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Abstract

This article analyzes the rationality of customer’s behavior when switching mobile services. Customer rationality is manifested by selecting the service provider capable of delivering the highest amount of benefits to the customer. A model based on multinomial logistics regression that makes use of the rational choice theory is developed to emulate customers’ service switching behavior and predicts the probability of switching mobile services. Customer rationality is assessed by comparing the choice predicted by the model with the true choice of customers and rationality is considered as achieved when a customer chooses a service provider that maximizes his/her benefit. The model was tested and validated by modeling the behavior of customers in the Jordanian telecommunication market and a rationality assessment was conducted. The results show that mobile service customers in Jordan are making rational decisions and would be willing to switch their current service provider if offered a better service. The study encourages service providers to challenge their assessment of customer loyalty and investigate new ways to enhance customer experience and guarantee customer loyalty.

Keywords

Rational choice mobile service switching services multinomial logistics regression Jordan telecommunication market

Introduction

There is a growing interest by mobile service providers to study the behavior and logic used by customers when selecting a service provider and the reasons for customer retention due to the negative effects resulting from customer switching service providers, such as reduced market share, impaired profitability, and increased costs.¹ Customer retention results in favorable behavioral consequences that are beneficial to the mobile service provider. Thus, mobile service providers are focusing on establishing and maintaining long-term relationships with their existing customers^2,3 by ensuring that customers make subsequent purchases or by extending the customers’ contract with the service provider. Customers may be forced to make future purchases from the current service provider because they are prevented by some barrier from changing or switching providers. On the other hand, customers may voluntarily choose to make subsequent purchases from their current service provider because they have a favorable attitude toward the service provider. When such a voluntarily behavior exists, the term customer loyalty is used to describe the business relationship between the service provider and the customer. Customer loyalty is defined as the customer’s intention or predisposition to repurchase from the same firm again.⁴ That is, customer loyalty can be viewed as the willingness of consumers to subscribe to the company on an ongoing basis, purchasing and using goods and services on a repeated and preferably exclusive basis, and recommending the firm’s products and services to friends and associates.⁵ Loyal customers are less likely to be motivated to search for information on alternatives, are more resistant to persuasion by competitors, and are more likely to engage in word-of-mouth communications to project a favorable image about the product or service.^6
–8 Customer loyalty has been linked to customer satisfaction, in the sense that satisfied customers are more likely to be loyal customers.^9
–11 Additionally, it has been also argued that:

Satisfaction and loyalty can be strong predictors of customer retention.^12,13

The cost of attracting new customers (potential switchers) from a competitor exceeds the cost of retaining existing customers.^14,15

Customer switching to other service providers has many unfavorable consequences such as loss of market share due to decreased sales and increased marketing cost to retain existing customers and win new customers leading to a decrease in the overall profitability of the firm.^16
–18 Service switching can be viewed as a consumer behavior that indicates a person’s desire to replace or change the current service provider with another competitor and is usually caused by dissatisfaction, higher cost, low quality, lack of features, and privacy concerns.¹⁹ There are many determinants that could inhibit customer switching, such as service quality, customer satisfaction, and switching barriers that include switching cost.²⁰ A major barrier to switching services is the burden of switching if customers are forced to change their current phone numbers. It is highly unlikely that customers will switch service providers if they had to change their phone numbers due to the inconvenience associated with such a change.²¹ This barrier has been overcome in countries where the regulations allow customers to maintain their phone numbers after switching services in what is known as mobile number portability (MNP) regulations. The absence of such regulations in some markets may give false indications about customer satisfaction and loyalty, in the sense that customers could be dissatisfied but are unwilling to switch to another service provider because of the switching burden they will have to deal with when changing their mobile numbers.^21,22

Customers’ choice when selecting service providers could be caused by many reasons, and the resulting choice can be characterized as being consistent among various segments of customers. That is, the customer choice is thought to be “rational,” in the sense that customers have a set of identifiable and expected benefits that they seek to get from the service provider and will choose the service provider that maximizes these benefits. Thus, choice rationality implies that customer behavior could be predicted if the choice process is emulated by developing models capable of assessing customer preference toward a service provider based on the features offered by the service provider. This research proposes using rational choice theory as the basis to model the behavior of customers when choosing a service provider and use this model to predict the probability of switching mobile services. Next, the probability estimated is used to determine customer rationality and loyalty. The model will be made possible by investigating the factors that affect the decision of switching service providers and using these factors in the construction of the model.

Previous studies on consumer choice and service switching behavior

A classification of switching reasons and their relative frequency in service industries has been conducted by Keaveney²³ where eight casual antecedents to service switching behavior were identified; namely, price, inconvenience, core service failure, service encounter failure, response to a service failure, competition, ethical problems, and involuntary switching. The results indicated that two variables can be considered as key determinants of switching behavior: service performance (i.e. core service failure, service encounter failure, response to a service failure, and ethical problems) and costs of switching (i.e. price, inconvenience, and competition). Table 1 provides a list of studies that investigated the behavior of customers when choosing mobile service providers and the factors that affect switching mobile service providers.

