Determinants of customer loyalty in the Korean smartphone market: Moderating effects of usage characteristics

Accepted Manuscript Determinants of Customer Loyalty in the Korean Smartphone Market: Moderating Effects of Usage Characteristics Moon-Koo Kim, Siew Fan Wong, Younghoon Chang, Jong-Hyun Park PII: DOI: Reference:

S0736-5853(15)30047-2 http://dx.doi.org/10.1016/j.tele.2016.02.006 TELE 773

To appear in:

Telematics and Informatics

Received Date: Revised Date: Accepted Date:

19 October 2015 28 January 2016 21 February 2016

Please cite this article as: Kim, M-K., Wong, S.F., Chang, Y., Park, J-H., Determinants of Customer Loyalty in the Korean Smartphone Market: Moderating Effects of Usage Characteristics, Telematics and Informatics (2016), doi: http://dx.doi.org/10.1016/j.tele.2016.02.006

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: Determinants of Customer Loyalty in the Korean Smartphone Market: Moderating Effects of Usage Characteristics Journal name: Telematics and Informatics

Authors: Moon-Koo Kim Creative Future Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 291 Gajeong-ro, Yuseong-gu, Daejeon 305-700, South Korea Voice: +82-42-860-1182 Fax: +82-42-860-6504 E-mail: [email protected] Siew Fan Wong Department of Information Systems, Sunway University No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor, Malaysia Voice: +60-3-7491-8622 Ext. 7150 Fax: +60-3-5635-8633 Email: [email protected] Younghoon Chang

1

Division of Business and Management BNU-HKBU United International College 28 Jinfeng Road, Tangjiawan, Zhuhai, Guangdong Prov. 519085, China Voice: +86-756-362-0000 Fax: +86-756-362-0888 Email: [email protected] Jong-Hyun Park Creative Future Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 291 Gajeong-ro, Yuseong-gu, Daejeon 305-700, South Korea Voice: +82-42-860-1081 Fax: +82-42-860-6504 E-mail: [email protected]

1

Corresponding author

Moon-Koo Kim ([email protected]) is a principal researcher in the Creative Future Research Laboratory, Electronic and Telecommunications Research Institute (ETRI). He received his BA degree in Business Administration from Yonsei University, South Korea and his MA degree in Management from Korea Advanced Institute of Science and Technology (KAIST), South Korea. He is mainly interested in marketing strategies, forecasting and business modeling, competitiveness, and IT policy. His articles have appeared in Telecommunications Policy, ETRI Journal and as well as in many proceedings. One of his papers published in Telecommunications Policy is honored as the most-cited paper since his publication in 2004. Siew Fan Wong ([email protected]) is an associate professor in the Department of Computing and Information Systems at Sunway University, Malaysia. She received her PhD degree in MIS from the University of Houston, Texas. Her research includes IT outsourcing and organizational IT strategy. Her publications have appeared in journals such as Telematics & Informatics, Journal of Global Information Management, Information Development, and International Journal of Business Data Communications and Networking. Younghoon Chang ([email protected]) is an assistant professor in the Division of Business and Management at BNU-HKBU United International College, Zhuhai, China. He received his PhD degree in Business & Technology Management from Korea Advanced Institute of Science and Technology (KAIST), South Korea. Younghoon’s research interests include Information privacy, ICT4D, e-business, business analytics and IoT. His articles have appeared in Telematics and Informatics, Journal of Global Information Management, Behaviour and Information Technology, Telecommunications Policy, Industrial Management & Data Systems, and Information Development as well as in the proceedings of international conferences. Jong-Hyun Park ([email protected]) is a senior researcher in the Creative Future Research Laboratory, Electronic and Telecommunications Research Institute (ETRI). He received his BA degree in Economics from Korea University, South Korea and his MA degree in management from Korea Advanced Institute of Science and Technology (KAIST), South Korea, in 2000 and 2002, respectively. He is mainly interested in the management of

technology, IT consumer behavior, marketing strategies and business modeling, competitiveness, and IT policy.

Determinants of Customer Loyalty in the Korean Smartphone Market: Moderating Effects of Usage Characteristics

ABSTRACT Since the Korean smartphone market has reached a saturation state, device manufacturing companies are refocusing their resources and capabilities to enhance customer loyalty in order to retain existing customers and attract new customers. However, there are only few previous studies on customer loyalty of smartphones. The purposes of this study are to examine the effect of customer satisfaction and switching barriers on customer loyalty, and evaluate the moderating effects of usage characteristics in the Korean smartphone market. To test the research model, we collected data from 700 smartphone users via a face-to-face survey method. The results show that customer satisfaction and switching barriers (alternative attractiveness and switching cost) have significant impacts on customer loyalty. The device features (functions, usability, and design) and corporate factors (customer support and corporate image) significantly influence customer satisfaction. Usage characteristics (relationship length and usage experience) moderate some of the links in the research model.

Keywords: Customer loyalty, Smartphone, Customer satisfaction, Switching Barriers, Market Saturation

1. Introduction Korea is one of the fastest growing smartphone markets due to the proliferation of high speed 3G and 4G mobile communication networks, marketing activities by mobile service providers and device manufacturers, and innovative information and communications technology (ICT) environment. As of December 2014, more than 80% of Korea's population used smartphones (MISP, 2015). According to a report by KT economic and business research institute (2015), Korea has the fourth highest smartphone penetration rate in the world. However, the Korean smartphone market has reached a saturation state. The overall market growth has slowed down, and is dependent on upgrades and replacements of devices by existing users. On the average, device replacement has a short cycle of less than 20 months (KISDI, 2014). Accordingly, device manufacturers are changing their marketing strategies. Those with dominant market positions are making efforts to maintain their high market share with repeated purchases from their powerful customer base. Other companies also try to

win customers from these market-dominating manufacturers while trying to protect their own share at the same time. Retaining existing customers through increasing satisfaction and attracting dissatisfied customers of competitors are strategic necessities to companies in the maturity stage (Anderson and Zeithaml, 1984; Kim et al., 2004). Companies would gain enormous benefits and competitive advantage from customer loyalty, including reducing marketing and operational costs, increasing profit per customer, and building brand assets (Reichheld and Teal, 2001). Besides customer satisfaction, other factors within the industries also play important roles as the determinants of customer loyalty (Mittal and Kamakura, 2001). Therefore, it is crucial to understand customer loyalty in the smartphone market and identify the factors (including the determinants and the moderators) that affect their loyalty. Existing customer behavioral studies mainly focus on the adoption and use of smartphone in the market growth stage (e.g., Joo and Sang, 2013; Lee, 2014; Risselada et al., 2014). Some studies investigated critical success factors of smartphone diffusion from the perspectives of the market and the ecosystem (e.g., ArrudaFilho et al., 2010; West and Mace, 2010). There are also studies that investigate influential factors of customer satisfaction from the aspects of benefits and costs of smartphone use (Ha and Park, 2013) and examined the relationship between quality, satisfaction and loyalty of smartphones (Kim et al., 2015). However, only a few studies have investigated customer loyalty among smartphone users (Kim et al., 2015; Lin et al., 2015). Thus, more studies on customer loyalty in the smartphone market need to be conducted to better understand customer attitude and behavior. According to preceding studies, customer satisfaction and switching barriers are the core factors that affect customer loyalty (e.g., Lee et al., 2001). Specifically, switching barriers play an important role in forming customer loyalty especially in the saturation phase (Yi and Lee, 2005). Despite the importance of the topic, existing studies that investigate the effects of switching barriers on customer loyalty are rare with the context of the smartphone market. A few studies have examined the moderating effects of usage characteristics on the links of customer satisfaction and customer loyalty (e.g., Deng et al., 2010; Karjaluoto et al., 2012; Rodgers et al., 2005). However, they are not focusing on the smartphone context. Since it is likely that usage characteristics will affect the relationship between switching barriers and customer loyalty, it is valuable to conduct a study on the moderating effect of usage characteristics. Considering the gaps in previous research, the purposes of this study, therefore, are to investigate the effect

of customer satisfaction and switching barriers on customer loyalty of smartphones among Korean users, and identify the moderating effect of usage characteristics on the relationship among customer satisfaction, switching barriers and customer loyalty. In the process, it also studies two sets of antecedents to customer satisfaction.

