Characterizing the Technological Evolution of : Insights from Performance Benchmarks

Qiwei Han Daegon Cho Department of Engineering and Public Policy College of Business Carnegie Mellon University KAIST Pittsburgh PA 15213 Seoul, Korea 02455 [email protected] [email protected]

ABSTRACT by advanced computing capability and network connectiv- Recent technological advancements in have paved ity [25]. In essence, the accelerated convergence of mobile the way for the rapidly growing . As smart- telephony, personal computing and services leads to phone vendors launch the products with a rich variety of the emergence of multi-sided technological and commercial technical features for different end-user market segments, platforms that involve interdependent stakeholders, includ- understanding the evolution of these features is of vital im- ing chipset makers and component suppliers, smartphone portance to all stakeholders in the smartphone industry. We vendors, mobile network operators (MNOs), mobile OS and address this issue by exploring technical specifications of application developers [7]. These stakeholders together con- smartphones at both the feature and the device level. In par- tribute complementary innovations and integrate both hard- ticular, we introduce the benchmarks to operationalize the ware and artifacts into smartphones that provide overall performance of smartphone models, such that multi- users with “over-the-top” services, such as Internet brows- dimensional technical features can be quantitatively summa- ing, video streaming, online gaming, etc. [24]. Meanwhile, rized into a single index. Through the analysis of a compre- the smartphone industry has continuously witnessed that hensive dataset entailing technical features for smartphone new entrants such as Apple and Samsung outcompete the models launched during the years 2012-2015, we show that incumbents for their superior product development and de- although certain features have become the standard func- sign strategies [14, 31]. This phenomenon leads to both the tionality, the smartphone industry is largely innovative and proliferation of new phone models and high variations of continues to evolve over time. We believe our findings may technical features among heterogeneous manufacturers, im- provide important insights into the future development and plying that product differentiation still characterizes this in- design strategies of smartphones. novative 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 CCS Concepts response to the ever increasing performance demanded by •Information systems → Data analytics; •Human- the market than merely imitating from competitors [27]. centered computing → Smartphones; 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. Keywords MNOs are challenged by the declining voice and SMS usage Smartphone; Mobile Technology; Performance and substantial investment in handling network capacity due to the surge in mobile data traffic. As smartphone users have higher willingness to add to tariff 1. INTRODUCTION plans as add-on services, MNOs endeavor to close the rev- Smartphones nowadays have quickly replaced feature phones enue gap by inducing subscribers to adopt more advanced to become the dominant configuration for mobile handsets. smartphone models and transform tariff structures to be- Gartner estimates that smartphones account for 82 percent come more data-centric [29]. Moreover, mobile application of mobile handset shipment by the end of 2016 [13]. The developers heavily rely on the technical features embedded popularity of smartphones reflects the fast technological evo- in smartphones as enablers of their services [25]. For ex- lution of mobile handsets from communication devices with ample, the market potential for location-based mobile ap- fixed functionalities to general-purpose devices empowered plications would be limited without a large installed base of smartphones equipped with GPS sensors. Lastly, improve- Permission to make digital or hard copies of all or part of this work for personal or ment in smartphone features may increase consumer utility classroom use is granted without fee provided that copies are not made or distributed and in turn spur widespread adoption [33]. For example, for profit or commercial advantage and that copies bear this notice and the full cita- the early success of Apple’s iPhone can be attributed to the tion on the first page. Copyrights for components of this work owned by others than enhanced and touchscreen technology that pro- ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior specific permission vide its users with a compelling mobile Internet experience and/or a fee. Request permissions from [email protected]. and thus generate positive network effects [12, 37]. ICEC ’16 August 17–19, 2016, Suwon, Republic of Korea However, the increasingly sophisticated feature combina- c 2016 ACM. ISBN 978-1-4503-4222-3/16/08. . . $15.00 tions that smartphone vendors leverage to build the prod- DOI: http://dx.doi.org/10.1145/2971603.2971635 ucts for different end-user segments complicates our under- rapidly lost its market share to Apple and Android standings on how smartphone technologies evolve for the (e.g. Samsung). Moreover, the market share of top five following reasons. First, as smartphones typically contain a vendors dropped from 80% in 2008 to 47% in 2015, indi- rich variety of technical characteristics, the objective eval- cating that mobile handset market has become ever more uation of overall device performance is underexplored. For fragmented (Fig. 1b). Third, the market share by mo- example, tightly integrated technical specifications such as bile OS can further reflect the fundamental shift in market CPU frequency, memory size and power consumption are at dominance from Nokia’s and RIM’s Blackberry to play in partially affecting system performance of a smart- Android and iOS once developed around 2008 [31]. More phone. Second, comparative