Table 1.

Service switching reasons.

Study author(s)	Switching reasons analyzed
Birke and Swann²⁴	• Total number of subscribers for each service provider• The service provider of other household members
Shin and Kim²¹	• Customer satisfaction and switching barriers
Rahman et al.²⁵	• Price or call rate, service quality, and service availability and promotion
Shi et al.²²	• Customer satisfaction (including call quality, mobile device, and customer complains)• Switching costs and attractiveness of alternatives
Zhou²⁶	• Expectation confirmation, perceived ease of use, perceived usefulness, and usage cost
Sobolewski and Czajkowski²⁷	• Consumer’s social group using the same service provider and the magnitude of on-net price discounts
Karacuka et al.²⁸	• Local network effects (the choice of family and friends within their local area)
Wirtz et al.²⁹	• Overall satisfaction, performance perception, and monetary switching costs
Calvo-Porral and Lévy-Mangin³⁰	• Corporate image
Varshney³¹	• Service quality, switching barriers, and customer loyalty
Calvo-Porral et al.³²	• Service quality, availability of attractive alternatives
Aslam and Frooghi³³	• Inconvenience, service encounter failure, and attraction by competitors
Jacob and Subramoniam³⁴	• Switching cost, price-cut, and brand image
Saroha and Diwan³⁵	• Quality of service, value for money perceived by the customer, the price paid by the customer, and image of the telecom operator

The literature reviewed shows that there are many factors that could cause customers to switch their mobile service provider, but the main reasons could be classified as the dissatisfaction with service offered by the current service provider. This dissatisfaction is usually caused by the following factors.

Price

Price plays a crucial role in the mobile telecommunication market, in the sense that companies that offer lower charges are able to attract more customers. The success of a mobile service provider is dependent on the provider’s pricing policy.³⁶ A common strategy adopted by many mobile service providers is to differentiate their offerings vertically.³⁷ The effect of service pricing on customer switching is also affected by the price sensitivity level of customers. Munnukka³⁸ found that customers with moderate usage of mobile services are the least price sensitive, while intensive and low-end users are most sensitive to price change.

Service quality

Quality can be defined as a characteristic that goods or services must possess to be perceived as useful and thus meet or exceed consumers’ expectations.³⁹ Mobile service quality is considered an important factor to assess a provider’s performance in the sense that it provides an overall impression of the relative effectiveness and efficiency of the provider services. It has been found that consumers generally prefer service quality when other factors such as cost are held constant.⁴⁰ Assessing the quality of services is complex since different customers may associate different features with quality. Mobile service quality has been associated with the level of service availability or network coverage. In addition, mobile service quality could be assessed through the quality of the voice services provided by the provider, and these services include voice clarity, transfer delay rate, and signal coverage rate.

Availability of attractive alternative services

Customer switching is not always caused by the dissatisfaction of existing service providers, it could be caused by the availability and attractability of alternative service providers.⁴¹ For example, there could be other competitors who are offering better value-added services to attract consumers such as free-on net calling and handsets at a discounted price, and so on. If these alternatives are considered more attractive to the customer, then the customer may switch services.

Switching cost

Switching cost refers to the burden endured by the subscriber when switching services. This burden is manifested through the monetary investment that a subscriber has to make and the time it will take the individual to make the switch. Shi et al.⁴² argued that consumers will be less likely to switch if the cost was higher than the benefits they might get from switching the service. Barroso and Picón⁴³ stated that switching cost should be addressed by analyzing the customers’ perception of the time, effort, and money involved in the switching process. The magnitude of switching burden or cost is magnified if the consumer had to change his/her phone number as part of the process of switching services. Changing phone numbers entails informing all contacts of the new phone number and this is usually a cumbersome process. Additionally, service providers usually adopt lock-in strategies to deter the subscriber from switching.⁴³ These strategies include hard lock-in strategies that are based on imposing financial barriers and contractual agreements that prevent switching. Some service providers also use soft lock-in strategies that are based on providing subscribers with relational benefits as a result of his/her continued subscription, such strategies usually take the form of rewards programs, bundling of services or special services that are related to the length of the subscription.

The reviewed literature shows that there is a wealth of literature that analyzes the switching behavior of customers but—to the best of the authors’ knowledge—none addressed the rationality nature of the choice process and used it to predict the behavior of customers. Additionally, none of the reviewed literature provided a quantitative model to estimate the probability of switching services.

This article addresses service switching behavior by adopting the notion that consumers will be making rational decisions when selecting a service provider. Rationality will be manifested by selecting the service provider capable of delivering the highest amount of benefits or the value to the customer. That is, the article relies on the rational choice theory that states that customers may switch services if the perceived utility or value from another service is higher than the utility or value they are receiving from their current service provider. That is, this article objective is to operationalize the rational choice theory through the use of the multinomial logistic (MNL) regression model to estimate the probability of customers’ switching mobile service providers.

