Predicting young Chinese consumers' mobile viral ...

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Predicting young Chinese consumers’ mobile viral attitudes, intents and behavior Hongwei “Chris” Yang Appalachian State University, Boone, North Carolina, USA

Hui Liu Beijing International Studies University, Beijing, China, and

Mobile viral attitudes

59 Received 9 January 2011 Revised 28 August 2011 Accepted 17 September 2011

Liuning Zhou University of Southern California, Los Angeles, California, USA Abstract Purpose – The purpose of this paper is to integrate the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM) and Palka et al.’s model to predict young Chinese consumers’ mobile viral attitudes, intents and behavior. Design/methodology/approach – A paper survey was administered to 835 college students in Beijing, Shanghai, Kunming, and Liuzhou in summer and fall, 2010. The data were subject to statistic analyses including Pearson correlation, structural equation modeling, and backward regression with SPSS and AMOS. Findings – The SEM model testing results confirmed the chain of young Chinese consumers’ viral attitudes to intents to actual behavior. Subjective norm, perceived cost and pleasure were significant predictors of their viral attitudes. Their viral attitudes, perceived utility and subjective norm predicted their intent to pass along entertaining electronic messages. Their intent to forward useful electronic messages was determined by their viral attitudes, perceived utility and market mavenism. Their viral attitudes, intents and market mavenism predicted their mobile viral behavior. Practical implications – It pays to foster Chinese consumers’ favorable attitudes toward mobile viral marketing. It is advisable to know both target consumers and their associates very well. It is recommended to convince Chinese consumers that their friends and relatives can benefit greatly from viral content forwarding. Mobile messages with entertaining, useful, relevant and self-involved values can go viral more easily. Originality/value – The paper is probably the first study the integration of the TPB, TAM and Palka et al.’s model to predict Chinese consumers’ mobile viral attitudes, intents and behavior. Keywords China, Young consumers, Consumer behaviour, Mobile communication systems, Marketing communications, Mobile viral marketing, Theory of planned behaviour, Technology acceptance model Paper type Research paper

Introduction Mobile viral marketing, a new trend of viral marketing characterized by its ubiquity, immediacy, credibility, and legitimacy, has become a most efficient means for promotional messages to achieve exponential dissemination at little or no extra costs to advertisers. Mobile viral marketing is defined as the: The authors would like to thank two anonymous reviewers for their valuable suggestions and comments.

Asia Pacific Journal of Marketing and Logistics Vol. 24 No. 1, 2012 pp. 59-77 q Emerald Group Publishing Limited 1355-5855 DOI 10.1108/13555851211192704

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[. . .] distribution or communication that relies on consumers to transmit content via mobile communication techniques and mobile devices to other potential consumers in their social sphere and to animate these contacts to also transmit the content (Wiedemann, 2007, p. 53).

Mobile viral marketing offers interactive marketers some unique advantages. First, the ubiquity of mobile devices allows consumers to pass along mobile viral content anywhere anytime, thus giving immediacy to a viral marketing campaign. Furthermore, as mobile viral content usually comes from relatives or close friends, recipients often feel it is more acceptable and credible than promotional messages directly sent by advertisers (Wiedemann et al., 2008a). Finally, strict government regulation of mobile spamming makes forwarded mobile promotions more valuable to advertisers in China where mobile spams are closely monitored by the Ministry of Industry and Information Technology local communication bureaus and state-owned wireless service providers to protect consumer interests. The success of any mobile viral marketing campaign ultimately depends on the message recipients’ attitudes, intent and behavior regarding forwarding marketing messages via cell phones. Thus, it is essential for marketing researchers and practitioners to understand what factors predict Chinese consumers’ mobile viral attitudes, intents and behaviors. Limited research now has focused on consumers’ mobile viral marketing attitudes, intents and behaviors (Chen et al., 2008; Okazaki, 2008; Palka et al., 2009; Wiedemann et al., 2008a, b). No empirical study has examined mobile viral marketing in China except for theory building efforts (Liu et al., 2010). Absent empirical evidence, understanding of consumer attitudes, intents and behavior in relation to mobile viral marketing in the world’s second largest market is limited. In order to tap the full potentials of mobile viral marketing to reach more than 850 million wireless subscribers (MIIT, 2010), Chinese and international marketers are in need of theoretical and more importantly, practical guidance. This study intends to construct and test a conceptual model of mobile viral marketing that integrates the theory of planned behavior (TPB), technology acceptance model (TAM) and Palka et al.’s (2009) model. It examines the attitudes, intent and behavior of young Chinese consumers in relation to mobile viral marketing with data collected at multiple locations in China, and focuses on the following variables: subjective norm, perceived behavioral control, perceived utility, enjoyment, costs, and market mavenism. The following sections review the current research literature to introduce the underlying theories and formulate the hypotheses. Then, the survey procedures and scales are presented, followed by structural equation modeling (SEM) results. After discussing the findings and implications, the paper concludes with limitations and directions for future research. Review of literature and hypotheses eWOM and mobile viral marketing Mobile viral marketing is a special form of eWOM marketing that encourages and facilitates consumers to share opinions on a product, service, company, or brand favorably in various virtual social avenues such as SNS as well as pass along promotional messages to their relatives and friends on the internet (Hennig-Thurau et al., 2004). In addition to credibility and trustworthiness associated with WOM, eWOM marketing provides relatively low cost, target marketing, and high and rapid response rate (Okazaki, 2008). Moreover, it offers an unprecedented opportunity for individuals