measurement of smartphone specifically, Android now represented over 80% of market models across different dimensions (e.g. mobile OS plat- share and together with iOS (16%) dominated the market, forms) remains elusive. Third, interactions between smart- leaving Microsoft’s Windows Phone (2%) far behind even phone’s hardware components and its built-in software fur- after its acquisition of Nokia (Fig. 1c). Fourth, the increas- ther plague the issue. A wide range of smartphone vendors ing availability of mobile applications (commonly known as that appropriate and customize the open standard Android apps) that users can download from app stores significantly platform may yield different usability even with similar hard- enriches the value of smartphone usage [17]. The annual ware configuration, due to the own proprietary design and downloads are predicted to exceed 220 billion in engineering process [31]. After all, the combinatorial nature 2016, of which nearly 14 billion is from paid apps (Fig. 1d). of smartphone related technologies cautions researchers to This fact presents exciting opportunities to understand the operationalize features beyond synergies between technical great potential of mobile commerce by exploring the behav- specifications [38]. ior of mobile app users [11, 21]. In this paper, we aim to characterize the recent techno- logical evolution of smartphones. We do so by exploring a 2.2 Development of Mobile Handset Features comprehensive dataset entailing technical specifications of The development of mobile technology features has been smartphone models launched during the years 2012-2015 at studied within the IS discipline since the feature phone era both the feature level and the device level. In particular, we [28]. Traditionally, new features are first introduced into introduce the benchmarks to operationalize the overall per- handset models and further upgraded to result in signifi- formance of smartphone models, such that multidimensional cant performance improvement. For example, the successive technical features can be quantitatively summarized into a generations of core mobile communication technologies (e.g. single index. To the best of our knowledge, this paper is the from to , etc.) tend to be consistently more preferred first attempt that leverages the benchmark to measure the over the predecessors [23]. However, features are more often performance of smart devices such as smartphones. added to the handsets as the complementary functionalities We organize the rest of paper as follows. Section 2 pro- that rarely define generational changes in product evolu- vides the overview on the smartphone market and related tion [26]. Also, handset manufacturers strategically choose work on the development of technique features incorporated among a set of features to launch products for differentiation into the . Section 3 describes the background purposes [34]. Thus Koski and Kretschmer (2007) identify of smartphone benchmarks. Section 4 explains the collected two distinct strategies for the development of mobile hand- dataset of smartphone models with technical specification sets as vertical innovation and horizontal innovation, respec- details. Section 5 demonstrates the technological evolution tively [26]. The former refers to the incremental improve- in terms of the technical feature as well as the overall per- ment to the existing technical features (e.g. battery life), formance. Section 6 discusses the managerial implication of i.e., the quality ranking for all end-users, whereas the latter our findings and future research. refers to the technical characteristics (e.g. ) val- ued by heterogeneous end-user segments differently. They 2. BACKGROUND AND RELATED WORK demonstrate that product innovations at feature level occur across both vertical and horizontal dimensions [27]. 2.1 Smartphone Market Overview The emergence of smartphones has redefined the competi- tive landscape of mobile handset industry through a series of The smartphone marketplace has experienced substantial technological developments in both the hardware and soft- growth and drastic changes since 2008 with the arrival of Ap- 1 ware components. In particular, a subset of the features as ple’s iPhone and smartphones based on ’s Android. the results of either vertical or horizontal innovations gains We compile the analyst reports from Gartner through 2008- the general acceptance as the standard functionality and 2015 to demonstrate the trends using four important market physical design [8, 32]. The trend of convergence in technical statistics as shown in Figure 1. First, the mobile handset features is even intensified as Google licensed the Android sales worldwide have been steadily growing. In particular, freely to attract a majority of smartphone vendors to enter smartphone sales increased by over 10 times, reaching 1.4 the market and launch the products with similar specifica- billion units and surpassed that of feature phones since 2013 tions [31]. More recently, smartphone market growth is ma- (Fig. 1a). Second, new entrants such as Apple, Google jorly driven by replacement demand as the penetration rates and Microsoft brought core strength from personal comput- approach saturation [13], and ones with higher technological ing and Internet industry to build mobile platforms differ- sophistication tend to be diffused through the market faster ent from incumbents [24]. The long-standing market leader and have longer unit lifetime [35]. Therefore, the industrial 1We do not aim to survey the early stage of smartphones dynamics and the heterogeneity of consumer prompt smart- as it is beyond our objective. Interested readers may refer phone vendors to continuously improve the quality and per- to e.g. [7, 8] for historical background of smartphone from formance of existing features and meanwhile introduce new PDA since 1990s. 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 ; (d) mobile downloads.