Rational choice theory

Rational choice occurs when the choice is said to be deliberative and consistent in the sense that the decision maker can give a reasoned justification for the choice.^44,45 It is expected that rational choice leads to stable, consistent and thus predictable choices. Rational choice is based on the notion that consumers have transitive preferences and seek to maximize the utility that they derive from those preferences, subject to various constraints. Transitive preferences are those for which, if some good or bundle of goods denoted A is preferred to another good or bundle of goods denoted B and B is preferred to a third good or bundle of goods denoted C, then it must be the case that A is preferred to C. By contrast, if it were the case that A was preferred to B, B was preferred to C, and C was preferred to A, we would find that distinctly odd—indeed, irrational.

There is a general acceptance by practitioners that the theory of rational choice is capable of describing what rational consumers should do and predicts what consumer in fact does. The theory of rational choice states that individual consumers have a well-ordered preference for any set of choice alternative and they choose the alternative that maximizes their preference.^44,45 The implied assumption of the rational choice theory is that individuals are capable of making logical and prudent decisions that provide them with the maximum benefits and that these decisions are in their best self-interest. The theory of rational choice could be operationalized using the MNL regression model which provides a probabilistic framework to predict the probability that a customer choosing each of several alternatives on a particular choice or purchase occasion.⁴⁶ In each choice occasion, the MNL model assumes that the unobserved utility that customer i get from choice alternative k is given by:

U_{k}^{i} = A_{k}^{i} + ∊_{k}^{i}

where:

$∊_{k}^{i}$ is the random component of the customer’s utility and they are assumed to be distributed independent Gumbel distribution, which implies that the knowledge of the value of the random component for any customer choice or purchase occasion does not provide information about the value of the random component for another customer choice or purchase occasion.

The utility $U_{k}^{i}$ is the sum of observable component $A_{k}^{i}$ and an unobservable component $∊_{k}^{i}$ making it unobservable or latent.

$A_{k}^{i}$ is the inferred preference or utility value (overall attractiveness) of alternative k to customer i and is defined as the following.

A_{k}^{i} = \sum_{j} β_{j} X_{i j k}

where:

$X_{i j k}$ is the measured or observed value of the contextual variable j for product alterative k on a given choice or purchase occasion.

$β_{j}$ is the importance weight associated with variable j.

It is assumed that customer i chooses the product which offers him/her the highest utility. The probability that customer i will choose alternative k is given by:

P_{i k} = P {U_{k}^{i} \geq U_{m}^{i}; for all m in the choice set}

That is $P_{i k}$ is the probability that the utility of product k will be at least as high as the utilities of any other product on that purchase occasion. Then, the individual i’s probability of choosing product 1 or choice alternative 1 ( $P_{i k}$ ) is given by:

P_{i k} = \frac{e^{A_{1}^{i}}}{\sum_{k} e^{A_{k}^{i}}} for k = 1, 2, \dots . K

Thus, the logit model is a sequence of K equations (where K is the number of alternatives). Applying the logit model to the mobile service provider choice problem, the model components will have the following interpretations:

X_ijk = customer i’s evaluation of mobile service provider j on service attribute (factor) k, where the summation is over all service providers that individual i is considering. Service providers understudy are Umniah, Zain, and Orange. While the service factors or attributes being investigates are price, coverage, customer service, billing accuracy, and rewards.

β_j = “revealed importance weight” showing the degree to which attribute j influences service provider preferences. These parameter estimates are revealed by an analysis of the past behavior of customers rather than by directly asking consumers. This can be done by investigating whether customers switched service providers recently. The importance weights can be broadly interpreted in much the same way as regression coefficients.

$\sum_{j} β_{j} X_{i j k}$ = overall attractiveness (utility) of service provider k for customer i.

The probability of choosing a specific product can be used to assess the rationality of customers when compared with the actual or true choice of customers. That is, if there is a match between the product selection using the MNL model and the true choice of the customer, then the customer decision is considered rational since it is based on the maximization of the benefit (utility) gained from the selection made. On the other hand, if the true selection of the customer does not match the prediction obtained by a valid MNL model, then it could be said that the customer selection is “irrational” since it does not maximize the customer benefit (utility). An irrational customer is simply a customer whose choice is influenced by other factors that were not captured as part of the factors addressed in the MNL. Furthermore, loyalty could also be analyzed based on the estimated probability of purchase, in the sense that a customer could be considered loyal if he/she had a high probability of purchasing from their current service provider. This simply means that customers perceive that their current service provider is delivering the highest level of benefit to them and there is no need to switch to another service provider.

Case study

The model was tested and validated by assessing the rationality of customers in the Jordanian telecommunication market where data from 450 customers were gathered and used to construct the model as shown in the following sections.

