This study applied mathematical programming approach to investigate the brand efficiency of smartphone brands by collecting data of 2013–2015 from
According to the report by TrendForce (
Pandey and Nakra [
Liu and Liang [
To compare brand efficiency across multiple smartphone brands, a common metafrontier was defined as the boundary of an unrestricted technology set. Group frontiers were also defined as the boundaries of restricted technology sets, with restrictions derived from a lack of economic infrastructure or other characteristics of the production environment. Importantly, the metafrontier envelops the group frontiers (thus, the metafrontier is related to the concept of the metaproduction function defined by Hayami and Ruttan (1971): “the metaproduction function can be regarded as the envelope of commonly conceived neoclassical production functions” (p. 82)). Thus, efficiencies measured relative to the metafrontier can be decomposed into two components: one that measures the distance from an input-output point to the group frontier and the other that measures the distance between the group frontier and the metafrontier.
To determine brand efficiency, this study first employed data envelopment analysis (DEA) [
The objectives of this study are listed as follows. First, this study compares the function performance in different smartphone brands and explores their strengths and weakness. The second objective is to evaluate the brand efficiency of smartphone by using the mathematical programming approach in DEA. The relationship between smartphone brand efficiency and telecommunication operators was also explored. Finally, the metafrontier concept firstly was employed to measure the technology gap ratio of various smartphone manufacturers and provide a reference for decision makers.
The remainder of this paper is organized as follows. Section
Tellis and Wernerfelt [
Chaudhuri and Holbrook [
Jiang and Balasubramanian [
Lee et al. [
Because of the growth and competition in the smartphone industry, Yeh et al. [
To summarize, this study offers several important information including the following. Firstly, this study extends prior research to explore the function performance in different smartphone brands and to analyze their strengths and weakness. Secondly, this is the first study to use the mathematical programming approach to evaluate the brand efficiency of smartphone in the metafrontier concept.
Consumer Reports (the Consumer Reports Survey Research Department, a team of highly trained social scientists, surveys millions of consumers each year using state-of-the-art techniques; collecting feedback on a broad range of real-world experiences with products, services) is open and credible. Therefore, most scholars use this Consumer Reports for marketing research [
Input and output definitions.
Variable | Definition | |
---|---|---|
Input | Price | The product purchase price. The buyer owns the product after purchase, and the ownership is transferred from the seller to the buyer |
|
||
Output | Ease of use (Q1) | How easy is it is to access the phone’s various features and modes |
Messaging (Q2) | Assessing keyboard ergonomics, e-mail readability, attachment capabilities, and text-messaging features | |
Web browsing (Q3) | Assess browser capabilities | |
Display quality (Q4) | Representing overall picture quality, including pixel resolution, contrast under normal and bright lighting (outdoor use) conditions, and color accuracy | |
Voice quality (Q5) | Incorporating listening and talking in noisy and quiet settings while on a phone call | |
Phoning (Q6) | Considering the step-saving functions for making and receiving calls, including hands-free capabilities such as voice command and Bluetooth, speed dialing, ringer controls, call timers, and more. We also evaluated keypad readability under different lighting conditions | |
Battery life (Q7) | Representing tests under nominal cell-network signals, including battery consumption while performing tasks that involve voice, data, display, and other factors | |
Camera image quality (Q8) | Evaluating resolution, dynamic range, color accuracy, and visual noise | |
Camera video quality (Q9) | Judging recorded video images shot at the highest quality setting available | |
Portability (Q10) | Representing our judgment based on the ideal combination of size and weight |
Smartphone brands and models.