However, current literature only focuses on the existence and Bajari (2005) show that omitting CPU benchmark may of technical features incorporated in the handset models but cause a large bias in estimating the quality changes of per- fail to account for the variations in quality and performance sonal computers due to the unobserved characteristics [4]. of each feature [8, 25, 34, 35]. To capture the evolution of However, using the benchmark to operationalize the per- these features, researchers need to objectively measure the formance of smartphones remains challenging for its complex performance and allow the comparison across smartphone underlying synergies between heterogeneous components [10]. models. This salient observation leads us to link with an- In contrast to personal computers, smartphones have even other stream of literature that leverages the concept of the more unobserved performance characteristic (e.g. mobile ap- benchmark as the performance characteristic. plications usability) beyond technical specifications [22]. As such, performance measurement on application-architecture interactions should mirror interactive user activities, such 2.3 Performance Measurement for IT Prod- as web browsing, gaming and video streaming. Only recent ucts work by Riikonen et al. (2016) suggests that it would be The benchmark scores have been used by economists as re- salient to include performance-related variables into the fea- liable proxies for the relative quality of numerous complex IT ture set of smartphones to evaluate the technological sophis- artifacts, such as CPUs [6, 18, 19, 30], desktop computers [4, tication, but they do not provide any solid measures either 5] and computers [9]. In general, benchmark enables [35]. the direct measurement of product performance based on ac- tual computational workload tests on routine tasks obtained by a user rather than conventional input characteristics [6]. 3. SMARTPHONE BENCHMARKS For example, clock speed (GHz) cannot sufficiently gauge Similar to common practices in the computer industry, the computational power of CPU due to differences in chip smartphone benchmark evaluates smartphone performance architectures (e.g. non-homogeneity across CPU brands and through workload analysis on different models. Typically, all families). Thus, the use of benchmark may facilitate the per- users can easily install benchmarking tools on their smart- formance comparison between different CPU models [19]. phone as mobile apps. There have been various well-known Moreover, as the overall performance of computer systems benchmark apps available for download from mobile appli- is determined through the interaction of a combination of cation stores to measure both the system-level performance hardware and software components, the system-level bench- of device, such as AnTuTu [2], Basemark OS II [3], and spe- mark can quantitatively summarize multidimensional tech- cialized components, such as 3DMark and GFXBench for nical specifications into a single index [9]. In particular, graphic features [1, 16], Geekbench for processors [15] and such interactions are not easily measurable by accounting Browsermark for browsers [3]. for observed technical specifications independently. Benkard We decide to choose Basemark OS II (referred only as Basemark hereafter for the sake of brevity) as the exemplary – Fragmentation performance test is similar to vari- benchmark tool in this study for the following reasons: First, able file size performance test, but measures trans- it is supported by all three leading popular mobile platforms, fer rate inside a fragmented memory scenario. including Android, iOS and Windows Phone. Second, it tests different technical features of the device and produces • Graphics tests an objective overall score. Third, it closely cooperates with – Shader effect test displays several 2D/3D graphics numerous major players in embedded industry that ensures 2 inside the same scene and measures the GPU pixel its wide acceptance . In summary, Basemark allows easy processing speed. comparison of the overall device performance for almost all smartphone models across mobile platforms. Figure 2 shows – Rendering effect tests display 100 particles with the user interface and benchmark result obtained from a free one draw calls and renders the scene to a full high- version of its mobile app that runs on Samsung Galaxy S6. definition resolution offscreen buffer 100 times be- fore 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 ob- jects transformed. Transform functions use a quasi- random number as an argument. – HTML5 canvas test creates HTML graphic ob- jects 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 ob- Essentially, each group of tests produces a group score tained from Basemark OS II installed on Samsung (SGi, i = 1, 2, 3, 4) and then Overall Score (SO) is calcu- Q4 1/4 Galaxy S6. lated as: SO = ( i=1 SGi) . This reflects that overall system-level performance can be thought of as being multi- Basemark features a comprehensive suite of tests in four plicative in the performance of