Characterizing the Technological Evolution of
Smartphones: Insights from Performance Benchmarks
Qiwei Han
Daegon Cho
Department of Engineering and Public Policy
Carnegie Mellon University
Pittsburgh PA 15213
College of Business
KAIST
Seoul, Korea 02455
qiweih@cmu.edu
ABSTRACT
Recent technological advancements in smartphone have paved
the way for the rapidly growing mobile commerce. As smartphone vendors launch the products with a rich variety of
technical features for different end-user market segments,
understanding the evolution of these features is of vital importance to all stakeholders in the smartphone industry. We
address this issue by exploring technical specifications of
smartphones at both the feature and the device level. In particular, we introduce the benchmarks to operationalize the
overall performance of smartphone models, such that multidimensional technical features can be quantitatively summarized into a single index. Through the analysis of a comprehensive dataset entailing technical features for smartphone
models launched during the years 2012-2015, we show that
although certain features have become the standard functionality, the smartphone industry is largely innovative and
continues to evolve over time. We believe our findings may
provide important insights into the future development and
design strategies of smartphones.
CCS Concepts
•Information systems → Data analytics; •Humancentered computing → Smartphones;
Keywords
Smartphone; Mobile Technology; Performance
1.
INTRODUCTION
Smartphones nowadays have quickly replaced feature phones
to become the dominant configuration for mobile handsets.
Gartner estimates that smartphones account for 82 percent
of mobile handset shipment by the end of 2016 [13]. The
popularity of smartphones reflects the fast technological evolution of mobile handsets from communication devices with
fixed functionalities to general-purpose devices empowered
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DOI: http://dx.doi.org/10.1145/2971603.2971635
daegon.cho@kaist.ac.kr
by advanced computing capability and network connectivity [25]. In essence, the accelerated convergence of mobile
telephony, personal computing and Internet services leads to
the emergence of multi-sided technological and commercial
platforms that involve interdependent stakeholders, including chipset makers and component suppliers, smartphone
vendors, mobile network operators (MNOs), mobile OS and
application developers [7]. These stakeholders together contribute complementary innovations and integrate both hardware and software artifacts into smartphones that provide
users with “over-the-top” services, such as Internet browsing, video streaming, online gaming, etc. [24]. Meanwhile,
the smartphone industry has continuously witnessed that
new entrants such as Apple and Samsung outcompete the
incumbents for their superior product development and design strategies [14, 31]. This phenomenon leads to both the
proliferation of new phone models and high variations of
technical features among heterogeneous manufacturers, implying that product differentiation still characterizes this innovative and competitive market [8]. Therefore, smartphone
vendors have strong incentives to build the products at the
technological edge, because this may create more values in
response to the ever increasing performance demanded by
the market than merely imitating from competitors [27].
Characterizing the technological evolution of smartphones
along a set of features is also of vital importance to other
stakeholders in the wireless industry across the value chain.
MNOs are challenged by the declining voice and SMS usage
and substantial investment in handling network capacity due
to the surge in mobile data traffic. As smartphone users
have higher willingness to add mobile broadband to tariff
plans as add-on services, MNOs endeavor to close the revenue gap by inducing subscribers to adopt more advanced
smartphone models and transform tariff structures to become more data-centric [29]. Moreover, mobile application
developers heavily rely on the technical features embedded
in smartphones as enablers of their services [25]. For example, the market potential for location-based mobile applications would be limited without a large installed base of
smartphones equipped with GPS sensors. Lastly, improvement in smartphone features may increase consumer utility
and in turn spur widespread adoption [33]. For example,
the early success of Apple’s iPhone can be attributed to the
enhanced web browser and touchscreen technology that provide its users with a compelling mobile Internet experience
and thus generate positive network effects [12, 37].
However, the increasingly sophisticated feature combinations that smartphone vendors leverage to build the prod-
ucts for different end-user segments complicates our understandings on how smartphone technologies evolve for the
following reasons. First, as smartphones typically contain a
rich variety of technical characteristics, the objective evaluation of overall device performance is underexplored. For
example, tightly integrated technical specifications such as
CPU frequency, memory size and power consumption are at
play in partially affecting system performance of a smartphone. Second, comparative measurement of smartphone
models across different dimensions (e.g. mobile OS platforms) remains elusive. Third, interactions between smartphone’s hardware components and its built-in software further plague the issue. A wide range of smartphone vendors
that appropriate and customize the open standard Android
platform may yield different usability even with similar hardware configuration, due to the own proprietary design and
engineering process [31]. After all, the combinatorial nature
of smartphone related technologies cautions researchers to
operationalize features beyond synergies between technical
specifications [38].
