Pricing of mobile phone attributes at the retail level in a developing country: Hedonic analysis

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Pricing of mobile phone attributes at the retail level in a developing country: Hedonic analysis Waseem Ahmada,∗, Tanvir Ahmedb, Bashir Ahmadc a b c

Institute of Business Management Sciences, University of Agriculture, Faisalabad, Pakistan Department of Economics, Forman Christian College (A Chartered University), Lahore, Pakistan Institute of Agricultural and Resource Economics, University of Agriculture, Faisalabad, Pakistan

A R T IC LE I N F O

ABS TRA CT

Keywords: Mobile phone attributes Hedonic model Retail market price Developing country

This study has been undertaken to determine the effect of mobile phone attributes on their retail market prices. A log-linear hedonic price model was fitted to a total of 348 handsets, for which data were collected about various attributes from different websites, while the price data were obtained from mobile phone retailers in two major cities of Pakistan from November 2016 to February 2017. Results indicate that brand, battery capacity, weight, operating system, RAM, memory size and display size have a significant positive effect on mobile phone prices. Given the significant premium associated with various characteristics, manufacturers need to formulate strategies to emphasize the battery capacity of 2000-3000 mAH, RAM of more than 1GB, screen size of more than 5 inches, memory size of more than 8GB, back camera of over 15MP, 4G network mode, front camera and FM radio.

1. Introduction Mobile phones have become indispensable for socio-economic development all over the world. In developing countries, many farflung areas have limited access to communication roads, health, postal, financial services, etc. However, the mobile phone represents the first modern infrastructure that is available in these interior regions. The mobile phone market has undergone rapid changes since its inception. In the beginning, the adoption of these phones increased at the rate of 25–35 percent annually over the period 1983–1997 (Hausman, 1999). However, the latest estimates show that the number of mobile phones has increased by 250 percent from 2.21 billion in 2005 to 7.74 billion in 2017 in the world. In developed countries, this number increased by 62 percent from 0.99 billion to 1.61 billion, while in developing countries the number went up by 406 percent from 1.213 billion to 6.133 billion during the same period. Mobile density for 100 inhabitants increased from 33.9 to 103.5 in the world, 82.1 to 127.3 in developed countries and 22.9 to 98.7 in developing countries over the period 2005 to 2017. Thus the mobile telephone penetration is more than 100 percent in developed countries and close to 100 percent in developing countries (ITU, 2017). The mobile phone is now considered a necessity, and its industry is booming and is characterized by rapid technological advances. Mobile phones are no longer simple voice communication devices and contain many high-tech features in a single handset. Handsets have technical and performance features such as touch screens with more colors, enhanced connectivity, higher resolution cameras, large storing data capacity, the ability to run complex software applications, etc. Apart from functioning as a phone, they can be used as video recorders, photographic cameras, video game consoles, radios, televisions, GPS devices and so on. The mobile phone handsets market is served by a large number of firms. There is a large number of mobile phone brands and each brand has a variety of ∗

Corresponding author. Institute of Business Management Sciences, University of Agriculture, Faisalabad, Pakistan. E-mail addresses: [email protected] (W. Ahmad), [email protected] (T. Ahmed), [email protected] (B. Ahmad).

https://doi.org/10.1016/j.telpol.2018.10.002 Received 15 May 2018; Received in revised form 14 October 2018; Accepted 17 October 2018 0308-5961/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Ahmad, W., Telecommunications Policy, https://doi.org/10.1016/j.telpol.2018.10.002

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models. Further, there is a fierce competition among mobile phone producers and information regarding consumers’ preferences for various attributes is very important for future investment in the fast changing world. Consumers make a choice among the models of various brands based on their attributes like volume, battery duration, weight, etc. (Nazari, Kalejahi, & Sadeghian, 2011; Mostafavi, Roohbakhsh, & Behname, 2013; Kim, Wong, Chang, & Park, 2016). According to Lancaster (1966), consumers derive utility from the attributes of the product rather the product itself. For example, consumers do not purchase mobile phone; rather they buy different bundles of mobile phone attributes. For introducing or designing a mobile phone, manufacturers need information about how consumers are valuing different attributes of the mobile phone, which will help them in developing innovation strategies. Manufacturers need to incorporate preferred attributes in their products in order to reduce the chances of failure. Introduction of the less desired product attributes could cause wastage of resources and loss to the society (Ahmad & Anders, 2012). The information about product attributes is very important for the success of the mobile phone product. Winger and Wall (2006) reported that about 95 percent of new products fail in the market because of customer non-acceptability. Although, some studies analyzed mobile phone prices by using data of developed/high-income economies1 (Dewenter, Haucap, Luther, & Rötzel, 2007; Kim et al., 2016; Montenegro & Torres, 2016) but these economies have only 16% share of world population. Developing countries comprising of upper middle, lower middle and low-income economies have about 84 percent of the global population (World Bank, 2018). These economies have many common features that make them different from the developed economies of the world. Developing economies income per capita, living standards, and labor productivity are low while unemployment rate, population growth rate and percentage of population living in poverty are high as compared to developed countries. These countries are a big market for the mobile phone industry and represent a great potential in the telecommunication business. Some research work has been done in upper middle economies (Mostafavi et al., 2013; Nazari et al., 2011) sharing 34% of the world population. However, no study has been conducted in developing countries representing lower middle-income and lowincome economies sharing 50% of the global population (World Bank, 2018). Consumer's utility from mobile phone attributes might be different between developed and developing countries because of differences in socio-economic features. Therefore, this paper examines the pricing of mobile phone attributes in a developing country Pakistan, which is part of lower middle-income economies group but its development indicators lie between the average figures of lower middle-income and low-income economies. For example, gross national income per capita of Pakistan at purchasing power parity ($5320) is less to the average value of lower-middle income economies ($6409) but high to low-income economies of the world ($1602). Similarly, GDP per capita growth (2.6%) is less to the average value of the lower-middle income economies (3.8%) but is high to low-income economies (1.6%). As far as mobile cellular subscriptions per 100 people are concerned, it is high to the average value of low-income economies but below to lower-middle income economies. Similarly, individual using the internet as the percentage of the population is below to lower-middle economies but is high to the average value of low-income economies. Hence Pakistan lies close to lower end of developing economies and indicate a huge potential for communication technologies in future. The comparison of Pakistan's statistics for various development indicators against the average values across income groups of developing economies is reported in Table 1. As most of Pakistan's statistics lie between the average values of lower middle-income and the low-income group of economies of the world, so this paper makes a contribution to the existing literature by estimating the price of mobile phone attributes in Pakistan. It is a good example of lower-middle and low-income developing countries of the world, and in its market a wide variety of mobile phones having different attributes are traded. As no empirical work has been done to analyze the pricing of mobile phone attributes in Pakistan, therefore, to improve the understanding of consumer demand for the mobile attributes, present study is conducted. This pricing of mobile phone attributes will help manufacturers/importers/suppliers/retailers to know the preferences and interests of consumers that are formed on the basis of attributes of mobile phone handsets in order to make their business successful in developing countries similar to Pakistan.