each aspect [9] and as being groups, including system, memory, graphics and web brows- proportional for comparison to other smartphone models. ing. Admittedly other benchmark tools may include differ- ent tests; we believe tests used by Basemark are represen- 4. DATA tative to capture important system-level performance. We We implement a web crawler and collect detailed informa- list the detailed score breakdown from each aspect below: tion about full technical specifications of smartphone models • System tests launched between July 2012 and July 2015 from GSM Arena [20]. GSM Arena is an independent website that compiles – Math test measures the CPU processing speed of mobile device information from manufacturers and conducts integers and floating points operations. editorial reviews on popular models since 2000. Our deci- – XML parsing test measures the CPU utilization sion to use GSM Arena for obtaining smartphone features is of parsing XML files. consistent with prior studies that collect phone specifications – CPU single core test measures how fast a sin- during the years 1992-2002 [26] and between January 2004 gle CPU core can perform image processing in and August 2012 [8] from the same source. We remove fea- 2048x2048 pixels, 32-bit image. ture phones (models without mobile OS) and tablets (models without cellular connectivity and screen size is larger than – CPU multi core test measures how fast all the 7 inches) from the crawled data and the resulting dataset CPU cores together can perform image processing contains 1,743 smartphone models released by 62 vendors. in 2048x2048 pixels, 32-bit image. As smartphones are expected to continuously improve over • Memory tests time, we follow the standard in the industry to organize the smartphones based on the quarterly launched date. Figure – Fixed file size performance test measures the read- 3 shows the number of new models based on mobile OS from ing/writing transfer rate (MB/s) to load/create 2012Q3 to 2015Q3. files in the internal device storage. We find that the Android clearly is the dominant mobile – Variable file size performance test measures the OS that 57 smartphone vendors choose to build their prod- reading/writing transfer rate (MB/s) to load/create ucts due to its relative openness, whereas Apple builds the files in the internal device storage. own proprietary mobile platform and only launches products once a year. This indicates that for most of the smartphone 2Basemark initiates a Benchmark Development Program with members from smartphone industry including AMD, vendors, they need to invest in the development of technical ARM, Broadcom, Digital Media Professionals, Imagination features that help differentiate their products once the mo- Technologies, Intel, Marvell, MediaTek, Microsoft, Nvidia, bile OS is commodified [31]. Table 1 shows relevant technical Renesas, Samsung, and Xiaomi. features in the dataset. Among all of the technical features, ones marked in italic have been used in over 95% of smartphone models, imply- ing 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 dy- namics that still undergo considerable improvement in the next section.

5. TECHNICAL FEATURE DYNAMICS 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 func- tionality or jump to the next level through the percent- age 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 bench- mark score to describe the system-level performance of each smartphone, such that the evolution of multidimensional Figure 3: Number of new smartphone models by technical features described in the first two aspects can be mobile OS from 2012Q3 to 2015Q3 easily understood using just a single index value. 5.1 Categorical Features Fig. 4a demonstrates the extent of several key service enabling features to be diffused among the launched smart- phone models during the period of study. Smartphone ven- dors increasingly build the phone models to support net- works as the infrastructure becomes more widespread. More specifically, the percentage of smartphone models that sup- port 4G network increases from 20% to over 50% in con- trast 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 smart- phone vendors can only add extra value through improving Table 1: Extracted smartphone features list the camera resolution. We also observe the increased use Feature Type Feature of smaller-sized SIM card (in terms of both Micro-SIM and Brand; launch date (quarter); price Nano-SIM) over time, whereas the diffusion of NFC and in- Main group3 frared sensor still remains low. Physical display size (inches); resolution We demonstrate the substitution of internal memory and Display (pixel-per-inch); multi-touch screen storage as shown in Fig 4b. As larger internal memory allows Network 4G LTE; Wi-Fi faster multi-task processing, 2GB RAM is gradually growing Dimensions, weight; SIM type in use and nearly catch up with the 1GB RAM. Also, we Body (mini/micro/nano) see clear substitution between 8GB and 16GB storage and Primary camera resolution (mega-pixels); 4GB storage, which reflect the demand for larger storage Camera secondary