2. Overview of the Korean smartphone market Korea is one of the countries with rapid smartphone penetration in the world (Strategy Analytics, 2013). Smartphones were first introduced in Korea in December 2009. Although the launch was a few years behind countries such as the USA and the UK, the introduction marked the beginning of rapid proliferation of smartphones in Korea. As shown in Fig. 1, the penetration rate of smartphones has jumped significantly from a mere 1.7% in December 2009 to 70.9% of the overall Korean mobile communications market in December 2014 (MISP, 2015). Also, in December 2014, the mobile and smartphone penetration rate in Korea has reached 113.5% and 80.4% respectively. Clearly, Korean consumers react passionately toward smartphones.

Fig. 1. Smartphone Subscribers in Korea Source: Ministry of Science, ICT, and Future Planning (MSIP, 2015)

Adequate high-speed mobile infrastructure and speedy migration to the next generation network play a major role in encouraging rapid smartphone penetration in Korea. Korean mobile service providers were also among the first to offer LTE (Long Term Evolution) services in July 2011 and LTE-Advanced (LTE-A) services in June 2013 (Kim et al., 2014). These 4G services allow the data to transmit faster from smartphones. Another factor that fuels rapid smartphone penetration in Korea is its innovative use of ICT. According to the ICT Development Index of the International Telecommunications Union (ITU, 2012), Korea is ranked top in all three areas of ICT access, use, and skills, which lead to higher receptiveness of new innovations such as smartphones among its consumers. Competitions among smartphone manufacturers are fierce as every party strives to

maximize its share of the lucrative market. They use various innovative marketing strategies to attract and retain customers. They also implement market segmentation strategies by offering different functions, performance, price, and design for each device type. The competition among manufactures benefits Korean consumers as they are able to buy smartphones at a relatively lower price.

3. Theoretical background and hypotheses This study intends to investigate the relationships among customer satisfaction, switching barriers, and customer loyalty in the Korean smartphone market. It also identifies the antecedents of customer satisfaction. Specifically, we chose device features and corporate factors as the antecedents of customer satisfaction. We selected these two factors because they are associated with customer satisfaction formed from usage behavior and customer attitude. We constructed switching barriers with subcomponents of alternative attractiveness and switching cost to capture both external comparison and internal obstacles a customer would face. In studying the moderating effects, we focused on the relationship length and usage experience as the moderators as these are related to customers’ evaluation and commitment on a device. Our proposed research model is shown in Fig. 2.

Fig. 2. Proposed research model

3.1. Device Features and Customer Satisfaction Satisfaction refers to the level of cognitive or affective evaluation on purchasing and using a product or service (Edvardsson et al., 2000; Eshghi et al., 2007; Johnson and Fornell, 1991). Customers feel satisfied when their demands are met. Consequently, they will continue to buy and use the same product or service (Parasuraman et al., 1994; Oliver 1997). When a customer feels satisfied, it could be the results of emotional response based on his/her experience of the purchase and use of the product/service, or the cognitive evaluation between the level of expectation and the actual experience (Babin and Griffin, 1998; Oliver, 1997). Customer satisfaction refers to the customers’ overall evaluation of a company. Customers’ satisfaction toward a smartphone manufacturer is greatly influenced by the level of satisfaction they have with the attributes of the device and their evaluation while using the devices (Ha and Park, 2013). Five important attributes of a smartphone are functions, usability, design, applications, and price. Functions refer to the functional or physical performance of a smartphone (Andrews et al., 2012; Deng, et al., 2010; Sweeney and Soutar, 2001). Usability refers to the ease of using, learning, and operating a smartphone (Bevan, 2001; Belanche et al., 2012; Lee et al., 2015; Oghuma et al., 2016; Tseng and Lo, 2011). Design is the aesthetic quality of a smartphone (Hong et al., 2008; Tan and Sie, 2015; Tsai, 2011), and applications refer to the nature of the applications available on a smartphone including the variety of choices, usefulness of the applications, and joy in playing these applications (Kim et al., 2015; Xu et al., 2015; Zhu et al., 2011). Price is the monetary amount one has to pay to purchase a smartphone (Gerpott et al., 2001; Kim et al., 2004; Tseng and Lo, 2011). All these attributes translate into values that will directly influence customer satisfaction toward the manufacturers. Functions, usability, design, and applications create utilitarian and hedonic values (Wu and Lu, 2013) while price translates into economic values (Zeithaml, 1988). The higher the values consumers perceive from each of these attributes, the more satisfied they are toward the particular smartphone manufacturer. Therefore, we hypothesize a smartphone device with better functions, ease of use, aesthetic design, useful applications, and lower price will draw higher customer satisfaction toward the manufacturer.

H1a. Device functions are positively related to customer satisfaction. H1b. Device usability is positively related to customer satisfaction. H1c. Device design is positively related to customer satisfaction. H1d. Device applications are positively related to customer satisfaction. H1e. Device price is positively related to customer satisfaction.

3.2. Corporate Factors and Customer Satisfaction In a competitive market, having good customer relationship management is important to retain customers. One way to maintain good customer relationship is to provide excellent and comprehensive customer support. Customer support is the way companies provide aids to customers when responding to customer needs or complaints (Kim and Yoon, 2004; Kim et al., 2004; Kim et al., 2015; Rigopoulou et al., 2008). Examples of customer support include giving product or service recovery as a part of after-sales service, being responsive to customer questions, and attending to customer requests promptly. Customers who receive good support from a company when they need help will have a more satisfactory feeling toward the company. Therefore, the better customers perceive the support they receive from a smartphone manufacturer, the more satisfied they are toward the company.

H1f. Customer support is positively related to customer satisfaction.

Corporate image refers to customers' set of beliefs toward the company (Andreassen and Lindestad, 1998; Kotler, 1997; Wang, 2010). Corporate image is related to the overall impression or experience on a company and is an influential factor of customer satisfaction (Calvo-Porral and Lévy-Mangin, 2015). In the smartphone market, corporate image defines emotional fondness consumers hold toward smartphone manufacturers. Corporate image has a positive effect on customer satisfaction (Aydin and Özer, 2005; Gerpott et al., 2001; Kim and Hyun, 2011; Lai et al., 2009; Lee, 2011). The better the image of a smartphone manufacturer, the more satisfied the customers are toward the company.

H1g. Corporate image is positively related to customer satisfaction.

3.3. Customer satisfaction and Customer Loyalty Customer loyalty refers to a consumer’s commitment to repurchase or continue using the products/services from a company (Dick and Basu, 1994; Oliver, 1997). Loyal customers are critical to companies because it costs less to keep existing customers than to attract new customers (Anderson and Mittal, 2000). Having loyal customers allows companies to exercise price premium. In addition, loyal customers tend to make credible

recommendations to the people around them. Customer satisfaction is one of the main antecedents of customer loyalty (Bhattacherjee and Premukumar, 2004; Jones et al., 2000; Kim et al., 2004). A customer who is satisfied with a smartphone manufacturer will tend to have higher loyalty toward that company.

H2. Customer satisfaction is positively related to customer loyalty.

