efficacy Price Satisfaction Alternative Attractiveness
Intention to switch 0.50
MNP-Induced Self-Efficacy 0.29 0.57
Price 0.40 0.70 0.54
Satisfaction 0.45 0.72 0.57 0.87
Figure 3: Path Analysis
Bootstrapping technique in Smart PLS 3.0 with 5000 bootstrap samples was used to test whether the hypotheses were supported or not, the hypotheses were examined using bootstrapping technique by checking on the t-values (Hair et al., 2014). When the t-value is larger than the critical value in a certain error aspect, then the coefficient is considered significant. For the two-tailed tests, the critical values are at 1.96 at significance level of 0.05 or five percent, while 2.58 for significance level of 0.01 or one percent (Hair et al., 2014).
There were four hypotheses tested (H1, H2, H3 and H4). The bootstrapping results show that all the hypotheses in study is supported in which there is significant relationship between price and intention to switch (t-value=3.53, p-value=0.00), satisfaction and intention to switch (t- value=3.28, p-value=0.00), alternative attractiveness and intention to switch (t-value=5.03, p- value=0.00), and MNP-induced self-efficacy and intention to switch (t-value=4.49, p- value=0.00). The bootstrapping results is illustrated in Figure 4 and presented in Table 6.
Figure 4: Bootstrapping (t-values)
Table 6: Path Coefficients and Hypotheses Testing
4. Results and Discussion
Based on the hypotheses, the study has confirmed the positive and significant relationship between price and intention to switch. The finding of the study is in line with previous studies, such as Kaur and Sambyal (2016) and Sahi et al., (2016). The possible reason why price has become the important factor is because customers are more concern about price and which mobile service provider offering better services (Lim, Yeo & Ling, 2018). Therefore, mobile phone service provider must strategize to offer better price to prevent customer switching.
Relationships Std. Error β t-value p-value Decision H1: Price -> Intention to switch 0.08 0.28 3.53 0.00 Supported H2: Satisfaction -> Intention to switch 0.08 -0.27 3.28 0.00 Supported H3: Alternative Attractiveness-> Intention
to switch 0.04 0.20 5.03 0.00 Supported
H4: MNP-Induced Self-Efficacy->
Intention to switch 0.04 0.20 4.49 0.00 Supported
The result also revealed that satisfaction has a negative and significant influence on intention to switch. This finding is congruent with those of the previous studies, such as Nimako and Ntim (2015) and Sahi et al., (2016). Hence, to prevent customer switching, mobile phone service provider must ensure that their services satisfy the subscribers. Further, the outcomes reveal that alternative attractiveness has a positive and significant relationship with intention to switch. These findings seem to be consistent with research conducted by Chuang (2011).
This result may be explained by the fact that customers nowadays tend to switch their mobile service provider if they have better alternative (Lim, Yeo & Ling, 2018).
Finally, as hypothesized, MNP-induced self-efficacy positively influence switching intention.
The findings of study confirm that MNP-induced self-efficacy has a positive significant influence on switching intention. Customers tend to switch Prepaid mobile phone service because they believe that they have high capability to switch with MNP. The study outcome is consistent with prior result by Nimako et al., (2014).
5. Conclusion
In conclusion, the study provides an understanding on the factors that influence customer’s intention to switch in Malaysian Prepaid mobile phone services. The finding of the study suggests that customer’s intention to switch Prepaid mobile phone services depend on price, satisfaction, alternative attractiveness, and MNP-induced self-efficacy.
6. Acknowledgement
Thank to Othman Yeop Abdullah (OYA) Graduate School of Business, Universiti Utara Malaysia, MY BRAIN 15 scholarship for financial assistance, in addition to, respondents who participates in the survey.
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