to connect both domestically and internationally, and both synchronously and asynchronously (Subramani and Rajagopalan, 2003). It is recognized that eWOM marketing can influence consumers’ brand awareness (Ferguson, 2008), brand attitudes (Herr et al., 1991), brand loyalty (Sung et al., 2008), and purchase intention and decision (Riegner, 2007). Marketing scholars have investigated various influence factors predicting consumers’ participation in eWOM marketing communications. These factors include customer satisfaction (Casalo´ et al., 2008), social capital (Hung and Li, 2007), altruism, social benefits, advice seeking (Hennig-Thurau et al., 2004), cultural differences (Fong and Burton, 2008), and gender (Awad and Ragowsky, 2008). Previous studies have also examined consumers’ demographic characteristics, motivations, and behaviors of receiving, using, and forwarding viral content via the internet, especially by e-mail (Chiu et al., 2007, 2006; Gangadharbatla and Lisa, 2007; Huang et al., 2009; Phelps et al., 2004). Some recent studies have addressed the motives of using social networking web sites for information sharing (Bolar, 2009; Boyd and Ellison, 2007; Chu and Choi, 2010; Chu and Kim, 2011). Generally, the literature suggests that consumers are more likely to pass along an e-mail message if they perceive it to be entertaining or useful or beneficial or relevant to their recipients. They also tend to pass along online messages if they believe they will gain some social capital such as affection. Only a few studies, though, have shed light on the demographic, psychographic and behavioral characteristics of wireless service subscribers who engage in mobile eWOM communications and viral marketing behavior (Chen et al., 2008; Okazaki, 2008; Palka et al., 2009; Wiedemann et al., 2008a, b). Little is known about how subjective norms, behavioral control, perceived risks, perceived utility, pleasure, costs and market mavenism affect Chinese cell phone users’ mobile viral attitudes, intent and behavior. These important variables are shown to predict consumers’ adoption of e-commerce, internet and mobile marketing. Theory of planned behavior Ajzen’s (1991) theory of planned behavior (the TPB based on the theory of reasoned action) has been applied and validated directly or indirectly by previous studies about consumer adoption of e-commerce and mobile advertising in different countries (Bauer et al., 2005; Lee et al., 2006; Muk and Babin, 2006; Pavlou and Fygenson, 2006; Shen and Chen, 2008; Tsang et al., 2004; Wong and Tang, 2008; Zhang and Mao, 2008). Mobile viral marketing is a special form of mobile advertising with internet-enabled mobile phones as a new platform for e-commerce. Therefore, the TPB will be adopted in this study as a theoretical framework to explain young Chinese consumers’ mobile viral attitudes, intention and actual behavior. The TPB proposes that consumers’ intentions to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control. In this model, attitude is a person’s overall evaluation of performing the behavior and subjective norm concerns the individual’s perception of the expectations of important others about the specific behavior. Perceived behavioral control denotes a subjective degree of control over the performance of a behavior and should be read as “perceived control over the performance of a behavior” (Ajzen, 2002, p. 668). The TPB model is shown in Figure 1 (Ajzen, 1991).


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Attitude

62

Subjective norm

Figure 1.

