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Analysis of consumer preferences for prepaid mobile internet packages in Iran: A Discrete Choice Experiment

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As a first discrete choice experiment in Iran emerging telecommunication market, this paper studies consumer preferences for prepaid mobile internet packages with a combined software and paper-based interview. A two-stage Bayesian D-optimal design procedure is deployed to design choice sets of mobile internet packages from four main attributes. The utility structure and customers’ willingness-to-pay for mobile internet packages are analyzed. Findings/originality: The results indicate that even with a considerable price reduction, consumers avoid prepaying for data plans with commitment periods longer than six months and high traffic volume. Traffic volume and brand attributes are recognized as the two most influential factors on consumers’ behavior. Simulating the market demonstrates the competition between mobile internet operators in Iran market. The statistics express a significant effect of consumers’ current mobile operator on their preferences for the brand attribute.
Economic Journal of Emerging Markets, 12(1) 2020, 39-53
Econ.J.Emerg.Mark.
Economic Journal of Emerging Markets
Available at http://journal.uii.ac.id/index.php/jep
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Copyright @ 2020 Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License
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Analysis of consumer preferences for prepaid mobile internet packages in
Iran: A Discrete Choice Experiment
Arya Sohrabi1, Mir Saman Pishvaee2*, Ashkan Hafezalkotob3, Shahrooz Bamdad4
1, 3,4 School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
*Corresponding author: pishvaee@iust.ac.ir
Article Info
Article history:
Received : 1 November 2019
Accepted : 10 January 2020
Published : 8 April 2020
Keywords:
Discrete choice experiment,
willingness to pay (WTP), conjoint
analysis, mobile internet
JEL Classification:
D01, L96
DOI: 10.20885/ejem.vol12.iss1.art4
Abstract
As a first discrete choice experiment in Iran emerging telecommunication
market, this paper studies consumer preferences for prepaid mobile
internet packages with a combined software and paper-based interview.
A two-stage Bayesian D-optimal design procedure is deployed to design
choice sets of mobile internet packages from four main attributes. The
utility structure and customers’ willingness-to-pay for mobile internet
packages are analyzed. Findings/originality: The results indicate that
even with a considerable price reduction, consumers avoid prepaying for
data plans with commitment periods longer than six months and high
traffic volume. Traffic volume and brand attributes are recognized as the
two most influential factors on consumers’ behavior. Simulating the
market demonstrates the competition between mobile internet operators
in Iran market. The statistics express a significant effect of consumers’
current mobile operator on their preferences for the brand attribute.
Introduction
Mobile internet is increasingly being recognized as one of the most widely welcomed services in
meeting people’s communication needs and has surpassed other mobile communication services
like voice and SMS (Shih, Yang, & Yang, 2018). Different telecommunication companies compete
locally and globally to increase their market shares, optimize their revenues and manage customers
churn by offering a wide range of mobile internet plans and packages. Competing in this market
applies high fixed costs to telecommunication service providers. In such a growing and competitive
industry, utilizing effective marketing research methods becomes a target for service providers to
measure consumers’ purchase behavior, analyze firm-level brand equity, and design packages that
outperform substitute services.
There are different methods like ordered logit regression models to measure consumer
preferences (Luu, 2019). However, conjoint analysis, particularly discrete choice experiment
(DCE), is one of the most popular market research techniques that can be used in a
telecommunication market to satisfy the mentioned objectives. It is employed to measure
consumers’ preferences between products and services containing several attributes that are
characterized by their levels. The importance of attributes can also be ranked according to their
influence on consumers’ behavior. In a DCE questionnaire, respondents face different scenarios
of possible products or services and are requested to opt one of the alternatives in each choice
situation.
From a macro perspective, utilization of conjoint analysis to discover consumers’
preferences for mobile internet can help companies, market leaders, and lawmakers to design
effective strategies that optimize the GDP, penetration rate of mobile internet, and social welfare.
40 Economic Journal of Emerging Markets, 12(1) 2020, 39-53
Louviere, Flynn, and Carson (2010) state that DCEs are not a form of conjoint analysis. However,
mostly in the literature, both DCE and choice-based conjoint analysis (CBC) are used alternatively,
and DCE is considered as a particular form of conjoint analysis. The purpose of this paper is to
study Iranian consumers’ preferences for prepaid mobile internet packages by employing a discrete
choice experiment.
