# The Brand Effect: A Case Study in Taiwan Second-Hand Smartphone Market

Guan-Yuan Wang

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Guan-Yuan Wang. The Brand Effect: A Case Study in Taiwan Second-Hand Smartphone Market. Journal of Social and Economic Statistics, 2021, 10 (1-2), pp.30-42. 10.2478/jses-2021-0003. hal-05168448

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# THE BRAND EFFECT: A CASE STUDY IN TAIWAN SECOND-HAND SMARTPHONE MARKET

**Guan-Yuan WANG***

National Taiwan University, Taipei, Taiwan

## Abstract

Since the smartphone market is an oligopoly market structure, consumer purchase intention is usually driven by brand preference. This research analyses the customer-to-customer market of second-hand smartphones, pointing out how the brand factor affects the consumers' purchasing behaviour. It is found that the recovery value and life cycle of Apple smartphones are higher and longer than those of other brands. Moreover, the recovery value of other brand smartphones is significantly driven by the debut date of the Apple smartphones, implicitly forming a consumption cycle. In addition, through machine learning models, the predictability for the recovery value is able to reach 93.55%.

**Keywords:** Smartphone, Brand Value, Recovery Value, Consumer Purchase Intention

**JEL Classification:** C10, C53, L89, M20

<footnote>* Corresponding author, **Guan-Yuan Wang** – guanyuan.wnag.ntu@gmail.com</footnote>
# 1. Introduction

The nature of brand and branding dominates our economic and social world. A brand is not only regarded as one of the most valuable assets of a company, which represents the face of the company, but also establishes a connection among consumers and exists in people's mind. More specifically, a company creates an identity for the products by a recognizable logo, slogan, or mark that the public is able to easily associate with the company, which implicitly drives consumer purchase intention and draws prospective customers. Branding is the act of shaping our perceptions, is to build a psychological entity, and is immeasurably valuable to sway consumers' purchasing behaviour.

In many markets, the brand is the foremost factor in making products differentiate among others. To illustrate this, I note that the brand values of top global companies are more than five billion dollars, which is estimated by the Interbrand index*. This means a large part of the real economic value of these companies lies in their brands. Especially for the smartphone market, Apple and Samsung are the top two companies with high Interbrand index, ranking first and fifth position, respectively. Also, the smartphone market is an oligopoly market structure, and there might be complex interactive relationships among these few brands. It is of interest to analyse how the brand factor affects the market mechanism of smartphones and whether the brand factor prolongs the recovery value and life cycle.

This paper empirically shows that the brand factor has significant impacts on the smartphone market, affecting both used-price and using-period. I examine this cause and effect by using data from the largest Taiwanese customer-to-customer smartphone platform called *mobilesales*. Instead of estimating used-price, I define recovery value so as to compare different brand and smartphone models at the same price ratio level. I show that the recovery value of the Apple smartphone is much higher than other brand of smartphones and the life cycle of the Apple smartphone is longer than Samsung and HTC for about one year and two years, respectively.

Observing the transaction record, I find that every debut date of the disparate Apple smartphone models is key time points. In general, Apple smartphones are launched in September or October; therefore, transactions volume of all smartphones rapidly increase for around three months before the Apple's debut date and the recovery value of all smartphones dramatically decreases for around three months after the Apple debut date.

<footnote>* This index is measured by the Interbrand, a division of Omnico, specializing in brands and branding management. More specifically, the brand value for top five brands: Apple, Amazon, Microsoft, Google, and Samsung with $322,999m, $200,667m, $166,001m, $165,444m and $62,289m.</footnote>
I build machine learning models to predict recovery value including regression tree, random forest and nearest neighbour, and find that the brand factor has stronger predictive power than other factors. Compared with ordinary least squares (OLS) regression, the predictive accuracy (predicted R-squared) is improved by about 5%. In addition, regarding the importance of variables in these models, although machine learning models do not consider the brand factor as the most critical feature, it can enhance the prediction accuracy. The best model (random forest) is as high as 93.55%, indicating that the models are able to capture almost all possible second-hand smartphone states.