Brand | Year | Model |
---|---|---|
Apple | 2013 | iPhone 4S, iPhone 4, and iPhone 3GS |
2014 | iPhone 5, iPhone 4S, and iPhone 4 | |
2015 | iPhone 5S, iPhone 5C, and iPhone 4S | |
|
||
BlackBerry | 2013 | Torch 9810, Torch 9850, Bold 9900 4G, Bold 9780, and Bold 9930 |
2014 | Z10, Q10, Torch 9810, and Bold | |
2015 | Z10, Q10, Z30, and Bold 9930 | |
|
||
HTC | 2013 | One X, Vivid, Titan 2, Evo 4G LTE, Arrive, One S, Amaze 4G, My Touch 4G Slide, Radar 4G, and Trophy |
2014 | One, One X+, One VX, Windows Phone 8X, Windows First, Evo 4G LTE, 8XT, Driod DNA, and Windows Phone | |
2015 | One M8, One, One max, 8XT, One Remix, and Windows Phone 8X | |
|
||
LG | 2013 | Nitro HD, Viper, Optimus Elite, Spectrum, and Lucid |
2014 | Optimus G, Optimus G Pro, Optimus G, Optimus F3, Viper, Optimus Elite, Optimus L9, Spectrum 2, Lucid 2, and Intuition | |
2015 | G3, G2, G Pro, G Flex, Viper, Optimus L90, and Optimus F3Q | |
|
||
Motorola | 2013 | Atrix 2, XPRT, Droid Razr Maxx, Droid Razr, and Droid 4 |
2014 | Photon Q 4G LTE, Droid Razr Maxx HD, Droid Razr HD, Droid Razr M, and Droid 4 | |
2015 | Moto X, Droid Maxx, Droid Mini, Moto X, and Droid Razr M | |
|
||
Nokia | 2013 | Lumia 900 |
2014 | Lumia, Lumia 920, Lumia 925, Lumia 521, Lumia 928, and Lumia 822 | |
2015 | Lumia 1020, Lumia 1520, Lumia 925, Lumia 635, Lumia Icon, and Lumia 928 | |
|
||
Samsung | 2013 | Galaxy S 3, Galaxy S 2 Skyrocket, Galaxy Note, Galaxy S 2, Galaxy Exhilarate, Focus 2, Rugby Smart, Galaxy S2 Epic 4G Touch, Epic 4G, Conquer 4G, Replenish, Galaxy Blaze 4G, Galaxy Nexus, Srtatosphere, and Droid Charge |
2014 | Galaxy S4, Galaxy S4 Active, Galaxy S3, Galaxy Note |
|
2015 | Galaxy S5, Galaxy S5 Active, Galaxy S4, Galaxy S4 Active, Galaxy Note |
|
|
||
Sony | 2013 | Xperia ion and Xperia Play |
2014 | Xperia Z | |
2015 | Xperia Z1S |
Descriptive statistics (
Input | Output | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Price | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | |
Max. | 800 | 5 | 5 | 5 | 5 | 3 | 5 | 5 | 5 | 4 | 5 |
Min. | 500 | 3 | 4 | 2 | 3 | 2 | 2 | 2 | 1 | 1 | 1 |
Average | 587.75 | 4.73 | 4.63 | 4.54 | 4.67 | 2.81 | 4.05 | 3.63 | 3.47 | 2.98 | 3.90 |
SD | 86.11 | 0.48 | 0.48 | 0.72 | 0.54 | 0.39 | 0.56 | 0.75 | 0.80 | 0.69 | 0.86 |
A smartphone brand is defined as efficient if it provides the highest value per dollar spent for that set of function characteristics within a smartphone category. Since a smartphone’s efficiency is a complex phenomenon that cannot be characterized by just a single criterion, a number of studies have argued that a multifactor performance measurement model may be used [
Pearson’s correlations for inputs and outputs.
Price | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Price |
|
||||||||||
|
|||||||||||
Q1 |
|
|
|||||||||
|
|
||||||||||
Q2 |
|
|
|
||||||||
|
|
|
|||||||||
Q3 |
|
|
|
|
|||||||
|
|
|
|
||||||||
Q4 |
|
|
|
|
|
||||||
|
|
|
|
|
|||||||
Q5 |
|
|
|
|
|
|
|||||
|
|
|
|
|
|
||||||
Q6 |
|
|
|
|
|
|
|
||||
|
|
|
|
|
|
|
|||||
Q7 |
|
|
|
|
|
|
|
|
|||
|
|
|
|
|
|
|
|
||||
Q8 |
|
|
|
|
|
|
|
|
|
||
|
|
|
|
|
|
|
|
|
|||
Q9 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||
Q10 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
According to Battese and Rao [
Technical efficiencies and the metafrontier.