Intention

Behavior

perceived behavioral control

In this study, consumers’ viral attitudes are defined as their overall evaluation of the desirability of forwarding viral content electronically while subjective norm refers to the person’s perception of the expectations of important others about passing along electronic messages. Also, perceived behavioral control comes from a consumer’s uninhibited feeling and sense of ease in forwarding electronic messages to one’s friends or relatives. We conceptualize that cell phone users are more likely to approve forwarding electronic messages if the behavior is approved and practiced by the people who are important to them. A consumer will be more inclined to engage in mobile viral behavior if they consider it easy to pass along electronic viral messages. So, we propose the following hypotheses based on the TPB and previous studies: H1. Young Chinese consumers’ attitudes toward passing along electronic messages positively predict their intent to forward electronic messages (H1a) and their frequency of passing along mobile messages (H1b). H2. Young Chinese consumers’ intent to forward electronic messages positively predicts their frequency of passing along mobile messages. H3. Young Chinese consumers’ subjective norm will be positively related to their perceived behavioral control (H3a), positively predicts their attitudes toward passing along electronic messages (H3b), and positively predicts their intent to forward electronic messages (H3c). H4. Young Chinese consumers’ perceived behavioral control positively predicts their attitudes toward passing along electronic messages (H4a) and their intent to forward electronic messages (H4b). The technology acceptance model The TAM posits that an individual’s intention to adopt and use a new information technology is determined by both perceived usefulness (PU) and perceived ease of use (EOU). According to Davis (1989), PU refers to the individual’s subjective assessment of the utilities offered by the technology. For the purpose of this research, we define PU as the extent to which cell phone users perceive the received mobile messages to be entertaining, useful, credible, and self-involved for themselves while relevant and useful to their potential recipients. EOU refers to the cognitive effort that the individual puts

forward in learning the technology (Davis). In this study, EOU is defined as the degree to which a consumer believes that forwarding electronic messages would be effortless. The sense of effortlessness empowers young consumers so that they feel more in control, therefore, the construct of behavioral control in this study has incorporated EOU. TAM or its revised form has been adopted and validated by other scholars of e-commerce and mobile marketing in the USA, Taiwan, and China (Rohm and Sultan, 2006; Soroa-Koury and Yang, 2010; Wu and Wang, 2005; Yang, 2007; Zhang and Mao, 2008). In addition, the literature suggests that consumers are more likely to pass along an electronic message if they perceive it to be entertaining, useful, credible, and self-involved, and if they expect it to be relevant and interesting to their recipients (Chiu et al., 2006, 2007; Huang et al., 2009; Okazaki, 2008; Phelps et al., 2004). In other words, PU or utility has been found to strongly predict consumers’ acceptance of e-commerce, mobile advertising and e-mail viral marketing. Thus, this study extends the TAM to mobile viral marketing research by testing the following hypotheses: H5. The perceived utility (perceived entertainment, usefulness, credibility, involvement, relevancy, and interest to others) of electronic messages positively predicts young Chinese consumers’ intent to forward electronic messages (H5a) and their frequency of forwarding mobile messages (H5b). Palka et al.’s (2009) mobile viral marketing model Building on previous studies (Wiedemann et al., 2008a, b), Palka et al. (2009) constructed a conceptual model to explain mobile viral content forwarding. They propose that consumers’ actual behavior of forwarding mobile viral message is determined by social conditions (adherence of recipient’s interests, tie strength, subjective norm, and expressiveness), attitudinal conditions (PU of communicator, perceived user friendliness, perceived enjoyment, and attitude toward forwarding), personal conditions (market mavenism and altruism), resource-based condition (perceived costs), and consumption-based conditions (customer satisfaction and involvement of communicator). So far, no quantitative study has tested this model in China. Palka et al.’s model shares many constructs with the TPB and the TAM such as adherence of recipient’s interests, subjective norm, PU of communicator, user friendliness (EOU), forwarding attitude, and involvement of communicator. However, neither TPB, TAM, or Palka et al.’s model is sufficient to account for mobile viral attitudes, intents and behavior. Palka et al.’s model does not examine the dynamics of consumer mobile viral attitudes, intents and behavior. The TPB and TAM, on the other hand, fail to consider consumers’ market mavenism, perceived enjoyment and costs, which are important variables in the viral marketing context. This study integrates predictors in Palka et al.’s model into the TPB and TAM to construct and test a more comprehensive research model of mobile viral attitudes, intents and behavior. For this study, perceived enjoyment is the extent that a consumer derives fun or pleasure from passing along electronic messages. Perceived costs refer to a consumer’s belief that forwarding electronic messages will cause the recipients to lose time or money. Market mavens are individuals who have information about a variety of products, places to shop, and other market conditions, and initiate discussions with consumers and respond to requests from consumers for market information (Feick and Price, 1987). They are general opinion leaders with a solid overall market-related knowledge and a willingness to disseminate information which is not product specific


