Case Study in Mobile Internet Innovation: Does Advertising or Acquaintances Communication Decide Taiwan s Mobile Internet Diffusion?

Transcription

1 Case Study in Mobile Internet Innovation: Does Advertising or Acquaintances Communication Decide Taiwan s Mobile Internet Diffusion? Hui-Chih Wang Yi-Cheng Ku Her-Sen Doong National Chung Cheng University Providence University National Chiayi University hcwang@mis.ccu.edu.tw ycku@pu.edu.tw hsdoong@mail.ncyu.edu.tw Abstract Reporting the world s highest mobile phone penetration rate in 2002, Taiwan s experience in promoting mobile phone has offered global scholars a successful example in innovation marketing. Its current mobile Internet status is therefore received great attentions from international practitioners and researchers. However, despite various marketing efforts, Taiwan s mobile Internet services' diffusion is surprisingly slower than that of Korea and Japan. Aiming to provide some insights of innovation marketing, three diffusion models, i.e. external, internal, and mixed influence models, were applied in this study so as to reveal whether diffusion of mobile Internet services in Taiwan is affected by mass media advertising, interactions and imitations among acquaintances, or a combination of both. Findings indicated that the mixed influence model is the best fit, but acquaintance s influence is the dominant factor. The authors therefore argue that managers should consider Source-Strategy Fit, i.e. it is essential to understand the innovation product s features and its influential sources when developing adequate marketing strategies. 1. Introduction The blinding speed of technological advances in the Information Age has made the marketing strategies of innovations an increasingly crucial issue for any technology-oriented business. For example, being an innovation, mobile Internet services have caught the attention of individuals and companies world-wide. The attractiveness of the convenience of constant availability and connectivity have made the mobile phone a necessity nowadays, and led to the robust development of mobile Internet services. A recent report of the Strategy Analytics Wireless Network Strategies Service suggests that mobile phone users will reach 2.5 billion world-wide by 2006 and 3.5 billion world-wide by It also forecasts that in the coming decade, the mobile telephony industry will generate a service revenue of US$ 800 billion [23]. Another survey revealed an increase in usage of 145%, to achieve a total of 79 million users in 2003, while the number of global mobile Internet adopters is predicted to reach nearly 600 million by 2008 [8, 19]. Moreover, the Economist estimates that, by the year 2005, the number of Internet-connected mobile phones will exceed the number of Internet-connected PCs [25]. Seeing the great potential in mobile commerce (mcommerce), such as live sport news, mobile messengers, mobile , mobile Blog, or wireless payment, mobile service providers in technology advanced countries have rivaled for the priority to launch mobile Internet services, which are provided according to different communication protocols, e.g. the Wireless Application Protocol (WAP), General Packet Radio Service (GPRS), and Personal Handyphone System (PHS). Recently, new technology, the Third-Generation Wireless System (3G), which offers broad-band data transmission, has facilitated global mobile Internet services' diffusion. Indeed, the continuing investment that Japan, Korea, Hong Kong and Taiwan have made in 3G mobile Internet services has made these major Asian countries the engines of global m-commerce development [17]. Although mobile Internet services' diffusion plays a crucial role in m-commerce development, little is known about mobile Internet diffusion patterns, and how mobile service providers can build a successful marketing strategy to effectively promote the effective diffusion of mobile Internet services. The authors argue that the quickly updated technology has shortened the product life cycle, which leads to an extremely competitive market environment. The importance of marketing strategies for an innovation is therefore reaching its highness than ever for any manufacturing business /07 $ IEEE 1