Figure
As explained by Cooper et al. [
The no-oriented SBM model evaluates the metaefficiency of the target
Equation (
Finally, this paper can obtain a measure of how close the group-
First, the performance analysis was conducted using the
Annual qualitative analysis of smartphone brands (
Year | Brand | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 |
---|---|---|---|---|---|---|---|---|---|---|---|
2013 | Apple |
|
4.00 | 3.14 | 4.86 |
|
4.00 | 3.00 | 3.29 |
|
|
2013 | BlackBerry | 3.80 | 4.20 | 3.20 | 4.80 | 2.60 | 3.80 | 3.00 | 2.80 | 2.60 | 4.40 |
2013 | HTC | 4.50 | 4.70 | 4.40 | 4.50 | 2.90 | 4.00 | 3.00 | 2.70 | 2.60 | 3.50 |
2013 | LG | 4.80 | 4.80 | 4.60 | 4.40 | 2.40 | 3.80 | 3.00 | 2.80 | 2.00 | 4.00 |
2013 | Motorola | 4.80 | 4.80 | 4.60 | 3.80 |
|
4.00 |
|
|
2.60 | 3.60 |
2013 | Nokia | 4.00 |
|
|
|
|
4.00 | 3.00 | 2.00 | 3.00 | 3.00 |
2013 | Samsung | 4.84 | 4.79 | 4.79 | 4.63 | 2.79 |
|
3.05 | 3.37 | 2.53 | 3.79 |
2013 | Sony | 4.00 | 4.00 | 4.00 | 4.50 | 2.50 | 4.00 | 3.50 | 3.50 | 2.50 | 3.50 |
|
4.47 | 4.54 | 4.22 | 4.56 | 2.77 | 3.99 | 3.14 | 3.03 | 2.71 | 3.83 | |
|
|||||||||||
2014 | Apple |
|
4.00 | 4.00 |
|
|
4.00 | 3.00 |
|
|
|
2014 | BlackBerry | 4.38 | 4.50 | 4.25 | 4.63 | 2.63 | 4.00 | 3.88 | 3.63 | 2.88 | 4.13 |
2014 | HTC | 4.82 | 4.91 |
|
4.91 | 2.64 | 4.00 |
|
3.18 | 2.73 | 4.00 |
2014 | LG | 4.90 | 4.80 | 4.70 | 4.80 | 2.80 | 3.70 | 3.90 | 3.20 | 2.30 | 3.90 |
2014 | Motorola |
|
|
|
4.80 |
|
4.00 | 3.60 | 4.00 | 3.00 | 3.80 |
2014 | Nokia | 4.83 |
|
|
|
|
4.00 | 4.00 | 4.00 | 2.67 | 3.50 |
2014 | Samsung | 4.95 | 4.84 |
|
4.79 | 2.84 |
|
3.95 | 4.11 | 3.00 | 3.58 |
2014 | Sony |
|
|
|
|
|
4.00 | 4.00 | 4.00 | 3.00 | 4.00 |
|
4.86 | 4.76 | 4.74 | 4.87 | 2.86 | 4.01 | 3.80 | 3.79 | 2.95 | 3.99 | |
|
|||||||||||
2015 | Apple |
|
4.00 | 3.89 |
|
|
4.00 | 3.00 | 3.89 |
|
|
2015 | BlackBerry | 4.00 | 4.43 | 4.00 | 4.57 | 2.71 | 3.86 | 3.86 | 3.57 | 2.86 | 4.00 |
2015 | HTC | 4.70 | 4.70 | 4.70 | 4.90 | 2.40 | 3.90 | 3.60 | 2.90 | 3.00 | 3.80 |
2015 | LG | 4.73 | 4.87 | 4.67 | 4.20 | 2.87 | 4.07 |
|
3.33 | 2.87 | 3.47 |
2015 | Motorola |
|
4.33 | 4.83 | 4.17 | 2.83 |
|
3.83 | 3.00 | 3.00 | 4.17 |
2015 | Nokia | 4.57 | 4.29 | 4.43 | 4.71 |
|
3.71 | 3.29 | 3.29 | 3.00 | 3.43 |
2015 | Samsung | 4.67 | 4.71 | 4.76 | 4.62 | 2.81 | 4.14 | 4.24 | 3.62 | 3.38 | 3.52 |
2015 | Sony | 4.00 |
|
|
|
|
4.00 | 4.00 |
|
|
4.00 |
|
4.58 | 4.54 | 4.53 | 4.65 | 2.83 | 3.98 | 3.76 | 3.45 | 3.26 | 3.92 |
Other brands exhibited high performance in some indicators each year, although there was no consistent trend. For example, HTC had the poorest performance among the eight smartphone brands in both 2013 and 2015 but performed well in Q3 (web browsing) and Q7 (battery life) in 2014. Finally, all brands attained scores lower than 3 in Q5 (voice quality), indicating that all smartphone brands should prioritize improving the voice quality of their products. In addition, these smartphone brands exhibited inconsistent results in Q6 (phoning), Q7 (battery life), and Q8 (camera image quality), indicating that these three features are highly competitive in the smartphone market (i.e., they exhibited an inability to maintain an advantage and were likely to require replacement).