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(Wiedmann et al., 2001). Because of this marketplace orientation, market mavens become especially attractive targets to today’s big-box retailers. Specifically, retailers that sell a wide range of products are naturally drawn to market mavens who are highly likely to spread WOM communications across a variety of products (Clark and Goldsmith, 2005). Market mavens are more motivated than ordinary consumers to spread the word about the marketplace in both non-virtual and virtual communication contexts (Feick and Price, 1987; Walsh et al., 2004; Wiedmann et al., 2001). In addition, studies reveal that market mavens spend more time on the internet seeking marketplace information and sharing their opinions and experiences with others (Belch et al., 2005; Krentler and Singh, 2009; Ruvio and Shoham, 2007; Walsh and Mitchell, 2010). Thus, it is reasonable to assume that market mavens will be more likely than ordinary consumers to engage in mobile eWOM communications and viral behavior. An understanding of the attitudes, intents and behavior of market mavens will provide insight into their role in mobile viral content sharing, and thus facilitate the design of effective marketing strategies. Therefore, the following hypotheses will be addressed: H6. The perceived costs negatively predict young Chinese consumers’ attitudes toward forwarding electronic messages. H7. The perceived pleasure positively predicts young Chinese consumers’ attitudes toward forwarding electronic messages. H8. Market mavenism tendencies positively predict young Chinese consumers’ intent to forward electronic messages (H8a) and their frequency of forwarding mobile messages (H8b). The proposed research model and hypotheses are shown in Figure 2. Subjective Norm

Perceived Utility

H3c (+)

H5b (+)

H5a (+)

H3a (+) H3b (+) Behavioral Control

H4b (+)

H4a (+)

Viral Intent H1a (+)

H2 (+)

Viral attitudes

H6 (-)

Mobile Viral Behavior

H8a (+) Perceived Costs

Figure 2. The proposed chinese mobile viral model

H1b (+) H8b (+) H7 (+)

Perceived Pleasure

Market Mavenism

Method In May, June and October, 2010, a paper survey was administered at a big public university in Shanghai, two public universities in Beijing, three public universities and one junior college in Kunming (the capital city of Yunnan Province), and one junior college in Liuzhou, Guangxi. A college student sample is suitable for this study as younger consumers are heavy users of mobile technology and have been frequently targeted by major mobile marketing campaigns in Europe, USA, and the Asia-Pacific region (Choi et al., 2008). Our survey questionnaire consists of 49 questions adopted and adapted from previous studies (Feick and Price, 1987; Huang et al., 2009; Merisavo et al., 2007; Nysveen et al., 2005; Pavlou and Fygenson, 2006). Major scales are shown in Appendix. Finally, we included four demographic questions about respondents’ gender, age, SES and personal income. The English questionnaire was translated by the first author into Chinese and then back translated into English by the second author to test accuracy and content validity. Completed questionnaires of 835 were usable with no missing data. The survey data set was subject to statistical analyses including Pearson correlation, multiple regression analyses and SEM with SPSS-18 and AMOS-18.


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Results The descriptive statistics of 835 Chinese respondents are presented in Table I. The majority of our sample is female (62 percent), and the average age is (M ¼ 21.34). Their family annual income distribution skews toward lower income families. Similarly, their monthly personal income falls into the lower income brackets. Their use of cell phone voice and texting services varied considerably. Table II presents Cronbach coefficients (a) of all adopted and adapted scales and the results of exploratory factor analyses (principal axis factoring with varimax rotation). A liberal minimum requirement for scale reliability is 0.60 (Churchill, 1979; Peter, 1979), while a stricter minimum requirement of 0.70 is recommended (Nunnally and Bernstein, 1994). However, Nunnally and Bernstein also considered the reliability of 0.50-0.60 as sufficient for exploratory studies. So, three scales (market mavenism, viral attitudes and perceived utility) performed very well and two scales (subjective norm Gender Male 38.0% Family annual income (SES) (yuan) ,24,000 37.4% 24,001-36,000 20.2% 36,001-48,000 14.3% 48,001-60,000 11.4% 60,001-72,000 7.2% .72,000 9.5% Mean Phone calls (minutes) 61.8 Texting (messages) 28.9 Note: n ¼ 835

Female 62.0%

SD 88.3 59.1

Age Mean SD 21.34 3.75 Personal monthly income (yuan) ,500 44.3% 501-1,000 32.2% 1,001-1,500 11.9% 1,501-2,000 4.1% 2,001-2,500 1.7% .2,500 5.9% Median Mode Range 30 30 0-750 15 10 0-700

Table I. Descriptive statistics

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and perceived pleasure) are considered satisfactory according to the criteria of Churchill and Peter. The reliability of other two scales (i.e. perceived control and perceived costs) is also acceptable for this exploratory study based on the standards of Nunnally and Bernstein. The maximum likelihood method of SEM with AMOS 18.0 was employed to test our proposed research model and directional hypotheses. The fitness indexes of two final models are shown in Table III. Two significant x 2 statistics imply that the model fit is not perfect but if the normed 2 x is in the 2:1 or 3:1 range, the model can be considered acceptable (Carmines and McIver, 1981). As the likelihood ratio test is very sensitive to sample size, assuming that the model fits perfectly in the population, other fitness indexes were developed to address this problem (Byrne, 2010). The widely accepted cutoff values of these fitness indexes include the root mean square error of approximation (RMSEA) # 0.06, goodness of fit index (GFI) $ 0.90, comparative fit index (CFI) $ 0.90 and Tucker-Lewis index (TLI) $ 0.95 (Hu and Bentler, 1999; Schumacker and Lomax, 2004). Our fitness indexes have met the first three standards except that two TLIs fell slightly below 0.95. As Marsh et al. (2004) argue that the cutoff value of 0.95 for the TLI is too stringent for hypothesis testing, the overall fitness of four models can be considered satisfactory. Figures 3 and 4 show the standardized path estimates of four proposed models. The significant path estimates connecting viral attitudes, intents and behavior in two final models strongly supported H1a, H1b, and H2. Young Chinese consumers’ viral attitudes positively predicted their intents to pass along electronic messages and actual behavior of forwarding mobile messages. Their viral intents positively influenced their mobile viral behaviors. Construct