Iran with a population of nearly 80 million people, is the second-largest country in the
Middle East (Pourmokhtar, Moghaddasi, Nejad, & Hosseini, 2018). In recent years, Iranians
quickly adopt the internet as the primary tool to facilitate their daily activities. Some examples are
employing social media, teleworking, reading, and shopping online. Moreover, by implementing
the e-government project in Iran, citizens and businesses are increasingly relying on the internet
for doing their daily routines. All these transformations have increased the demand for the internet
data services. By providing the internet in almost all situations and places, mobile internet services
are responding to a significant proportion of Iranians’ internet demand.
The ITU ICT Development Index (IDI) is a unique benchmark of the level of ICT
development in countries across the world. The International Telecommunication Union (ITU) in
a report entitling “Measuring the Information Society” publishes the IDI annually. Iran’s 2017 IDI
value is 5.58, 1.32 points above the average for developing countries, and 0.57 points above the
world average. The IDI ranks of Iran are 81 among 176 counties, and 37 among 130 developing
countries. Iran is the second most dynamic country by 0.54 points in IDI value improvement
between 2016 and 2017. Comparing Iran with Iceland, the first 2017 IDI publication reveals a
significant difference between the sub-indexes of active mobile broadband subscriptions per 100
inhabitants. Iran’s 33.77 active mobile-broadband subscriptions per 100 inhabitants is 70.22 less
than Iceland’s 103.99 active mobile-broadband subscriptions per 100 inhabitants. Furthermore, the
percentage of individuals using the internet in Iran is only 53.23, 45.01 percent lower than Iceland,
which results in Iran’s 3.54 low IDI use sub-index. This significant difference between the IDI
index and IDI use sub-index demonstrates that a potential market for mobile internet still exists in
Iran. Based on the reports published by Statistical Centre of Iran at www.amar.org.ir, and Ministry
of I.C.T of Iran at www.ict.gov.ir, Table 1 summarizes the total active mobile subscribers in Iran
from 1997 through the fourth quarter of 2017.
Table 1. Total active mobile subscribers in Iran (in million)
Mar-97
Mar-07
Mar-14
Mar-15
Mar-17
Dec-17
0.599
15.385
63.831
70.920
82.986
87.245
Source: Ministry of ICT of Iran
Three mobile network operators, MCI, MTN Irancell, and Rightel, offer consumers two
types of services, prepaid and postpaid plans. Table 2 compares the total active population of
prepaid and postpaid subscribers. Prepaid plans with 80% share are more popular than postpaid
plans. Due to its high popularity, this study focuses on prepaid plans. Before launching MTN
Irancell in 2006 and the entrance of Rightel to the mobile telecommunication market in 2010, MCI,
which was the monopoly power in Iran mobile telecommunication market from 1994, had been
offering only prepaid plans. As a result, MCI has the lowest share of prepaid customers from its
total active customers in comparison with the other two operators. According to the Iran
Telecommunication Condition report, published by Ministry of I.C.T of Iran at www.ict.gov.ir on
January 2018, the pie chart in Figure 1 demonstrates that Rightel has the lowest market share in
the prepaid plan market. MTN, with its 47 percent share is competing closely with the
governmental operator, MCI.
There is a vast literature on conjoint analysis and discrete choice experiment field.
Thurstone (1927) introduced the psychological aspect of DCE to measure psychological values
Analysis of consumer preferences for prepaid ... (Sohrabi, et al.) 41
and qualitative comparative judgments. Bradley and Terry (1952) studied a two-by-two factorial
experiment. Louviere and Woodworth (1983) developed a design method for multiple-choice
alternatives. Following these articles, several developments in the discrete choice experiment field
have occured.
Figure 1. Active prepaid subscribers
The examples of utilization of conjoint analysis, as one of the most popular market research
methods, can be found in a broad array of disciplines. Strauss, George, and Rhodes (2018) applied
it in healthcare. Paci, Danza, Del Nobile, and Conte (2018) conducted a study in the food industry.
Kim, Chung, Petrick, and Park (2018) analyzed the tourism industry using conjoint analysis. In
addition to other economic and marketing methods, Conjoint analysis methods and DCEs have
been applied in the telecommunication industry extensively (Confraria, Ribeiro, & Vasconcelos,
2017; Koh & Lee, 2010; Kwak & Yoo, 2012; Sobolewski & Kopczewski, 2017). However, few
studies investigate consumers’ preferences for Internet services using conjoint analysis. Madden,
G., and Simpson (1997) investigate the demand for residential broadband services through a
discrete choice experiment and analyze the influences of socioeconomic elements on the
consumption of the Internet by surveying Australian households. Ahn, Lee, Lee, and Kim (2006)
employed a conjoint analysis to study consumer preferences for wireless data communication
services, including wireless LAN and mobile Internet services by interviewing 500 respondents in
South Korea. Rosston, Savage, and Waldman (2010) investigated consumers' willingness to pay
(WTP) regarding eight factors influencing consumers’ preferences for broadband Internet services.
