Antecedents of consumers’ intentions to upgrade their mobile phones

Telecommunications Policy 35 (2011) 74–86

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Telecommunications Policy URL: www.elsevierbusinessandmanagement.com/locate/telpol

Antecedents of consumers’ intentions to upgrade their mobile phones Fang-Mei Tseng a,n, Hui-Yi Lo b a b

Faculty of International Business, College of Management, Yuan Ze University, 135 Yuan-Dung Rd., Chung-Li, Taoyuan 320, Taiwan Faculty of Marketing, College of Management, Yuan Ze University, 135 Yuan-Dung Rd., Chung-Li, Taoyuan 320, Taiwan

a r t i c l e in fo

abstract

Available online 7 January 2011

The fourth generation (4G) mobile phone will soon be launched. Marketers need to know which factors determine whether customers choose to upgrade their mobile phones, as this will affect the diffusion of third generation (3G), 4G, and Worldwide Interoperability for Microwave Access phones. This study integrates pre- and post-adoption theories, upgrading, and value-based theory to examine plans to upgrade to a newer model mobile phone among second generation (2G) and 3G mobile phone users. The empirical results show that the technology acceptance model fails to explain consumers’ intentions to upgrade in sequence. Although customers perceived next-generation mobile phones as being easier to use and more useful than their current model phones, this did not directly influence them to upgrade. When users were satisfied with their current model, they were not willing to upgrade to a newer generation model. Moreover, value assessments affect users’ plans to upgrade to next-generation mobile phones. & 2010 Elsevier Ltd. All rights reserved.

Keywords: Upgrade Mobile phone Technology acceptance model (TAM) WiMAX

1. Introduction Compared to other durable goods, technology-based products have a distinctively brief lifecycle. Firms develop nextgeneration products to profit from consumers’ desires to upgrade. For example, older model cell phones are rapidly replaced by newer models with augmented functions. The penetration of mobile phone use in Taiwan was anticipated to reach 114.69% in 2009 (National Communications Commission, 2009), which suggests that every individual in the country has at least one mobile phone. The National Communications Commission report also shows that the number of second generation (2G) and 2.5 generation (2.5G) users has decreased annually, whereas the number of 3G and 3.5 generation (3.5G) users has increased, indicating that Taiwanese consumers tend to upgrade their mobile phones. However, the 4G mobile phone will soon be launched, and thus it is necessary to analyze the factors that determine whether customers upgrade their mobile phones. Upgrading in turn affects the diffusion of 3G, 4G, and even Worldwide Interoperability for Microwave Access (WiMAX) phones. Previous studies have used logit regression or duration formulation and measurable variables to analyze the explanatory variables associated with upgrading behavior (Kim & Srinivasan, 2009; Kim, Srivastava, & Han, 2001). However, these methods cannot measure consumers’ cognitive or emotional perceptions of a product (e.g. a mobile phone). Yet emotional assessments, cognitive perceptions, and other psychological factors combine to determine whether consumers will use or buy a mobile phone (Asthana, 2009). They also affect consumers’ subsequent plans to upgrade their mobile phone to the nextgeneration model. Although upgraded phones have some innovative functions, they share the same basic functions as older models. Therefore, customers’ experiences with the earlier model also influence their desire to upgrade (Kim & Srinivasan, 2009).

n

Corresponding author. Tel.: 886 3 4638800 x 2691; fax: 886 3 4633824. E-mail addresses: [email protected] (F.-M. Tseng), [email protected] (H.-Y. Lo).

0308-5961/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.telpol.2010.11.003