Jordan telecommunication market

Mobile telecommunications services in the form of “2G” services were first introduced into Jordan in 1995, with the operation of FastLink (what is now Zain). The second mobile license was issued in 2000, to Mobily (what is now Orange Mobile). There are currently three public mobile wireless service providers licensed to provide mobile telecommunications services in Jordan. These are Zain, Orange Mobile, and Umniah. A fourth mobile service provider “Friendi” was launched in 2010 after the Jordanian market opened for mobile virtual network operators. All three leading providers (Zain, Orange, and Umniah) have achieved high levels of population and territorial coverage in Jordan and offer similarly broad baskets of mobile services to retail customers. Those services consist of both a range of voice call-related services and data transfer services. It is worth mentioning here that “Friendi” only offers pre-paid services and does not yet have a significant market share.

Jordan active mobile services subscribers amounted to 16,746,094 by the end of September 2016, and the country cellular market penetration rate stood at an estimated 1168.0% by the end of September 2016. Mobile services providers offer two types of service subscriptions: pre-paid services and post-paid services. The number of subscribers as of September 2016 is shown in Table 2.

Table 2.

Active mobile phone subscriptions in Jordan.^a

Provider	Post-paid	Pre-paid	Total
Zain	802,697	5,100,236	5,902,933
Orange	235,641	5,184,456	5,420,100
Umniah	193,268	5,194,945	5,388,213
Friendi	0	34,848	34,848
Total	1,231,606	15,514,488	16,746,094

^aData obtained from telecommunication regulatory commission (TRC) in Jordan(http://www.trc.gov.jo). http://trc.gov.jo/EchoBusV3.0/SystemAssets/PDF/AR/مراجعة%20سوق%20الاتصالات/بمراجعة%20أسواق%20الاتصالات/Mobile%20suscriptions%20%20arabic%20.pdf.

Jordan was the first country in the Middle East and North Africa region to begin liberalizing its telecommunications market, and thus has the most deregulated telecom market in this region. Nevertheless, the absence of a regulation that allows MNP has hampered customer churn between different mobile service providers. It is believed that many customers in Jordan subscribe to more than one mobile service provider to take advantage of the offers made by some providers and to reduce the cost of off-network calls. It should be noted here that there is a lack of credible data regarding how many people actually subscribe to more than one mobile service provider.

Examining the nature of the current mobile service market, one can notice that all three service providers provide handset subsidies to their customers through the availability of various deals. Usually, the subsidy level is determined on the basis of the length of the subscription period. Furthermore, all the providers are offering identical services at comparable prices making it difficult to differentiate between providers. In addition, the Jordanian market is thought to have reached saturation levels which makes the competition even more intense since mobile service providers need to try to convince subscribers of other providers to switch service while maintaining their current subscribers.

This article will try to shed some light on the nature of the current telecommunication market in Jordan and provide some insights into the rationality of consumer behavior regarding switching mobile services. The article will strive to provide decision makers with a model that is able to explain the current behavior of their customers and can be used to predict the expected behavior of customers.

Data description and survey

A panel of experts composed of industry experts and academician was utilized in determining the factors that affect the rational choice of customers when selecting a mobile service provider. The panel was asked to use the literature related to customer choice of mobile services along with their knowledge of the local market to identify the major factors they believe might affect the customer. In addition, the panel was asked to consider the ability of the mobile service provider to control or affect the factor in the short term. That is, the factors selected should be factors that could be used as design factors when proposing a new service offering. This means that factors such as “brand image” and “reputation” are excluded since varying these factors in the short term is difficult, if not impossible, which renders them impractical to be included in a new service offering. The selected factors are:

the price of the service subscription (Price),

the quality of the network coverage (Coverage),

the quality of the customer services (Customer Service),

the perceived accuracy of the billing (Billing), and.

the attractiveness of the rewards programs (Rewards).

Individual-specific responses regarding these factors were gathered by administering a consumer survey (Appendix A) in which a stratified target sample was used. The survey was carried out via face-to-face interviews in Amman (the capital city of Jordan). The total number of respondents was 450 distributed across the three major mobile service providers were 165 are Zain subscribers, 145 are Orange subscribers, and 140 are Umniah subscribers. The targeted respondents were selected such that they meet the following criteria:

Mode of payment: post-paid

Length of subscription: at least 3 years

Number of mobile services: only one mobile service

Average monthly bill: at least 50 Jordanian dinars (about US$70)

Contractual obligation: none.

This criteria was formed to ensure that customers have enough length of experience in terms of the service provided by their respective mobile service provider, and that they will form their judgment with regard to other mobile service providers based on the perceptions they have formed through several methods such as advertisement, professional reviews, and word of mouth. In addition, mobile service providers consider post-paid subscribers who are not obliged by a contract to stay with their current service providers as a loyal customer, which means that analyzing such customers could reveal good insights about their rationality and loyalty.

Descriptive statistics

Each respondent was asked to rate the three mobile service providers with respect to the factors on a scale of 10, where 10 means the best and 1 means the worst. This scale was used since most people are familiar with the concept of rating out of 10.⁴⁷ In addition, respondents were asked if they plan to switch their service within the next 3 months. Table 3 shows a partial listing of the data gathered.

Table 3.

Partial listing of the data gathered.