2. Materials and methods 2.1. Model specification Mobile phones are high technology products and have a large number of technical and performance attributes. This makes them differentiated products with a number of alternative designs and selling prices in the market. Market prices of phones depend on consumer preferences for the set of attributes embodied in them. The demand for a particular handset depends on the implicit value that consumers attach to each attribute. The unobserved implicit price of each attribute can be estimated by using the hedonic price model. Two basic approaches have contributed towards the theoretical framework of hedonic pricing. The first approach was developed by Lancaster (1966), and the second by Rosen (1974). Both approaches incorporated prices and attributes based on the relationship between the actual prices of differentiated products and the attributes associated with these products. The Lancaster model assumes that goods are the member of a group and consumer consumes some or all of the goods or combinations subject to the 1 According to new country classification by income level for 2017–18, World Bank has classified countries on the basis of GNI per capita as lowincome economies with GNI per capita of $1005 or less, middle-income economies with GNI per capita income between $1006 and $3955, uppermiddle economies with GNI per capita between $3956 and $12235, and high-income economies with GNI per capita of $12236 and more (World Bank, 2017b). According to The World Development Indicators 2009, on page XXI, low and middle-income economies are sometimes referred to as developing economies (World Bank, 2009).

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Table 1 Development indicator of Pakistan in comparison to various income group averages of developing economies. Source: World Bank, 2017a. Indicator

Upper middle- income economies

Lower middle- income economies

Low- income economies

Pakistan

Gross national income per capita $ purchasing power parity (2015) Gross domestic product per capita growth (2014–15) Gross savings (% of GDP 2015) Urban population (% of total population 2015) Maternal mortality ratio (per 100,000 live births 2015) Labor force participation rate (% of population ages 15 and older 2015) Unemployment (% of total labor force 2016) Access to improved water source (% of total population 2015) Domestic credit provided by financial sector (% of GDP 2015) Tax revenue collected by central government (% of GDP 2015) Mobile cellular subscriptions (per 100 people 2015) Individuals using the internet (% of population 2015)

15627

6409

1602

5320

2.4 32.0 64 54 66

3.8 27.9 39 251 58

1.6 14.6 31 496 76

2.6 23.3 39 178 54

6 95

5 90

6 66

6 91

143.2

63.8

26

48.6

12.5

11.4

n/a

10

105 52

90 29

60 9

67 18

budget constraint. Rosen's model presumes that a consumer chooses a good from the range of goods and consumes it discretely or separately. The hedonic price model does not require the joint consumption of goods within a group. Lancaster's model is based on the assumption that there is a linear relationship between the prices of goods and their characteristics and implicit prices are constant over the range of characteristics. In the case of Rosen model, there is a non-linear relationship between the prices of goods and their inherent attributes. Hedonic price model decomposes the price of a product into respective components that determine the product price (MartinezGarmendia, 2010). According to Lancaster (1966; 1976; 1979), goods are observed as a bundle of quality characteristics, and the marginal values of these characteristics that consumers attribute to them explain the variation in prices of goods. According to Rosen (1974) and Oczkowski (1994), product price is determined by the attributes of the product. Functional form (i.e. mathematical form or model structure) is very critical in determining an accurate and consistent econometric model (Brown & Ethridge, 1995). As the economic theory of hedonic pricing provides little guidelines on the choice of proper functional form (Cropper, Deck, & McConnell, 1988; Haab & McConnell, 2002) so the use of an inappropriate form of hedonic price function may lead to biased estimates and thus mislead about the implicit prices of the characteristics. But when it comes to the valuation of product attributes, it is necessary to use a hedonic price function that can estimate the marginal attribute prices most accurately. Prices are treated as a dependent variable in the estimation of marginal values for these attributes. However, errors in the measurement of marginal prices vary depending upon the form of hedonic price function. Haab and McConnell (2002) have pointed out that in the selection of a functional form and a set of variables, one must consider the inevitable problem of collinearity. Further, high collinearity makes the choice of a flexible functional form less useful, as the interactive terms of a flexible functional form lead to high collinearity. Since the dataset of present study comprises of many binary variables, it limits the choice of functional form to linear or log-linear specification. For testing the correct specification of the model, Ramsey Regression Equation Specification Error Test (RESET) was applied. On the basis of RESET and following Cropper et al. (1988), and Haab and McConnell (2002), this study uses a log-linear functional form. The econometric model for hedonic analysis of mobile phone is as follows:

ln (PGi ) = x i′ β + ei

(1)