2 Apple s ipod, for example, would not be so popular and its diffusion would not be so successful if the appropriate marketing strategies had not been undertaken. Product managers now realize that a new and fantastic technology is not the only key to a bestselling product; a fitting marketing strategy serves a no less important role. The innovation diffusion models offer a unique lens through which influential sources can be examined and fitting marketing strategies can be developed. In the context that mobile Internet services can be viewed as innovations, this study used three different diffusion models to investigate the mobile Internet diffusion process in Taiwan. According to the ITU World Telecommunication Indicator Database, Taiwan s mobile phone penetration rate exceeded one hundred percentage in 2000, and it was the highest in Asia and the world in 2000 and 2002, respectively. However, this high mobile phone penetration rate did not lead to rapid mobile Internet services' diffusion. World-wide Mobile Internet Survey reports [5] indicate that in 2003 and 2004, the mobile Internet adoption rates of top Asian countries were as follows: Japan (65%, 51%), South Korea (52%, 64%), Hong Kong (44%, 55%), and Taiwan (38%, 43%), respectively. In other words, although nearly everyone has a mobile phone in Taiwan, and some even have two or more phones, not many have started to use mobile Internet services. As one of the leading countries in terms of mobile phone usage, this dynamic market is worthy of investigation and identification of the reason(s) for such asymmetric results is very important for managers in related industries, e.g. mobile service providers and mobilecontent providers, and 3G mobile handset manufacturers. The case of Taiwan s mobile Internet services adoption, will also offer fruitful insights of how marketing strategies should be designed and implemented for different innovation product so as to ensure its success. In the sections below, the current state of Taiwan's mobile Internet is introduced, followed by a review of the literature on innovation diffusion theory. Subsequently, innovation diffusion econometric models developed by Mahajan and Peterson [11, 12, 13] are discussed. Empirical results derived from longitudinal statistics, conclusions and implications are consecutively presented. 2. The status of mobile communication in Taiwan Reporting a tremendous growth in the past decade, Taiwan has become one of the most dynamic mobile communication markets in Asia. The ITU World Telecommunication Indicator Database indicates that Taiwan s mobile penetration rate was the highest in Asia in 2000 (100.07%), and also the highest in the world in 2002 (108%). This may have been due to relatively cheap tariffs, the variety of services offered, and a subsidized program for producing popular mobile handsets. The high growth rate suggests a quickly matured market, which has forced Taiwan s mobile service providers to shift their marketing focus from mobile phone user acquisition to improving earnings ability from their existing customers. In other words, the more premium mobile services the customer uses, the higher the Average Revenue per Unit (ARPU) the mobile service providers make. Premium mobile services include the Short Message Service (SMS), Multimedia Message Service (MMS), and mobile Internet services (e.g. Web browsing, mobile messenger, mobile Blog). The SMS is reported to have been the most popular premium service in Taiwan during As regards mobile Internet services, in June 2005, revenue from then only accounted for 6% of total mobile services' revenue in Taiwan, whilst that in Japan and Korea accounted for 26% and 22%, respectively (see Table 1). Moreover, the ARPU of the mobile Internet services of Taiwan (US$1.16) was also greatly lower than that of Japan (US$16.18) and Korea (US$9.5). This suggests there is a huge market potential for Taiwan s mobile Internet services, and mobile service providers should not ignore such potential. Taiwan launched 3G services relatively late compared to mobile telephony in advanced countries in Asia. The major telecom corporate in Japan (NTT Docomo), Korea (SK Telecom), and Hong Kong (Dualband) launched 3G services in late 2001, the end of 2003, and early 2004, respectively, whereas the major telecom corporate in Taiwan (Chunghwa Telecom) only did so in mid 2005 (see Figure 1) [5]. 3G technologies offer faster data transmission than previous WAP and GPRS communication protocols and thus obviate the major problem of lack of acceptance of mobile Internet services. As a result, although the growth of users of mobile Internet services slowed down in Taiwan during 2004, it 2

3 accelerated after the introduction of 3G technology (see Table 2) [5]. Table 1. Comparison of mobile Internet services' revenue between Taiwan, South Korea and Japan South Taiwan Japan Country Korea (US$) (US$) Description (US$) Mobile Internet Services' Average Revenue per Unit (ARPU), June, 2005 Monthly National Income 1,100 1,200 3,000 Monthly National Income Index ARPU/Index Mobile Internet Services' Revenue/Total Mobile Services' Revenue 6% 22% 26% tariff per 10MB data transmission highly decreased in 2006, and is now cheaper (US$6.1) than that of Japan (US$148.8), Korea (US$26.6) and Hong Kong (US$66.0) [5]. In sum, given the existing advantages of high mobile phone penetration rate and attractive price strategies, mobile service providers in Taiwan need to be aware of underlying reasons why mobile Internet services' diffusion is not as good as expected in the country. Using the diffusion models discussed below, this study aims to provide market insights. Table 2. Growth of mobile Internet users in Taiwan Date No. of users Date No. of users 2001/Q3 90, /Q4 3,500, /Q4 280, /Q1 4,280, /Q1 390, /Q2 4,670, /Q2 730, /Q3 4,910, /Q3 1,150, /Q4 5,350, /Q4 1,550, /Q1 6,060, /Q1 1,790, /Q2 6,850, /Q2 2,230, /Q3 7,860, /Q3 2,770, /Q4 8,070,000 Table 3. Comparison of 3G mobile Internet tariffs in major Asian countries Country Tariff per 10MB data Transmitted (US$) 10MB fee/national Income Taiwan % Japan % South Korea % Hong Kong % Fig 1. 