Analysing how brands affect the market is important, this is because consumer goods industries tend to be dominated by a small number of branded goods and brand preferences drive consumer demand (Bronnenberg, Dub'e, and Moorthy (2019)). An increasing number of studies investigates the economics of brand (Bronnenberg, Dub'e, and Gentzkow (2012), AlMakrami (2013), Baur and Nyström (2017), Bronnenberg and Dub'e (2017), and Baidya, Maity, and Ghose (2017)), showing that brand culture represents a phenomenon of implicitly guiding consumer purchase choices. In the contemporary market, this phenomenon is inevitable. For the relationships between brand effects and consumers' smartphone choices, Kim, Lee, and Lee (2020) use the mixed logit model to show that the brand is the most important attribute of smartphone and Apple is the strongest in brand loyalty. William, Muhammad and Abdul (2020) use multiple regression to analyse Apple smartphone users, finding that brand image has a positive and significant effect on purchase intention. Indriany (2013), Salmi Mohd Isa, Lai Kelly and Shaian Kiumarsi (2020), Yalcıntekin and Saygili (2020) use a regression model to show brand has a positive influence on the development of consumers' loyalty to smartphone brands.

I examine the research focusing on the second-hand smartphone market to check whether there exists another situation that does not relate to phone condition but affects consumer to buy a smartphone and to assess the environmental impacts of second-hand products so as to seek possible strategy to promote sustainable consumption. Wieser and Troeger (2017) prove that if a smartphone is malfunctioning, people would not fix the phone and feel obsolete. Instead, they will buy a new smartphone. Kim, Thurston, and Kwak (2012) and Makov et al. (2018) find that brand also has a significant impact on second-hand prices, showing that brand is the intangible property to promote consumption. It meets our expectation that brand has a high influence on customers while buying a second-hand phone. This is because brands carry a trust value for customers and it also represents the quality of trustable products (Louis and Lombart (2010), Hassan, Rafi, and Kazmi (2015), and Valette-Florence and Valette-Florence (2020)) and because brands establish a great image that helps customers associate with a high quality of products (Tse and Tan (2012)).

As recent machine learning literature for economics and econometrics is increasing, I view these supervised learning methods in the machine learning field as important for empirical
researches in economics, as discussed in Mullainathan and Spiess (2017) and Athey and Imbens (2019). Therefore, I use not only OLS regression but machine learning methods to predict recovery value.

The paper proceeds as follows. Section 2 describes the data and methods. Section 3 introduces the machine learning models. Section 4 shows the findings. Section 5 discusses our results.

## 2. Data

In order to estimate the influence of brand on the smartphone market, I collect data from the Taiwanese smartphone sales platform: PTT mobilesales, which is the biggest customer-to-customer second-hand smartphone market in Taiwan. The number of the total smartphone transaction records is 107459, I delete the missing data, and then divide the data into with-in-sample (70%) for training and out-of-sample data (30%) for testing. The data range from 2013 to 2020 for Apple, Samsung and HTC with the number of 78215, 16476 and 12768, respectively, because Apple, Samsung and HTC are the first three brands dominating market-share during these years. I define *Duration Day* is the period between the launch date of smartphones and the date when it was sold, and define *Recovery Value* as:

$$ \text{Recovery Value} = 100\% \times \frac{P_{\text{resale}}}{P_{\text{launch}}} \quad (1) $$

where the $P_{\text{resale}}$ is the resale price on the platform, the $P_{\text{launch}}$ is the Taiwan retail launch price. In this way, the second-hand price ratio is rescaled to the same level so as to compare across disparate smartphone models and brands.

On this sales platform, people can either buy or sell smartphones, and they are required to provide certain information to describe the status of smartphones. Hence, I can know whether the warranty of the smartphone expires, what the brand and model are, the date when people sell it and other buy/sales condition. In addition, because Apple launch smartphones in September or October, the use of other brand of smartphones would also be affected. Then, I use the *month* dummy variable to capture a seasonal effect. The chosen variables are presented in table no. 1.