3.4. Switching Barriers and Customer Loyalty Satisfaction with a company or a brand is not enough to explain customer loyalty (Kim et al., 2004). In fact, a satisfied customer may not be loyal to a company and may leave the company at any time (De Ruyter, et al., 1998). Switching barrier is another factor that might influence customer loyalty. Switching barriers refer to a series of factors that make it difficult or expensive for a customer to switch to another service provider (Jones et al., 2000). We explore two types of switching barriers in terms of external comparison and internal obstacle: alternative attractiveness and switching cost. Alternative attractiveness refers to a level at which competitors draw the attention and interest of existing customers (Jones et al., 2000). In the smartphone market, manufacturers compete to launch new devices. Each product targets different market segments and each newer version has more functions and appealing features than the previous ones. As smartphone choices that are available in the market increase, and as competitors offer better products and services, the attractiveness of competing manufacturers rises. This reduces the level of loyalty customers have toward their existing smartphone manufacturers. On the contrary, if other manufacturers are unable to provide different or better products and services, then customers will stay with their current providers (Kim et al., 2004; Ha and Park, 2013; Haenlein and Kaplan; 2012; Picón et al., 2014). Therefore, we propose, the higher the level of alternative attractiveness, the lower the level of customer loyalty.

H3a. Alternative attractiveness is negatively related to customer loyalty.

Switching cost refers to the price incurred when changing from one product/service to another (Kim et al., 2004). It includes monetary, psychological, and time sacrifices that result from discontinuation (Jones et al., 2002). When switching from one smartphone manufacturer to another, customers may have to learn a totally new system. Some may perceive the need to learn something that they are not familiar with a hassle and not

worthwhile. The more they see difficulties or troubles in learning to use new smartphones, the higher they will perceive the switching cost. In addition, as consumers move from one smartphone manufacturer to another, they have to incur monetary cost in purchasing new devices. The more expensive the new smartphones are, the higher the perceived switching cost. When the switching cost is high, customers will tend to stay loyal and remain with the current manufacturer (Burnham et al., 2003; Lin et al., 2015; Liu et al., 2011; Jung and Kwon, 2015; Picón et al., 2014). Therefore, the higher the switching cost, the higher the customer loyalty.

H3b. Switching cost is positively related to customer loyalty.

3.5. Moderating Effects of Usage Characteristics We proposed two moderators that will influence the relationship between customer satisfaction, switching barriers and customer loyalty in terms of a customer’s evaluation and commitment toward a device: relationship length and usage experience. We argued that these two moderators are likely to significantly strengthen and weaken some of the links between customer satisfaction, switching barriers, and customer loyalty because they are related to a customer’s cognitive and affective involvement with a device. Relationship length refers to how long users have been with the current smartphone manufacturer (Bell et al., 2005). It depends on a customer’s confidence or evaluation toward the manufacturer (Karjaluoto et al., 2012). When customers have higher evaluation toward a company, they will tend to form longer and lasting relationship with the company. In such a situation, the effect of customer satisfaction and switching barriers on customer loyalty will be strengthened. However, previous studies have not shown consistent results on the moderating effect of relationship length on customer satisfaction and their loyalty (Balaji, 2015; Deng et al., 2010; Seo et al., 2008; Wang and Wu, 2012). Also, there are only few studies that investigate the moderating effect of relationship length on the link between switching barriers and customer loyalty. Therefore, it is essential to investigate the role of relationship length as a moderator in a smartphone context.

H4a. Relationship length has a moderating effect on the relationship between customer satisfaction and customer royalty, as well as between switching barriers and customer loyalty.

Usage experience refers to how much a smartphone user uses the device (Rogers et al., 2005). It is associated with the relationship depth between a customer and a company (Aurier and N’Goala, 2010). When

customers have higher usage experience, they tend to have stronger commitment toward a company or a product. This will enhance the relationship depth. Therefore, usage experience is likely to affect the relationships among customer loyalty, customer satisfaction and switching barriers. A few studies have carried out the moderating effect of usage experience on the relationship between customer satisfaction and customer loyalty (Rogers et al., 2005). But, none has looked at the link between switching barriers and customer loyalty. As such, more empirical studies are required to further investigate the role of usage experience as a moderator.

H4b. Usage experience has a moderating effect on the relationship between customer satisfaction and customer loyalty, as well as between switching barriers and customer loyalty.

4. Research methodology

4.1. Measurement Development To examine the research model and the hypotheses, we adopted a survey research method. All measurement items for this study were adapted and modified from previous research. All items were measured using 7-point Likert scales (1-Strongly disagree and 7-strongly agree). Table 1 shows the measurement items along with the related literature. The functions of a smartphone are measured in terms of its functional performance, high performance, and stable quality (Anderson and Shrinivasan, 2003; Andrews et al., 2012; Deng et al., 2010). Usability is measured by ease of use, ease of learning the operating systems (OS), and ease of performing certain tasks (Tseng and Lo, 2011; Belanche et al., 2012). Design is measured based on the attractiveness of a smartphone, its visual appeal, and good-look (Cyr et al., 2006; Tsai, 2011). Applications are measured from the perspectives of varieties, usefulness, and entertainment nature of the applications available on a smartphone (Zhu et al., 2011). Price measures consumers’ acceptance toward the price of a smartphone, the worthiness of the device, and their willingness to pay the price (Chen and Dubinsky, 2003; Tseng and Lo, 2011). Customer support is measured based on a manufacturer’s response to complaints as well as adequacy and speed of after-sales services (Kim et al., 2004). Corporate image measures the reliability, innovative quality, and value of the manufacturer (Aydin and Özer, 2005; Bayraktar et al., 2012; Kim and Hyun, 2011). Alternative attractiveness is measured in terms of perception of user satisfaction for other manufacturers, and price and quality of services provided by other manufacturers (Ha and Park, 2013; Yim et al., 2007). Switching cost

captures switching problems, costs, and time spent as a result of the change (Gefen, 2002; Kim et al., 2004). Customer satisfaction measures the overall satisfaction, satisfaction of the expectations, and satisfaction of consumer needs/wants (Bhattacherjee, 2001; Lam et al., 2004). Customer loyalty is measured based on repurchase intention, price tolerance at the time of repurchase, and recommendation of the manufacturer to other people (Bayraktar et al., 2012; Tsai, 2011). Relationship length and usage experience as moderating variables are each measured using a single question. For relationship length, we asked how long (in months) users have used the smartphone provided by the current manufacturer (Deng et al., 2010; Gerpott et al., 2013; Raimondo and Costabile, 2008). Depending on the length of smartphone use, we grouped the users into long-term users and short-term users. For usage experience, we asked the average monthly amount of data usage over last three months (Gerpott et al., 2013; Lopez et al., 2006). Depending on the level of smartphone use, we grouped the users into high-experience users and low-experience users.

Table 1 Measurement Items Construct Functions

Usability

Design

Applications Price

Customer support Corporate image Alternative attractiveness Switching

Measurement Items The functional performance of my smartphone is good. My smartphone offers high performance. My smartphone offers stable quality. My smartphone is easy to use. Learning to operate my smartphone is easy. It is easy to use my smartphone to perform what I want it to do. My smartphone looks attractive. The look and feel of my smartphone are visually appealing. The design of my smartphone is good. There are varieties of applications on my smartphone. Applications on my smartphone are useful. Applications on my smartphone are fun to play with. The price of my smartphone is acceptable. My smartphone is worth the price I paid. I am pleased with the price that I paid for my smartphone.

Related Studies Anderson and Shrinivasan, 2003; Deng et al., 2010 Belanche et al., 2012; Tseng and Lo, 2011; Cyr et al, 2006; Tsai, 2011 Zhu et al., 2011 Chen and Dubinsky, 2003; Tseng and Lo, 2011

My smartphone manufacturer responds immediately to my complaints. My smartphone manufacturer provides adequate after-sales services. My smartphone manufacturer is quick to provide after-sales services.

Kim et al., 2004

The manufacturer of my smartphone is reliable. The manufacturer of my smartphone is innovative and forward looking. The manufacturer of my smartphone adds value to me.