			Factors^a
Customer ID	Service provider	Price	network Coverage	Customer service	Billing	Reward programs	Will you switch service?	Current service provider
1	Umniah	10	5	8	5	1	No	Umniah
	Orange	2	7	3	7	2
	Zain	5	8	4	8	7
2	Umniah	7	3	5	9	2	No	Orange
	Orange	6	9	8	9	2
	Zain	4	8	7	7	4
3	Umniah	9	7	8	8	7	No	Orange
	Orange	7	7	4	6	7
	Zain	4	10	4	5	6
4	Umniah	9	8	9	10	10	No	Zain
	Orange	10	9	8	10	10
	Zain	10	10	9	10	10
5	Umniah	8	8	9	7	6	No	Orange
	Orange	7	9	8	9	9
	Zain	6	6	7	8	7
6	Umniah	9	3	7	10	7	Yes to Umniah	Zain
	Orange	9	10	10	10	9
	Zain	3	5	8	7	5
7	Umniah	9	4	8	8	7	No	Orange
	Orange	10	8	9	8	9
	Zain	7	9	9	9	8

^a10 is best performance and 1 is worst performance.

The survey was analyzed using SPSS® and the results of customers’ ratings of each attribute with respect to the three service providers are shown in Table 4. For example, Orange rating with respect to the “Price” attribute was 6.8 with a standard deviation of 1.82, while Umniah had a rating of 8.52 with a standard deviation of 1.7.

Table 4.

Results of factor rating per service provider.

		Provider
		Orange	Umniah	Zain
Price	Mean	6.80	8.52	5.00
Price	Standard deviation	1.82	1.70	2.38
Coverage	Mean	8.29	5.21	8.81
Coverage	Standard deviation	1.31	2.11	1.51
Customer service	Mean	7.52	7.29	6.55
Customer service	Standard deviation	1.82	2.00	2.24
Billing	Mean	7.25	8.68	5.98
Billing	Standard deviation	1.93	1.63	2.37
Rewards	Mean	7.40	6.63	6.23
Rewards	Standard deviation	2.21	2.49	2.45

The results shown in Table 4 suggest that there could be significant differences between customer ratings of the attributes between the three service providers. Thus, the analysis of variance (ANOVA) will be used to test if there is a statistical difference between the attribute ratings assigned by customers to each service provider. The ANOVA test will lead to rejecting the null hypothesis if the significance level is 0.05 and concluding that there is a significant difference between the average ratings.⁴⁸ The results of the ANOVA test are shown in Table 5, which shows that a significant difference does exist between the mean ratings of the attributes per service provider.

Table 5.

ANOVA test between factors.

		Sum of squares	df	Mean square	F	Significance
Price	Between groups	2774.313	2	1387.156	350.841	0.000
	Within groups	5325.780	1347	3.954
	Total	8100.093	1349
Coverage	Between groups	3402.033	2	1701.016	604.498	0.000
	Within groups	3790.364	1347	2.814
	Total	7192.397	1349
Customer service	Between groups	232.413	2	116.207	28.295	0.000
	Within groups	5532.147	1347	4.107
	Total	5764.560	1349
Billing	Between groups	1642.224	2	821.112	205.353	0.000
	Within groups	5386.040	1347	3.999
	Total	7028.264	1349
Rewards	Between groups	315.850	2	157.925	27.794	0.000
	Within groups	7653.516	1347	5.682
	Total	7969.366	1349

ANOVA: analysis of variance.

Since the ANOVA test indicated that a difference between the means does exist, we need to perform some follow-up tests to isolate the specific differences. Any multiple comparison method, such as the Tukey’s range test,⁴⁹ could be used to find the means that are significantly different from each other. The results of the multiple comparisons using Tukey test are shown in Table 6, where a significance level less than 0.05 indicates that there is a statistically significant difference between the means. For example, there is a significant difference between the mean rating of “price” of Umniah, Zain, and Orange.

Table 6.

Multiple comparisons between factors (Tukey HSD).