Where ln(PGi ) is the natural log of the price of a mobile set; x i is the vector of independent variables (i.e. brand, battery duration, display size, weight, camera, radio, etc.); β is a vector of parameters to be estimated and ei is the error term. The conditional distribution of the errors given the matrix of explanatory variables have zero mean [E (ei ) = 0], constant variance [V (ei ) = σ 2] and zero covariance [E (ei Xi ) = 0] (Gujarati & Sangeetha, 2007). In order to ensure the reliability of the estimates, these assumptions must hold. For this purpose, the model should be tested for specification error, multicollinearity, and heteroscedasticity. If the model is misspecified, specification bias arises. When a relevant variable is excluded from the model, the coefficients of the included variables are generally biased as well as inconsistent. The error variance is incorrectly estimated and the usual hypothesis testing procedures are not valid. The inclusion of an irrelevant variable in the model yields unbiased and consistent estimates of the coefficients. Further, error variance is correctly estimated and the usual hypothesis testing methods are still valid. However, the estimated variances of the coefficients are large and consequently, probability inferences about the parameters are less precise (Gujarati & Sangeetha, 2007). One can use Ramsey's RESET test to check specification error. In the presence of high collinearity among the independent variables, estimates of regression coefficients are unbiased but their standard errors tend to be high. Consequently, the value of the coefficients cannot be estimated precisely. The variance inflating factor (VIF) is used to test the presence of multicollinearity. As in the presence of heteroscedasticity, the estimates of the regression coefficients under ordinary least squares (OLS) are unbiased, consistent but inefficient so the usual procedures for hypothesis testing are no longer applicable. If these 3

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procedures are applied then t and F tests based on them would be misleading, and results in erroneous conclusions (Gujarati & Sangeetha, 2007). In order to obtain efficient estimates in the presence of heteroscedasticity, one can use the robust estimation procedure (Verbeek, 2012). Heteroscedastic-consistent covariance matrix estimator can be used to obtain a consistent estimator of the covariance matrix. White (1980) developed the heteroscedastic-consistent covariance matrix estimator (HC0) that is asymptotically valid. MacKinnon and White (1985) recommended three alternative estimators using heteroscedastic consistent standard errors i.e. HC1, HC2, and HC3. Davidson and MacKinnon (1999) pointed out that HC0 is not the best covariance matrix estimator because least squares residuals tend to be too small. HC1 shows the same finite sample bias from which HC0 suffers (Cai & Hayes, 2008). Long and Ervin (2000) have reported that HC2 and HC3 are the best covariance matrix estimator and their superiority depends on the properties when testing coefficients are affected by heteroscedasticity. HC2 is superior for tests of coefficients that are least affected by heteroscedasticity, while HC3 is better for tests of coefficients that are most affected by heteroscedasticity. Various alternative covariance matrix estimators (HC2 and HC3) as suggested by Davidson and MacKinnon (1993), HC0 by White (1980) and of OLS are specified as follows:

OLS =

∑ ei2 (X ′X )−1 n−k

HC0 =

(X ′X )−1X ′diag (ei2) X

(2)

(X ′X )−1

(3)

ei2 ⎞ −1 HC2 = (X ′X )−1X ′diag ⎜⎛ ⎟ X (X ′X ) ⎝ 1 − hii ⎠

(4)

ei2 ⎞ −1 HC3 = (X ′X )−1X ′diag ⎜⎛ ⎟ X (X ′X ) 2 ⎝ (1 − hii ) ⎠

(5)

X ′ (X ′X )−1.

Where n is number of observations, k is the number of parameters estimated and hii = We estimated results with the covariance matrix of the error term of OLS, HC0 (White heteroscedasticity corrected standard error), HC2 and HC3. As HC3 is superior for tests of coefficients which are most affected with heteroscedasticity (Long & Ervin, 2000), therefore, we used HC3 for testing coefficients of the hedonic model. Version 12 of the Stata software was used for analysis in this study and the following hedonic model is developed to identify factors determining the mobile phone price.

∑ β1i BRANDi + ∑ β2i WEIGHTi + ∑ β3i BATTERYi + β4i OPERSYSi + ∑ β5i RAMi + ∑ β6i MEMORYi + ∑ β7i DISPLAYi + ∑ β8i MNTi + ∑ β9i BCAMi + β10i FCAMi + β11i FMRi + ei

Ln (PRICEi ) = β1 +

(6)

The dependent variable is the price of the mobile phone, measured in Rupees (Pakistani currency) but converted to US$ (1US $ = 110 Rupees). This is the price that is charged by the retailers when they sell mobile phones. To capture the impact of mobile features on its price, dummy variables for brand (BRAND), weight (WEIGHT), battery (BATTERY), ram (RAM), memory (MEMORY), display (DISPLAY), mobile network mode (MNT), back camera (BCAM), front camera (FCAM) and radio (FMR) are considered. It is expected that all these variables have the influence on the price of the mobile phones due to reasons enlisted under each subheading. 2.1.1. Brand There are a number of mobile phone brands like Qmobile, Rivo, Huawei, Samsung, Haier etc. Each brand is a product of a particular manufacturer which is differentiated by its name and get up. The brand manufacturer derives many benefits from its name. Firstly, it allows the manufacturer to communicate directly with consumers. If brands do not exist then all the mobiles will be treated as similar products. Secondly, it helps the brand manufacturer to develop consumer loyalty and add value to his products (Murphy, 1990). Brands also provide an assurance of quality, reliability etc. to the consumers and enable them to shop with confidence. 2.1.2. Weight Smartphone characteristics are generally related to performance and technical capabilities. However, the design characteristics like weight (in grams) are also valued by the users and can influence the demand for a particular phone. Therefore, the weight variable is included in the model due to its relevance to the consumer's choice. 2.1.3. Battery capacity The functionality of the smartphone is severely affected by battery life. Initially, the battery capacity was measured in hours (talk and standby time). However, with the introduction of smartphones, which are equipped with various applications, energy consumption has increased. In reality, making phone calls is just one “application” and there are many other applications and electronic components which make use of the battery. Therefore, we measured the battery capacity in terms of Milli Amperes (mAH) and included in the model. 2.1.4. Operating system Smartphones are designed to run specific software. Initially users purchased the mobile phone without knowing the operating system that was running on the device. With the passage of time consumers purchased the smartphone based on the functionality 4