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The existing marketing research has traditionally focused on pre-adoption or post-adoption decisions, with most research having been conducted on the adoption of single-generation products. Despite its importance to both academics and product manufacturers, upgrading behavior has rarely been studied. Therefore, to bridge the gap in research between consumers’ initial adoption of a particular innovation and their subsequent acceptance of innovations in that technology, this study explores the factors that affect consumers’ decisions to upgrade to next-generation mobile phones. This study provides industry managers with valuable insights that can be used to formulate effective marketing strategies. 2. Background Digital mobile telecommunications networks were introduced in the early 1990s (Gruber & Verboven, 2001), and Taiwan launched its first digital mobile phone soon thereafter in 1995. Since then, nationwide mobile communication systems have developed, with the penetration rate reaching 114.69% in 2009 (National Communications Commission, 2009). The first commercial launch of the 3G phone was by NTT DoCoMo in Japan in 2001 (UMTS, 2009). The technology reached Taiwan in 2005, when all of the major domestic telecommunications providers launched 3G cell phones (TEEMA, 2005). Although 2G mobile phones can support basic functions such as text messaging, voice calls, voicemail, and navigational mapping, 3G mobile phones offer enhanced functionality. For example, these models offer Global Positioning System navigation; music (MP3) and video (MP4) playback; personal digital assistant functionality; the ability to watch streaming video or to download video for later viewing; video calling; built-in digital cameras and camcorders (for video recording); ringtones; games; memory card readers; USB (2.0); infrared, Bluetooth (2.0), and WiFi connectivity; instant messaging; Internet e-mail and Web browsing; and wireless modem connections. 3G mobile phones will also soon serve as a ‘‘console’’ for online games and other high-quality games (Huh & Kim, 2008). Yet despite these advantages, 3G phones did not initially perform well in the market. According to the National Communications Commission, the penetration rate for 3G technology in Taiwan was 6% in 2005 and 14.75% in 2006. This suggests that mobile phones in Taiwan were still primarily used for voice, rather than data, transmission, and that few people used the 3G technology (Taipei Times, 2007). However, competition in the industry has helped keep prices low and has led to increased 3G sales since late 2008. According to a National Communications Commission report, 11.29 million 3G phones were in use in Taiwan in 2009, with about 60% of customers using value-added services (MEPO Humanity Technology Inc, 2009). Yet even as 3G keeps growing, 4G is emerging as the latest technology. 4G technology is marked by widespread availability, low-cost data delivery, and a high degree of personalization and synchronization between various user interfaces; its development is being driven by service (Rouffet, Kerboeuf, Cai, & Capdevielle, 2005). Telecommunications Infotechnology Forum (1996) reported that Long Term Evolution and 4G will be tested in North America, Western Europe, and East Asia in 2009. The number of Long Term Evolution users is expected to begin to increase in 2012. However, Taiwan has been an aggressive advocate of WiMAX, awarding six licenses in 2007; since then, the country has spent $614 million on the peripheral development of the technology (Lee, 2007). However, some have criticized this government-driven choice. For example, Tucker Grinnan, the head of regional telecoms equity research for the financial services company HSBC, argued that Long Term Evolution, not WiMAX, will be the technology of choice for 4G (Taipei Times, 2008). The cost of WiMAX will be too great for Taiwan’s telecommunications industry. Some Taiwanese service providers launched WiMAX in the first quarter of 2009. These services are cheaper than asymmetric digital subscriber lines and offer 3 to 4 times the speed. 3. Research model and hypotheses According to Davis, Bagozzi, and Warshaw’s (1989) technology acceptance model (TAM), users tend to adopt a new technology if they perceive that technology as being useful and easy to use. The TAM is applicable in the pre-adoption stage and in the repurchase stage (Bhattacherjee, 2001; Thong, Hong, & Tam, 2006). According to the expectation–confirmation model (ECM) of post-adoption stage behavior, consumer satisfaction is a significant determinant of repurchase intentions (Bhattacherjee, 2001; Thong et al., 2006). That is, the more satisfied a consumer is with his or her current product, the more willing that consumer is to make future purchases. However, studies using the ECM do not explain how a customer’s use of an older generation product affects his or her pre-adoption intentions for newer generation products. Consumers’ buying intentions can be key determinants of whether they will upgrade to a newer model product. Consumers may measure their own use of a product based on their perception of the services they are offered (Caruana, Money, & Berthon, 2000; Zeithaml, 1988), which directly influences their plans to upgrade. Therefore, the TAM, the ECM, upgrade decisions, and perceived value can all be combined to explain the gap between pre-adoption and post-adoption behavior. 3.1. TAM Several studies have used the TAM to discuss adoption behavior with regard to mobile telecommunications technology (Bruner & Kumar, 2005; Hung, Ku, & Chang, 2003; Lu, Liu, Yu, & Wang, 2008; Teng, Lu, & Yu, 2009). These studies have

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confirmed that perceived usefulness directly affects a consumer’s intention to use a new technology. The product’s perceived ease of use indirectly influences the consumer’s behavioral intention to use the new technology by directly impacting the consumer’s attitude toward the product and the perceived usefulness of the product. Perceived usefulness and perceived ease of use have significant effects on pre-adoption and post-adoption intentions (Bhattacherjee, 2001; SeJoon, Thong, & Tam, 2006). Consumers’ intentions to upgrade can be regarded as a type of new product adoption or repurchase behavior. However, these studies have all focused on single-generation products or services. According to Kim and Srinivasan (2009), a consumer’s plans to upgrade to a next-generation product can be influenced by the type of product (i.e. its relative advantage). Thus, if 2G/3G mobile phone users perceive 3G/4G to be more useful compared to 2G/3G, they will in turn consider 3G/4G mobile phones to be more useful than their 2G/3G counterparts. Therefore, the following hypotheses are proposed: Hypothesis 1. If users perceive a newer generation product as being more useful than a current product, they will have a higher intention to upgrade. Hypothesis 2. If users perceive a newer generation product as being easier to use than a current product, they will believe that the newer generation product is more useful than the current product. Hypothesis 3. If users perceive a newer generation product as being easier to use than a current product, they will have a higher intention to upgrade. The utility of the current and newer generation products can be an antecedent of the upgrading decision (Kim & Srinivasan, 2009). In other words, perceived value can influence the intention to upgrade because it reflects the utility of a product. Moreover, Kim, Chan, and Gupta (2007) noted that usefulness as a perceived benefit affects the overall measure of value and thus plays a critical role in adoption intention. Davis et al. (1989) found that usefulness is used to assess the performance of a specific application system before it is used. This concept of the task value of new technology is akin to the performance/ quality value in the multidimensional measure of perceived value (Sweeney & Soutar, 2001). Performance/quality value refers to ‘‘the utility derived from the perceived quality and expected performance of the product’’ (Sweeney & Soutar, 2001, p. 211). That is, the purpose of perceived usefulness and performance/quality value is to meet customers’ needs. Greater perceived usefulness or performance/quality value may result in a higher overall consumer assessment of the value of a product. Therefore, the following is expected: Hypothesis 4. If users perceive a newer generation product as being more useful than a current product, they will believe that the newer generation product is more valuable than the current product. Kim et al. (2007) suggested that perceived value is also influenced by perceived ease of use, which is defined as an element of technicality. They reported that technical excellence can reduce physical, mental, and learning efforts because in expectancy value models such as TAM, effort is considered an element of cost (a non-monetary sacrifice). Technical excellence refers to user friendliness and efficient technology adoption. Because perceived ease of use influences the ¨ & Brush, 2008), the following is hypothesized: perceived value of a purchase (Anderson & Srinivasan, 2003; Pihlstrom Hypothesis 5. If users perceive a newer generation product as being easier to use than a current product, they will believe that the newer generation product is more valuable than the current product. 3.2. ECM and satisfaction The ECM, proposed by Oliver (1980), holds that consumers’ intentions to repurchase a product or continue using a service are influenced primarily by their satisfaction with their prior use of that product or service. A user’s experiences with a current-model product influence his or her decision to upgrade to a newer model (Kim & Srinivasan, 2009; Kim et al., 2001). Sweeney and Soutar (2001) posited that satisfaction is derived from the experience of using a product. Thus, in the postpurchase stage, customer satisfaction with a current product is one of the most significant predictors of his or her behavior (Oliver, 1980), intention to purchase the product in the future (Hellier, Geursen, Carr, & Rickard 2003; Oh, 1999), and plans to continue using the product (Bhattacherjee, 2001). Similarly, there is also a relationship between satisfaction with the manufacturer and decision to upgrade (Bolton, Lemon, & Verhoef, 2008), such that satisfaction affects first the consumer’s intention to upgrade and then the decision as to which product to upgrade to. Here, the following is assumed: Hypothesis 6. Customer satisfaction with the current product positively influences the intention to upgrade. Kim and Srinivasan (2009) suggested that satisfaction may sometimes be used to evaluate post-adoption value. That is, the appraisal of a customer’s current experience influences how likely the customer will be to continue to use the product and to make repeat purchases, which is known as cumulative satisfaction. Based on previous studies, Bolton (1998) postulated that a customer’s subjective expected value of a product or service should depend primarily on his or her current cumulative satisfaction. This indicates that prior satisfaction may have a positive relationship with the perceived value of the newer generation product. Accordingly, the following is assumed: Hypothesis 7. Customer satisfaction with the current product positively influences the perceived value of a product upgrade.