Dependent variable			Mean difference (I–J)	Standard error	Significance	95% Confidence interval
Dependent variable			Mean difference (I–J)	Standard error	Significance	Lower bound	Upper bound
Price	Umniah	Orange	1.71333^*	0.13256	0.000	1.4023	2.0244
	Umniah	Zain	3.51111^*	0.13256	0.000	3.2001	3.8221
	Orange	Umniah	−1.71333^*	0.13256	0.000	−2.0244	−1.4023
	Orange	Zain	1.79778^*	0.13256	0.000	1.4867	2.1088
	Zain	Umniah	−3.51111^*	0.13256	0.000	−3.8221	−3.2001
	Zain	Orange	−1.79778^*	0.13256	0.000	−2.1088	−1.4867
Coverage	Umniah	Orange	−3.08000^*	0.11183	0.000	−3.3424	−2.8176
	Umniah	Zain	−3.59556^*	0.11183	0.000	−3.8579	−3.3332
	Orange	Umniah	3.08000^*	0.11183	0.000	2.8176	3.3424
	Orange	Zain	−0.51556^*	0.11183	0.000	−0.7779	−0.2532
	Zain	Umniah	3.59556^*	0.11183	0.000	3.3332	3.8579
	Zain	Orange	0.51556^*	0.11183	0.000	0.2532	0.7779
Customer service	Umniah	Orange	−0.23333	0.13511	0.195	−0.5503	0.0837
	Umniah	Zain	0.74000^*	0.13511	0.000	0.4230	1.0570
	Orange	Umniah	0.23333	0.13511	0.195	−0.0837	0.5503
	Orange	Zain	0.97333^*	0.13511	0.000	0.6563	1.2903
	Zain	Umniah	−0.74000^*	0.13511	0.000	−1.0570	−0.4230
	Zain	Orange	−0.97333^*	0.13511	0.000	−1.2903	−0.6563
Billing	Umniah	Orange	1.43111^*	0.13331	0.000	1.1183	1.7439
	Umniah	Zain	2.70000^*	0.13331	0.000	2.3872	3.0128
	Orange	Umniah	−1.43111^*	0.13331	0.000	−1.7439	−1.1183
	Orange	Zain	1.26889^*	0.13331	0.000	0.9561	1.5817
	Zain	Umniah	−2.70000^*	0.13331	0.000	−3.0128	−2.3872
	Zain	Orange	−1.26889^*	0.13331	0.000	−1.5817	−0.9561
Rewards	Umniah	Orange	−0.76222^*	0.15891	0.000	−1.1351	−0.3894
	Umniah	Zain	0.40444^*	0.15891	0.030	0.0316	0.7773
	Orange	Umniah	0.76222^*	0.15891	0.000	0.3894	1.1351
	Orange	Zain	1.16667^*	0.15891	0.000	07938	1.5395
	Zain	Umniah	−0.40444^*	0.15891	0.030	−0.7773	−0.0316
	Zain	Orange	−1.16667^*	0.15891	0.000	−1.5395	−0.7938

HSD: honestly significant difference.

*The mean difference is significant at the 0.05 level.

The results obtained through ANOVA and the multiple comparisons using Tukey test do indicate that customers perceive differences between the three service providers with respect to each factor when analyzed individually as follows:

Price: Umniah was rated to have the best prices (8.52) while Zain got the lowest ratings (5) for the price.

Coverage: Zain and Orange got the highest rating (8.81 and 8.29, respectively) with some advantage to Zain. While Umniah received the lowest score (5.21) in terms of its network coverage.

Customer service: Orange was ranked as having the best customer services (7.52), followed by Umniah (7.29) and Zain (6.55). It is worth mentioning here that all the service provider received a rating that is higher than average which indicates good overall customer service across all three service providers.

Perceived billing accuracy: Umniah was rated as having the most accurate billing (8.68) followed by Orange (7.25) and Zain (5.98).

Rewards programs: Orange received the highest rating (7.4) for the availability of attractive rewards programs followed by Umniah (6.63) and Zain (6.23).

Purchasing probability results

The purchasing probability was computed using equation (4), which requires estimating the overall attractiveness (utility) of each service provided to each individual customer using equation (1). The overall attractiveness was estimated after determining the revealed importance weights (β) using the logit model (in SPSS®) to determine the importance of each service attribute. The results of the logit model gave the β values as shown in Table 7.

Table 7.

Attribute importance weights ( $β) .$

	β	Standard error	Wald	df	Significance	Exp(B)
Price	0.173	0.029	34.872	1	0.000	1.189
Coverage	−0.060	0.022	7.219	1	0.007	0.942
Customer service	0.184	0.038	23.087	1	0.000	1.202
Billing	−0.253	0.033	60.539	1	0.000	0.776
Rewards	−0.126	0.028	19.594	1	0.000	0.882

The model goodness of fit is assessed by analyzing the prediction power or the accuracy of the model through the confusion/classification matrix which compares the actual outcomes to the predicted outcomes.⁵⁰ The current model had an overall accuracy of 68.5% which indicates a good prediction power and good overall fit.

The results in Table 7 show that all importance weights β’s are significant, since they had a significance level less than 0.05, and should be used as part of equation (2) to determine the overall attractiveness. Thus, overall attractiveness (A) of service provider (k) to customer (i) becomes:

A_{k}^{i} = 0.173 \times X_{i 1 k} - 0.060 \times X_{i 2 k} + 0.184 \times X_{i 3 k} - 0.253 \times X_{i 4 k} - 0.126 \times X_{i 5 k}

where

X_i _1k is the rating assigned by customer i to the service provided by company k with respect to Price.

X_i _2k is the rating assigned by customer i to the service provided by company k with respect to Coverage.

X_i _3k is the rating assigned by customer i to the service provided by company k with respect to Customer service.

X_i _4k is the rating assigned by customer i to the service provided by company k with respect to Billing accuracy.

X_i _5k is the rating assigned by customer i to the service provided by company k with respect to Rewards programs.

For example, the overall attractiveness for customer 1 is calculated as following:

Overall attractiveness of Umniah to customer 1 = (0.173 × 10) + (−0.06 × 5) + (0.184 × 8) + (−0.253 × 5) + (−0.126 × 1) = 1.511.