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offered by the software and now the operating system is considered to be a key factor regarding the user's choice of phone. 2.1.5. RAM RAM is the memory that a phone uses in current working. A phone with more RAM is more responsive to inputs and is good at working in lots of applications simultaneously without crashing. RAM varies among handsets and mobile phone response time increases if there is a mismatch between the requirements of the application and RAM, it results in user dissatisfaction. 2.1.6. Memory size Memory size is an important element of mobile phone and has an impact on its performance. A phone uses memory to store data, pictures, videos etc., and large memory means more data storage capacity in the phone. 2.1.7. Display Mobile phone display has undergone remarkable developments over time and is an important component in mobile phones. Initially, the function of the display was to show numbers for dialing but with current displays, it is possible to watch videos, play games, browse the internet etc. Although, with larger phone display size, one can view and type better while with small display, one faces difficulty in reading the small text but phones having large display size may be difficult to fit in the pocket. 2.1.8. Network mode Mobile network mode is identified by generation (G) and is used for communications. 1G is used for voice communication only while 2G also allowed short messaging. Besides services provided by early generations, 3G enabled faster communications and allowed transfer of high quality audio, video and data. The mobile phone connects to the internet under 3G and 4G network modes but usually, 4G is ten times faster than 3G. Customers interested in fast downloads prefer to buy phone having 4G network mode. As cellular companies are charging internet services on the basis of the amount of data download, so one-time investment on 4G mobile phone enabled the customer to avail internet at a faster speed. 2.1.9. Cameras Initially phone manufacturing companies introduced a back camera but now many companies added a second camera, the front camera, to their devices. This front camera enables consumers to make video calls and selfies. Camera specification is given in MP (Mega Pixels) and higher MP indicates the ability of the camera to capture quality pictures with more fine details. 2.1.10. Radio Mobile phone with radio enables the user to listen music, news, discussions on various issues, etc. This facility is especially helpful for the people living in developing countries. Table 2 provides the definitions of variables along with their mean and standard deviation. The model included all the variables as described in Table 2 except the variables for the base/benchmark categories i.e. OTHERB (Nokia, Alcatel, Dany, HTC, Maxx), WEIGHTU100 (weight up to 100 g), BATTERYU2 (battery capacity up to 2000 mAH), SAMB (Symbian operating system), RAMU1 (RAM up to 1 GB), MEM8 (memory up to 8 GB), DIS3 (display size up to 3 inches), MNT2 (network mode 2G or less), BC5 (back camera up to 5 MP), FCAM2 (no front camera) and FMR2 (no FM radio). The constant term includes the joint effect of the categories not included in the model. The semi-logarithmic specification of the hedonic price model assumes homotheticity of the utility function and zero degree homogeneity of demand for different mobile phone characteristics. Moreover, each marginal implicit price is a nonlinear function in terms of the entire characteristics (Ahmad & Anders, 2012). All the variables with the exception of the dependent were dummy variables showing whether a particular characteristic was present or not. The average value of the dummy variable shows the percentage of mobile phones in the sample having a particular characteristic. In order to eliminate the effect of influential observation, the present study used the DFFITS criterion as suggested by Belsley, Kuh, and Welsch (1980). The DFFITS criterion can be written as

DFFITS =

yˆi − yˆ(i) S(2i) − hii

(i = 1, 2,…n) (7)

Where numerator measure the difference in the predicted values when ith data point is included and excluded from the analysis, S(i) is the standard error obtained without the observation included and hii is the leverage for the observation, which is a measure of the distance from the other independent variables in the sample. To identify potential outlier, Belsley et al. (1980) suggested that any observation for which DFFITS > 2 k warrants attention. Where k is the number of parameters and n is the number of observations. n

2.2. Study area This study is confined to a developing country, Pakistan, which is the sixth most populous country of the world (Government of 2 originally data were collected for 363 handsets. 15 observations were dropped with the application of DFFITS test. Therefore, results of 348 handsets included in the analysis are presented here.

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Table 2 Definitions of variables and their statistics. Variable

Definition of variable

Mean

Std. Dev.

PRICE BRAND QMOBILE RIVO HUAWEI SAMSUNG HAIER IPHONE LENOVO OTHERB WEIGHT WEIGHTU100 WEIGHTB150 WEIGHTM150 BATTERY BATTERYU2 BATTERYB23 BATTERYM3 OPERSYS OSS OSA SAMB RAM RAMU1 RAMM1 MEMORY MEM8 MEMB816 MEMA16 DISPLAY DIS3 DIS315 DISA5 MNT MNT4 MNT3 MNT2 BCAM BC5 BC515 BCA15 FCAM FCAM1 FCAM2 FMR FMR1 FMR2