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3.3. Perceived value Kim and Srinivasan (2009) suggested that the enjoyment or satisfaction a customer receives from a current product and in the purchase of a next-generation product can affect the decision to upgrade. That is, perceived value affects consumers’ intentions to upgrade because it is derived from utility. Furthermore, perceived value may affect consumers’ repurchase intentions (Hellier, Geursen, Carr, & Rickard 2003; Oh, 1999) and pre-adoption intentions (Kim et al., 2007; Turel, Serenko, & Bontis, 2007). This means that perceived value can be used to measure consumers’ opinions of product upgrades, as in the following hypothesis: Hypothesis 8. If users perceive a newer generation product as being more valuable than a current product, they will have a higher intention to upgrade. According to Sweeney and Soutar (2001), monetary value is ‘‘the utility derived from the product due to the reduction of its perceived short term and longer term costs’’ (p. 211). They also indicated that emotional value denotes ‘‘the utility derived from the feelings or affective states that a product generates’’ (p. 211). Monetary value and economic value are both ¨ & Brush, 2008; Sachez, Callarisa, Rodriguez, & Moliner, 2006). Moreover, important dimensions of perceived value (Pihlstrom monetary value is significantly related to behavioral intentions (Hellier et al., 2003) and repurchase intentions (Hong, Thong, ¨ & Brush, 2008). Kim et al. (2007) proposed a similar concept of monetary value in which they & Tam, 2006; Pihlstrom confirmed the existence of a relationship between perceived price and perceived value. ¨ & Brush, 2008). In the TAM, Perceived enjoyment has also been analyzed in terms of repurchase intentions (Pihlstrom perceived enjoyment is considered to be an emotional state that leads to behavioral intentions (Ahn et al., 2007). In addition, Lim, Widdows, and Park (2006) suggested that perceived price may have a positive effect on perceived enjoyment. Sweeney and Soutar (2001) demonstrated an interrelationship between value dimensions. Here, the following is expected: Hypothesis 9. If users perceive a newer generation product as being more worthwhile than a current product, they will believe that the newer generation product is more valuable than the current product. Hypothesis 10. If users perceive a newer generation product as being less worthwhile than a current product, they will believe that the newer generation product is more enjoyable than the current product. Hypothesis 11. If users perceive a newer generation product as being more enjoyable than a current product, they will believe that the newer generation product is more valuable than the current product. Perceived enjoyment is predicted to be a strong antecedent of the intention to upgrade. It can also reflect a consumer’s perceived enjoyment of a product (Kim et al., 2007). Perceived enjoyment relates to feelings or affective states (Lim et al., 2006; Sweeney & Soutar, 2001). This is in line with perceived enjoyment and fun in the TAM. Perceived enjoyment is a benefit or a reward derived from the adoption of a technology (Nysveen, Pedersen, & Thorbjørnsen, 2005) or from an activity involving the technology (Kim et al., 2007). Therefore, it represents an affective and intrinsic benefit of technology (Kim et al., 2007). Perceived enjoyment and fun play a critical role in users’ intentions to adopt a technology (Ahn et al., 2007; Nysveen et al., 2005). Thus, it is posited that consumers’ intentions to adopt an upgraded model of a product are influenced by their perceived enjoyment of the product: Hypothesis 12. If users perceive a newer generation product as being more enjoyable than a current product, they will have a higher intention to upgrade. Davis et al. (1989) argued that perceived ease of use has a significant influence on the perceived usefulness of a technology, which is in turn related to its performance. Accordingly, performance affects perceived price, because it reflects the monetary value of a technology. However, ease of use influences savings in transaction costs or training costs (Thompson, Wang, & Leong, 2004). Perceived price can also be interpreted in terms of cost savings; therefore, there is a positive link between perceived ease of use and perceived price. Hence, the following is hypothesized: Hypothesis 13. If users perceive a newer generation product as being easier to use than a current product, they will believe that the newer generation product is cheaper than the current product. 4. Research methodology 4.1. Descriptive statistics An online empirical survey was conducted to test the model. A Web-based investigation was appropriate for this research because such an investigation uses reality-based pictures or dynamic videos. Discussions of the merits of Web-based testing may be found in Birnbaum (2000; 2004) and Skitka and Sargis (2006). This study used a scenario method. Participants were given a series of pictures that depicted users’ real-world behavior. Respondents were Taiwanese men and women recruited over the period from April 4 to May 5, 2009. A link to the online survey was given to mobile phone users who were students at Yuan Ze University in Chung-Li; a link to the survey was also