In summary, Apple had relatively outstanding performance in all indicators compared with other brands, indicating that other brands should recognize Apple as a benchmark. Although Samsung’s flagship models generally had the highest overall scores for each year, Samsung’s overall brand performance was more widely dispersed because the company released more smartphone models than any other brands and could not focus on one or two products as Apple did.
A brand can be defined as efficient if it provides the highest value for money for a set of characteristics [
After using
First, Table
Smartphone brand decomposition results stratified by brands: summary.
Brand | Year | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
DEA-MF | DEA-K | DEA-MTR | ||||||||||
2013 | 2014 | 2015 | Mean | 2013 | 2014 | 2015 | Mean | 2013 | 2014 | 2015 | Mean | |
Apple | 0.881 | 0.963 | 0.967 | 0.937 | 0.893 | 0.963 | 0.967 | 0.941 | 0.985 |
|
|
0.995 |
BlackBerry | 0.768 | 0.852 | 0.834 | 0.818 | 0.894 | 0.982 | 0.954 | 0.943 | 0.862 | 0.868 | 0.876 | 0.869 |
HTC | 0.797 | 0.881 | 0.831 | 0.836 | 0.843 | 0.956 | 0.896 | 0.898 | 0.945 | 0.922 | 0.928 | 0.932 |
LG | 0.780 | 0.853 | 0.865 | 0.833 | 0.823 | 0.902 | 0.939 | 0.888 | 0.948 | 0.946 | 0.923 | 0.939 |
Motorola | 0.837 | 0.921 | 0.882 | 0.880 | 0.883 | 0.963 | 0.966 | 0.937 | 0.946 | 0.956 | 0.913 | 0.938 |
Nokia | 0.769 | 0.896 | 0.811 | 0.825 | 0.833 | 0.966 | 0.892 | 0.897 | 0.923 | 0.928 | 0.911 | 0.921 |
Samsung | 0.834 | 0.927 | 0.898 | 0.886 | 0.840 | 0.937 | 0.898 | 0.892 | 0.993 | 0.990 |
|
0.994 |
Sony | 0.788 | 0.930 | 0.952 | 0.890 | 0.833 | 1.000 | 1.000 | 0.944 | 0.947 | 0.930 | 0.952 | 0.943 |
Mean | 0.807 | 0.903 | 0.880 | 0.855 | 0.959 | 0.939 | 0.944 | 0.943 | 0.938 |
Second, this study further examined the synergy between telecommunications operators and smartphone manufacturers. The four major telecommunications operators were AT&T, Sprint, T-Mobile, and Verizon. Some operators did not carry certain smartphone brands (these are marked with an “X” in Table
Average DEA-MF of telecommunication operators in 2013–2015.
Brand | Operator | 2013 | 2014 | 2015 | Average | Kruskal–Wallis test | |
---|---|---|---|---|---|---|---|
Operator | Brand | ||||||
Apple | AT&T | 0.857 | 0.963 | 0.947 | 0.922 | 0.100 | 0.000 |
Sprint | 0.899 | 0.958 | 0.976 | 0.944 | |||
T-Mobile | X | 0.983 | 1.000 | 0.992 | |||
Verizon | 0.899 | 0.951 | 0.952 | 0.934 | |||
BlackBerry | AT&T | 0.828 | 0.875 | 0.870 | 0.858 | 0.215 | |
Sprint | 0.784 | X | 0.870 | 0.827 | |||
T-Mobile | 0.754 | 0.899 | X | 0.827 | |||
Verizon | 0.719 | 0.836 | 0.807 | 0.787 | |||
HTC | AT&T | 0.817 | 0.925 | 0.896 | 0.879 | 0.063 | |
Sprint | 0.725 | 0.841 | 0.789 | 0.785 | |||
T-Mobile | 0.848 | 0.876 | 0.896 | 0.873 | |||
Verizon | 0.674 | 0.835 | 0.822 | 0.777 | |||
LG | AT&T | 0.744 | 0.874 | 0.873 | 0.830 | 0.259 | |
Sprint | 0.761 | 0.856 | 0.841 | 0.819 | |||
T-Mobile | X | 1.000 | 0.889 | 0.945 | |||
Verizon | 0.803 | 0.787 | 0.840 | 0.810 | |||
Motorola | AT&T | 0.909 | X | 0.857 | 0.883 | 0.352 | |
Sprint | 0.909 | X | 0.857 | 0.883 | |||
T-Mobile | 0.619 | 0.870 | 0.857 | 0.782 | |||
Verizon | 0.891 | 0.933 | 0.895 | 0.906 | |||
Nokia | AT&T | 0.769 | 0.923 | 0.809 | 0.834 | 0.818 | |
Sprint | X | X | X | X | |||
T-Mobile | X | 0.885 | 0.745 | 0.815 | |||
Verizon | X | 0.880 | 0.850 | 0.865 | |||
Samsung | AT&T | 0.876 | 0.950 | 0.871 | 0.899 | 0.302 | |
Sprint | 0.755 | 0.924 | 0.891 | 0.857 | |||
T-Mobile | 0.899 | 0.950 | 0.939 | 0.929 | |||
Verizon | 0.804 | 0.882 | 0.988 | 0.891 | |||
Sony | AT&T | 0.857 | X | X | 0.857 | 0.259 | |
Sprint | X | X | X | X | |||
T-Mobile | X | 0.930 | 0.952 | 0.941 | |||
Verizon | 0.719 | X | X | 0.719 | |||
|
|||||||
Average | 0.805 | 0.903 | 0.881 |