3G Mobile service providers in major Asian countries In Taiwan, the attempts of mobile service providers to promote mobile Internet services are clear. For example, Chunghwa Telecom offers a wide range of mobile Internet services, including e-card, sports news, auction biddings, a restaurant and shopping index, video games, astrology, divination, etc. GPS is also integrated into these services, e.g. mobile Internet users can view a digital map, look for an available parking space, search the nearest restaurant/store, and check instant traffic load on a particular route. As regards tariff (see Table 3), Taiwan s mobile Internet 3. Innovation diffusion theory and diffusion models Rogers' [21] innovation diffusion theory centers on the processes by which an innovation "is communicated through certain channels over time among members of a social system" [21]. Rogers argues that both mass media and inter-personal communication impact on the diffusion of an innovation. In other words, potential adopters rely on these two channels to obtain information related to an innovation (i.e. a new type of product or service such as mobile Internet services) in which they are interested. Notably, researchers concur that interpersonal communication plays a major role in 3

4 influencing the speed and shape of an innovation's diffusion [6, 15, 24]. In the context of mobile Internet services' diffusion, this study attempts to determine how the diffusion is influenced by interactions and imitations between acquaintances (e.g. friends, associates, relatives, family members, community members at the Website chat room or the Webblog, or simply those people around us everyday), marketing efforts of mobile service providers (i.e. advertising via the television, radio, magazines or the Interne, or a combination of these factors. Diffusion models assume that the timing of first time adoption of an innovation is distributed in some fashion over the population [21] and express diffusion rate at time t [13], as follows: dn(/dt = g([m - N(] where dn(/dt is the rate of diffusion at time t; N( t t0 n( dt N( is the cumulative number of adopters at time t; m is the total number of potential adopters in a population within a social system; N(t=t0) = m0, the initial number of adopters in a social system; g( is the rate at which adoption occurs. Building on diffusion theory, various econometric diffusion models have been developed so as to depict the diffusion pattern, predict future distribution of an innovation, illustrate possible effects of a policy, or plan marketing strategies for a new product. Mahajan and Peterson [12] have developed three general diffusion models: the external influence model, internal influence model, and the mixed influence model. The external influence model assumes that adoption is driven by information from sources such as advertising external to the social system and only by information from such sources. It describes situations where the adoption influence is exclusively from outside the adoption community and little or no other communication, which leads to effective imitation among members of the social system. The external diffusion model is expressed as equation (1) and the coefficient of diffusion is a constant. Where m is the number of potential adopters, p is commonly called the coefficient of innovation, and p and m must be positive in this model. pt N ( m(1 exp ) (1) The internal influence model hypothesizes that the diffusion rate is affected by interpersonal communication. Earlier adopters influence later adopters. The coefficient of diffusion is a function of the cumulative number of adopters, and the diffusion model is expressed as the equation (2). m -----(2) N( m m0 qmt 1 exp m0 where q, which is expected to be positive, is the coefficient of internal influence. This function is an S- shaped curve with m as the limiting value. The mixed influence model, equation (3), combines the effect of external and internal influences. The mixed influence model is the most widely used model. It yields an asymmetrical S-shaped adoption function where external influence results in more rapid early adoption than imitation alone. p( m m0 ) m exp( ( p qm) p qm (3) 0 N( q( m m 0) 1 exp( ( p qm) p qm 0 Since the mixed influence model combines external and internal influence effects, this research uses the mixed influence