Overall attractiveness of Orange to customer 1 = (0.173 × 2) + (−0.06 × 7) + (0.184 × 3) + (−0.253 × 7) + (−0.126 × 2) = −1.545.

Overall attractiveness of Zain to customer 1 = (0.173 × 5) + (−0.06 × 8) + (0.184 × 4) + (−0.253 × 8) + (−0.126 × 7) = −1.785.

Next, the probability of purchase is calculated using equation (4) as following:

Probability that customer 1 purchase from Umniah = $\frac{e^{1.511}}{e^{1.511} + e^{- 1.545} + e^{- 1.785}} = 92.25 %.$

Probability that customer 1 purchase from Orange = $\frac{e^{- 1.545}}{e^{1.511} + e^{- 1.545} + e^{- 1.785}} = 4.33 % .$

Probability that customer 1 purchase from Orange = $\frac{e^{- 1.785}}{e^{1.511} + e^{- 1.545} + e^{- 1.785}} = 3.42 %.$

The probability of purchase is computed for each individual customer surveyed and a sample of 15 out of 450 of the probabilities computed is shown in Table 8.

Table 8.

Sample of computed probability of purchase for each customer from each service provider.

Customer ID	Probability of purchasing from			Will you switch service?	Current service provider
Customer ID	Umniah	Orange	Zain	Will you switch service?	Current service provider
1	92.25%	4.33%	3.42%	No	Umniah
2	35.27%	35.84%	28.89%	No	Orange
3	50.75%	28.56%	20.69%	Yes to Umniah	Orange
4	33.49%	31.21%	35.30%	No	Zain
5	60.59%	16.50%	22.91%	Yes to Umniah	Orange
6	34.26%	30.30%	35.44%	No	Zain
7	43.42%	37.91%	18.68%	No	Orange
8	24.48%	41.03%	34.50%	No	Zain
9	78.88%	10.05%	11.07%	No	Umniah
10	47.53%	16.85%	35.62%	No	Umniah
11	16.38%	51.07%	32.55%	Yes to Orange	Zain
12	26.55%	43.87%	29.57%	Yes to Orange	Umniah
13	25.07%	58.64%	16.29%	No	Orange
14	30.57%	54.25%	15.18%	No	Orange
15	47.69%	32.30%	20.01%	No	Umniah

Rationality/loyalty analysis

The data analysis consists of two major phases: the first phase addresses the switch-ability of the subscribers. This analysis will also serve to validate the model by comparing the predicted switch-ability—as computed by the MNL model—with the stated intention to switch as stated by the customers. The second phase of the analysis will provide an assessment of the rationality and loyalty of the subscribers.

Analysis phase I: Switch-ability analysis

A customer will be predicted to have the intent to switch if the computed probability of purchasing from another subscriber is higher than the computed probability of purchasing from the current provider. For example, customer 11 (shown in Table 8) is a current subscriber of Zain and has a probability of purchasing from Orange of 51.07%, so this customer was found by the model as having the intention to switch which matches the result as stated by the customer, so the model prediction was correct. On the other hand, customer 8 had a probability of purchasing from Orange 41.03% and was predicted by the model to have the intent to switch from Orange to Zain but the customer did not state any intention to switch, so the model failed to predict the correct switch-ability of that customer. This logic was applied to all customers and it was found that the model was able to correctly predict the switch-ability of customers about 83% of the time, which provides significant evidence to the validity and strength of the model and its ability to predict the probability of purchasing from a specific mobile service provider.

Analysis phase II: Rationality and loyalty assessment

The computed probabilities of purchase are used to perform rationality and loyalty analysis by classifying the respondents/subscribers based on their computed probability of purchase. The classification logic is based on the idea that a customer choice could be deemed rational if the subscriber has the highest computed probability of purchasing from his/her current service provider or from the service provider that he/she intends to switch to as stated by the subscriber. That is, if the customer rating of the different factors assessed leads to a probability of purchasing from the existing service provider, then the customer is making a rational choice, and the loyalty will be classified high if the probability is more than 50%, while if the probability is less than 50%, then the customer is classified as making rational decisions but having questionable loyalty that may result in switching the service in the future, that is, the customer could become a “defector” who will switch to another service provider. On the other hand, a customer will be classified as “defector” if the probability of purchase was found to be the highest from the service provider that the customer is planning to switch to. All other customers who have the highest probability of purchasing from a service provider other than their existing one and did not indicate their intention to switch are classified as “irrational” choice making customers. This classification logic is illustrated using the classification algorithm shown in Figure 1.

Figure 1.

Rationality/loyalty classification algorithm.

A sample of the loyalty analysis performed using this classification logic is shown in Table 9, while Figure 2 shows the final results of the rationality and loyalty analysis. The results shown in Figure 2 indicate that the choice of mobile phone subscribers in the Jordanian telecommunication market is mostly rational.

Table 9.

Sample of rationality and loyalty analysis.