In this paper, we aim to characterize the recent technological evolution of smartphones. We do so by exploring a
comprehensive dataset entailing technical specifications of
smartphone models launched during the years 2012-2015 at
both the feature level and the device level. In particular, we
introduce the benchmarks to operationalize the overall performance of smartphone models, such that multidimensional
technical features can be quantitatively summarized into a
single index. To the best of our knowledge, this paper is the
first attempt that leverages the benchmark to measure the
performance of smart devices such as smartphones.
We organize the rest of paper as follows. Section 2 provides the overview on the smartphone market and related
work on the development of technique features incorporated
into the mobile device. Section 3 describes the background
of smartphone benchmarks. Section 4 explains the collected
dataset of smartphone models with technical specification
details. Section 5 demonstrates the technological evolution
in terms of the technical feature as well as the overall performance. Section 6 discusses the managerial implication of
our findings and future research.
2.
2.1
BACKGROUND AND RELATED WORK
Smartphone Market Overview
The smartphone marketplace has experienced substantial
growth and drastic changes since 2008 with the arrival of Apple’s iPhone and smartphones based on Google’s Android.1
We compile the analyst reports from Gartner through 20082015 to demonstrate the trends using four important market
statistics as shown in Figure 1. First, the mobile handset
sales worldwide have been steadily growing. In particular,
smartphone sales increased by over 10 times, reaching 1.4
billion units and surpassed that of feature phones since 2013
(Fig. 1a). Second, new entrants such as Apple, Google
and Microsoft brought core strength from personal computing and Internet industry to build mobile platforms different from incumbents [24]. The long-standing market leader
1
We do not aim to survey the early stage of smartphones
as it is beyond our objective. Interested readers may refer
to e.g. [7, 8] for historical background of smartphone from
PDA since 1990s.
Nokia rapidly lost its market share to Apple and Android
(e.g. Samsung). Moreover, the market share of top five
vendors dropped from 80% in 2008 to 47% in 2015, indicating that mobile handset market has become ever more
fragmented (Fig. 1b). Third, the market share by mobile OS can further reflect the fundamental shift in market
dominance from Nokia’s Symbian and RIM’s Blackberry to
Android and iOS once developed around 2008 [31]. More
specifically, Android now represented over 80% of market
share and together with iOS (16%) dominated the market,
leaving Microsoft’s Windows Phone (2%) far behind even
after its acquisition of Nokia (Fig. 1c). Fourth, the increasing availability of mobile applications (commonly known as
apps) that users can download from app stores significantly
enriches the value of smartphone usage [17]. The annual
mobile app downloads are predicted to exceed 220 billion in
2016, of which nearly 14 billion is from paid apps (Fig. 1d).
This fact presents exciting opportunities to understand the
great potential of mobile commerce by exploring the behavior of mobile app users [11, 21].
2.2
Development of Mobile Handset Features
The development of mobile technology features has been
studied within the IS discipline since the feature phone era
[28]. Traditionally, new features are first introduced into
handset models and further upgraded to result in significant performance improvement. For example, the successive
generations of core mobile communication technologies (e.g.
from 2G to 3G, etc.) tend to be consistently more preferred
over the predecessors [23]. However, features are more often
added to the handsets as the complementary functionalities
that rarely define generational changes in product evolution [26]. Also, handset manufacturers strategically choose
among a set of features to launch products for differentiation
purposes [34]. Thus Koski and Kretschmer (2007) identify
two distinct strategies for the development of mobile handsets as vertical innovation and horizontal innovation, respectively [26]. The former refers to the incremental improvement to the existing technical features (e.g. battery life),
i.e., the quality ranking for all end-users, whereas the latter
refers to the technical characteristics (e.g. ringtones) valued by heterogeneous end-user segments differently. They
demonstrate that product innovations at feature level occur
across both vertical and horizontal dimensions [27].