model as the basis for analysis. Diffusion models are built under some kind of pattern rather than a random process, therefore, it is necessary to verify the distribution of diffusion patterns after we have found the parameters. Mahajan et al. [15] provided the White-Noise model, equation (4), as the null hypothesis for testing the validity of diffusion models. A diffusion model is useful when it can better explain the innovation-adopting process than the White-Noise model. X( = x(t-1)+( (4) where: x( is the number of adopters at time t; ( is assumed to be N(0, ) Econometric diffusion models are widely used to investigate the diffusion of phone, mobile phone or Internet usage, and their prediction ability is reported to be sound. For example, Chaddha and Chitgopekar [2] used a logistic model to estimate the demand for 4

5 Bell System residence telephones during Wang and Kettinger [27] applied a logistic model to study the diffusion of mobile phone users and cell sites in the United States between December 1984 and June Results suggested that USA mobile phone users would start flattening out in 2015, while USA cell site numbers will reach a saturation point in Kauffman and Techatassanasoontorn [9] applied a modified Bass model and a coupled-hazard survival model to investigate how environmental factors of country, industry, and technology policy impact on the diffusion speed of digital and analogue mobile phones. Multiple standards and high prices were found to slow down digital mobile phone diffusion from the Introduction stage to the Partial Diffusion stage. Competition in both the analogue and digital mobile phone industries also shapes the growth of digital mobile phone diffusion. Diffusion of the Internet, regarded as a technological innovation [18], has also been investigated frequently using econometric diffusion models. Gurbaxani [7] indicated that BITNET s diffusion pattern is S-shaped, suggesting a symmetrical growth process. Rai et al. [20] adopted three diffusion models, i.e. logistic, Gompertz and exponential models, to forecast future Internet development. Using data from August 1981 to January 1994, findings implied that the exponential model was the best fit in the initial growth stage of the Internet. External, internal and mixed influence models have also been used widely to examine different topics. For example, Loh and Venkatraman [10] used these three models to investigate the adoption of information technology (IT) outsourcing. Findings indicated that the internal influence was greater than the external influence among 60 outsourcing contracts during April 1988 and August Dos Santos and Peffers [4] also verified competitor and vendor influences on the adoption of the automated teller machine (ATM) system with these three models. The external influence model was reported to have negative parameters of p and m (i.e. the model was meaningless), while the mixed influence model was found to be the best model fit, and the internal influence model revealed that competitor influence (i.e. interpersonal communication) dominated the bank s decision to adopt the ATM system. 4. Research methodology The current study focused on mobile Internet users in Taiwan sourced from the Focus on Internet News and Data Website (FIND, Longitudinal data was provided quarterly for Q3, 2001 to Q4, 2005 (see Table 2). FIND is organized and sponsored by the Department of Industrial Technology of the Ministry of Economic Affairs in Taiwan. It aims to provide abundant information on Information Communication Technology (ICT) trends in terms of important global news and statistics within the context of development and usage in Taiwan. Its survey results and analyses are referenced and discussed frequently in Taiwan and by international associations. To determine which influence model best explains Taiwan s mobile Internet diffusion pattern, longitudinal data was used to fit external, internal and mixed influence models. NLIN, a non-linear regression procedure of the SAS software package, was employed to estimate the parameters of the three models. The performance of each model was then measured in the fit (adjusted R 2 ) of the estimation model. In addition, to confirm model patterns were not random results, Mahajan et al. s [14] White-Noise model was used. Mahajan et al. [14] contend that a diffusion model is useful when it explains the innovation-adoption process better than the White- Noise model. Therefore, a J-test [3] was applied to test the external influence model, external influence model, and mixed influence model against the White-Noise model. 5. Results Table 4 summarized the parameter estimation and model fit for external, internal and mixed influence models. All coefficients are statistically significant (p<0.05). However, the estimated coefficients p and m of the external influence model are negative, i.e. they are meaningless since they suggest the number of potential adopters is negative and the number of adopters per period will increase boundlessly as the number of remaining adopters decreases. In other words, the external influence model, which asserts mobile Internet diffusion is purely via advertising, is not able to describe the actual diffusion pattern of Taiwan s mobile Internet diffusion. 5