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posted on the PTT (the largest bulletin board system in Taiwan) of National Taiwan University, with members ranging in age from 14 to 39 years old. Respondents were excluded from the study if they failed to complete the survey, had previously completed it, or were not mobile phone users. Participants were informed that they would be entered into a prize drawing if they registered and completed the entire questionnaire. Winners were notified by e-mail. With regard to the sample of this study, Huh and Kim (2008) suggested that age has a significant negative influence on the intention to purchase next-generation products, which suggests that younger people adopt technologies earlier and are more willing to upgrade to next-generation products. The purpose of this research was to investigate antecedents to the intention to upgrade. Thus, focusing on younger subjects can help elucidate the relationship between those critical antecedents and the intention to upgrade. MIC (2006) also suggested that the age group between 16 and 35 represents a large fraction of mobile phone consumers in Taiwan. In this survey, of the 1153 individuals who were initially recruited, 770 respondents were included in the study, representing an effective response rate of 67%. The majority of respondents were between 19 and 24 years old. Table 1 presents a profile of the respondents included in the study. There were approximately equal numbers of men and women. Sony Ericsson was the most preferred brand of phone among the respondents. The most frequently used functions were voice calls and messaging. The usage of 2G users (i.e. 2 hours per week) was less than that of 3G users (i.e. more than 6 hours per week). Most of the respondents had replaced their mobile phones within the past 2 years and had bought them at subsidized prices. 4.2. Measurement The variables in the model were taken from the existing literature. Before the study, the items were pilot-tested. The wording of items was reviewed and modified by experts in quantitative research based on the outcome of the pilot test. The items used to measure each construct are shown in the Appendix. All items were measured on a 7-point Likert scale, where 1 is disagree strongly and 7 is agree strongly. 4.3. Questionnaire design and procedure There were four parts to the survey: introduction, registration, state of the current mobile phone, and questionnaire. The introduction gave a detailed description of the survey, noted that it had been approved by the Department of International

Table 1 Sample characteristics. Items

Gender Age

Education

Occupation

Income (NT dollars)

Total

Male Female Less than 19 years 19–24 years 25–29 years 30–34 years 35–39 years 40 years and above Junior high school (inc. below) Senior high school College/ Undergraduate Graduate Student Education Public servant Business and Information Financial service industry Service industry Industry Others Below 15,000 (inc.) 15,001–25,000 25,001–35,000 35,001–45,000 45,001–55,000 55,001 and above

2G users

3G users

Freq.

Percent.

Freq.

Percent.

Freq.

Percent.

401 369 49 496 172 41 6 6

52.1 47.9 6.4 64.4 22.3 5.3 .8 .8

234 211

52.6 47.4

20 19 572 154 565 26 16 20 7 34 7 36 601 46 39 44 21 19

2.6 2.5 74.3 20.6 73.4 3.4 2.1 2.6 .9 4.4 .9 4.7 78.1 6.0 5.1 5.7 2.7 3

32 281 99 26 4 3 17 15 321 92 322 17 9 8 4 23 6 17 350 24 20 27 16 8

7.2 63.1 22.2 5.8 .9 .6 3.8 3.4 72.1 20.7 72.4 3.8 2.0 1.8 .9 5.2 1.3 3.8 78.7 5.4 4.5 6.1 3.6 1.7

167 158 17 215 73 15 2 3 3 4 251 67 243 9 7 12 3 11 1 19 251 22 19 17 5 11

51.4 48.6 5.2 66.2 22.5 4.6 .6 .9 .9 1.2 77.2 20.6 74.8 2.8 2.2 3.7 .9 3.4 .3 5.8 77.2 6.8 5.8 5.2 1.5 3.3

Note: N = 770 (N2G= 445, 57.8%; N3G = 325, 42.2%); N2G: Number of participants who were 2G mobile users; N3G: Number of participants who were 3G mobile users.

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Business ethic committee at Yuan Ze University and described the reward scheme. In the registration stage, participants’ demographic information was recorded. The state of the current mobile phone identified whether participants were mobile phone users and whether they used 2G or 3G. The questionnaire obtained self-reported data on perceptions of using Name Email Gender Age Income Education Occupation

Mobile Phone Usage

Yes

Generation

3G

2G

Which brand? Which functions? How long? How often (usage)? How often (replacement)?