<table><caption>Table no. 1: The Description of Chosen Variables</caption><thead><tr><th>Numeric Variable</th><th>Min</th><th>Max</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td>Recovery Value</td><td>0.06462</td><td>0.99998</td><td>0.562</td><td>0.269</td></tr><tr><td>Duration Day</td><td>1</td><td>2366</td><td>544.076</td><td>344.365</td></tr><tr><th>Dummy Variable</th><th colspan="4">Description</th></tr><tr><td>Expired</td><td colspan="4">1 binary indicator for warranty period. Warranty expired is the reference.</td></tr><tr><td>Buy/Sale Condition</td><td colspan="4">2 binary indicators for buy, sale, and uncertain. The uncertain is the reference.</td></tr></tbody></table>
<table><tr><td>Location</td><td>1 binary indicator for location (Taipei metropolitan area). Not in the Taipei metropolitan area is the reference.</td></tr><tr><td>Phone Condition</td><td>2 binary indicators for new, second hand, and uncertain. The uncertain is the reference.</td></tr><tr><td>Brand</td><td>2 binary indicators for Apple, HTC and Samsung. HTC is the reference.</td></tr><tr><td>Month</td><td>11 binary indicators for each month except for September. September is the reference.</td></tr></table>

### 3. Model

#### 3.1. Ordinary Least Square Linear Regression

I apply ordinary least square (OLS) linear regression on with-in-sample data for modelling recovery value of smartphones with their characteristics, including linear and quadratic forms of *Duration Day* to capture time trend. The following form of the OLS regression model is to estimate the effect of brand and to predict recovery value:

$$Y = \beta_0 + S\beta_s + \varepsilon \quad (2)$$

Where *Y* is a vector of *recovery values*, *S* is a matrix of attributes of smartphones, as in table no.1. The $\beta_0$ is the vector of the constant terms, $\beta_s$ is the vector of the corresponding parameters. OLS regression method is a powerful analysis that can simultaneously analyse multiple smartphone features to estimate which one has a stronger effect on the recovery value of smartphones. In addition, this is also a common analytical method for predicting out-of-sample data.

#### 3.2. Regression Trees

Regression trees (Classification And Regression Trees (1984)) has become a popular and effective method for estimating regression functions. The algorithm allocates the data into the first two partitions based on a threshold of the feature, minimizing the sum of the squared deviations from the mean in the two separate partitions. This process as binary recursive partitioning continues until each node reaches an optimal size. Before splitting data, the sum of the squared deviations is:

$$\sum_{i=1}^{N} (Y_i - \bar{Y})^2 \quad (3)$$

where $\bar{Y} = \frac{1}{N} \sum_{i=1}^{n} Y_i$. After splitting based on the k variable with the threshold c, the sum of the squared deviation is:

$$\sum_{i:X_{ik} \le c} (Y_i - \bar{Y}_{k,c,l})^2 + \sum_{i:X_{ik} > c} (Y_i - \bar{Y}_{k,c,r})^2 \quad (4)$$

where c is threshold, *l* and *r* denote left and right nodes, $\bar{Y}_{k,c,l} = \sum_{i:X_{ik} \le c} Y_i / \sum_{i:X_{ik} \le c} l$ and $\bar{Y}_{k,c,r} = \sum_{i:X_{ik} > c} Y_i / \sum_{i:X_{ik} > c} r$.
### 3.3. Random Forest

Random Forest (Breiman (2001)) combines regression tree and bootstrapping method, consisting of a large number of individual regression trees with a subset of data and features. Each individual tree in the random forest provides a prediction and the value with the most votes becomes the model's prediction.

### 3.4. Nearest Neighbours

An alternative effective way to predict value is to use the nearest neighbours algorithm (Cover and Hart (1967)). Given target observation in a feature space, this method selects the nearest neighbour to a target point based on the Euclidean distance, then compute the simple average within the space. Therefore, the importance of variables has not been defined. Despite the lack of interpretability, the algorithm does not need to calculate any specific parameters or values, and can still provide high accuracy.

## 4. Results

R version 3.6.1 in RStudio version 1.2.5019 were used for collecting data, analysis and visualization.

Before analysing the brand effect on the second-hand smartphone market, I visualize the relationship between the number of transaction and the recovery value of Apple flagship models (from iPhone 6 plus to iPhone 12 Pro Max) to illustrate the following findings. From figure no.1 (the dash lines represent the debut dates), I find that when Apple launches a new iPhone model, there is a surge of people selling their phone. The supply of second-hand phones increases, the price therefore decreases followed by the law of demand and the recovery value also decreases by the advent of the price drop. This phenomenon provides insight into the brand effect on second-hand smartphone.
<figure><img src="image_2.png" /><figcaption>Figure no.1: Recovery Value and Transaction Number for iPhone Flagship Models</figcaption></figure>