Price of Mobile phone (US $) Brand of mobile phone QMOBILE = 1, if the brand of the mobile is Q Mobile, 0 otherwise RIVO = 1, if the brand of the mobile is Rivo, 0 otherwise HUAWEI = 1, if the brand of the mobile is Huawei, 0 otherwise SAMSUNG = 1, if the brand of the mobile is Samsung, 0 otherwise HAIER = 1, if the brand of the mobile is Haier, 0 otherwise IPHONE = 1, if the brand of the mobile is iPhone, 0 otherwise LENOVO = 1, if the brand of the mobile is Lenovo, 0 otherwise OTHERB = 1, if the brand of the mobile is Nokia or Alcatel or Dany or HTC or Maxx, 0 otherwise (base category) Weight of mobile phone (grams) WEIGHTU100 = 1, if the weight of the mobile phone is up to 100 g, 0 otherwise (base category) WEIGHTB150 = 1, if the weight of the mobile phone is more than 100 but up to150 g, 0 otherwise WEIGHTM150 = 1, if the weight of the mobile phone is more than 150 g, 0 otherwise Battery capacity (mAH) BATTERYU2 = 1, if the battery life is up to 2000 mAH, 0 otherwise (base category) BATTERYB23 = 1, if the battery life is more than 2000 but up to 3000 mAH, 0 otherwise BATTERYM3 = 1, if the battery life is over 3000 mAH, 0 otherwise Operating system of mobile OSS = 1, if the operating system of the mobile is IOS, 0 otherwise OSA = 1, if the operating system of the mobile is Android, 0 otherwise SAMB = 1, if the operating system of the mobile is Symbian, 0 otherwise (base category) Ram of the mobile (GB) RAMU1 = 1, if the ram of the mobile is up to 1 GB, 0 otherwise (base category) RAMM1 = 1, if the ram of the mobile is more than 1 GB, 0 otherwise Memory of the mobile phone (GB) MEM8 = 1, if the memory of the mobile phone is up to 8 GB, 0 otherwise (base category) MEMB816 = 1, if the memory of the mobile phone is more than 8 but up to16 GB, 0 otherwise MEMA16 = 1, if the memory of the mobile phone is more than 16 GB, 0 otherwise Display size (inches) DIS3 = 1, if the display size of the mobile phone is up to 3 inches, 0 otherwise (base category) DIS315 = 1, if the display size of the mobile phone is more than 3 but up to 5 inches, 0 otherwise DISA5 = 1, if the display size of the mobile phone is more than 5 inches, 0 otherwise Mobile network mode MNT4 = 1, if the mobile network mode is 4G, 0 otherwise MNT3 = 1, if the mobile network mode is 3G, 0 otherwise MNT2 = 1, if the mobile network mode is 2G or less, 0 otherwise (base category) Back Camera (MP) BC5 = 1, if the back camera of the mobile phone is up to 5 MP, 0 otherwise (base category) BC515 = 1, if the back camera of the mobile phone is more than 5 but up to15 MP, 0 otherwise BCA15 = 1, if the back camera of the mobile phone is more than 15 MP, 0 otherwise Front Camera (MP) FCAM1 = 1, if the mobile phone has front camera, 0 otherwise FCAM2 = 1, If the mobile phone has not front camera, 0 otherwise (base category) FM Radio FMR1 = 1, if the mobile phone has FM Radio, 0 otherwise FMR2 = 1, If the mobile phone has not FM Radio, 0 otherwise (base category)

136.35

180.86

0.365 0.115 0.112 0.083 0.060 0.029 0.055 0.181

0.482 0.319 0.316 0.277 0.238 0.167 0.228 0.386

0.382 0.371 0.247

0.487 0.484 0.432

0.483 0.374 0.144

0.500 0.484 0.351

0.034 0.632 0.333

0.183 0.483 0.472

0.644 0.356

0.480 0.480

0.509 0.299 0.193

0.501 0.458 0.395

0.273 0.319 0.408

0.446 0.467 0.492

0.624 0.213 0.164

0.485 0.410 0.371

0.641 0.218 0.141

0.480 0.414 0.349

0.638 0.362

0.481 0.481

0.773 0.227

0.420 0.420

Pakistan, 2017) and its 67% of the population is mobile cellular subscribers (World Bank, 2017a). It represents very well the case of people living in low-income and lower middle-income economies. Demand for mobile phones has increased considerably over the past decade in Pakistan like other developing countries. The total number of phones has increased by 193 percent from 40.48 million to 118.61 million over the period 2005-06 to 2014–15. Phone density increased from 26.2 percent to 62.8 percent during this period (Government of Pakistan, 2015). Cellular mobiles contributed 97 percent towards both the total number of phones and phone density. There are a large number of firms that are supplying their brands to the customers and the competition between them is getting fierce with the passage of time. The mobile phone market is one of the fastest growing markets in Pakistan. There were about 139.11 million mobile phone subscribers at the end of March 2017 (Government of Pakistan, 2017). Pakistan is expected to spend $ 800 million on mobile phones imports in the financial year 2018, roughly $2 million more than in the financial year 2017. According to Pakistan Telecommunication Authority (PTA), the revenue of the telecommunication companies was Rs. 452.8 billion during 2015-16. Further, mobile phone users spent a total of Rs. 98 billion during 2015-16 by using 3G and 4G internet services (Ali, 2016) while the number of subscribers of 3G and 4G LTE was 39.88 million (Government of Pakistan, 2017). According to mobile phone industry sources, about one million mobile phones are being imported every month in Pakistan. Assuming that the average price of a smartphone is $ 200, the import bill per annum would be $ 2.4 billion. These imports are not reflected in the official figures mainly due to smuggling so there is a tax loss of Rs. 35–40 billion per annum to the government (BR Research, 2017).

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Table 3 Number of handsets by firm.2. Firm

QMOBILE RIVO HUAWEI SAMSUNG HAIER IPHONE LENOVO OTHERBRAND Total

Number of Handsets

Price (US $)