Aydin and Özer, 2005; Bayraktar et al., 2012; Kim and Hyun, 2011

The users of other smartphone manufacturers are more satisfied. The smartphone price offered by other smartphone manufacturers is more reasonable. Other smartphone manufacturers give better services. Switching to smartphones made by other manufacturers would cause too many problems. Switching to smartphones made by other manufacturers would be too

Ha and Park, 2013; Yim et al., 2007 Gefen, 2002; Kim et al., 2004

Cost

expensive. Switching to smartphones made by other manufacturers would require too much learning.

Customer Satisfaction Customer Loyalty

I am very satisfied with the manufacturer of my smartphone. The manufacturer of my smartphone meets my expectations. The manufacturer of my smartphone fits my needs/wants.

Bhattacherjee, 2001; Lam et al., 2004

I will surely repurchase smartphones from the current manufacturer. I will recommend my smartphone manufacturer to others. I will buy smartphones from my current manufacturer even if the phones are more expensive than that of other manufacturers.

Bayraktar et al., 2012; Tsai, 2011

4.2. Sample We collected data from 700 smartphone users aged between 15 and 49 who resided in the Seoul metropolitan area and other 14 major cities. A stratified sampling method was applied to the 2010 national census to extract the samples. To collect the data, we engaged a research panel company who employed trained interviewers to conduct face-to-face surveys. The sample consists of 50.4% males and 49.6% females. About half of the respondents are working adults, and 22.7% are students and 11.4% are housewives. The majority of the respondents (58.5%) use Samsung phones. 15.9% use LG phones and 9.9% use Apple phones. Table 2 shows the respondents’ profiles.

Table 2 Respondents’ Profiles N=700

Category Gender Age

Occupation

Smartphone Corporate

Male Female 15~19 20~29 30~39 40~49 Business Worker Student House wife Others Apple Samsung LG Pantech Others

Frequency 353 347 90 177 214 219 67 354 159 80 40 69 410 111 82 28

Percentage 50.4 49.6 12.8 25.3 30.6 31.3 9.6 50.6 22.7 11.4 5.7 9.9 58.5 15.9 11.7 4.0

5. RESULTS We used partial least square (PLS) to test our research model. PLS is a second generation analytical

technique that handles complex models robustly and imposes minimal restrictions on sample size, measuring scales, and residual distribution (Chin et al., 2003). The PLS tool used was Smart PLS 2.0 (http://www.smartpls.de).

5.1. Measurement Model To ensure the reliability and convergent validity of our measurement model, first we assessed the composite reliability and the average variance extracted (AVE) of each construct. Table 3 showed that the composite reliabilities for all constructs surpass the cut-off value of 0.7 (Hair et al., 2009), and the AVEs are greater than 0.5 for all constructs (Fornell and larcker, 1981). Furthermore, all items load highest on their intended constructs with factor loadings greater than 0.7 (see Table 5) (Nunnally, 1978). Therefore, the constructs demonstrate good reliability and convergent validity.

Table 3 Reliability and Convergent Validity Items per construct 3 3 3 3 3 3 3 3 3 3 3

Construct Functions Usability Design Applications Price Customer support Corporate image Alternative attractiveness Switching cost Customer satisfaction Customer loyalty

Mean

S.D

C.R.

AVE

4.736 5.017 4.795 4.999 4.376 4.705 4.740 3.789 4.174 4.776 4.413

1.566 0.940 1.149 1.168 1.337 1.096 1.125 1.264 1.403 1.164 0.924

0.981 0.963 0.981 0.969 0.978 0.961 0.975 0.967 0.975 0.963 0.983

0.945 0.897 0.944 0.912 0.938 0.892 0.929 0.906 0.930 0.897 0.951

Note: S.D. = Standard Deviation, α = Cronbach’s Alpha, C.R. = Composite Reliability, AVE = Average Variance Extract

Next, we conducted a test of discriminant validity. From Table 4, the square roots of all the AVEs are greater than the correlations between any two constructs. Table 5 also shows that all items load highest on their intended constructs than any other constructs. Meeting these two criteria provides evidence of discriminant validity for our measurement model (Gefen et al., 2003).

Table 4 Discriminant Validity Construct

FCN

UBT

Functions (FCN) Usability (UBT)

0.972 0.301

0.947

DSN

APP

PRC

SUP

IMG

AA

SC

SAT

LOY

Design (DSN) Applications (APP) Price (PRC) Customer support (SUP) Corporate image (IMG) Alternative attractiveness(AA) Switching cost (SC) Customer satisfaction (SAT) Customer loyalty (LOY)

0.439 0.333 0.022 0.429 0.424 -0.382 0.262 0.498 0.552

0.352 0.400 0.005 0.370 0.349 -0.243 0.129 0.428 0.289

0.972 0.503 -0.037 0.410 0.453 -0.390 0.215 0.564 0.451

0.955 0.044 0.456 0.460 -0.330 0.223 0.469 0.398

0.968 -0.020 0.014 -0.059 0.098 -0.025 0.066

0.944 0.478 -0.419 0.202 0.527 0.501

0.964 -0.457 0.298 0.508 0.514

0.952 -0.377 -0.431 -0.478

0.964 0.246 0.412

0.947 0.524

0.975

Note: Diagonals represent the square root of the AVE. Other entries represent the correlations.

Table 5 Loadings and Cross-loadings Item APP1 APP2 APP3 SUP1 SUP2 SUP3 SC1 SC2 SC3 DSN1 DSN2 DSN3 UBT1 UBT2 UBT3 FCN1 FCN2 FCN3 IMG1 IMG2 IMG3 AA1 AA2 AA3 PRC1 PRC2 PRC3 SAT1 SAT2 SAT3 LOY1 LOY2 LOY3

APP 0.96 0.96 0.95 0.45 0.41 0.43 0.22 0.21 0.21 0.50 0.47 0.50 0.40 0.37 0.37 0.33 0.33 0.32 0.45 0.43 0.45 -0.30 -0.31 -0.33 0.05 0.03 0.05 0.44 0.46 0.43 0.39 0.39 0.39

SUP 0.43 0.44 0.44 0.95 0.94 0.94 0.21 0.18 0.20 0.38 0.40 0.41 0.36 0.35 0.34 0.43 0.41 0.41 0.45 0.47 0.47 -0.36 -0.42 -0.41 0.01 -0.03 -0.03 0.50 0.51 0.49 0.50 0.48 0.48

SC 0.23 0.19 0.22 0.20 0.20 0.18 0.96 0.96 0.97 0.21 0.20 0.21 0.13 0.11 0.13 0.27 0.25 0.24 0.27 0.30 0.29 -0.38 -0.33 -0.37 0.11 0.08 0.10 0.24 0.24 0.22 0.39 0.41 0.40

DSN 0.49 0.49 0.47 0.38 0.38 0.40 0.21 0.21 0.20 0.96 0.96 0.99 0.34 0.34 0.32 0.44 0.42 0.43 0.46 0.41 0.44 -0.38 -0.35 -0.39 -0.04 -0.04 -0.03 0.56 0.55 0.49 0.43 0.44 0.45

UBT 0.37 0.38 0.39 0.35 0.35 0.35 0.13 0.13 0.12 0.34 0.33 0.36 0.95 0.95 0.94 0.30 0.29 0.29 0.33 0.35 0.33 -0.21 -0.25 -0.23 0.01 0.00 0.01 0.41 0.42 0.38 0.28 0.28 0.29

FCN 0.33 0.30 0.32 0.41 0.40 0.40 0.26 0.24 0.25 0.40 0.44 0.43 0.30 0.28 0.27 0.97 0.97 0.98 0.40 0.40 0.43 -0.35 -0.36 -0.38 0.02 0.02 0.02 0.49 0.50 0.43 0.53 0.55 0.53

IMG 0.44 0.44 0.44 0.46 0.45 0.44 0.29 0.28 0.29 0.43 0.44 0.45 0.34 0.33 0.32 0.42 0.40 0.42 0.96 0.96 0.98 -0.40 -0.45 -0.45 0.03 0.00 0.02 0.50 0.50 0.44 0.51 0.51 0.49