They estimated a random utility model using discrete choice analysis. Kwak and Yoo (2012)
represented the first study focusing on the consumers’ choices behavior for 4G technology in
South Korea. They evaluated WTP for attributes of the 4G technology by applying a choice
experiment. Srinuan, Srinuan, and Bohlin (2012) conducted a discrete choice model in Thailand to
determine the factors influencing consumers’ choice behavior for mobile internet services. They
also recognized inelasticity in demand attributable to a noncompetitive market. Choi and Han
(2015) studied the attributes affecting Korean consumers’ preferences for mobile data servizces.
Nakamura (2015) deployed a conjoint analysis to explore substitutability between mobile internet
access services and fixed broadband access services in Japan. Dagli and Jenkins (2016)
demonstrated consumers’ willingness to pay for enhancements in mobile services. They have
focused on 4G upgrades and roaming services in North Cyprus.
The results obtained from previous studies could not be generalized to Iranian consumers,
because their choice behavior may be different due to the various market and demographic
characteristics. Moreover, we define and analyze different attributes and levels, which potentially
determine current prepaid internet packages in Iran. To the best of the authors’ knowledge, this
paper is the first that uses discrete choice analysis to measure consumers’ preferences for prepaid
50%
47%
3%
MCI
MTN
Rightel
42 Economic Journal of Emerging Markets, 12(1) 2020, 39-53
mobile internet packages in Iran. This paper represents an empirical analysis of the main
determinants of consumers’ behavior in Iran’s mobile data market.
The remainder of this paper is organized as follows. Section 2 explains the study
methodologies. Section 3 describes in detail the selected attributes, their levels, and the survey
design. Section 4 presents the results of the analysis. A simulation study that is drawn from the data
is also demonstrated in section 4, where the respondents are also clustered according to their
current operator. Recommendations and policy implications are also discussed. Finally, the study
is concluded in section 5.
Methods
This paper employs a discrete choice analysis to discover preferences regarding prepaid mobile
data services. In a DCE, a respondent opts the service that maximizes her satisfaction. Random
utility framework, which was pioneered by McFadden (1974), is used to study consumer behavior.
In a random utility framework, a scale called utility measures the consumer’s level of satisfaction
for a service.
The theory of discrete choice and random utility is based on characteristics demand theory
(Lancaster, 1966). In this theory, it is assumed that the consumer's preferences are measured from
the attributes of a service, instead of the service as an integrated unit. The utility parameters can be
calculated using the multinomial logit model developed by McFadden (1974).
Assuming that the respondent i is supposed to select the alternative perceived as yielding
the highest utility among J mobile internet services in each of t scenarios, the person’s utility when
choosing alternative j in choice set t can be written as:
  
  (1)
Consumer i chooses service j in comparison with k other alternatives, in the event of
Uijt>Uikt for any j≠k. During the consumer buying process, an individual compares the utility of
each and purchases the service with the maximum utility. The probability of purchasing internet
package j by individual i, namely the probability of Uijt>Uikt for all j≠k , is expressed as:
  
   
 ) for all    (2)
Rearranging (2) yields:
  
  
     for all    (3)
Considering a typical assumption that the distribution of random disturbance is
independent and identical extreme value, the choice probability of alternative j by person i from
the choice set can be expressed by:
  


(4)
Following Bridges et al. (2011), the steps of the study are as follwos. A discrete choice
experiment should clearly state a research objective that defines what the experiment intends to
measure. After delineating the study perspective, we identify and select relevant attributes and
assign their appropriate levels. The attributes identification should be supported by evidence on
current services offered in the market and the potential range of factors that may influence
consumers’ preferences. By striking a balance between what may influence consumers’ preferences,
restrictions of the study and the guidelines in the literature, we select the most relevant attributes.