I then use the OLS regression method and observe that the recovery value of Apple smartphones is significantly higher than Samsung and HTC smartphones, as shown in table no.2 (The values represented in brackets are standard errors of the estimators). This result demonstrates that the smartphone brand has a significant and meaningful impact on the second-hand smartphone market. The value difference between Apple and other brands is around 16%. Controlling the month dummy variables, it can be observed that after the iPhone debut dates, each brand smartphones experience a decrease in recovery value until February, then follow a slight upward trend until the next debut date, forming a pricing pattern. Also, the place people sell or buy a smartphone does not matter the recovery value; the warranty of a smartphone expiring leads to recovery value drop; buying prices are generally higher than selling prices; the longer used-day of smartphones, the lower recovery value of smartphones.

<table><caption>Table no. 2: OLS Estimates of the Effect of Smartphone Attributes on Recovery Value</caption><thead><tr><th></th><th>Dependent Variable</th></tr></thead><tbody><tr><td></td><td>Recovery Value</td></tr><tr><td>Samsung</td><td>0.017***<br>(0.001)</td></tr><tr><td>Apple</td><td>0.176***<br>(0.001)</td></tr><tr><td>Expired</td><td>-0.017***<br>(0.001)</td></tr><tr><td>Buy</td><td>0.025***<br>(0.004)</td></tr><tr><td>Sale</td><td>0.014***<br>(0.004)</td></tr><tr><td>Location</td><td>-0.0005<br>(0.001)</td></tr><tr><td>New</td><td>0.053***<br>(0.001)</td></tr><tr><td>SecondHand</td><td>-0.008***<br>(0.001)</td></tr></tbody></table>
<table><thead><tr><th></th><th>Dependent Variable</th></tr></thead><tbody><tr><td>DurationDay</td><td>-0.001***<br>(0.000)<sup>1</sup></td></tr><tr><td>DurationDay<sup>2</sup></td><td>0.0000***<br>(0.000)<sup>1</sup></td></tr><tr><td>Jan</td><td>-0.017***<br>(0.002)</td></tr><tr><td>Feb</td><td>-0.010***<br>(0.002)</td></tr><tr><td>Mar</td><td>0.004***<br>(0.002)</td></tr><tr><td>Api</td><td>0.006***<br>(0.002)</td></tr><tr><td>May</td><td>0.012***<br>(0.002)</td></tr><tr><td>Jun</td><td>0.018***<br>(0.002)</td></tr><tr><td>Jul</td><td>0.029***<br>(0.002)</td></tr><tr><td>Aug</td><td>0.031***<br>(0.002)</td></tr><tr><td>Oct</td><td>-0.024***<br>(0.001)</td></tr><tr><td>Nov</td><td>-0.032***<br>(0.001)</td></tr><tr><td>Dec</td><td>-0.027***<br>(0.001)</td></tr><tr><td>Constant</td><td>0.777***<br>(0.004)</td></tr></tbody></table>

Observation: 75268

R²: 0.883

Adjusted R²: 0.883

Residual Std. Error: 0.091

F Statistic: 26981.190***

Note: *p<0.1; **p<0.05; ***p<0.01

1. The actual values are 1.943e-07 and -8.776e-04

As the recovery value of three brand smartphones is significantly different, I investigate whether the life cycle of the smartphones would be also disparate. From figure no.2, I fit the recovery values on the logarithm of duration day. I notice that Apple smartphones retain most of the recovery value among the three brands. Under the same level of recovery value, the life cycle of the Apple smartphones is longer than that of the Samsung and HTC smartphones for around one and two years, respectively.
<figure><img src="image_3.png" /><figcaption>Figure no.2: The Life Cycle of Three Brand Smartphones</figcaption></figure>

In predicting recovery values of smartphones, I use out-of-sample data to verify the performance of the model. Using predicted R-squared values to determine the prediction performance of the models, the traditional OLS regression method can well predict the recovery values. However, machine learning models can further improve the prediction accuracy for about 5%, and the best model, random forest, can reach the accuracy of 93.55%, as shown in table no.3. I then examine those variables in models to know which one has stronger predictive power helping us to predict value correctly. Comparing the importance of each variable†, I find these machine learning models do not consider the brand factor as the foremost feature but this factor indeed provides stronger predictive power than other factors.