127 40 39 29 21 10 19 63 348

Average

Minimum

Maximum

56.39 48.86 293.26 266.52 50.21 869.73 172.99 97.29 136.35

12.64 14.91 23.64 21.68 14.09 654.55 59.09 12.05 12.05

250.00 150.00 635.45 718.18 177.27 1152.73 331.82 450.00 1152.73

2.3. Data and variables Data on prices were collected from mobile phone retailers in two major cities i.e. Lahore and Faisalabad of the Punjab province during the period 2016–17. All the handsets reported in the mobile phone price list were included in the sample. The data about the performance and technical characteristics of 363 handsets were obtained from www.gsmarena.com, www.whatmobile.com.pk, and mobile phone producers’ websites. Application of DFFITS criterion resulted in the exclusion of 15 observations, reducing our sample to 348 observations. Table 3 summarizes the number of mobile phones in our sample and reports their average prices. Qmobile dominates the retail mobile phone market. There are 127 models of Qmobiles, 40 Rivo, 39 Huawei, 29 Samsung and 21 Haier models. The number of models of other firms varied from 10 to 19. Wide variations were observed in the mobile phone prices of models under each brand and can be seen through their minimum and maximum price. 3. Results and discussion In order to test the robustness of the estimated model, various tests are applied. The computed value of Ramsey RESET (1.77) was below the critical value at one percent level of significance, so we did not reject the null hypothesis and concluded that the model is correctly specified. The mean value of VIF is 3.04 which ranged from 1.48 to 7.29 for various coefficients. Since these values are less than 10 i.e. the rule of thumb maximum value (Gujarati & Sangeetha, 2007), so multicollinearity is not a problem. Chi-square value of Breusch-Pagan/Cook-Weisberg test for heteroscedasticity came to 3.56 and was significant at 10 percent level of significance. Hence we rejected the null hypothesis of constant variance and used HC3 for testing coefficients of the model (Long & Ervin, 2000). The results of the estimated hedonic regression are reported in Table 4. The estimated regression model explained about 92 percent of price variability across phones and mobile phone attributes have a jointly significant impact on their prices. For dummy variables, the semi-elasticity has been estimated by using the framework proposed by Kennedy (1981). Each mobile phone brand is a product of a particular manufacturer, which is differentiated by its name and appearance. Mobile phone buyers often use the brand as a proxy for quality. Consistent with the findings of Nazari et al. (2011) and Dewenter et al. (2007), the brand had a significant effect on mobile price in the present study, mainly due to the inclusion of some features that were liked by the users. The brand effect shows that five of the seven popular mobile phone brands have a price premium over the category of other recorded brands. iPhone is ranked as a top brand with a 240.42 percent premium price in the market. Montenegro and Torres (2016) also reported that consumers are willing to pay a high premium of up to 95 percent for Apple smartphone. Users are paying the highest premium for iPhone, which could be due to fact that the iPhone is a luxury product and is considered as a status symbol. Another possible explanation is the familiarity of buyers with this device who are using and want to use Apple products. Further, iPhone applications have the best features and designs, and users have access to the latest software and great support for solving problems. The second most important mobile phone brand is Rivo, which enjoys a 65.97 percent premium price that is much lower than the premium rate of iPhone. Samsung, Haier and Huawei brands have a price premium of 32.36 percent, 30.05 percent and 22.66 percent respectively over the category of other brands. The possible explanation for paying a premium for these brands could be that all these brands are international, use the latest technology and trends, offer good quality, lightweight, and good performance phones. Qmobile is the lowest ranked mobile phone with a 22.03 percent discount. The possible explanation for paying a discount price for Qmobile brand by consumers could be that it is a local brand and the company offers a phone with a slogan of affordable phone prices. Khasawneh and Hasouneh (2010) concluded that brand name influences user evaluation and affect their buying decisions. Further, Rahim, Safin, Kheng, Abas, and Ali (2016) reported a significant relationship between the brand name and purchase intention. Smartphone users change their phones regularly in this fast changing world (Hew, Badaruddin, & Moorthy, 2017) due to improvements in performance and technical capabilities. The attribute indicating the weight of a mobile handset suggests a positive effect leading to a price increase of approximately 15.83 percent and 16.18 percent in the case of mobile phones weighing 100–150 g, and above 150 g, respectively over the benchmark mobile set of weight up to 100 g. It is true that some mobile phone attributes like the battery, screen size, etc. add to the weight, but there is no significant correlation between weight and these attributes as indicated by the correlation coefficient values. The results of the study show low correlation coefficient values between weight and battery 7

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Table 4 Estimated results of OLS hedonic model. Variable

Coefficient

Standard Error HC3

Relative impact %

Constant QMOBILE RIVO HUAWEI SAMSUNG HAIER IPHONE LENOVO WEIGHTB150 WEIGHTM150 BATTERYB23 BATTERYM3 OSA OSS RAMM1 MEMB816 MEMA16 DIS315 DISA5 MNT3 MNT4 BC515 BCA15 FCAM1 FMR1 R2 F test RESET Specification Test Breusch-Pagan/Cook-Weisberg test for heteroscedasticity Mean VIF

2.198*** −0.246*** 0.514*** 0.207** 0.287** 0.272* 1.235*** −0.082 0.148*** 0.152** 0.180*** 0.134 0.320*** 0.269 0.222*** 0.135** 0.514*** 0.042 0.340*** 0.136** 0.511*** 0.025 0.216* 0.627*** 0.593*** 0.9204 250.94 F(2, 320) = 1.77 3.56* 3.04

0.148 0.070 0.153 0.085 0.134 0.159 0.256 0.081 0.054 0.068 0.052 0.088 0.073 0.268 0.060 0.065 0.071 0.068 0.103 0.065 0.082 0.065 0.111 0.089 0.131

−22.03 65.97 22.66 32.36 30.05 240.42 −8.24 15.83 16.18 19.55 13.91 37.47 27.24 24.67 14.25 66.87 4.06 39.96 14.40 66.42 2.31 23.53 86.81 80.12

***, ** and * show statistical significance at 1, 5 and 10 percent level of significance. The relative impact measures the percentage effect of the particular attribute on the mobile phone price against its average price.