Once attributes have been selected, their levels should be assigned. In the experimental design step,
we combine the selected attributes and levels to form hypothetical choice situations. In this phase,
Analysis of consumer preferences for prepaid ... (Sohrabi, et al.) 43
we determine the number of questions, the number of choices in each question, and the optimum
number of questionnaire blocks. Other specifications of the experiment like choosing between the
full profile and partial profile design are also specified in the design phase. By considering the study
perspective and limitations, we choose how to present questionnaires to respondents and how to
offer them sufficient motivation to respond to questions. In the next step, we collect the data, and
finally, different analyses of the data are carried out . These steps are amplified in the next sections.
One of the critical steps of designing a good DCE is identifying and selecting relevant
attributes and assigning their appropriate levels based on the purpose of the study (Hensher, Rose,
& Greene, 2005). By studying Iran mobile broadband market, several attributes for internet bundles
including validity interval, volume, price, brand, free off-peak volume offering, speed, purchasing
options can be identified. The more attributes to define a service in a conjoint study, the more
complex questionnaires are essential. Consequently, Respondents may utilize simplifying strategies
to handle the complexity of questions (Green & Srinivasan, 1978). Maximum of eight attributes
are advised for a full profile study (Wittink & Cattin, 1989). However, a more conservative
approach by (Schwabe, Grasshoff, Großmann, & Holling, 2003), recommends four attributes
maximum in a choice experiment. In order to decrease the risk of employing the simplifying
strategies by respondents, the attributes are limited to four main ones. The attributes that are
required to define any internet bundle are validity interval, traffic volume, price, and brand.
Attributes and levels are carefully determined based on the discussions with telecommunication
experts and recognizing the most influential attributes on the sale volume and consumers’ choice.
After recognizing the attributes, their levels should be assigned. When defining the attribute levels,
two guidelines are considered, limiting the number of levels to five to obtain more precise part-
worth (Orme, 2010) and equilibrating the number of levels of all attributes to improve the
comparability of attribute importance (Wittink, Krishnamurthi, & Reibstein, 1990). Five levels were
all assigned except Brand attribute. Due to Iran’s tripoly telecommunication market, the brand
attribute was defined by three levels.
In order to detect the current attribute levels offered in the market, all the mobile internet
packages offered by three operators were categorized in separated and combined pivot tables
according to each attribute. A hierarchical approach was adopted to define the levels of validity
interval, volume and price attributes. 10 detected levels of the validity interval were reduced to five
that were detected in packages with a higher market share. These five levels are identical among
the packages of all operators. In the next step, we recognized 15 levels of volume attribute for each
level of the validity attribute. Five levels with the highest sale volume with the objective of covering
the broadest possible range of volume levels were selected. In the final step, five levels are assigned
to price attribute. Considering USD to characterize the price levels could limit the market
simulation study. Alternatively, the price attribute is characterized by USD per gigabyte. We should
mention that the prices were converted into the Iranian currency in questionnaires. Table 3 presents
the attributes and their selected levels.
Once attributes and levels are selected, they must be combined to form hypothetical choice
situations. The possible mobile internet services are shown to respondents to select a favorable
one. In this study, a two-stage Bayesian D-optimal design was applied to construct a partial profile
choice experiment (Kessels, Jones, & Goos, 2011). By maximizing the DB-criterion introduced
first by Kessels, Jones, Goos, and Vandebroek (2011), the two-stage approach allows for the
creation of DB-optimal partial profile design. In this process, the prior distribution of expected
parameter values is assumed. At the first stage, the constant attributes in each choice set are
determined. At the second stage, the levels of the non-constant attributes are determined. We
designed a pilot survey to use the results as prior parameters necessary to design the final survey.
We deployed the SAS-based software, JMP, to design the experiment.
44 Economic Journal of Emerging Markets, 12(1) 2020, 39-53
Table 2. Attributes and levels
Attribute
Levels
Definition
Validity period
1 Day
7 days
30 Days
180 Days
360 Days
validity period after which the package expires even
if the subscriber fails to exhaust the entire
purchased data volume
Traffic Volume
0.2 Gigabyte
1 Gigabyte
3 Gigabyte
12 Gigabyte
24 Gigabyte
Maximum traffic volume which a customer is
allowed to use for a fixed prepaid cost; the bundle
should be renewed after exhausting the entire data
volume available even if the validity period is not
expired
Price
2.125 USD/Gigabyte
1.75 USD/Gigabyte
1.375 USD/Gigabyte
1 USD/Gigabyte
0.625 USD/Gigabyte
Price per gigabyte defines the total fixed prepaid
package price multiplied by package traffic volume
Brand
MTN
MCI
Rightel
Brands of mobile telecommunication operators in
Iran’s triopoly market
Johnson and Orme (1996) studied surveys containing up to 20 choice sets. They concluded
that respondents provide high-quality data at a much faster rate in the last stages of choice surveys.