Which brand? Which functions? How long? How often (usage)? How often (replacement)?

Satisfaction with 2G

Satisfaction with 3G

Compare 2G and 3G 1. Perceived usefulness 2. Perceived price 3. Perceived ease of use 4. Perceived enjoyment 5. Perceived value 6. Intention to upgrade

Compare 3G and 4G 1. Perceived usefulness 2. Perceived price 3. Perceived ease of use 4. Perceived enjoyment 5. Perceived value 6. Intention to upgrade

End

Fig. 1. The flowchart of online survey.

No

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next-generation mobile phones. Participants first saw two generations of mobile phones (e.g. 2G vs. 3G) and were given descriptions of each. They were asked to compare the two generations of phones when responding to each question. The details of the procedure are shown in Fig. 1. As can be seen in the questionnaire section of Fig. 1, if the participants were 2G users, they answered two sets of questions. The first had to do with their adoption of and satisfaction with 2G (e.g. which brand and functions they used). The second elicited their comparisons of the 2G and 3G phones. 3G mobile phone users answered the same questions as the 2G users, but all of the questions and pictures referred to 3G and 3G/4G. 4.4. Reliability and validity analysis Composite reliability is the most common index of the convergent validity of measures. It is used to check whether the scale items measure the construct in question or other (related) constructs; a value of .70 or above is deemed acceptable (Fornell & Larcker, 1981). Cronbach’s coefficient alpha was used to test the inter-item reliability of the scales used in this study. Cronbach’s alpha assesses how well the items in a set are positively correlated with one another. In general, reliability of less than .60 is considered poor, reliability in the .70 range is considered acceptable, and reliability greater than .8 is considered good (Sekaran, 2003). As shown in Table 2, all of the alpha values were greater than the recommended level and showed good reliability with Cronbach’s alpha ( 4.7) in each construct. Discriminant validity is the extent to which a measure diverges from other similar measures. Testing for discriminant validity involves checking whether the items measure the construct in question or other constructs. With the exception of a strong correlation between some constructs (e.g., perceived usefulness, perceived enjoyment), correlations were moderate, weak, or nonexistent (Table 3). Additionally, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) can be used to evaluate it (Table 2). For adequate discriminant validity, the square root of the AVE should be greater than the construct’s correlation coefficient. As shown in Table 3, the AVE for all constructs was higher than their shared variances. These results indicate that these constructs have adequate discriminant validity. Convergent validity is also supported as the average variance extracted clearly exceeded .50 for all dimensions (Fornell & Larcker, 1981). In addition, exploratory factor analysis was used to examine construct validity. The Kaiser–Meyer–Olkin test and Bartlett’s test of sphericity were first used to assess the appropriateness of the correlation matrices for factor analysis. The Kaiser–Meyer–Olkin value of .912 for 2G versus 3G use exceeded the meritorious limit of .80 set forth by Hair, Anderson, Table 2 Reliability and average variance extracted (AVE).

Constructs

2G/3G AVE

Cronbach Alpha

3G/4G AVE

Cronbach Alpha

Prior satisfaction Perceived usefulness Perceived price Perceived ease of use Perceived enjoyment Perceived value Intention to upgrade

.506 .834 .762 .738 .884 .770 .886

.899 .898 .898 .796 .930 .874 .940

.723 .839 .788 .757 .891 .789 .896

.895 .903 .865 .842 .938 .861 .941

Table 3 Correlations coefficient and AVE between constructs. Constructs

SAT

PU

MON

PE

EMO

PV

INT

(a) 2G to 3G model Prior satisfaction (SAT) Perceived usefulness (PU) Perceived price (MON) Perceived ease of use (PE) Perceived enjoyment (EMO) Perceived value (PV) Intention to upgrade (INT)

.711 .110(*) .145(**) .097 .159(**) .108(*) .079

.913 .676(**) .548(**) .687(**) .705(**) .583(**)

.873 .497(**) .595(**) .679(**) .490(**)

.859 .549(**) .502(**) .376(**)

.940 .737(**) .694(**)

.877 .737(**)

.941

(b) 3G to 4G model Prior satisfaction (SAT) Perceived usefulness (PU) Perceived price (MON) Perceived ease of use (PE) Perceived enjoyment (EMO) Perceived value (PV) Intention to upgrade (INT)

.850 .241(**) .233(**) .138(*) .291(**) .263(**) .254(**)

.916 .688(**) .578(**) .724(**) .721(**) .576(**)

..888 .492(**) .635(**) .623(**) .484(**)

.870 .538(**) .458(**) .335(**)

.944 .725(**) .662(**)

.888 .729(**)

.947

Note. The diagonal elements represent the square roots of the average variance extracted (AVE, Table 2). *p o .05. **p o .01.

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Table 4 Factor loading. (A) Comparison between 2G and 3G model 2G/3G M s Intention to upgrade (INT) INT3 4.69 1.547 INT1 4.85 1.439 INT2 4.87 1.473

INT

SAT

MON

PE

PU

EMO

PV

Comm.