The emergence of smartphones has redefined the competitive landscape of mobile handset industry through a series of
technological developments in both the hardware and software components. In particular, a subset of the features as
the results of either vertical or horizontal innovations gains
the general acceptance as the standard functionality and
physical design [8, 32]. The trend of convergence in technical
features is even intensified as Google licensed the Android
freely to attract a majority of smartphone vendors to enter
the market and launch the products with similar specifications [31]. More recently, smartphone market growth is majorly driven by replacement demand as the penetration rates
approach saturation [13], and ones with higher technological
sophistication tend to be diffused through the market faster
and have longer unit lifetime [35]. Therefore, the industrial
dynamics and the heterogeneity of consumer prompt smartphone vendors to continuously improve the quality and performance of existing features and meanwhile introduce new
features to serve market niches [8].
(a)
(b)
(c)
(d)
Figure 1: Global mobile handset market statistics through 2008-2015 synthesized from Gartner press releases:
(a) mobile handset sales; (b) mobile handset market share by vendor; (c) smartphone market share by
Operating System; (d) mobile app store downloads.
However, current literature only focuses on the existence
of technical features incorporated in the handset models but
fail to account for the variations in quality and performance
of each feature [8, 25, 34, 35]. To capture the evolution of
these features, researchers need to objectively measure the
performance and allow the comparison across smartphone
models. This salient observation leads us to link with another stream of literature that leverages the concept of the
benchmark as the performance characteristic.
2.3
Performance Measurement for IT Products
The benchmark scores have been used by economists as reliable proxies for the relative quality of numerous complex IT
artifacts, such as CPUs [6, 18, 19, 30], desktop computers [4,
5] and laptop computers [9]. In general, benchmark enables
the direct measurement of product performance based on actual computational workload tests on routine tasks obtained
by a user rather than conventional input characteristics [6].
For example, clock speed (GHz) cannot sufficiently gauge
the computational power of CPU due to differences in chip
architectures (e.g. non-homogeneity across CPU brands and
families). Thus, the use of benchmark may facilitate the performance comparison between different CPU models [19].
Moreover, as the overall performance of computer systems
is determined through the interaction of a combination of
hardware and software components, the system-level benchmark can quantitatively summarize multidimensional technical specifications into a single index [9]. In particular,
such interactions are not easily measurable by accounting
for observed technical specifications independently. Benkard
and Bajari (2005) show that omitting CPU benchmark may
cause a large bias in estimating the quality changes of personal computers due to the unobserved characteristics [4].
However, using the benchmark to operationalize the performance of smartphones remains challenging for its complex
underlying synergies between heterogeneous components [10].
In contrast to personal computers, smartphones have even
more unobserved performance characteristic (e.g. mobile applications usability) beyond technical specifications [22]. As
such, performance measurement on application-architecture
interactions should mirror interactive user activities, such
as web browsing, gaming and video streaming. Only recent
work by Riikonen et al. (2016) suggests that it would be
salient to include performance-related variables into the feature set of smartphones to evaluate the technological sophistication, but they do not provide any solid measures either
[35].
3.
SMARTPHONE BENCHMARKS
Similar to common practices in the computer industry,
smartphone benchmark evaluates smartphone performance
through workload analysis on different models. Typically, all
users can easily install benchmarking tools on their smartphone as mobile apps. There have been various well-known
benchmark apps available for download from mobile application stores to measure both the system-level performance
of device, such as AnTuTu [2], Basemark OS II [3], and specialized components, such as 3DMark and GFXBench for
graphic features [1, 16], Geekbench for processors [15] and
Browsermark for browsers [3].
We decide to choose Basemark OS II (referred only as
Basemark hereafter for the sake of brevity) as the exemplary
benchmark tool in this study for the following reasons: First,
it is supported by all three leading popular mobile platforms,
including Android, iOS and Windows Phone. Second, it
tests different technical features of the device and produces
an objective overall score. Third, it closely cooperates with
numerous major players in embedded industry that ensures
its wide acceptance2 . In summary, Basemark allows easy
comparison of the overall device performance for almost all
smartphone models across mobile platforms. Figure 2 shows
the user interface and benchmark result obtained from a free
version of its mobile app that runs on Samsung Galaxy S6.