Their findings support the practice of requesting respondents to perform many choice questions.
Relying on their study, we designed 60 scenarios and divided them into three sets of twenty. In
each Task, a respondent was presented with five alternatives characterizing the mobile internet
bundles and the choice of not selecting any bundle.
The survey was conducted during the 19th international exhibition of telecommunications,
information technology & innovative CIT solutions in Tehran in 2018. Customers of all three
telecommunication operators who had good knowledge about the mobile telecommunication
market and mostly up to 50 years old were attendants in the exhibition. Therefore, we could obtain
meaningful results from their answers. Prior to starting the survey, the respondents were informed
that gifts would appreciate them. This action persuaded them to overcome the burden of questions.
Simple customized software for doing a discrete choice survey was developed and installed
on the tablets. In the introductory page of the software, we presented the respondents with
instructions and examples to help them to participate in the study. After answering each task, the
next task was available automatically. The software could record the answering time for each
question and the time spent on the introductory page, which could be a useful statistic for furthered
analysis. We implemented a hybrid data collection method including both paper-based and
software-based surveys to interview the respondents. Some university students were available to
guide the respondents during the interview voluntarily. After filtering the unreliable and incomplete
questionnaires, 196 questionnaires were analyzed in this study. During the survey, we asked the
participants to illustrate the information about their current mobile operator, sex and marital status.
Result and Discussion
Table 4 presents the descriptive statistics obtained from conducting a multinomial logit model that
includes the part-worth, standard error, lower and upper limit of each level of the attributes. The
marginal utility values sum to zero for each attribute. The last level of each attribute is adjusted
according to the part-worth of other levels. Fig. 3 is the graph description of the part-worth in Table 4.
Analysis of consumer preferences for prepaid ... (Sohrabi, et al.) 45
Table 3. Part-worth of attribute levels
Term
Estimate
Std Error
Lower 95%
Upper 95%
Traffic Volume[0.2GB]
-0.051
0.038
-0.127
0.024
Traffic Volume[1GB]
0.540
0.033
0.474
0.604
Traffic Volume[3GB]
0.211
0.037
0.138
0.284
Traffic Volume[12GB]
-0.167
0.039
-0.245
-0.090
Price[2.125 USD/Gigabyte]
-0.278
0.043
-0.364
-0.193
Price[1.75 USD/Gigabyte]
-0.229
0.039
-0.307
-0.152
Price[1.375 USD/Gigabyte]
0.024
0.036
-0.047
0.093
Price[1 USD]
0.150
0.036
0.077
0.221
Validity[1d]
-0.429
0.050
-0.529
-0.330
Validity[7d]
0.089
0.033
0.022
0.155
Validity[30d]
0.134
0.038
0.057
0.209
Validity[180d]
0.152
0.042
0.069
0.233
Brand[Rightel]
-0.189
0.028
-0.244
-0.134
Brand[MTN]
0.311
0.025
0.262
0.360
No Choice Indicator
0.202
0.042
0.118
0.285
Figure 3. Part-worth of attribute levels
Table 5 reports the consumers’ estimated WTP changes, the standard deviation calculated
by an alpha method, upper and lower limits by 95 percent confidence interval. The Rightel, 24 GB
volume, one-day validity interval and 0.625 USD per gigabyte levels were considered as a baseline
-1 -0,5 0 0,5 1
0.2GB
1GB
3GB
12GB
24GB Traffic Volume
-0,4 -0,2 0 0,2 0,4
0.625 USD/GB
1 USD/GB
1.375 USD/GB
1.75 USD/GB
2.125 USD/GB
Price/Gigabyte
-0,2 0 0,2 0,4
MTN
MCI
Rightel
Brand
-0,6 -0,4 -0,2 0 0,2
1d
7d
30d
180d
360d
Validity Period
46 Economic Journal of Emerging Markets, 12(1) 2020, 39-53
for measuring WTP changes. For a better illustration, the column labeled “New WTP” presents
new WTP by adding 0.625 USD to the measured WTP. Consumers’ WTP is expressed in USD per
gigabyte.