To determine whether efficiency differs significantly among the telecommunication operators and brands, this study adopted the Kruskal–Wallis test. At the significance level of 5%, we observed no significant difference in the performance of telecommunication operators’ efficiency among the regions. However, a significant difference was observed in the performance of the economic efficiency among the brands. Table
To understand the technology gap between different brand smartphone frontiers and all brand smartphone frontier, the metafrontier concept was first-time employed to measure the technology gap of various smartphone manufacturers in this study. The DEA-MTR is the most crucial indicator for this evaluation. An increasing DEA-MTR leads to a reduction in the technology gap between the global frontier and group frontier.
Based on the aforementioned definition, a higher value indicates a lower DEA-MTR. Table
To understand whether smartphone brands affect market efficiency, this study analyzed a total of 200 observations of eight smartphone brands from 2013 to 2015. The main objective was to compare market efficiency according to smartphone brand and understand the variance by calculating the DEA-MTR of each group through the metafrontier concept. Recommendations are proposed for the alleviation of DEA-MTR and improvement for inefficient brands.
The study results yielded three findings. First, from the brand efficiency perspective, each year featured a different market leader, and most brands exhibited a consistent trend. However, some brands departed from the trend line, potentially causing variance in the data for a particular year. For example, Nokia diverged from the general trend because of its strategic policy. Second, from the DEA-MTR perspective, LG, Motorola, and Nokia exhibited a trend of annual changes different from other brands. Finally, for all four major US telecommunications operators, Apple was the most efficient brand. The second-most efficient brand was Samsung for AT&T and Sprint, Sony for T-Mobile, and Motorola for Verizon.
Samsung is an interesting case. The average DEA-MF score shows that Samsung ranked third among brands. This result proved that product diversified strategy is very successful. Samsung’s average DEA-K score ranked seventh among brands, which shows that the efficiency between functions and price in different model has a large gap. This indicates that Samsung adopted an effective product segmentation strategy. The average DEA-MTR score shows that Samsung ranked second. This points out that Samsung has an excellent level of production technology, second only to Apple. Overall, Samsung’s mobile phone department has an extremely good competitiveness.
Several suggestions are proposed for smartphone consumers and manufacturers. First, consumers may refer to the annual change in brand efficiency when they purchase smartphones and select efficient products according to future trends in brand efficiency. Second, the concepts of benchmark enterprises and annual changes in brand efficiency may serve as reference for technological improvement for smartphone manufacturers. The DEA-MTR indicator also identifies the variation of overall technology between brands by analyzing whether brands are annually trending toward or away from the general population. Trending toward the general population indicates gradual technological improvement annually, whereas trending away from the general population indicates gradual technological decline. Smartphone manufacturers with obsolete technologies may eventually be eliminated from the market.
To overcome the data collection limitations encountered in this study, recommendations are proposed for future research. First, the data obtained from
Finally, the brands selected in this study were the top brands in market share in each year; other brands were not considered. As mentioned in Section
The authors declare no conflicts of interest regarding the publication of this paper.