AA -0.32 -0.32 -0.31 -0.41 -0.38 -0.40 -0.36 -0.38 -0.36 -0.36 -0.40 -0.38 -0.24 -0.24 -0.21 -0.37 -0.37 -0.37 -0.44 -0.45 -0.43 0.94 0.94 0.98 -0.06 -0.06 -0.06 -0.42 -0.43 -0.37 -0.46 -0.47 -0.47

PRC 0.04 0.04 0.04 0.01 -0.02 -0.04 0.11 0.08 0.09 -0.03 -0.04 -0.04 0.01 0.00 0.01 0.03 0.00 0.04 0.00 0.02 0.02 -0.06 -0.05 -0.06 0.94 0.97 0.99 -0.04 -0.02 -0.02 0.07 0.06 0.06

SAT 0.44 0.45 0.46 0.50 0.49 0.50 0.24 0.23 0.24 0.53 0.55 0.57 0.40 0.42 0.39 0.49 0.48 0.49 0.49 0.49 0.49 -0.40 -0.41 -0.42 -0.02 -0.03 -0.02 0.96 0.96 0.92 0.50 0.51 0.52

LOY 0.38 0.37 0.38 0.47 0.48 0.47 0.41 0.38 0.40 0.43 0.44 0.45 0.29 0.27 0.26 0.54 0.54 0.53 0.49 0.50 0.50 -0.46 -0.44 -0.46 0.07 0.05 0.07 0.51 0.52 0.46 0.98 0.97 0.98

t-statistic 236.55 225.88 186.73 186.85 143.62 181.96 237.83 285.86 295.73 258.27 303.75 1245.03 155.91 199.54 145.13 330.40 335.34 484.25 208.47 232.96 350.00 196.82 167.72 310.81 7.47 7.58 8.38 221.87 182.15 61.24 394.59 330.14 361.35

5.2. Structural Equation Model We examined the predictive power of our research model, by assessing the R2 value of endogenous constructs. As shown in Table 6 and Fig. 3, the model explains 50% of the variance in customer satisfaction, and about 40% of the variance in customer loyalty. Since the percentages of variances explained are greater than or equal to 10 percent, it implies a satisfactory and substantive model (Falk and Miller, 1992). All hypotheses are supported with the path coefficients significant at p<0.001, except H1d (applications on customer satisfaction) and H1e (price on customer satisfaction). The results show that customer loyalty toward smartphones is determined by individual’s satisfaction toward the manufacturer and the presence of switching barriers, particularly the attractiveness of other manufacturers

and switching costs. Customer satisfaction has the strongest effect on customer loyalty (β=0.366), followed by switching cost (β=0.234) and alternative attractiveness (β=-0.232). When analyzing the effect of device characteristics on customer satisfaction, it is found that the design of smartphones (β=0.257) exerts the strongest effect on customer satisfaction. This is followed by the functions that are available on smartphones (β=0.177), and usability (β=0.132). The applications available on smartphones and the price of the devices do not have significant effect on customer satisfaction. For corporate variables, both customer support (β=0.193) and corporate image (β=0.144) significantly influence customer satisfaction.

Table 6 Results of the hypotheses test Path H1a FCN → SAT H1b UBT → SAT H1c DSN → SAT H1d APP → SAT H1e PRC → SAT H1f SUP → SAT H1g IMG → SAT H2 SAT → LOY H3a AA → LOY H3b SC→ LOY *p < 0.05; **p < 0.01; ***p < 0.001

Coefficient

S.E

t-value

Testing Hypothesis

0.177 0.132 0.257 0.074 -0.022 0.193 0.144 0.366 -0.232 0.234

0.035 0.037 0.041 0.043 0.032 0.040 0.039 0.038 0.043 0.033

5.158*** 3.527*** 5.961*** 1.828 0.669 4.981*** 3.898*** 9.341*** 5.590*** 7.247***

Supported Supported Supported Not supported Not supported Supported Supported Supported Supported Supported

Fig. 3. Results of the path analysis

5.3. Analysis of the Moderators First, we classified the users into long-term users and short-term users based on the average months (=20.2 months) of relationship length. Long-term users (n=337) were those who have more than 20.2 months of relation length while short-term users (n=363) were those with less than 20.2 months of relationship length. Next, we differentiated between high-experience users and low-experience users based on the average monthly amount (=2.4 gigabytes) of data usage over 3 months. High-experience users (n=328) are those who use data with an average of 3.7 gigabytes a month while low-experience users (n=372) are those who use data with an average of 1.2 gigabytes a month. Comparisons between each group were conducted by assessing the path coefficients for the respected groups. We adopted pairwise t-tests approach to calculate structural differences between the coefficients of each path (Chin 1998; Vinzi et al., 2010). This technique is widely adopted by studies that test discrete moderators like those in our study to examine differences between groups (Vinzi et al., 2010). The tests were calculated using the following equation:


=



1)


ℎ  − 
ℎ  

( − ( − 1) 1 1 × . .   + × . .    ×  +  ( +  − 2) ( +  − 2)

~


where Pathsample1/2 = the path coefficient in subsamples m/n = number of cases in subsamples s.e.sample1/2 = standard error of the path coefficient in subsamples

Table 7 shows the results of comparative analysis between long-term users and short-term users (i.e., relationship length). These two groups of users differ in several ways. The impact of customer satisfaction on customer loyalty, and the impact of switching cost on customer loyalty are stronger for long-term users than that of short-term users. Short-term users see much stronger impact for the relationship between alternative attractiveness and customer loyalty. In sum, relationship length moderates the links between (i) alternative attractiveness and customer loyalty, (ii) switching cost and customer loyalty, and (iii) customer satisfaction and customer loyalty (H4a is supported).

Table 7 Hypotheses comparison between long-term users and short-term users (relationship length)

Path

Long-term users (n=337) Coefficient S.E t-value

Short-term Users (n=363) Coefficient S.E t-value

Group Difference Difference t-statistics

H1a: FCN → SAT

0.237

0.053

4.491***

0.153

0.046

3.323**

0.085

1.213

H1b: UBT→ SAT

0.183

0.056

3.252**

0.120

0.047

2.542*

0.063

0.869

H1c: DSN → SAT

0.210

0.057

3.688***

0.266

0.059

4.488***

-0.056

0.676

H1d: APP → SAT

0.012

0.038

0.318

0.100

0.054

1.855

-0.088

1.313

H1e: PRC → SAT

0.006

0.030

0.198

-0.032

0.029

1.097

0.038

0.902

H1f: SUP → SAT

0.166

0.049

3.380***

0.219

0.054

4.023***

-0.053

0.726

H1g: IMG → SAT

0.145

0.049

2.986**

0.138

0.052

2.670**

0.007

0.094

H2: AA → LOY

-0.123

0.055

2.225*

-0.292

0.055

5.293***

0.169

2.161*

H3a: SC→ LOY

0.343

0.043

7.891***

0.160

0.046

3.511***

0.183

2.891**

H3b: SAT → LOY

0.466

0.056

8.299***

0.308

0.052

5.928***

0.157

2.063*

Note: SE = standard error; * p < 0.05; ** p < 0.01; *** p < 0.001

Table 8 shows the results of comparative analysis between high-experience users and low-experience users (i.e., usage experience). The impact of customer satisfaction on customer loyalty, and the impact of switching cost on customer loyalty are stronger for high-experience users. In sum, usage experience moderates the links between (i) switching cost and customer loyalty, and (ii) customer satisfaction and customer loyalty (H4b is partially supported).