Customer ID	Probability of purchasing from			Will you switch service?	Current service provider	Rationality	Loyalty
Customer ID	Umniah	Orange	Zain	Will you switch service?	Current service provider	Rationality	Loyalty
1	92.25%	4.33%	3.41%	No	Umniah	Rational–loyal	High
2	35.27%	35.84%	28.89%	No	Orange	Rational–loyal	Questionable
3	50.75%	28.56%	20.69%	Yes to Umniah	Orange	Rational–defector
4	33.49%	31.21%	35.30%	No	Zain	Rational–loyal	Questionable
5	60.59%	16.50%	22.91%	Yes to Umniah	Orange	Rational–defector
6	34.26%	30.30%	35.44%	No	Zain	Rational–loyal	Questionable
7	43.42%	37.91%	18.68%	No	Orange	Not rational
8	24.48%	41.03%	34.50%	No	Zain	Not rational
9	78.88%	10.05%	11.07%	No	Umniah	Rational–loyal	High
10	47.53%	16.85%	35.62%	No	Umniah	Rational–loyal	Questionable
11	16.38%	51.07%	32.55%	Yes to Orange	Zain	Rational–defector
12	26.55%	43.87%	29.57%	Yes to Orange	Umniah	Rational–defector
13	25.07%	58.64%	16.29%	No	Orange	Rational–loyal	High
14	30.57%	54.25%	15.18%	No	Orange	Rational–loyal	High
15	47.69%	32.30%	20.01%	No	Umniah	Rational–loyal	Questionable

Figure 2.

Rationality and loyalty analysis results.

Discussion

Implications related to Jordan telecommunication market

The results found indicate that most customers in the Jordan telecommunication market are making rational decisions with regard to the selection of mobile phone service providers. In addition, the results suggest that about 50% of the customers of two mobile phone service providers (Zain and Orange) are likely to switch and can be considered potential “defectors” if were made an attractive offer. A similar argument could be made about loyal customers since about half of them had “questionable loyalty” which means that they could be potential defectors. This finding supports the belief that the Jordan telecommunication market has reached its maturity level which intensifies competition to maintain existing customers and switch others. It can also be argued that the possible reason that is keeping customers from switching could be the fact that customers are not allowed to maintain their phone numbers if they switch services. The absence of MNP in the Jordan telecommunication market could be viewed as an inhibiting factor limiting customer switching decision. Thus, liberating the telecommunication market in Jordan by allowing MNP could lead to a change in the market dynamics by facilitating customer switching decisions.

Managerial implications

The study provides some important managerial implications for service providers. Mainly in the way that Zain, Orange, and Umniah assess the effectiveness of their current policy with regards to determining loyal customers. The service providers should try to propose new offerings and use the model to test the effect of such an offering on customers.

The study also showed that Umniah’s customers are found to be more loyal than Zain and Orange, which means that Umniah should try to target Zain and Orange customers. The only possible thing that could hinder such an effort is the issue of maintaining current phone numbers. So, Umniah should try to push for MNP.

Generalizing the model

The model presented in this article can be applied in other contexts where there is a need to analyze customer switching behavior while assuming that the switching behavior is mainly caused by rational decisions and is motivated by selecting the service or buying the product that maximizes the customer’s benefit (or utility). That is, a customer is expected to switch to a new product or service if the utility of other products or services is higher than the utility of the currently owned product or service.

In such cases, the MNL model will need to be formulated by identifying the attributes or factors that affect customer choice and increase the benefits gained by the customer. Then the analysis will proceed toward determining the effect or importance weight of each attribute or factors. Next, the overall attractiveness of each service or product is determined and the probability of purchase is estimated. Finally, the probability of purchase is used to assess whether a current customer is likely to switch to a different service or product.

Conclusion

A model capable of emulating the behavior of customers when selecting a mobile service provider was developed in this article. The developed model is based on the rational choice theory that states that customers may switch services if the perceived utility or value from another service is higher than the utility or value they are receiving from their current service provider.

The rational choice theory was operationalized in this article by constructing an MNL regression model to estimate the probability of customers’ subscribing to specific mobile service provider. The first step in constructing the model is the identification of factors that affect the decision of switching mobile service providers. Next, the constructed model is used to estimate the probability of switching mobile service providers. After that, the validity of the model is checked by comparing the computed probability of switching to customers’ stated intention of switching. Finally, the customers’ rationality is assessed by comparing the choice predicted by the model with the true choice of customers. Rationality is said to exist when customers choose a service provider that delivers the maximum benefit.

The model was implemented in a case study that included 450 customers in the Jordan telecommunication market, and it was found that the constructed model had 83% chance of predicting the correct service provider. It was also found that customers in the Jordan telecommunication market are mostly making rational decisions when selecting a mobile service provider.

Limitations and further research

The model developed in this article was based on several attributes that are believed to affect consumer assessment of the current mobile service and are actionable from the point of view service providers. The selection of these attributes was deemed acceptable since it led to an acceptable model validity as measured with the predictability of the model. Nevertheless, the model could be further enhanced if more or different attributes were investigated.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

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