Table II. Scale reliability and EFA results

Variance explained (%)

0.720 0.739 0.671 0.586 0.594 0.687 0.800

46.9 29.1 50.4 34.7 42.2 52.3 40.7

Attitude toward forwarding electronic messages Perceived utility Subjective norm Perceived control Perceived costs Perceived pleasure Market mavenism Note: n ¼ 835

Model Viral model 1 Viral model 2 Table III. Fit indices for two proposed models

Cronbach’s a

x 2(df)

Nmd x 2

RMSE

GFI

TLI (NNFI)

CFI

611.6 (267) * 622.1 (268) *

2.29 2.32

0.039 0.040

0.947 0.946

0.923 0.920

0.936 0.934

Notes: Significant at: *p , 0.01; RMSEA – root mean square error of approximation, GFI – goodness of fit index, TLI – the Tucker-Lewis index or NNFI – non-normed fit index, CFI – comparative fit index

Subjective Norm 0.52 **


Perceived Utility 0.18 (2.15*) 0.19 (3.18**)

0.14 (1.80)

–0.20 (–4.34**)

67 Viral Intent1

Behavioral Control

–0.10 (ns)

0.01 (ns) –0.06 (–1.69)

0.23 (3.86**) Viral attitudes

0.27 (7.68**)


–0.04 (ns) 0.19 (4.27**)

Perceived Costs

0.27 (6.79**) 0.80 (10.52**)

Perceived Pleasure

Market Mavenism

Notes: Significance at: *p < 0.05, **p < 0.01, model fit: x2 = 611.6, df = 267, p < 0.01; RMSEA = 0.039; GFI = 0.947; TLI = 0.923; CFI = 0.936; n = 835; significance of the path estimates are shown in parentheses (critical ratio); ns = not significant

H3a was supported as the estimated correlation between subjective norm and perceived behavioral control is significant (r ¼ 0.54, p , 0.001). However, H3b was half supported as there is a significant path coefficient from subjective norm to viral attitudes in Figure 4, but the path estimate is marginally significant in Figure 3. H3c was also half supported. Subjective norm can only predict young Chinese consumers’ their intent to forward entertaining messages in Figure 3. Surprisingly, H4a and H4b were both rejected. Young Chinese consumers’ behavioral control did not influence their viral attitudes or viral intents. H5a was confirmed but H5b was not supported. Perceived utility positively predicted the viral intents among Chinese respondents. However, perceived utility negatively affected their mobile viral behaviors. H6 was rejected as perceived costs only marginally predicted young Chinese consumers’ viral attitudes in Figure 3. On the other hand, H7 was firmly supported as perceived pleasure strongly and positively predicted young Chinese consumers’ viral attitudes. H8a was half supported that young Chinese consumers’ market mavenism tendencies predicted their intent to forward useful messages but not entertaining messages. However, H8b was confirmed as young Chinese consumers’ market mavenism predicted their frequency of passing along mobile viral messages. Four backward regression procedures were conducted to further explore what demographic, psychographic, and behavioral factors predict young Chinese consumers’ mobile viral attitudes, intents and behavior. The results are shown in Table IV. Generally, the results of our backward regression procedures confirmed two structural equation models.

Figure 3. Structural equation model 1 with standardized path estimates

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Subjective Norm 0.52 **

Perceived Utility –0.06 (ns) 0.21 (3.70**)

0.17 (2.12*)

68 Behavioral Control

–0.16 (–3.40**)

Viral Intent2 –0.01 (ns) 0.30 (4.85**)

0.01(ns) Viral attitudes

–0.05 (ns)

0.23 (5.29**)

Perceived Costs

Table IV. Predictors of Chinese mobile viral attitudes, intents and behavior


0.15 (3.95**) 0.23 (5.76**)

0.79 (10.55**)

Figure 4. Structural equation model 2 with standardized path estimates)

0.15 (4.16**)