Table 4. Estimation of willingness to pay
Factor
Feature
Setting
WTP
Change
Std Error
Lower 95%
Upper
95%
New
WTP
Traffic Volume
0.2GB
1.12
0.16
0.82
1.43
1.75
Traffic Volume
1GB
2.48
0.21
2.06
2.90
3.10
Traffic Volume
3GB
1.72
0.17
1.39
2.05
2.35
Traffic Volume
12GB
0.85
0.14
0.57
1.13
1.48
Traffic Volume
24GB
0.00
0.14
-0.28
0.28
0.63
Validity
1d
0.00
0.14
-0.28
0.28
0.63
Validity
7d
1.19
0.12
0.95
1.43
1.82
Validity
30d
1.30
0.13
1.06
1.55
1.93
Validity
180d
1.35
0.13
1.10
1.61
1.98
Validity
365
1.13
0.13
0.86
1.39
1.75
Brand
Rightel
0.00
0.13
-0.26
0.26
0.63
Brand
MTN
1.17
0.12
0.93
1.40
1.79
Brand
MCI
0.16
0.09
-0.02
0.34
0.78
The coefficients of the price attribute in Table 4 admits that the reduction in price will raise
consumers’ utility. By expanding the validity interval to 180 days, the utility continues to improve.
However, shifting from 180 days to 365 days validity results in a significant decrease in utility. The
information indicates that consumers were not willing to accept a one-year subscription period due
to their price sensitivity and risk considerations. The WTP changes resulting from different validity
intervals demonstrate the same conclusion as Table 4. Rephrasing it in terms of WTP, as an
example, a discount of 0.18 USD per gigabyte is required to tempt a consumer subscribing a bundle
with a one-year commitment period instead of purchasing a bundle with one-month expiration
interval. Increasing the validity interval from one day to seven days results in a significant 1.19 USD
per gigabyte WTP increase. Consumers are willing to pay 0.11 USD per gigabyte more to extend
the validity period of their package from seven days to 30 days. The operators can charge
consumers only 0.05 USD per gigabyte more for offering six times more validity interval than a 30
days validity interval.
According to Table 4, a rise in the size of traffic volume does not necessarily result in an
increase in consumers’ utility value. The more the volume of an internet bundle, the more the
consumers should prepay for the bundle. So even packages with long validity periods and
competitive prices cannot compete with a 1 GB bundle with medium validity. Based on Table 5,
considering the 0.2 GB traffic volume as the baseline, consumers are willing to pay 1.36 USD more
per GB to purchase a 1 GB bundle. To persuade consumers to shift from a 1 GB to a 3 GB data
plan, a 0.76 USD per gigabyte price reduction is needed, and to a 12 GB bundle, a 1.63 USD per
GB price reduction is needed. Consumers are willing to pay 0.85 USD per GB less to shift from a
12 GB package to a 24 GB package with the same volume and brand. Therefore, optimized pricing
strategies should be applied to encourage consumers to shift to long-term and high-volume
packages.
The results indicate that while the ascendant brand in the market is MTN, MCI and Rightel
are in the next positions. The considerable dominance of MTN over other brands is reasonable in
packages with volume under one gigabyte. However, for long-term and high-volume bundles, this
WTP and part-worth difference cannot be applied in pricing decisions. Thus, we recommend an
interaction study to rank the brands more precisely.
Analysis of consumer preferences for prepaid ... (Sohrabi, et al.) 47
The P-value and LogWorth values defined as -log10(p-value) in Table 6 verifies that the
effects of all four selected attributes are statistically significant and all of the attributes affect the
respondents’ utility.
Table 5. P-values and LogWorth values
Source
LogWorth
P-Value
Traffic Volume
73.503
<0.001
Brand
33.236
<0.001
Price
21.134
<0.001
Validity
17.751
<0.001
No Choice Indicator
5.589
<0.001
LogWorth values in Table 6 and the range of variation between the upper and lower limits
of attributes’ utility demonstrate that traffic volume is the most influential attribute in Iranian
consumers’ choice behavior. The Brand attribute is perceived to be the second important attribute.
Price and validity factors are in the third and fourth priority when choosing mobile internet
bundles. However, it should be noted that the excellence of traffic volume attribute from the
attribute’s importance perspective, might be the result of its more extensive range of levels. This
problem can be examined in further studies.