Table 8 Hypotheses comparison between high-experience users and low-experience users (usage experience) Path

High-experience users (n=328) Coefficient S.E t-value

Low-experience users (n=372) Coefficient S.E t-value

Group Difference Difference t-statistics

H1a: FCN → SAT

0.240

0.032

7.454***

0.128

0.044

2.907**

0.112

2.001*

H1b: UBT→ SAT

0.230

0.045

5.078***

0.096

0.046

2.061*

0.134

2.056*

H1c: DSN → SAT

0.158

0.045

3.547***

0.276

0.056

4.891***

-0.118

1.615

H1d: APP → SAT

0.093

0.037

2.476*

0.058

0.049

1.188

0.035

0.552

H1e: PRC → SAT

-0.014

0.018

0.765

-0.032

0.032

0.996

0.018

0.475

H1f: SUP → SAT

0.288

0.044

6.607***

0.133

0.050

2.645**

0.155

2.300*

H1g: IMG → SAT

0.085

0.035

2.400*

0.187

0.053

3.512***

-0.102

1.553

H2: AA → LOY

-0.126

0.048

2.631*

-0.265

0.060

4.421***

0.139

1.780

H3a: SC→ LOY

0.297

0.045

6.659***

0.158

0.048

3.323***

0.139

2.111*

H3b: SAT → LOY

0.517

0.050

10.365***

0.268

0.053

5.054***

0.249

3.405***

Note: SE = standard error; * p < 0.05; ** p < 0.01; *** p < 0.001

6. DISCUSSIONS AND IMPLICATIONS

6.1. Discussions Our study has investigated the effect of customer satisfaction and switching barriers on customer loyalty of smartphones among Korean users, and identified the moderating effect of usage characteristics. Our study also have examined two sets of antecedents to customer satisfaction. Our findings that customer satisfaction exerting the strongest effect on customer loyalty is supported by previous studies that also examine similar relationship within the smartphone context (e.g., Kim et al., 2015; Lin et al., 2015; Tan et al., 2015). Similarly, the findings on device features including functions, usability, and design that have significant influence on customer satisfaction are supported by previous studies (e.g., function: Deng et al., 2010, design: Xu et al., 2015, and usability: Lee et al., 2015). However, pricing of smartphones does not have significant effect on customer satisfaction as supported by previous studies that also examine the relationship between perceived price and customer satisfaction (Lee, 2011; Xu et al., 2015). This finding can be explained using the Korean market structure. The smartphone market in Korea is divided into high and low-price phones. The expectation of customers differs according to the price positioning. Perceived price consists of price-quality ratio, relative price, price reliability, price fairness as well as price level (Matzler et al., 2006). It is possible that the confirmation level of expectation by pricing bandwidths rather than the pricing level that affect customer satisfaction. Our results also show that customer support and corporate image significantly affect customer satisfaction. Korean smartphone users are very sensitive to customer service and warranty services (KISA, 2014) as indicated in the literature (customer support: Kim et al., 2004; Kim et al., 2015; Rigopoulou et al., 2008, and corporate image: Aydin and Özer, 2005; Lai et al., 2009; Lee, 2011). We also find that switching barriers have significant effect on customer loyalty. Both types of switching barriers that we proposed (alternative attractiveness and switching cost) significantly influence customer loyalty. The role of alternative attractiveness and switching cost are also identified in previous studies (alternative attractiveness: Kim et al., 2004; Haenlein and Kaplan; 2012, and switching cost: Lin et al., 2015; Jung and Kwon, 2015). As for the moderating effect of relationship length on customer satisfaction and their loyalty, there is a lack of consistency in the literature (Balaji, 2015; Deng et al., 2010; Seo et al., 2008). Actually, studies that

investigate the role of usage experience on switching barriers and their loyalty are rare. Therefore, it is meaningful to find that relationship length and usage experience play important roles as moderators in the links among customer satisfaction, switching barriers and customer loyalty. Specifically, the effects of customer satisfaction and switching cost on customer loyalty are different depending on the levels of relationship length and usage experience.

6.2. Theoretical and practical implications Our study presents several implications from the theoretical perspective. First, it analyzes the links among customer satisfaction, switching barriers and customer loyalty in the Korean smartphone market. A few studies have investigated the influential factors of customer satisfaction in the smartphone context (e.g., Ha and Park, 2013; Kim et al., 2015). However, studies that analyze the relationship among these constructs are very rare in the smartphone market. Thus, our study has made contribution to enhance theoretical foundations of customer loyalty in the context of smartphones in particular and smart media such tablet PCs and smart TV in general. Second, our study identifies the effect of usage characteristics as the moderators to smartphone loyalty. There is a lack of existing studies that investigate the role of usage characteristics on customer loyalty. Furthermore, the results of these few studies are inconsistent. By empirically investigating the moderating effect of usage characteristics on switching barriers and customer loyalty, our study has helped to close the research gaps and enrich the literature on the role of usage characteristics. Our study also presents useful implications to smartphone manufacturers who are in the mature market. First, device design has the strongest effect on customer satisfaction. Companies, therefore, should channel their creative and innovative minds into producing the designs that appeal to customer taste. Good design of smartphone gives aesthetic attractiveness to users. Aesthetic attractiveness is associated with customer satisfaction in terms of hedonic value (Tan and Sie, 2015; Xu et al., 2015). Design of smartphone should provide both psychological comfort and aesthetic fulfilment, meeting functional convenience and self-efficiency at the same time. The design eventually should be balanced with creativity, availability, amusement, and aesthetic appearance. Second, good functions and usability are basic necessities of a smartphone. However, providing many functions do not always meet customer satisfaction. It is necessary to identify what consumers want in the use of smartphones, and crucial to concentrate on the functions that satisfy customers’ underlying needs. Examples of these needs are clear calls, fast Internet access, and good camera for taking vivid photos. The core of usability in

smart media including smartphones is simplicity and interactivity (Lee et al., 2015). When designing new devices, manufacturers should focus on interface intuitiveness and convenience for users. They should develop smartphones that are neither complicated in the menu configuration nor difficult to learn. It is also best to provide interfaces that can be altered based on users’ experience (Belanche et al., 2012; Lee et al., 2015). Third, corporate factors positively affect customer satisfaction of smartphones. In a mature market, device manufacturers need to focus on strengthening customer support by responding to customer complaints and providing more after-sales services. It is necessary to improve corporate image because it influences consumers' selection of a device manufacturer. It is particularly important to impose a consumer-friendly, innovative, smart, and customer value-oriented image of a company on consumers. Fourth, our findings show that when switching barriers are high, customers tend to stay with the current manufacturers. As such, building switching barriers may be a more efficient alternative than enhancing customer satisfaction toward a company in the fierce competitions of a mature market (Yi and Lee, 2005). Therefore, manufacturers ought to enhance relative advantage of their companies over competitors. They should also devise switching cost strategies to tie customers to their companies and their devices (Kim et al., 2004). Finally, our results suggest that manufacturers should pursue customer segmentation strategies based on usage characteristics to effectively strengthen customer loyalty. For example, providing additional promotions to those customers who have long-term relationship or high-level of usage experience would be a proper method to increase switching cost.

6.3. Limitations for further studies This study has several limitations for further studies. First, it focuses on the Korean smartphone market and uses survey sample from the Korean population. In order to generalize our findings, comparative studies in different contexts should be conducted. The determinants of customer loyalty may differ by industries, countries or market stage of a product life cycle. Common results from future comparative studies could be generalized to strengthen research theories while research gaps from inconsistent findings could be understood from internal and external characteristics such as cultural differences. Comparative studies between the USA and European countries, between developed and developing countries, and between smartphones and notebooks would be examples of future studies. Second, to test the moderating effect of usage characteristics, we only applied relationship length and usage experience. We recognize that other moderators may exist in the links between customer satisfaction, switching

barriers, and customer loyalty. Therefore, future studies could expand to identify moderating effects of individuals’ characteristics such as genders, ages, and occupations. The moderating effects of operating platforms such as Android and iOS also need to be investigated.