– Fragmentation performance test is similar to variable file size performance test, but measures transfer rate inside a fragmented memory scenario.
• Graphics tests
– Shader effect test displays several 2D/3D graphics
inside the same scene and measures the GPU pixel
processing speed.
– Rendering effect tests display 100 particles with
one draw calls and renders the scene to a full highdefinition resolution offscreen buffer 100 times before being drawn onto the screen to measure GPU
vertex operations.
• Web browsing tests
– CSS 3D rendering test is based on several CSS3
3D transformations and measures number of objects transformed. Transform functions use a quasirandom number as an argument.
– HTML5 canvas test creates HTML graphic objects to measure rendering performance.
– CSS resize test emulates screen size change (e.g.
when orientation changes) by resizing multiple
objects inside one master container.
Figure 2: User interface and benchmark results obtained from Basemark OS II installed on Samsung
Galaxy S6.
Basemark features a comprehensive suite of tests in four
groups, including system, memory, graphics and web browsing. Admittedly other benchmark tools may include different tests; we believe tests used by Basemark are representative to capture important system-level performance. We
list the detailed score breakdown from each aspect below:
• System tests
– Math test measures the CPU processing speed of
integers and floating points operations.
– XML parsing test measures the CPU utilization
of parsing XML files.
– CPU single core test measures how fast a single CPU core can perform image processing in
2048x2048 pixels, 32-bit image.
– CPU multi core test measures how fast all the
CPU cores together can perform image processing
in 2048x2048 pixels, 32-bit image.
• Memory tests
– Fixed file size performance test measures the reading/writing transfer rate (MB/s) to load/create
files in the internal device storage.
– Variable file size performance test measures the
reading/writing transfer rate (MB/s) to load/create
files in the internal device storage.
2
Basemark initiates a Benchmark Development Program
with members from smartphone industry including AMD,
ARM, Broadcom, Digital Media Professionals, Imagination
Technologies, Intel, Marvell, MediaTek, Microsoft, Nvidia,
Renesas, Samsung, Qualcomm and Xiaomi.
Essentially, each group of tests produces a group score
(SGi , i = 1, 2, 3, 4) and then Overall Score (SO) is calcuQ
lated as: SO = ( 4i=1 SGi )1/4 . This reflects that overall
system-level performance can be thought of as being multiplicative in the performance of each aspect [9] and as being
proportional for comparison to other smartphone models.
4.
DATA
We implement a web crawler and collect detailed information about full technical specifications of smartphone models
launched between July 2012 and July 2015 from GSM Arena
[20]. GSM Arena is an independent website that compiles
mobile device information from manufacturers and conducts
editorial reviews on popular models since 2000. Our decision to use GSM Arena for obtaining smartphone features is
consistent with prior studies that collect phone specifications
during the years 1992-2002 [26] and between January 2004
and August 2012 [8] from the same source. We remove feature phones (models without mobile OS) and tablets (models
without cellular connectivity and screen size is larger than
7 inches) from the crawled data and the resulting dataset
contains 1,743 smartphone models released by 62 vendors.
As smartphones are expected to continuously improve over
time, we follow the standard in the industry to organize the
smartphones based on the quarterly launched date. Figure
3 shows the number of new models based on mobile OS from
2012Q3 to 2015Q3.
We find that the Android clearly is the dominant mobile
OS that 57 smartphone vendors choose to build their products due to its relative openness, whereas Apple builds the
own proprietary mobile platform and only launches products
once a year. This indicates that for most of the smartphone
vendors, they need to invest in the development of technical
features that help differentiate their products once the mobile OS is commodified [31]. Table 1 shows relevant technical
features in the dataset.
Among all of the technical features, ones marked in italic
have been used in over 95% of smartphone models, implying these features have become standard functionality that
smartphone vendors must include in their product design
strategies even before our study period. Thus we leave these
features out and alternatively focus on technical feature dynamics that still undergo considerable improvement in the
next section.
5.