Market Mavenism

Perceived Pleasure

Notes: Significance at: *p < 0.05, **p < 0.01, model fit: x2 = 622.1, df = 268, p < 0.01; RMSEA = 0.040; GFI = 0.946; TLI = 0.920; CFI = 0.934; n = 835; significance of the path estimates are shown in parentheses (critical ratio); ns = not significant

Utility Norm Control Costs Pleasure Mavenism Gendera Age SES Income Phoning Texting Attitudes Intent 1 Intent 2 Total R 2

Attitudes b

Intent 1 b

Intent 2 b

Viral behavior b

ns 0.203 * * * ns 20.115 * * * 0.520 * * * ns ns ns ns ns ns ns – – – 0.444

0.167 * * * 0.136 * * * ns ns 0.160 * * * ns ns 2 0.069 * 2 0.089 * * ns ns ns 0.118 * * – – 0.196

0.159 * * * ns ns ns 0.104 * 0.163 * * * ns ns 2 0.059† ns 0.081 * ns 0.175 * * * – – 0.187

20.095 * * ns 20.057† ns 0.140 * * * 0.191 * * * ns 20.128 * * 20.125 * * * 0.074† 0.150 * * * ns ns 0.221 * * * 0.082 * 0.247

Notes: n ¼ 835; *p , 0.05, * *p , 0.01, * * *p , 0.001, †p , 0.10; aGender: dummy 2 coded as 1 – male, 2 – female; ns – not significant; backward regression results

Discussion and managerial implications This pioneering study of mobile viral marketing in relation to young Chinese consumers has integrated the TPB, TAM and Palka et al.’s (2009) model to predict young Chinese consumers’ mobile viral attitudes, intents and behavior. The final two models have achieved satisfactory fit and suggest that the underlying dynamics of young consumers’ mobile viral attitudes, intents and behavior are influenced by subjective norm, perceived utility, costs, enjoyment, and market mavenism in China. Our findings contribute to the concerted effort of constructing a comprehensive model of predicting young Chinese consumers’ mobile viral marketing attitudes, intents and behavior. The study has provided considerable empirical evidence that the TPB is applicable to mobile viral marketing in China. The findings suggest that young Chinese consumers are more willing to forward entertaining and useful electronic messages if they hold positive attitudes toward viral marketing. As a matter of fact, their stronger intention to forward viral content did translate into more frequent behavior of passing along entertaining and useful mobile messages. These findings confirmed the chain reaction of attitude-intention-behavior, which is consistent with previous research on mobile marketing (Bauer et al., 2005; Lee et al., 2006; Muk and Babin, 2006; Pavlou and Fygenson, 2006; Shen and Chen, 2008; Tsang et al., 2004; Wong and Tang, 2008; Zhang and Mao, 2008). This study also confirms the impact of subjective norm on young Chinese consumers’ viral attitudes and intention to forward entertaining electronic messages. It is good news to mobile marketers that young Chinese consumers are more likely to embrace viral marketing and pass along entertaining messages if their close friends or relatives consider it acceptable to pass them along to each other. Mobile viral marketing will gain more grounds if viral content forwarding becomes a social norm among consumers of all age groups. So, mobile marketers still need to engage in promotional efforts to encourage all mobile phone users to spread marketing messages. A variety of strategies can be used. For example, mobile marketers can offer incentives to urge consumers to pass along mobile promotions and coupons. Only when a critical mass of cell phone users gets used to passing along mobile messages, will mobile viral marketing gain success. The study did not find behavioral control as a significant predictor of young Chinese consumers’ attitudes toward viral marketing. Their perceived behavior control did not influence their viral attitudes and intents probably because they feel less inhibited to forward viral messages because their recipients do not have to pay. Another explanation is that Chinese cell phone users have become used to mobile viral content as they receive mobile spams on a daily basis (Wang, 2008). As Chinese wireless subscribers are not charged for receiving incoming mobile viral content, international marketers should take advantage of this market reality when designing digital advertising campaigns in China. This empirical study shows that the TAM is also suitable for explaining mobile viral intents as the perceived utility of viral messages strongly affected young Chinese consumers’ intents to forward entertaining and useful electronic messages. It has confirmed the key role of perceived utility in predicting consumers’ eWOM intentions (Chiu et al., 2006, 2007; Huang et al., 2009; Okazaki, 2008; Phelps et al., 2004). This finding has important implications for mobile marketers. It suggests that mobile advertising campaigns with entertaining, useful, self-involved and relevant values should prompt young Chinese consumers to pass them along to their friends or relatives. If young consumers conclude that mobile advertising messages will be relevant and interesting