References Anderson, C.R., Zeithaml, C.P., 1984. Stage of the product life cycle, business strategy, and business performance. Academy of Management journal 27(1), 5-24. Anderson, E.W., Mittal, V., 2000. Strengthening the satisfaction–profit chain. Journal of Service Research 3, 107–120. Anderson, R.E., Srinivasan, S.S., 2003. E-satisfaction and e-loyalty: a contingency framework. Psychology and Marketing 20(2), 123-138. Andreassen, T.W., Lindestad, B., 1998. The effect of corporate image in the formation of customer loyalty. Journal of Service Research 1(1), 82-92. Andrews, L., Drennan, J., Russell-Bennett, R., 2012. Linking perceived value of mobile marketing with the experiential consumption of mobile phones. European Journal of Marketing 46 (3-4), 357-386. Arruda-Filho, E., Cabusas, J., Dholakia, N., 2010. Social behavior and brand devotion among iPhone innovators. International Journal of Information Management 30, 475–480. Aydin, S., Özer, G., 2005. The analysis of antecedents of customer loyalty in the Turkish mobile telecommunication market. European Journal of Marketing 39(7/8), 910-925. Aurier, P., N’Goala, G., 2010. The differing and mediating roles of trust and relationship commitment in service relationship maintenance and development. Journal of the Academy of Marketing Science 38(3), 303325. Babin, B.J., Griffin, M., 1998. The nature of satisfaction: an updated examination and analysis. Journal of Business Research 42(February), 127-136 Balaji, M.S., 2015. Investing in customer loyalty: the moderating role of relational characteristics. Service Business 9(1), 17-40. Bayraktar, E., Tatoglu, E., Turkyilmaz, A., Delen, D., Zaim, S., 2012. Measuring the efficiency of customer satisfaction and loyalty for mobile phone brands with DEA. Expert Systems with Applications 39(1),

99–106. Belanche, D., Casaló, L.V., Guinalíu, M., 2012. Website usability, consumer satisfaction and the intention to use a website: the moderating effect of perceived risk. Journal of Retailing and Consumer Services 19(1), 124-132. Bell, S.J., Auh, S., Smalley, K., 2005. Customer relationship dynamics: service quality and customer loyalty in the context of varying levels of customer expertise and switching costs. Journal of the Academy of Marketing Science 33(2), 169-183. Bevan, N., 2001. International standards for HCI and usability. International Journal of Human-Computer Studies 55(4), 533-552. Bhattacherjee, A., 2001. Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly 25, 351–370. Bhattacherjee, A., Premkumar, G., 2004. Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS quarterly 28(2), 229-254. Burnham, T.A., Frels, J.K., Mahajan, V., 2003. Consumer switching costs: a typology, antecedents, and consequences. Journal of the Academy of Marketing Science 31(2), 109-126. Calvo-Porral, C., Lévy-Mangin, J.P., 2015. Switching behavior and customer satisfaction in mobile services: analyzing virtual and traditional operators. Computers in Human Behavior 49, 532-540. Chen, A., Dubinsky, A.J., 2003. A conceptual model of perceived customer value in e-commerce: a preliminary investigation. Psychology and Marketing 20, 323–347. Chin, W.W., 1998. The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates. Chin, W.W., Marcolin, B.L., Newsted, P.R., 2003. A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research 14(2), 189–217. Cyr, D., Head, M., Ivanov, A., 2006. Design aesthetics leading to m-loyalty in mobile commerce. Information and Management 43(8), 950-963. De Ruyter, K., Wetzels, M., Bloemer, J., 1998. On the relationship between perceived service quality, service loyalty and switching costs. International Journal of Service Industry Management 9(5), 436-453. Deng, Z., Lu, Y., Wei, K.K., Zhang, J., 2010. Understanding customer satisfaction and loyalty: an empirical

study of mobile instant messages in China. International Journal of Information Management 30(4), 289-300. Dick, A.S., Basu, K., 1994. Customer loyalty: Toward an integrated conceptual framework. Journal of Academy of Marketing Science 22, 99–113. Edvardsson, B., Johnson, M.D., Gustafsson, A., Strandvik, T., 2000. The effects of satisfaction and loyalty on profits and growth: products versus services. Total Quality Management 11, 917–927. Eshghi, A., Haughton, D., Topi, H., 2007. Determinants of customer loyalty in the wireless telecommunications industry. Telecommunications Policy 31, 93-106. Falk, R. F., Miller, N.N., 1992. A Primer for Soft Modelling. Akron, OH: University of Akron Press. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1), 39-50. Gefen, D., 2002. Customer loyalty in e-commerce. Journal of the Association for Information Systems 3(1), 2751. Gefen, D., Karahanna, E., Straub, D.W., 2003. Trust and TAM in on line shopping: an integrated model. MIS Quarterly 27, 51–90. Gerpott, T.J., Rams, W., Schindler, A., 2001. Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market. Telecommunications policy 25(4), 249-269. Gerpott, T.J., Thomas, S., Weichert, M., 2013. Characteristics and mobile Internet use intensity of consumers with different types of advanced handsets: an exploratory empirical study of iPhone, Android and other web-enabled mobile users in Germany. Telecommunications Policy 37(4-5), 357–371. Ha, Y.W., Park, M.C., 2013. Antecedents of customer satisfaction and customer loyalty for emerging devices in the initial market of Korea: an Equity Framework. Psychology and Marketing 30, 676–689. Haenlein, M., Kaplan, A.M., 2012. The impact of unprofitable customer abandonment on current customers' exit, voice, and loyalty intentions: an empirical analysis. Journal of Services Marketing 26(6), 458-470. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2009. Multivariate Data Analysis. New Jersey: Upper Saddle River, Pearson Prentice Hall. Hong, S.W., Han, S.H., Kim, K.J., 2008. Optimal balancing of multiple affective satisfaction dimensions: a case study on mobile phones. International Journal of Industrial Ergonomics 38(3), 272-279. International Telecommunication Union (ITU), 2012. Measuring the Information Society Report 2012. Retrieved from .

Jones, M.A., Mothersbaugh, D.L., Beatty, S.E., 2000. Switching barriers and repurchase intentions in services. Journal of Retailing 76, 259–274. Jones, M.A., Mothersbaugh, D.L., Beatty, S.E., 2002. Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research 55(6), 441-450. Johnson, M.D., Fornell, C., 1991. A framework for comparing customer satisfaction across individuals and product categories. Journal of Economic Psychology 12, 267–286. Joo, J., Sang, Y., 2013. Exploring Koreans’ smartphone usage: an integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior 29(6), 2512-2518. Jung, W., Kwon, Y., 2015. Differences between LTE and 3G service customers: business and policy implications. Telematics and Informatics 32(4), 667-680. Karjaluoto, H., Jayawardhena, C., Leppäniemi, M., Pihlström, M., 2012. How value and trust influence loyalty in wireless telecommunications industry. Telecommunications Policy 36(8), 636-649. Kim, H.S., Yoon, C.H., 2004. Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy 28(9), 751-765. Kim, J.H., Hyun, Y.J., 2011. A model to investigate the influence of marketing-mix efforts and corporate image on brand equity in the IT software sector. Industrial Marketing Management 40(3), 424-438. Kim, M. K., Park, J. H., Paik, J. H., 2014. Relationship between service-related activities, service capability and market diffusion: Case of WiBro. ETRI Journal 36(3), 490-497. Kim, M.K., Park, M.C., Jeong, D.H., 2004. The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications policy 28(2), 145-159. Kim, M., Chang, Y., Park, M.C., Lee, J., 2015. The effects of quality on the satisfaction and the loyalty of smartphone users. Telematics and Informatics 32(4), 949-960. Korea Information Society Development Institute (KISDI), 2014. 2012 Korean Media Panel Survey. Korean Internet Security Agency (KISA), 2014. The Survey Report on the Smartphone Usage. Kotler, P., 1997. Marketing Management: Analysis, Planning, Implementation, and Control. NJ: Prentice Hall International. KT economic and business research institute, 2015. Mobile Trends in the First Half of 2015. Lai, F., Griffin, M., Babin, B.J., 2009. How quality, value, image, and satisfaction create loyalty at a Chinese