Figure 3: Number of new smartphone models by
mobile OS from 2012Q3 to 2015Q3
We intend to explain the evolution of technical features
used in smartphone models mainly from three aspects. First,
we look at categorical features that either add new functionality or jump to the next level through the percentage of smartphones including each of these features (Fig.
4a-c). Second, we discuss continuous features that keep
evolving over time through the averaged value of each of
these features (Fig. 4d-f). Third, we introduce the benchmark score to describe the system-level performance of each
smartphone, such that the evolution of multidimensional
technical features described in the first two aspects can be
easily understood using just a single index value.
5.1
Table 1: Extracted smartphone features list
Feature Type Feature
Brand; launch date (quarter); price
Main
group3
Physical display size (inches); resolution
Display
(pixel-per-inch); multi-touch screen
4G LTE; Wi-Fi
Network
Dimensions,
weight;
SIM
type
Body
(mini/micro/nano)
Primary camera resolution (mega-pixels);
Camera
secondary camera
Manufacturer; multi-core (dual/quad
Chipset
/octa)
RAM size; internal memory size
Memory
3.5mm audio jack ; loudspeaker
Sound
Bluetooth; infrared port; NFC; miCommunication
croUSB ; radio; GPS
Capacity (mAh)
Battery
Benchmark score
Performance
TECHNICAL FEATURE DYNAMICS
Categorical Features
Fig. 4a demonstrates the extent of several key service
enabling features to be diffused among the launched smartphone models during the period of study. Smartphone vendors increasingly build the phone models to support 4G networks as the infrastructure becomes more widespread. More
specifically, the percentage of smartphone models that support 4G network increases from 20% to over 50% in contrast to the findings by Cecere et al. (2015) that only 6%
of smartphone models do so before 2012Q3 [8]. Meanwhile,
the secondary camera gains even more popularity, indicating
that dual-camera becomes the dominant design that smartphone vendors can only add extra value through improving
the camera resolution. We also observe the increased use
of smaller-sized SIM card (in terms of both Micro-SIM and
Nano-SIM) over time, whereas the diffusion of NFC and infrared sensor still remains low.
We demonstrate the substitution of internal memory and
storage as shown in Fig 4b. As larger internal memory allows
faster multi-task processing, 2GB RAM is gradually growing
in use and nearly catch up with the 1GB RAM. Also, we
see clear substitution between 8GB and 16GB storage and
4GB storage, which reflect the demand for larger storage
space for increased smartphone usage. However, the 32GB
storage has not widely diffused, indicating that the demand
for larger physical storage cannot justify the extra cost yet.
As multi-core CPUs generally boost the performance at
more efficient energy consumption, smartphone vendors tend
to choose carefully the chipset that embeds with the more
powerful CPU. From Fig 4c we find that the quad-core CPU
has quickly replaced the dual-core CPU as the standard configuration of the chipset during the studied period. We also
notice the emergence of octa-core CPU and expect it to surpass the dual-core CPU soon.
5.2
3
Continuous Features
The price group represents the price in a scale of 1-9 for the
comparison reference because smartphone price may change
dynamically and differ from countries.
(a)
(b)
(c)
(d)
(e)
(f)
Figure 4: Evolution of technical characteristics for smartphone models launched between 2012Q3 and 2015Q3:
(a) 4G LTE and accessories; (b) Internal storage and RAM; (c) Multi-core CPU; (d) Average height and
weight; (e) Display screen size and resolution; (f ) Camera resolution and battery capacity
We evaluate the physical design of smartphones from two
aspects. The body dimension of mobile handsets has experienced substantial changes during the feature phone era, i.e.,
the phone becomes smaller and lighter [27]. Interestingly, we
observe that both the size and weight of smartphone models
has increased during the studied period (Fig. 4d). In particular, the development of physical display makes the average
size of touch screen increase from 4 inches to nearly 5 inches
and average screen resolution is approaching 300 pixels per
inch, the industry standard beyond which the human eye
can hardly recognize the difference (Fig. 4e). This is consistent with findings from [8] that smartphone vendors compete
in the horizontal innovations such as physical design to differentiate the products. This results in the emergence of
smartphones with ever-larger screen (commonly known as
phablets), as the boundary between smartphone and tablet
is less clear.