capacity variables as well as between weight and screen size variables. Thus an increase in mobile weight though positively but insignificantly related to battery capacity which in turn may be due to the usage of heavier batteries having a longer life or extended capabilities. Similarly, increase in the weight of a mobile handset is related to the screen size. Lin and Chen (2013), and Montenegro and Torres (2016) also reported results similar to our findings showing that weight was positively related to the mobile phone price. However, Dewenter et al. (2007) indicated that battery duration tends to increase the battery weight and thereby also the mobile phone weight. Put differently, the user may prefer a longer battery duration but not a heavier handset. Further, similar to this study, weekly poll results by Peter (2018) also indicated that about 43% and 39% people had the opinion that ideal weight for a phone is 140 g and 170 g, respectively. The battery capacity of the mobile handset shows a price premium of 19.55 percent for mobile phones having the battery capacity of 2000–3000 mAH over the base category i.e. up to 2000 mAH. However, mobile sets with a battery capacity of over 3000 mAH have a price premium 13.91 percent, but is insignificant over the base category. From this, it can be concluded that consumers prefer sets with a battery capacity of 2000–3000 mAH and thus the mobile phone industry and other stakeholders must pay greater attention to battery capacity. The results of other studies (Dewenter et al., 2007; Lin & Chen, 2013; Montenegro & Torres, 2016; Mostafavi et al., 2013) also show that battery capacity/life is positively related with mobile prices. However, large battery capacity does not mean long battery life, as this depends on the usage of applications on the mobile sets (Pathak, Hu, & Zhang, 2012). Basically, factors such as voice communication, audio and video playback, web browsing, short message and email communication, gaming, etc. determine how long a mobile phone remains on a charge. The higher-capacity battery generally lasts longer than the lower-capacity battery but the battery capacity is not important if the user does frequent battery charging. Banerjee, Rahmati, Corner, Rollins, and Zhong (2007) have reported important findings in this regard. According to them, the majority of the users recharge their devices having a large percentage of charged battery remaining. For mobile phones, more than 50 percent of recharge occurs when the battery is more than 50 percent remaining, while 80 percent of the phone recharge occurs when the battery is more than 20 percent remaining. Similarly, charging by mobile users is driven by context and battery levels rather than the low battery levels. Further, results of phone user interviews show that 40 percent of users charge their phone once or twice a day even without looking at the battery indicator. Most of the phone users also do not carry the phone charger with them as they charge their sets at a single location. Even 28 percent mobile users when charge at a “low battery level”, the average remaining battery level is 40 percent. Operating systems play an important role in the price determination of smartphones. Smartphones offer different operating conditions and features depending on the type of operating system installed on them. Other things being equal, the Andriod system price was significantly higher while the IOS operating system showed a non-significant impact on price with reference to the base 8

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category, Symbian. The plausible explanation of this finding could be the availability of many applications for the Android operating system while Symbian has very few applications. The computing power and the screen resolution of the Android operating system are more than phones having the Symbian operating system (Malhotra, 2014). Further, Android is an open source operating system and it allows users to download, install third-party applications and these applications can be freely downloaded while with the IOS operating system, third-party applications do not work. Bala, Sharma, and Kaur (2015) concluded that Android is the best smartphone operating system and is superior to Symbian and IOS. Its market share is 59.15% compared to 23.51% for IOS and 1.70% for Symbian. Russell (2012) concluded that consumers in Malaysia mostly preferred Android (40%) followed by IOS (18%), BlackBerry (6%) and Windows (6%). RAM plays an important role in a mobile phone's response to user inputs as well as in running applications simultaneously. Other things being equal, phone prices were significantly higher if the RAM of a handset is more than one GB compared to handsets with RAM up to one GB. Mobile phones with RAM more than one GB exhibit price premiums of 24.67 percent when compared to base category mobile sets. Thus buyers prefer the purchase of mobile phone with RAM of more than 1 GB, which is probably due to its ability to perform simultaneous tasks on smartphones and efficient functioning of the mobile phone. The results of the study show that mobile phones with a memory size of more than 8 GB but less than 16 GB, and 16 GB & above have a significant positive effect on phone prices compared with the base category and received a premium of 14.25 percent and 66.87 percent, respectively. The results obtained in this study are consistent with the findings of Mostafavi et al. (2013), and Montenegro and Torres (2016). They confirm that other things being equal, memory size had a significant influence on mobile phone prices. The users are willing to pay a premium for higher memory due to its ability to store more data such as text, games, photos, videos, and music on the handset. The higher memory of mobile is also required for installing applications on the phone from a large and diversified set of mobile applications. Further, mobile phone applications are more data-centric and require smartphones to be equipped with larger and faster memory system (Duan, Bi, & Gniady, 2011). Better memory capacity is also required to meet the requirements of the latest mobile phone software and applications. Phones are available in a wide variety of display sizes. A larger display size of the mobile phone means that one can see and type better, although a larger size may make it difficult to fit in one's pocket. In the case of a smaller display size, one faces difficulty in seeing small text. In this study, handsets were classified into three groups on the basis of screen size i.e. small (display size is up to three inches), medium (display size is more than 3 but up to 5.0 inches) and large (display size is over 5 inches). The results show that the coefficient of a medium size screen phone is positive but non-significant compared with the base category i.e. small screen phone. However, the large size screen has a significant positive effect on phone prices as compared to the base category. Large size screen phones received a premium of 39.96 percent compared to small size screen phones. There could be various reasons for the preference of large sized screen phones. Firstly, these phones are great for reading e-books, watching videos and running more applications side by side. Secondly, they are helpful in better typing. Thirdly, mobile users who want to use their device for seeking information i.e. internet browsing, are more efficient if they have a device with a large screen size. Fourthly, the mobile users having large screen size enjoyed more when they use the device to play games. Our research results are supported by various previous studies. For example, Raptis, Tselios, Kjeldskov, and Skov (2013) reported the significant effect of screen size on efficiency especially for tasks that are not easy and require the significant amount of low level interactions such as scrolling. Jones, Buchanan, and Thimbleby (2003) also reported that internet searching tasks are faster on larger screen mobiles. A phone's network mode is an important determinant of its price. Mobile phones coming to the markets in Pakistan have a network mode of up to 4G to utilize the wireless network. Other things being equal, the phone price was significantly higher if the mobile had a network mode of 4G and 3G compared to the handset with a network mode of up to 2G. Thus, users prefer 4G and 3G network services over up to 2G network services. Mobile phones with a network mode of 4G enjoyed a higher price premium of 66.42 percent compared to 14.40 percent for 3G. These results can be explained in terms of services and the speed at which the services are provided to the users under various network modes. For 1G and 2G network modes, maximum bandwidth is 9.6 kB/s and these systems are used for voice transmission and data services like picture messages and MMS. In 3G, systems are designed for voice, video calling and mobile internet. The data transmission speed ranges from 384 kB/s to 2 MB/s. 4G mobile communication systems provide high quality voice, high definition videos and high speed data transfer. The speed of data transmission on a 4G network ranges between 100 MB/s to 1 GB/s (Krishna, 2011). Megapixels (MP) are used as a rough measure of quality photographs. More pixels mean better clarity, zooming, and cropping. Mobile phone users prefer camera features that can deliver high-quality images and videos. It may be noted that people use mobile phone cameras to capture special moments instantly and show those events to friends and families via email or post them on social and image sharing sites. The results show that if the back camera of the mobile phone is over 15 MP, the estimated coefficient is positive and significant. It enjoys a premium of 23.53 percent than a phone with a camera up to five MP. However, it was observed that the coefficient for a camera of more than 5 but up to 15 MP was positive and insignificant. From this, one can conclude that buyers value a back camera of over 15 MP on the mobile phone. Further, the estimated coefficient of the front camera is positive and highly significant. The premium associated with a front camera is 86.81 percent than a similar phone without this characteristic. In fact, a front camera is highly valued by the consumers due to its usage for selfies. According to Ling, Hwang, and Salvendy (2006) camera is one of the five characteristics of the mobile phone that is preferred by consumers. Many mobile users, especially in the middle age group are more interested in taking photos and they always value a good quality camera on the basis of the MP count. The camera allows users to capture and construct personal and group memory, maintain social relations and express their identity (Gye, 2007). Finally, a mobile phone with a FM radio enjoys a premium price of 80.12 percent than a similar phone without this feature. This may be due to the fact that FM radio helps users to listen music and is the best way to know the latest news.