.878 .839 .871

.020 .000 -.004

.127 .166 .162

.114 .102 .074

.140 .182 .127

.172 .265 .269

.169 .177 .150

.878 .877 .901

Prior satisfaction (SAT) SAT2 4.91 SAT1 4.92 SAT4 4.89

1.285 1.256 1.337

-.041 -.007 .050

.933 .928 .880

.014 .004 .061

.002 -.020 .012

.025 .032 -.068

-.006 -.043 .064

.013 .009 .007

.873 .864 .790

Perceived price (MON) MON1 4.02 MON3 4.02 MON2 4.37

1.309 1.346 1.334

.109 .218 .194

.038 .025 .061

.867 .797 .771

.211 .161 .093

.150 .318 .221

.157 .118 .278

.107 .173 .212

.869 .855 .815

Perceived ease of use (PE) PE1 3.83 PE2 3.93

1.268 1.267

.129 .123

.011 -.019

.167 .210

.829 .826

.276 .120

.186 .203

.019 .206

.844 .840

Perceived usefulness (PU) PU1 3.98 PU2 4.29 PU3 4.50

1.407 1.422 1.367

.129 .245 .331

-.010 -.015 .019

.291 .261 .336

.292 .156 .145

.808 .777 .607

.153 .327 .399

.132 .164 .162

.879 .891 .798

Perceived enjoyment (EMO) EMO3 4.68 EMO1 4.55 EMO2 4.50

1.392 1.307 1.309

.282 .301 .373

.013 .002 -.009

.167 .256 .227

.132 .257 .254

.209 .257 .246

.791 .762 .737

.265 .159 .181

.864 .894 .892

Perceived value (PV) PV2 4.62 PV1 4.74 PV3 4.16

1.075 1.028 1.389

.302 .308 .461

.004 .110 -.096

.234 .283 .345

.117 .165 .240

.163 .190 .357

.252 .387 .158

.799 .632 .446

.889 .799 .750

Extraction sums of squared loadings Total % of Variance Cumulative (%)

9.613 48.064 48.064

2.544 12.721 60.785

1.620 8.1 68.886

1.074 5.37 74.256

.815 4.073 78.328

.788 3.938 82.267

.609 3.045 85.311

INT

SAT

MON

PE

PU

EMO

PV

(B) Comparison between 3G and 4G model 3G/4G M r Intention to upgrade (INT) INT2 5.4 1.16 6 3 INT1 5.3 1.20 2 5 INT3 5.4 1.27 2 1 Prior satisfaction (SAT) SAT2 5.4 2 SAT1 5.3 9 SAT4 5.3 8 Perceived price (MON) MON1 4.4 5 MON3 4.5 7 MON2 4.9 1 Perceived ease of use (PE) PE1 4.2 5 PE2 4.3 9

Comm.

.864

.115

.147

.120

.164

.252

.202

.927

.860

.138

.151

.052

.137

.223

.217

.900

.841

.071

.150

.096

.205

.214

.169

.861

1.05 3 .958

.062

.914

.048

.036

.026

.068

.056

.852

.088

.898

.117

-.005

.069

.132

.085

.857

1.15 8

.104

.883

.026

.066

.080

.042

.021

.804

1.14 7 1.13 0 1.111

.130

.075

.832

.243

.147

.098

.115

.818

.168

.047

.810

.141

.238

.207

.160

.832

.220

.156

.674

.096

.299

.367

.175

.791

.082

.078

.196

.861

.183

.187

.052

.863

.123

.006

.178

.844

.210

.176

.152

.858

1.13 5 1.20 1

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Table 4. (continued ) Perceived usefulness (PU) PU1 4.7 4 PU2 5.0 7 PU3 5.1 4 Perceived enjoyment (EMO) EMO3 5.3 2 EMO1 5.11

1.19 8 1.17 5 1.18 7

5.0 7

1.16 3 1.08 3 1.10 9

Perceived value (PV) PV2 5.0 3 PV1 5.2 1 PV3 4.7 1

1.00 1 1.02 5 1.22 8

EMO2

Extraction sums of squared loadings Total % of Variance Cumulative (%)

.227

.082

.250

.309

.750

.123

.227

.846

.246

.128

.275

.228

.742

.291

.206

.882

.232

.063

.326

.158

.675

.411

.160

.840

.316

.138

.221

.143

.177

.790

.205

.885

.302

.130

.238

.273

.250

.743

.220

.903

.346

.123

.237

.276

.306

.686

.207

.874

.340

.069

.185

.111

.183

.256

.778

.871

.335

.165

.209

.130

.298

.364

.620

.806

.468

.079

.301

.204

.311

.123

.549

.771

2.253

1.621

1.006

.779

.706

.574

8.105

5.030

3.897

3.532

2.870

10.10 2 50.50 8 50.50 8

11.26 5 61.77 3

69.87 8

74.90 8

78.80 6

82.33 8

85.20 8

Note. (A) Comm. indicates communalities. KMO measure is .920, Bartlett’s test is significant (w2 (190, N = 445)= 7588.626, p o .001). Note. (B) Comm. indicates communalities. KMO measure is .931, Bartlett’s test is significant (w2 (190, N = 325)= 5603.4, po .001).