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to their friends or relatives, they have one more reason to forward them. For example, a mobile application or advertising campaign tied into holidays such as New Year’s Day or Valentine’s Day will probably go viral more easily if it offers young consumers free e-greeting cards and/or e-coupons. Similarly, a mobile application or advertising campaign will have a better chance of being forwarded by young consumers if it connects with their personal interests such as sports and fashion. Moreover, a mobile viral campaign will be more successful if a mobile marketer can assure young consumers that their friends or relatives will be interested and will benefit if they send them promotions. Therefore, marketing practitioners should understand the mindset of prospective consumers and their friends and relatives equally well. It is advisable to mine consumer data collected from social networking web sites (such as Renren.com.cn and qq.com.cn in China) for more effective mobile marketing campaigns in the future. Our study has also validated Palka et al.’s (2009) mobile viral marketing model by showing that young Chinese consumers’ mobile viral attitudes, intents and behavior are influenced by their adherence of recipient’s interests (their perception that the viral content will be interesting and relevant to their friends and relatives), involvement of communicator, subjective norm, perceived costs, pleasure, and mavenism. It has advanced our knowledge by showing Palka et al.’s (2009) model originally developed in Germany can be replicated in China. Perceived costs did not turn out to be an inhibiting factor for mobile viral marketing in China. As the cost barrier to mobile viral marketing is minimal, marketers can feel free to take advantage of this marketing channel in this fast growing market. Perceived pleasure stands out as the most important predictor of young Chinese consumers’ attitudes toward sharing viral content. It is intuitive that young Chinese consumers who enjoy passing along electronic messages should harbor more positive attitudes toward passing along viral content. Mobile marketers should recruit viral agents among young consumers who derive pleasure in forwarding electronic messages in China. Applying the concept of market mavenism to mobile viral marketing, this research discovered that young Chinese market mavens are more willing to pass along useful electronic messages and also engage in mobile viral behavior more frequently than non-mavens. These findings suggest that mobile marketers should take advantage of market mavens who possess rich marketplace knowledge, and who are eager to share their experiences with friends and relatives. Contrary to previous studies that find female consumers are more inclined to engage in eWOM communication on the internet (Awad and Ragowsky, 2008; Garbarino and Strahilevitz, 2004), our results did not establish gender as a significant predictor of young Chinese consumers’ mobile viral attitudes, intents and behavior. Rather, this study challenges the long-held assumption that young women are more likely to pass along electronic messages than young men, especially considering that a good majority (62 percent) of Chinese respondents were female. In this respect, the findings suggest that mobile marketers should promote their products or services without gender discrimination. Age plays an inhibited role in predicting young Chinese consumers’ intent to pass along entertaining electronic messages and actual forwarding behavior. The fact that young Chinese consumers tend to distance them from viral marketing as they grow older indicates that the best mobile viral agents are probably those under 35.

Similarly, young Chinese consumers’ SES, defined by their family annual income, was a negative predictor of both their intent to pass along entertaining electronic messages and their actual behavior of passing along mobile messages. On the other hand, the study also revealed that young Chinese consumers’ monthly personal income marginally predicted their actual mobile viral behavior. These seemingly contradictory results suggest that mobile marketers need to distinguish between young Chinese consumers from upper class families and those with a higher monthly personal income. Young adults from upper class families probably do not have to rely on mobile viral behavior to gain social capital as they can afford to invite their friends to various social occasions such as parties or dinners. At the same time, young Chinese consumers who earn their incomes may be more likely to spread a promotional message to their friends or relatives as they are business savvy with more knowledge of the marketplace. Further research should investigate whether such a distinction is necessary. Last but not least, young Chinese consumers’ cell phone calling can predict their intent to pass along useful electronic messages and their mobile viral behavior. It confirmed that stronger relationship/attachment to the mobile devices was positively associated with stronger likelihood of consumers’ participation in mobile marketing as shown in previous studies ( Jun and Lee, 2007; Maneesoonthorn and Fortin, 2006; Okazaki, 2008; Wiedemann et al., 2008a). So, mobile marketers are advised to recruit heavy cell phone users as viral agents in their future marketing activities. Conclusion This study has integrated the TPB, TAM and Palka et al.’s (2009) model to predict young Chinese consumers’ mobile viral attitudes, intents and behavior. Young Chinese consumers’ viral attitudes positively predict their intents to pass along viral content, and their forwarding attitudes and intents combine to influence their mobile viral behavior. Our proposed model fits the survey data fairly well. It suggests that constructs developed in Western culture such as subjective norm, perceived utility, enjoyment, costs, and market mavenism can be transplanted to predict mobile viral marketing attitudes, intents and behaviors in an Asian culture. Specifically, young Chinese consumers’ perceived subjective norm influences their viral attitudes and intent to forward entertaining electronic messages. Perceived behavioral control and costs did not significantly predict young Chinese consumers’ attitudes toward forwarding viral content and their intents to pass along electronic messages. However, perceived utility of being entertaining, useful, credible, self-involved, relevant and interesting has positively influenced young Chinese consumers’ intents to forward viral content. Perceived pleasure serves as the most important predictor of young Chinese consumers’ viral attitudes. Market mavenism tendencies positively affect young Chinese consumers’ intent to forward useful electronic messages and mobile viral behavior. Gender has no influence on young Chinese consumers’ mobile viral attitudes, intents and behaviors while age negatively influences young Chinese consumers’ intent to pass along entertaining electronic messages and their mobile viral behavior. Family annual income (SES) plays an inhibiting role in predicting young Chinese consumers’ forwarding intents and mobile viral behavior while their monthly personal income did not significantly influence their viral attitudes, intents and behavior. Heavy cell phone talkers should be motivated to participate in mobile viral marketing in China.