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4. Conclusions and policy implications This study has estimated the implicit retail prices for the mobile phone attributes by using the hedonic pricing approach. The combinations of mobile attributes lead to many heterogeneous phones with different prices. Hedonic pricing models are quite helpful in estimating the implicit prices of the characteristics of heterogeneous products such as a mobile phone. The estimated model explained about 92 percent of price variability across mobile phones. To capture the effect of mobile phone attributes on its prices, dummy variables were incorporated in the model and their effects on price have been estimated by using the framework proposed by Kennedy (1981). Given the rapid technological changes in the mobile phone industry, future telecommunication policies should place more emphasis on the preferences of users. The estimation of implicit prices of mobile characteristics is important for the industry to identify consumers’ preferences and to produce mobile phones with the set of attributes valued by consumers. It is useful for policymakers to incorporate consumer preferences and technical advances into communication policies. The results of the study showed the positive brand effect on price for some brands. It suggests that manufacturers/importers should place emphasis on the brand name during the marketing of their phones. Further, brand managers can draw a good loyalty program for their users in order to retain/improve their market share. The result obtained for weight indicated that the estimated coefficient is positive and significant. In principle, mobile phone attributes such as the battery, casing, etc. add to the weight. In order to maintain a reasonable weight of handset, manufacturers should use thermoplastic resin (Lee & Oh, 2010) and other light materials in batteries. Similarly, mobile phone producers should use aluminum and the thermoplastic resin in its casing, a strategy being followed by iPhone (Montenegro & Torres, 2016). Low battery capacity of mobile phones may be a great inconvenience for some users. Therefore, mobile phone manufacturers (like Samsung and Apple) can emphasize wireless charging technologies in order to ensure automatic charging. This is important for those mobile users that require all-day power and do not have time to charge them. Another possibility is the replacement of current Li-ion battery technology in devices with promising Sodium-ion battery which has the ability to discharge to zero percent without damaging the active materials. Large size screen mobile phones received a premium as compared to small size screen phones. Results of a survey by Business Wire (2012) also indicated that more than 90 percent of consumers seek larger screen than those on the device they currently use and the bigger screen is required for better browsing, watching videos, video conferencing, etc. This result is important for all the players involved in the supply chain and they must keep in view the large screen while developing new strategies for mobile phone. Mobile phone prices were significantly higher if the RAM of a handset is more than 1 GB. Basically, consumers require more RAM in the device to park more applications and multimedia tasks, so manufacturers are required to meet their demand by increasing the RAM of mobile phones. An increase in memory size has a significant positive effect on mobile prices. It is expected that in future resolutions will get higher, multimedia will become richer and cameras will get better in the mobile phones. For this reasons, the manufacturer should employ a better, faster and more sophisticated memory and storage system in mobile devices to meet the expected future demand of consumers. As mobile phones with network mode of 4G enjoyed the highest premium so mobile phone manufacturers should produce 4G enabled devices keeping in view 5G developments in order to be competitive in the market and to increase market share. The regression coefficients for the back camera and front camera are positive and significant. It suggests that manufacturers should focus on the good quality-image camera to make consumers to buy their products. Further, manufacturers need to incorporate a dual camera on their phones to meet the demand of consumers. The consumer would love to have access to radio and this feature enjoys a premium price. It suggests that manufacturers should provide radio on their mobile phone to cater the premium price. One major limitation of this study is its confinement to only one developing country, Pakistan. Since developing countries comprising of low and middle-income economies share about 84 percent of the global population and there may be differences in consumers utility from mobile phone attributes because of differences in terms of per capita income, educational level, health status, etc., therefore, more studies on the pricing of mobile phone attributes need to be done in other developing countries. Similarly, mobile phone companies should understand the strategic importance of consumers’ preferences and they must adopt customized production and marketing mix strategies to influence the purchase of mobile phone among consumers. They can design and offer different bundles of mobile phone attributes (i.e. product mix) based on their importance to the different consumers in order to generate revenue. Since consumers around the world vary tremendously in social, demographic, cultural and psychological characteristics, therefore, identification of mobile phone attribute requirements across homogeneous subsets of customer groups (e.g. low, middle and high income) in local and international markets should be done in future studies. Further, there is a need to apply other methods like Artificial Neural Network to study the impact of mobile phone features on its price. Acknowledgements The authors thank and appreciate four anonymous reviewers for their comments on the original manuscript for its improvement. We also acknowledge the funds provided by the Higher Education Commission, Pakistan for this study. References Ahmad, W., & Anders, S. (2012). The value of brand and convenience attributes in highly processed food products. Canadian Journal of Agricultural Economics, 60, 113–133. Ali, M. (2016). Mobile phone users spent Rs. 98 bln in one year on 3G/4G: PTA. 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