Lastly, we show the technological evolution of camera and
battery embedded in the smartphone in terms of camera
resolution and battery capacity, respectively (Fig 4f). The
average camera resolution has increased from 6 mega-pixels
to 9 mega-pixels, and the average battery capacity has increased by over 500 mAh. Thus the improvement of the
listed two technical characteristics would be very important
to yield better image quality and longer battery life.
5.3
Benchmark Performance
As explained earlier, we use the Basemark benchmark
scores provided by GSM Arena instead of other sources (e.g.
smartphone vendors) to represent the performance of each
smartphone model, because the website performs an independent editorial review that helps us to avoid the so-called
“bench-marketing” issue. However, as GSM Arena reviews a
subset of popular smartphone models since 2013Q3, we only
retain 142 models with the benchmark score released by 22
smartphone vendors for our analysis. We note that despite
the limited size of this benchmark dataset, it features almost all leading smartphone models across popular mobile
platforms from major vendors. Therefore, we believe the
dataset should suffice our needs to characterize the device
performance.
We first account for the smartphone performance by mobile platform (Android, iOS and Windows Phone) over time
in Fig. 5a. We clearly identify that Apple and other Androidbased vendors have different product design strategies. More
specifically, although iPhone is launched once each year, it
outperforms other contemporaneous competing models and
maintains high performance except for 2013Q3 that Apple
released low cost iPhone 5c, which may help explain its market success. In contrast, Android-based smartphones cover
a very wide range of performance from high-end models to
compete with Apple (e.g. Samsung Galaxy S series) to lowend models that aim at developing country market. Windows phones tend to have relatively lower performance compared to other competing models, which may help explain
its limited market share.
Moreover, we examine smartphone performance by price
range in Fig. 5b. More specifically, based on the price group
information we categorize smartphones into low-end ([1:3]),
mid-end ([4:6]) and high-end ([7:9]) models. We show that
the device performance smartphone models can be very indicative of the market segment they belong to, i.e. the highend smartphone models tend to have higher performance
than mid-end smartphone models, which in turn are better than low-end smartphone models. This implies that the
benchmark performances may help proxy the relative quality of the smartphones without including a large variety of
technical features. Our findings also reveal product pricing strategies that may be insightful for understanding the
competitive landscape of smartphone marketplace. For example, Chinese vendor Xiaomi follows aggressive strategy
by launching ”monster performance” models with the much
lower price than competing models that have comparable
performance.
6.
DISCUSSIONS AND FUTURE RESEARCH
Through analysis of technical features incorporated into
the smartphone models, we have the following findings on recent industrial dynamics in the smartphone industry. First,
novel categorical features are in different stages of diffusion, implying that smartphone vendors might choose to
retrofit certain features to smartphone models in response
to market demand. For example, the need for taking selfportrait photos popularizes smartphones equipped with secondary cameras. Second, core hardware components, such
as CPU, RAM and storage still undergo significant improvement to support faster processing power, as more computing
resources are required for the increasing usage of sophisticated mobile apps. Third, smartphone screens have become
larger with a better resolution due to the advances in display technology. Fourth, more battery capacity may help to
ensure steady battery life given the growing power consumption. Fifth, performance benchmarks further provide us
with a holistic view of the technological evolution of smartphones. More specifically, smartphone models designed for
different end-user market segments exhibit distinct performance. During each quarter, high-end smartphones built at
the technological edge tend to achieve better performance
over those launched previously. Therefore, the smartphone
industry is largely innovative and continues to evolve over
time, although certain features have emerged as the standard functionality.
Our use of benchmark may not only help capture the overall device performance, but also shed some light on future research. As benchmark has been widely used to measure the
quality changes of high technology products with rich technical characteristics in constructing the hedonic price index,
it may also be adapted to adjust the quality of smartphones
in hedonic regression for its even more complex synergies
between technical features [9, 10]. Moreover, benchmark allows customers to easily grasp the smartphone performance
and compare across models without having to comprehend
all technical specification details. Thus learning how objective benchmark score is linked with the user evaluation
of smartphone performance would be important to understand perceived value and customer satisfaction (e.g. [36]).
Lastly, we believe that the benchmark can be generalized
to study the performance of other smart devices, such as
tablets, wearable accessories, etc. in a similar fashion.
7.
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