telecom. Journal of Business Research 62(10), 980-986. Lam, S.Y., Shankar, V., Erramilli, M.K., Murthy, B., 2004. Customer value, satisfaction, loyalty, and switching costs: an illustration from a business-to-business service context, Journal of the Academy of Marketing Science 32, 3, 293–311. Lee, D., Moon, J., Kim, Y.J., Mun, Y.Y., 2015. Antecedents and consequences of mobile phone usability: linking simplicity and interactivity to satisfaction, trust, and brand loyalty. Information & Management 52(3), 295-304. Lee, J., Lee, J., Feick, L., 2001. The impact of switching costs on the customer satisfaction-loyalty link: mobile phone service in France. Journal of services marketing 15(1), 35-48. Lee, S.Y., 2014. Examining the factors that influence early adopters’ smartphone adoption: the case of college students. Telematics and Informatics 31(2), 308-318. Lee, J.W., 2011. Critical factors promoting customer loyalty to smartphone and mobile communications service providers. Academy of Marketing Studies Journal 15, 59-69. Lin, T.C., Huang, S.L., Hsu, C.J., 2015. A dual-factor model of loyalty to IT product–The case of smartphones. International Journal of Information Management 35(2), 215-228. Liu, C.T., Guo, Y.M., Lee, C.H., 2011. The effects of relationship quality and switching barriers on customer loyalty. International Journal of Information Management 31(1), 71-79. Lopez, J.P.M., Redondo,Y.P., Olivan,F.J.S., 2006. The impact of customer relationship characteristics on customer switching behavior. Managing Service Quality 16(6), 556–576. Matzler, K., Würtele, A., Renzl, B., 2006. Dimensions of price satisfaction: a study in the retail banking industry. International Journal of Bank Marketing 24(4), 216-231. Ministry of Science, ICT and Future Planning (MSIP), 2015. Korean Fixed and Mobile Telecommunication Service Statistics. Retrieved from . Mittal, V., Kamakura, W.A., 2001. Satisfaction, repurchase intent, and repurchase behavior: investigating the moderating effect of customer characteristics. Journal of marketing research 38(1), 131-142. Nunnally, J.C., 1978. Psychometric Theory. New York, NY: McGraw-Hill. Oliver, R.L., 1997. Satisfaction: a behavioral perspective on the consumer. Boston: Irwin McGraw-Hill. Oghuma, A.P., Libaque-Saenz, C.F., Wong, S.F. and Chang, Y., 2016. An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics 33(1), 34-47. Parasuraman, A., Zeithaml,V.A., Berry, L.L., 1994. Reassessment of expectations as a comparison standard in

measuring service quality: implications for further research. Journal of Marketing 58, 111-124. Picón, A., Castro, I., Roldán, J.L., 2014. The relationship between satisfaction and loyalty: a mediator analysis. Journal of Business Research 67(5), 746-751. Raimondo, M.A., Costabile, M., 2008. How relationship age moderates loyalty formation the increasing effect of relational equity on customer loyalty. Journal of Service Research 11(2), 142-160. Reichheld, F.F., Teal, T., 2001. The loyalty effect: the hidden force behind growth, profits, and lasting value. Harvard Business Press. Rigopoulou, I.D., Chaniotakis, I.E., Lymperopoulos, C., Siomkos, G.I., 2008. After-sales service quality as an antecedent of customer satisfaction: the case of electronic appliances. Managing Service Quality: An International Journal 18(5), 512-527. Risselada, H., Verhoef, P.C., Bijmolt, T.H., 2014. Dynamic effects of social influence and direct marketing on the adoption of high-technology products. Journal of Marketing 78(2), 52-68. Rodgers, W., Negash, S., Suk, K., 2005. The moderating effect of on-line experience on the antecedents and consequences of on-line satisfaction. Psychology & Marketing 22(4), 313-331. Seo, D., Ranganathan, C., Babad, Y., 2008. Two-level model of customer retention in the US mobile telecommunications service market. Telecommunications Policy 32(3), 182-196. Strategy Analytics, 2013. Global Smartphone Vendor Market Share for 15 Countries. Retrieved from. Sweeney, J. C., Soutar, G.N., 2001. Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77, 203–220. Tan, W.K., Lee, P.W., Hsu, C.W., 2015. Investigation of temporal dissociation and focused immersion as moderators of satisfaction–continuance intention relationship: smartphone as an example. Telematics and Informatics 32(4), 745-754. Tan, W.K., Sie, M.S., 2015. The impact of personal innovativeness on product aesthetics and self-connection with brand: a case study of mobile phone users. Behaviour & Information Technology 34(3), 316-325. Tsai, S.P., 2011. Fostering international brand loyalty through committed and attached relationships. International Business Review 20(5), 521–534. Tseng, F.-M., Lo, H.-Y., 2011. Antecedents of consumers’ intentions to upgrade their mobile phones. Telecommunications Policy 35(1), 74–86. Vinzi, V.E., Chin, W.W., Henseler, J., Wang, H., 2010. Handbook of Partial Least Squares: Concepts, Methods

and Applications. Berlin: Springer-Verlag, Springer Handbooks of Computational Statistics. Wang, C.Y., 2010. Service quality, perceived value, corporate image, and customer loyalty in the context of varying levels of switching costs. Psychology & Marketing 27(3), 252-262. Wang, C. Y., Wu, L.W., 2012. Customer loyalty and the role of relationship length. Managing Service Quality: An International Journal 22(1), 58-74. West, J., Mace, M., 2010. Browsing as the killer app: Explaining the rapid success of Apple's iPhone. Telecommunications Policy 34(5–6), 270–286. Wu, J., Lu, X., 2013. Effects of Extrinsic and Intrinsic Motivators on Using Utilitarian, Hedonic, and DualPurposed Information Systems: A Meta-Analysis. Journal of the Association for Information Systems, 14(3), 153-191. Xu, C., Peak, D., Prybutok, V., 2015. A customer value, satisfaction, and loyalty perspective of mobile application recommendations. Decision Support Systems 79, 171-183. Yi, Y.J., Lee C.G., 2005. Antecedents and consequences of switching costs: the moderating role of service subscription types, Journal of Korean Marketing Association 20(3), 1-28. Yim, C.K.B., Chan, K. W., Hung, K., 2007. Multiple reference effects in service evaluations: Roles of alternative attractiveness and self-image congruity. Journal of Retailing 83(1), 147-157. Zeithaml, V.A., 1988. Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. The Journal of Marketing 52(3), 2-22. Zhu, B., Kim, T.W., Kim, S.W., 2011. A study on formulating the classification model for smartphone’s satisfaction factors. Information Systems Review 13(3), 47-63.

Highlights The highlights of this paper are as follows.




This study examines the effect of customer satisfaction and switching barriers on customer loyalty and evaluates the moderating effects of usage characteristics on the relationships among customer satisfaction, switching barriers and customer loyalty in the Korean smartphone market.




The attributes of smartphone devices (functions, usability, and design) and corporate factors (customer support and corporate image) significantly influence customer satisfaction.




Customer satisfaction and switching barriers (alternative attractiveness and switching cost) have significant impacts on customer loyalty.




Relationship length moderates the links between (i) alternative attractiveness and customer loyalty, (ii) switching cost and customer loyalty, and (iii) customer satisfaction and customer loyalty.




Usage experience moderates the links between (i) functions and customer satisfaction, (ii) usability and customer satisfaction, (iii) customer support and customer satisfaction, (iv) switching cost and customer loyalty, and (v) customer satisfaction and customer loyalty.




This paper contributes to the literature in customer loyalty and provides valuable implications for smartphone manufacturers.