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Limitations and future research The external validity of our findings should be tested by future research as this study relied on a convenience sample collected from six public universities and two junior colleges in China. In addition, gender distribution of our sample skews toward female (62 percent) and is not representative of Chinese college student population. This study is exploratory in nature. Future studies should endeavor to construct a comprehensive model of Chinese consumers’ mobile viral marketing attitudes, intents and behavior by examining more determinants such as customer satisfaction, social tie strength, perceived social benefits, reward, personality strength, opinion leadership, and altruism. Furthermore, it will be interesting to examine these factors in a cross-cultural context and in countries where self-regulation trumps government regulation and where receiving mobile messages incurs fees. Research studies in those different cultural and regulatory contexts may yield different but important findings.

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Table AI. New media use and marketing web survey (selected scales)

1. How much time do you spend talking on your cell phone on a typical day?__________hours _________minutes 2. How many text messages do you send with your cell phone on a typical day? _________messages Frequency of forwarding How often do you forward a message (e.g. a text message, a multimedia viral messages message, a web link, an application, a ringtone) from your cell phone to your friends or relatives (1) Never; (2) Seldom; (3) Sometimes; (4) Often; (5) Very Often; (6) Always Intent of forwarding viral 1. I would pass along entertaining messages (e.g. a viral video, a joke, a messagesa funny image, a chain letter, a crazy stunt, a prank) to my friends or relatives 2. I would pass along useful messages (e.g. e-coupons, freebies, news for entertainment events, movies, music downloads, new product such as IPad, product reviews, shopping tips) to my friends or relatives Perceived utilityb 1. The electronic message I pass along to my friends or relatives is often entertaining 2. The electronic message I pass along to my friends or relatives is often useful 3. The electronic message I pass along to my friends or relative is often credible 4. I think that the electronic message that I pass along to my friends or relatives is often relevant to them 5. I think that the electronic message that I pass along to my friends or relative is often interesting to them 6. The electronic message that I pass along to my friends or relatives is often what I care about 7. The electronic message that I pass along to my friends or relatives is often what connects with myself Subjective normc 1. Most people who are important to me think it is good to pass along electronic messages to friends or relatives 2. Most people who are important to me would pass along electronic messages to friends or relatives Perceived controld 1. I feel free to pass along electronic messages to my friends or relatives if I like to 2. Passing along electronic messages to my friends or relatives is entirely within my control 3. It is easy to pass along electronic messages to friends or relatives Perceived costs or riskse 1. The problem with passing along messages to friends or relatives is their loss of time 2. The problem with passing along mobile messages to friends or relatives is their monetary costs Attitude toward forwarding 1. My attitude toward passing along electronic messages is positive c electronic messages 2. Generally, I think it is good to pass along electronic messages to friends or relatives 3. I honestly do not like passing along electronic messages to friends or relatives

Notes: aAll response options ranged from 1 – strongly disagree to 5 – strongly agree; adopted or adapted from Huang et al. (2009); adapted from bHuang et al. (2009) and Nysveen et al. (2005); cPavlou and Fygenson (2006); dPavlou and Fygenson (2006) and Nysveen et al. (2005); eMerisavo et al. (2007)

About the authors Hongwei “Chris” Yang PhD is keenly interested in advertising via new media (internet, mobile devices, social media, etc.). He is also interested in international marketing and media planning as well as regulation and self-regulation of new media advertising. He has presented and published research in these areas. Hongwei “Chris” Yang is the corresponding author and can be contacted at: [email protected] Hui Liu (PhD) focuses his research interest on persuasion of entertainment media, science communication and public opinion. He is also very interested in media planning and marketing communication. He has presented and published research in these fields. He is also a reviewer of Science Communication. Liuning Zhou, PhD, holds a Doctoral degree in Communication from the Annenberg School for Communication & Journalism at the University of Southern California, and is a Research Associate with the Center for the Digital Future at the Annenberg School. His research is focused on international telecommunications, new communication technologies, communication policy, and marketing, and he has presented and published research on those topics. He has been involved in many survey projects conducted by and for the Center for the Digital Future.

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