camera space for increased smartphone usage. However, the 32GB Manufacturer; multi-core (dual/quad storage has not widely diffused, indicating that the demand Chipset /octa) for larger physical storage cannot justify the extra cost yet. As multi-core CPUs generally boost the performance at Memory RAM size; internal memory size more efficient energy consumption, smartphone vendors tend Sound 3.5mm audio jack; loudspeaker to choose carefully the chipset that embeds with the more Bluetooth; infrared port; NFC; mi- Communication powerful CPU. From Fig 4c we find that the quad-core CPU croUSB; radio; GPS has quickly replaced the dual-core CPU as the standard con- Battery Capacity (mAh) figuration of the chipset during the studied period. We also Performance Benchmark score notice the emergence of octa-core CPU and expect it to sur- pass the dual-core CPU soon. 5.2 Continuous Features

3The 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 battery embedded in the smartphone in terms of camera aspects. The body dimension of mobile handsets has experi- resolution and battery capacity, respectively (Fig 4f). The enced substantial changes during the feature phone era, i.e., average camera resolution has increased from 6 mega-pixels the phone becomes smaller and lighter [27]. Interestingly, we to 9 mega-pixels, and the average battery capacity has in- observe that both the size and weight of smartphone models creased by over 500 mAh. Thus the improvement of the has increased during the studied period (Fig. 4d). In partic- listed two technical characteristics would be very important ular, the development of physical display makes the average to yield better image quality and longer battery life. size of touch screen increase from 4 inches to nearly 5 inches and average screen resolution is approaching 300 pixels per 5.3 Benchmark Performance inch, the industry standard beyond which the human eye can hardly recognize the difference (Fig. 4e). This is consis- As explained earlier, we use the Basemark benchmark tent with findings from [8] that smartphone vendors compete scores provided by GSM Arena instead of other sources (e.g. in the horizontal innovations such as physical design to dif- smartphone vendors) to represent the performance of each ferentiate the products. This results in the emergence of smartphone model, because the website performs an inde- smartphones with ever-larger screen (commonly known as pendent editorial review that helps us to avoid the so-called ), as the boundary between smartphone and tablet “bench-marketing” issue. However, as GSM Arena reviews a is less clear. subset of popular smartphone models since 2013Q3, we only Lastly, we show the technological evolution of camera and 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 al- industry is largely innovative and continues to evolve over most all leading smartphone models across popular mobile time, although certain features have emerged as the stan- platforms from major vendors. Therefore, we believe the dard functionality. dataset should suffice our needs to characterize the device Our use of benchmark may not only help capture the over- performance. all device performance, but also shed some light on future re- We first account for the smartphone performance by mo- search. As benchmark has been widely used to measure the bile platform (Android, iOS and Windows Phone) over time quality changes of high technology products with rich tech- in Fig. 5a. We clearly identify that Apple and other Android- nical characteristics in constructing the hedonic price index, based vendors have different product design strategies. More it may also be adapted to adjust the quality of smartphones specifically, although iPhone is launched once each year, it in hedonic regression for its even more complex synergies outperforms other contemporaneous competing models and between technical features [9, 10]. Moreover, benchmark al- maintains high performance except for 2013Q3 that Apple lows customers to easily grasp the smartphone performance released low cost iPhone 5c, which may help explain its mar- and compare across models without having to comprehend ket success. In contrast, Android-based smartphones cover all technical specification details. Thus learning how ob- a very wide range of performance from high-end models to jective benchmark score is linked with the user evaluation compete with Apple (e.g. Samsung Galaxy S series) to low- of smartphone performance would be important to under- end models that aim at developing country market. Win- stand perceived value and customer satisfaction (e.g. [36]). dows phones tend to have relatively lower performance com- Lastly, we believe that the benchmark can be generalized pared to other competing models, which may help explain to study the performance of other smart devices, such as its limited market share. tablets, wearable accessories, etc. in a similar fashion. 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]), 7. REFERENCES mid-end ([4:6]) and high-end ([7:9]) models. 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