Tatham, and Lack (1998).1 Moreover, the correlation matrix was significant at the p o.001 level as assessed using Bartlett’s test. Thus, both results demonstrated the factorability of the matrices considered. Principal components factor analysis with varimax rotation and a factor loading of .5 was performed. Factor analysis revealed seven factors with eigenvalues totaling of 85.76% (2G vs. 3G).2 All seven factors showed a number of strong loadings, and all variables loaded substantially on only one factor. The results of this analysis provided evidence of construct validity (Table 4). 5. Results 5.1. Model estimation Two linear structural equation models using LISREL 8.5 were used to test for possible patterns of causal dependency among variables. Then, a chi-square goodness-of-fit test was used to assess the overall fit of the models and to compare competing models; indices based on derivatives of the fitting function were used to suggest better fitting models. Under the assumptions justifying maximum likelihood estimation, a chi-square goodness-of-fit measure allows a test of the null hypothesis that a given model provides an acceptable fit to the observed data. A model is said to have good fit when the goodness-of-fit index, comparative fit index, normed fit index, and non-normed fit index values are higher than .90; the root mean square error of approximation value is less than .05; and the standardized root-mean-square residual is less than .08 (Browne & Cudeck, 1993). The overall results suggested that the two structural models (2G vs. 3G and 3G vs. 4G) provided satisfactory model fit (Table 5). 5.2. Hypotheses testing: the model with upgrading from 2G to 3G The hypotheses were tested with a path analysis. Model A tested the determinants of the intentions of 2G users to upgrade to 3G technology (Fig. 2). Perceived usefulness and perceived ease of use did not influence 2G users’ intentions to upgrade in sequence; thus, H1 and H3 were not supported, even though perceived price and perceived usefulness partially mediated the influence of perceived ease of use on perceived value. This meant that perceived usefulness and perceived ease of use influenced 2G users’ intentions to upgrade in sequence. However, perceived ease of use indirectly affected perceived value through perceived price and perceived usefulness, with perceived usefulness significantly affecting perceived value; thus, H2, H4, H9, and H13 were supported. Perceived price positively affected the perceived enjoyment of 3G technology, supporting H10. In addition, consumers’ 1 2

The Kaiser–Meyer–Olkin value was .931 for 3G versus 4G use. Eigenvalues totaled 85.21% for 3G versus 4G use.

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83

Table 5 Model fit for models. Fit criteria

Recommended value

w2/df RMSEA Normed fit index (NFI) Non-normed fit index (NNFI) Comparative fit index (CFI) Standardized RMR Goodness of fit index (GFI)

Results

Between 2 and 5 o .08 4 .9 4 .9 4 .9 o .08 4.8

Perceived price

2G/3G

3G/4G

4.25 .086 .96 .97 .97 .080 .85

3.70 .078 .97 .97 .98 .063 .87

0.69*

Perceived enjoyment

0 .7

8*

Perceived usefulness

0 .3 1

* 0.91

*

0.43*

0.11*

Perceived ease of use 0.14

- 0.01

-0 .23

0.12

Perceived value

0.27*

Satisfaction

Prior satisfaction of 2G

-0.01

* 0.62

Intentionto upgrade

Comparison between 2G and 3G Fig. 2. Test results for 2G users: comparison between 2G and 3G.

perception of 3G technology as being more enjoyable than the 2G technology positively affected perceived value, which in turn had a significant effect on the intentions of 2G users to upgrade to 3G phones. Thus, H8 and H11 were supported. Similarly, perceived enjoyment affected intentions to upgrade, supporting H12. Conversely, satisfaction with 2G had no significant direct effects on perceived value or on users’ intentions to upgrade; thus, H6 and H7 were not supported. 5.3. Hypotheses testing: the model with upgrading from 3G to 4G Model B tested the determinants of the intentions of 3G users to upgrade to 4G technology (Fig. 3). The results were the same as those for Model A. 6. Discussion and implications 6.1. Discussion 6.1.1. Partial support for the TAM According to the TAM, if customers perceive a technology as being useful and easy to use, they will adopt that technology (Davis et al., 1989). However, this study found that users who perceived newer generation products as more useful and easier

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F.-M. Tseng, H.-Y. Lo / Telecommunications Policy 35 (2011) 74–86

Perceived price

0.80*

Perceived enjoyment

0 .8

6*

Perceived usefulness

0 .2 0

* 0.90

*

0.42*

0.48*

Perceived ease of use -0.1 6

-0 .22

0.05

0.00

Perceived value

0.26*

Satisfaction

Prior satisfaction of 3G

0.03

* 0.73

Intention to upgrade

Comparison between 3G and 4G Fig. 3. Test results for 3G users: comparison between 3G and 4G.

to use did not necessarily have a greater intention to upgrade in sequence. Perceived usefulness and perceived ease of use only partially influenced the purchase of the next-generation product through their effects on the perceived overall value of the product. Moreover, perceived ease of use affected consumers’ intentions to upgrade through monetary value and perceived usefulness. These findings provide only partial support for the TAM. One explanation for this is that today’s new technologies encourage more frequent upgrading. Consumers’ desires and behavior are influenced by their lifestyle and situational factors. Lifestyles involve a combination of products, consumption styles, and patterns of behavior that reflect consumers’ choices for how they spend time and money. These patterns of behavior are frequently affected by the surroundings and situational factors. Nowadays, mobile technologies reach customers around the world, and they are changing exponentially faster every day. Consumers’ attitudes toward upgrading may be more closely linked to their perceived overall value of the product than to its ease of use or usefulness. Thus, technology users who postpone upgrading take into consideration the gap between their current model and newer models. 6.1.2. No support for the ECM The ECM, which seeks to explain post-adoption behavior, posits that consumer satisfaction is a significant determinant of intentions to repurchase (Bhattacherjee, 2001; Thong et al., 2006). However, it does not explain the change in users’ behavior from using an older generation product to intending to adopt a newer generation product and the present study found that satisfaction with the current product did not influence consumers to upgrade. Although this result goes against the ECM, it is reasonable. The literature upon which the ECM is built examined business-to-business service contracts, which are different from contracts for consumer-oriented technological products. It may be that individuals do not make plans to upgrade to a newer model because they either are satisfied with their current model or have no contractual constraints. Alternatively, the college students in the present sample may have had budget constraints that made them less willing to purchase nextgeneration mobile phones when they were satisfied with their current models. In addition, as mentioned previously, mobile technologies change rapidly over time, which can affect consumers’ motives for using a product. If consumers are satisfied with their current models, they may not want to upgrade and have to constantly change their user behavior or learn a new technology. 6.1.3. Upgrading behavior is determined primarily by perceived value Perceived value was the most critical factor influencing consumers’ intentions to upgrade in sequence. That is, individuals who compare their current mobile phones with next-generation mobile phones and perceive the latter as being of greater value tend to upgrade. Their decision to upgrade is determined by their subjective perception of value. In addition, perceived