Decision-makers to experiment pricing and product development decisions in a
competitive environment can deploy marketplace simulation. The simulator reflects the probability
of consumers choosing each alternative. Sum of the probability of choosing packages or none of
the alternatives by an individual in each choice situation equals one. We used choice data to estimate
the probability of choosing each package. Fig. 4 simulates a choice occasion consisting of two
product profiles. In this scenario, it is supposed that in a 30 days validity interval, MCI is offering
a 1 GB service for 1.75 USD per GB. MCI is analyzing how to encourage its customers to shift to
a 3 GB bundle. Fig. 4 and Fig. 5 demonstrate that a 0.375 USD per GB price decrease cannot
satisfy the MCI objective. A 0.75 USD per gigabyte price decrease is required to tempt consumers
to buy a 3 GB service rather than a 1 GB bundle with a 30 days validity interval.
Figure 4. Choice probability comparison of a 1 GB, MCI, 30 days validity at 1.75 USD per
GB with a 3 GB, MCI, 30 days validity at 1.75 USD per GB
48 Economic Journal of Emerging Markets, 12(1) 2020, 39-53
Figure 5. Choice probability comparison of a 1 GB, MCI, 30 days validity at 1.75 USD per GB
with a 3 GB, MCI, 30 days validity at 1 USD per GB
In Table 7, the same services shown in Fig. 5, beside two other hypothetical services from
other operators, are analyzed in a competitive environment. Table 7 reveals that although
respondents prefer the new 3 GB MCI service to a Rightel service with the same specifications and
the MCI 1 GB data plan, consumers would rather choose the MTN 3 GB package at 0.75 USD
more per GB. This comparison can be made about a broader range of service profiles to study the
products in a competitive environment. Moreover, the market reaction can be studied before
introducing new services.
Table 6. Simulation of four packages in a competitive market
Brand
Validity
Traffic Volume
Price
Probabilities
MCI
30 days
1 GB
1.75 USD
0.199
MTN
30 days
3 GB
1.75 USD
0.22
Rightel
30 days
3 GB
1 USD
0.196
MCI
30 days
3 GB
1 USD
0.209
No Choice
0.176
Table 8. Respondent Groups, according to the mobile operators of their current SIM cards
Respondent Group
Respondents’ current SIM card
operators
1
MNT
2
MCI
3
Rightel
4
MNT, MCI
5
MCI, Rightel
6
MNT, Rightel
7
MNT, MCI, Rightel
One of the useful analyses of choice data is segmenting the market into clusters based on
their characteristics and homogeneous preferences. In this section, the interaction between
respondents’ demographic characteristics and attributes of services is studied. Respondents are
asked about their demographic characteristics, including their current mobile operator, sex, and
marital status during the experiment. The analysis reveals that only the respondent’s current
Analysis of consumer preferences for prepaid ... (Sohrabi, et al.) 49
network operator has a statistically significant effect on describing her preference for the brand
attribute. Each of the respondents belongs to one of the seven groups that are defined in Table 8
according to their network operators. Table 9 demonstrates that respondents’ group influences
their choice behavior about brand factor. Table 10 and Fig. 6 present the interaction between brand
and respondent operator group.
Table 9. P-values and LogWorth values for respondent Group and Brand interaction
Source
LogWorth
P-Value
Traffic Volume
72.87
<0.001
Price
21.269
<0.001
Validity
17.727
<0.001
Respondent Group *Brand
5.535
<0.001
Brand
3.252
<0.001
Table 10. Interaction between respondent Group and Brand
Operator Group
1
2
3
4
5
6
7
Estimate
Rightel
-0.119
-0.408
0.37
-0.22
-0.169
-0.059
-0.334
MTN
0.246
0.252
0.279
0.489
-0.158
0.092
0.341
MCI
-0.127
0.156
-0.649
-0.269
0.327
-0.033
-0.007
Figure 6. Interaction between brand and respondents group
Results from Table 10 and Fig. 6 demonstrate that consumers in groups one and three,
who are subscribing only one operator, perceive the brand of their current operator as the most
favorable. Group 2, MCI consumers, prefer MTN to their current brand, but they are not ready to
incur the switching cost. Groups 4 to 6 who are subscribing two different operators at the same
time are willing to choose one of their current subscribers as the most preferred option. While it is
typical in Iran to use dual subscriber identity module (SIM) smartphones, it can be inferred that
when choosing a mobile internet subscriber, respondents prefer the operator of the SIM card that
is assigned for using mobile internet. The choice data about respondents belonging to group 7,
who have the experience of using all three operators at the same time, indicate the same behavior
as when not considering the clusters. The results are relevant and validate the findings. The
simulation study indicates that in such an environment, by adopting price discrimination policies
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
Rightel MTN MCI
1
2
3
4
5
6
7
50 Economic Journal of Emerging Markets, 12(1) 2020, 39-53
between subscribers of different network operators, churn management and marketing strategies
of a firm can be optimized.