<table><caption>Table no. 3: Prediction Results of Machine Learning Models</caption><thead><tr><td>Model</td><td>RMSE</td><td>R<sup>2</sup></td></tr></thead><tbody><tr><td>OLS regression</td><td>0.0912</td><td>0.8793</td></tr><tr><td>Regression Tree</td><td>0.0742</td><td>0.9216</td></tr><tr><td>Random Forest</td><td>0.0673</td><td>0.9355</td></tr><tr><td>Nearest Neighbours</td><td>0.0701</td><td>0.9303</td></tr></tbody></table>

<footnote>† The variable importance in different models is defined as: OLS—the absolute value of the t-statistic for each variable; Regression Tree—summation of the reduction in mean squared error attributed to each variable at each split; Random Forest—calculating the difference in mean squared error between the shuffled out-of-bag samples and the average overall trees, then averaging the effect of each variable obtained by regressing this difference on the shuffled variables.</footnote>
<figure><img src="image_4.png" /><figcaption>Figure no.3: The Importance of Variables in Different Machine Learning Models</figcaption></figure>

From the aspect of prediction, it is of interest to construct the models only including the variables with high predictive power. I therefore develop two regression models for comparison with the full model (including all the variables). One is just contained the *duration day* variable, and the other one includes *duration day* and *brand*. From table no.4, it can be seen that when only including *duration day* and *brand* two variables, the prediction accuracy of the model is almost the same as that of the full model. However, predictive ability not only needs to be concerned but also interpretability is a major purpose. Hence, adopting the full model is beneficial to explain the purchase pattern of consumers in the second-hand smartphone market.

<table><caption>Table no. 4: Prediction Results of OLS regression Models</caption><thead><tr><td>Model</td><td>RMSE</td><td>R<sup>2</sup></td></tr></thead><tbody><tr><td>OLS (Full)</td><td>0.0912</td><td>0.8793</td></tr><tr><td>OLS (DurationDay)</td><td>0.1182</td><td>0.8007</td></tr><tr><td>OLS (DurationDay + Brand)</td><td>0.0934</td><td>0.8756</td></tr></tbody></table>
5. Concluding Remarks

The intangible asset, brand, plays a pivotal role in shaping consumption. Particularly, the brand might be especially important for consumers in second-hand markets because the Apple brand retains most of the recovery value and prolong the life cycle compared to the other two brands, helping product reuse. The secondary market is the major venues for consumers seeking used-product, where the brand is a status signalling showing the basic quality and is prominent in identifying consumption patterns of second-hand products.

Intuitively, a prestigious brand can affect the economic lifespan and value in the market because the industry can strictly control its product line and directly face its customers so as to establish a good reputation. As a result, the risk of purchasing an uncertain product would be eliminated in the secondary market. Second-hand products align strongly with sustainable development, which the UN put emphasis on by proposing Sustainable Development Goals (SDGs). Prestigious industries may improve our sustainable environments, utilizing the power of the brand.

This research has several limitations. First, this analysis draws on the transaction of second-hand smartphones from the Taiwan customer-to-customer trade platform. Even though that platform is the biggest market for second-hand phones in Taiwan, it still cannot represent the whole smartphone market. In addition, I only include the first three market share brand and the number of the transaction record among the three brands is also quite unbalanced, although the apple actually accounts for the relatively bigger market share than the other two. Final, the retail price of smartphones is not static, it dynamically changes over time. There exist different pricing strategies to promote the sale of smartphones such as discounts, selling along with a phone contract and other business strategies. This therefore makes the definition of recovery value be slightly improper as I am based on the retail price at the debuted date to normalize the price.

On the other hand, this research can be used in developing a pricing strategy or helping people to estimate their second-hand smartphones. As far as the pricing strategy is concerned, because the location has nothing to do with smartphone prices, the company is more willing to charge different prices in different regions according to the average disposable income of the cities. In addition, since Apple usually launches smartphones in September or October, other companies may stagger the launch date to maximize profit. Estimating the second-hand price is beneficial for those with a tight budget, who can find the best time to buy and sell second-hand smartphones.

My findings focus on how the brand affects a product, highlighting the brand effect on prices and life cycle in terms of smartphones. Although I discuss the case of smartphones, it is of interest to analyse different markets such as the car, laptop and so on and so forth. Cross-
country comparisons would also be of interest to analyse how the brand influences consumers
behaviour and product.

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