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85

enjoyment reflects whether users are willing to purchase next-generation mobile phones directly. That is, when consumers perceive that an upgraded model provides greater enjoyment and value, their desire to upgrade to that model increases. However, the perception that a newer generation product is cheaper than a current product only partially influences consumers’ intent to upgrade. The degree of influence depends on the customers’ perceptions that the newer generation of product is more valuable than the current product. In addition, perceived value is influenced primarily by perceived enjoyment among 2G users and perceived usefulness among 3G users. Perceived enjoyment is very subjective. Mood or other physiological conditions (e.g., inner conflict) influence what consumers buy and how they decide to upgrade. Therefore, if consumers tend to upgrade from their current model, they may perceive being able to obtain gratification or pleasure from their new model, especially if it is not expensive. 6.2. Implications This research helps explain the relationship between satisfaction with an existing mobile phone and a consumer’s intention to upgrade to a newer model. 2G/3G users’ satisfaction with their current mobile phone does not affect their upgrading behavior. However, the perception of the newer generation product as being easier to use, more useful, cheaper, or more enjoyable or providing more value will entice customers to upgrade. Therefore, managers should launch promotions that boost value perceptions of the newest model. Moreover, mobile phones normally have a very short lifecycle because of rapidly changing technology. Thus, upgrading to a newer generation model may reflect a consumer’s good taste and sense of innovation. By contrast, some individuals may be overwhelmed by such a dynamic market because they may be resistant to change. These results suggest that when launching a new generation of product, marketers should consider the gap between the current generation and the new generation, and the upgrading process should follow a proper sequence (e.g. 3G to 3.5G, 3.5G to 4G).

Acknowledgments This work was partially supported by Grant NSC 97-2410-H-155-022-MY2 from the National Science Council of the Republic of China. The authors would like to thank the anonymous reviewers for their insightful and constructive comments. Appendix See Table A1 here. Table A1 Construct

Measurement

References

Perceived ease of use

Compared with the 2G, the 3G mobile phone is easier to use. Compared with the 2G, learning to operate the 3G mobile phone is easy. Compared with the 2G, the 3G mobile phone makes it easier to do what I want it to do. Compared with the 2G, using the 3G mobile phone saves me time. Compared with the 2G, using the 3G mobile phone improves my efficiency. Compared with the 2G, the 3G mobile phone is useful to me. Compared with the 2G, using the 3G mobile phone gives me pleasure. Compared with the 2G, using the 3G mobile phone makes me feel good. Compared with the 2G, using the 3G mobile phone provides me with a lot of enjoyment. Compared with the 2G, the price of the 3G mobile phone is more acceptable. Compared with the 2G, the 3G mobile phone is more worthwhile. Compared with the 2G, I am more pleased with the price that I paid for the 3G mobile phone. I am very satisfied with my overall 2G mobile phone experience. I am very pleased with my overall 2G mobile phone experience. It was a wise choice to use my 2G mobile phone. Compared with the 2G, please describe the overall value you perceive from the 3G mobile phone. Compared with the 2G, how would you rate the 3G mobile phone in terms of overall value? Compared with the 2G, I think the 3G mobile phone is more valuable. Compared with the 2G, I intend to buy the 3G mobile phone rather than the current one. Compared with the 2G, I intend to upgrade to the 3G mobile phone instead of using the current one. Compared with the 2G, it is very possible that I will upgrade to the 3G mobile phone.

Gefen, Karahanna, and Straub (2003), Kim et al. (2007), and Nysveen et al. (2005) Gefen et al. (2003), Kim et al. (2007), and Nysveen et al. (2005)

Perceived usefulness

Perceived enjoyment

Perceived price

Satisfaction

Perceived overall value

Future intention

Kim et al. (2007), Nysveen et al. (2005), Pura (2005), and Sweeney and Soutar (2001) Chen and Dubinsky (2003), Kim et al. (2007), Pura (2005), and Sweeney and Soutar (2001) Bhattacherjee (2001), Lim et al. (2006), and Thong et al. (2006) Garbarino and Johnson (1999), and Oh (1999)

Bhattacherjee (2001), Castaneda, Munoz-Leiva, and Luque (2007), Jones, Mothersbaugh, and Beatty (2000), Lin, Sher, and Shih (2005), and Nysveen et al. (2005)

Note. This appendix shows items seen by 2G users; 2G and 3G were substituted with 3G and 4G, respectively, for 3G users.

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