We demonstrate how telecommunication companies and industries in Iran and other
emerging markets can deploy conjoint analysis as a proved efficient method for analyzing their
local and global target markets. Market players can deploy the result of this study to take specific
actions. From a revenue and churn management perspective, we conclude that improving service
characteristics like volume and validity interval is not always the best strategy to maximize the
consumers’ welfare. The results reveal that consumers' risk perceptions and average consumption
affect their decisions. Therefore, consumers are not willing to prepay for long commitment periods
and high volume packages. Consequently, even with considerable price discounts, operators cannot
be successful in increasing the sale of packages with long-term validity periods.
Generally, network operators offer two kinds of SIM cards in Iran, prepaid SIM card with
negligible or zero fixed cost and postpaid SIM card that is offered with a fixed cost and guarantees
of the post-payment of the bill. In order to prevent non-payments, network operators do not offer
postpaid plans to prepaid SIM card owners. One risk-free strategy that can be studied to encourage
consumers with prepaid SIM cards to purchase services with high validity periods is designing an
installment plan. For instance, the network operator can offer its subscribers to prepay around 60%
of the total price when purchasing and around 40% after consuming half of the volume or in the
middle of the commitment period.
Practitioners can analyze pricing and product development decisions in a competitive
environment by utilizing marketplace simulation. The simulator demonstrates the probability of
consumers choosing each alternative. The findings from simulation suggest that the brand position
has a significant effect on setting the pricing and marketing strategies. Without considering the
brand position in pricing, the strategies can cause the firm significant losses or reduction in profit.
Another notable result is about the preferences of respondents in different segments, who
are clustered based on their current mobile operators. It can be inferred that considerable discounts
and incentives are essential to persuade MTN customers to shift to other operators. On the other
hand, MCI is in a vulnerable situation and may experience churn in its customers if it is not
spending enough to promote its brand equity. The operators, especially MCI and Rightel should
analyze different cooperative scenarios and optimize their packages to maximize their revenue and
control the churning of their customers. The brand study indicates that MTN has a superior
position in the market. Therefore, to survive in the market, other operators should improve their
competitive advantages like improving data transmission speed, expanding the coverage area,
offering new payment terms and modern services.
For policymakers, the results reveal that consumers hesitate to purchase high volume
bundles because of their high expenses, and this limits the internet consumption rate. New policies
such as reducing the tax for mobile internet service providers and applying discounts on customs
duties for telecommunication devices, investing in the development of infrastructure, and
implementing new technologies can lead to fixed costs cutting and, therefore, a price reduction of
internet services. According to cluster analysis, the policymaker can set different rules for each
operator to prevent a monopoly market and to keep firms in competition besides increasing
consumers’ satisfaction. They can also direct consumers to have interests, especially in less
developed areas, in essential social and scientific training programs. The policymaker can achieve
this objective by putting obligations or giving tax exemption for operators in exchange for putting
incentives, gift packages or discounts on the internet expended on training programs and videos.
Conclusions
In this study, we designed a DCE to study consumers’ preferences for mobile internet packages in
Iran. Four attributes, including traffic volume, validity period, brand and price are selected to define
Analysis of consumer preferences for prepaid ... (Sohrabi, et al.) 51
alternatives in choice sets. The statistics indicate that all the attributes influence the consumers’
choices behavior. Based on the results, the traffic volume is the most critical attribute for
customers. The estimations reveal that consumers avoid subscribing packages with a validity period
longer than six months because of their risk considerations and avoiding of higher prepaid
expenses. To motivate consumers to buy long-term and high-volume bundles, designing a price
optimization model with the objective of increasing the revenue and sale of target packages is
recommended. The comparison of the utility values over consumers showed that their current
network operator influenced their preferences about brand attribute. As our knowledge, this study
is the first practical DCE in Iran telecommunication industry. Furthermore, we could not find any
paper that employs DCE to analyze mobile internet packages by focusing on prepaid Plans.
Nevertheless, our study has limitations that further studies are needed in order to overcome them.
Interaction analysis between volume and validity interval attributes in a broader sample can help
decision-makers to study the market more precisely and implement strategies that are more
effective.
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