6 Table 4. Parameter estimation and model fit Parameter Estimates Model Adjusted R 2 Parameter Value p *** External m *** q *** Internal m *** p ** Mixed q ** m * * p<0.05 ** p<0.01 *** p<0.001 Ten Thousand People Num ber of A doptors a /Q3 2001/Q4 Mobile Internet Adoptions Internal Influence Model 2002/Q1 2002/Q2 2002/Q3 Fig. 2 Internal influence model fit 2002/Q4 2003/Q1 2003/Q2 2003/Q3 2003/Q4 2004/Q1 2004/Q2 2004/Q3 2004/Q4 2005/Q1 2005/Q2 2005/Q3 2005/Q4 In contrast, both internal and mixed influence models fit the data points with very high adjusted R2, reporting and 0.993, respectively (see Table 4). Supportively, the data plot (see Fig. 2 and 3) also shows that the mixed influence model has a better visual fit, since the internal influence model underestimates the diffusion pattern between 2002/Q2 to 2004/Q2, but over-estimates it between 2004/Q3 to 2005/Q1. Given the mixed influence model is the additive combination of external and internal influences, it is not surprising to find the mixed influence model is the best fit. However, since the internal influence model reported a very high-adjusted R2 of , it is reasonable to infer that interactions and imitations among acquaintances (i.e. interpersonal communication) prevail in Taiwan s mobile Internet diffusion pattern. Ten Thousand People 900 N um ber of U sers a Mobile Internet Adoptions Mix Influence Model 2001/Q3 2001/Q4 2002/Q1 2002/Q2 2002/Q3 Fig. 3 Mixed influence model fit 2002/Q4 2003/Q1 2003/Q2 2003/Q3 2003/Q4 2004/Q1 2004/Q2 2004/Q3 2004/Q4 2005/Q1 2005/Q2 2005/Q3 2005/Q4 To ensure the robustness of the results, a J-test was carried out (see Table 5). Both internal and mixed influence models were statistically significant (p<0.001) when tested against the White-Noise model using the J-test. In other words, the mobile Internet diffusion pattern described by these two models is not a random process. In sum, this study s findings suggest that the mixed influence model can accurately reproduce Taiwan s mobile Internet diffusion. Further, consistent with previous studies [16, 21], acquaintances communication predominantly affects Taiwan s mobile Internet diffusion pattern. Table 5. J-test model comparisons Null Model Alternative Models White Noise Internal Model Mixed Model t-statistic p-value 0.011* *** * p<0.05 ** p<0.01 *** p<0.001 Supportively, the World-wide Mobile Internet Study [26] revealed that in 2004 and 2005, friends and Internet communities were the two dominant factors influencing individual users adoption of mobile services in the U.S. (see Figure 4). In contrast, fewer respondents reported that their mobile phone adoption resulted from the mass media, such as TV, TV events, radio, and movies. This empirical report provides strong evidence confirming our findings. 6

7 Fig. 4. How people learn about mobile services 6. Discussions Although technology innovation is crucial for any company or industry in the context of new product development, it will fail or even disappear without a fitting marketing strategy. Indeed, while the product innovation is assumed to increase consumer utility, it is effective only if the innovating firm invests in marketing, so that consumers become aware of the newly developed product. Dos Santos and Peffers [4] argue that the advertising effect is very important during the first few years after the innovation introduction. However, every innovation has its own features. Some may continue to diffuse quickly if the advertising approach is undertaken, whilst others may require the acquaintance s recommendation approach for the diffusion to take off. Still others may involve the hybrid approach. For example, Brancheau and Wetherbe [1] reported that early adopters were more likely to rely on mass media advertising in the adoption of spreadsheet software. However, amongst later adopters, interpersonal communication was instrumental in the diffusion of spreadsheet software. Managers should thus be aware of and answer this critical question once the number of adopters begins to grow: For a specific innovation, which truly is the engine underlying its diffusion in different stages? In the case of Taiwan s mobile Internet diffusion, this study indicated that the mixed influence model describes the diffusion pattern of Taiwan s mobile Internet services reasonably well and acquaintances influence in terms of interactions and imitations is the dominant factor. Consistently, the network consumption externalities concept [22] asserts that the value of a communications network is determined by the size and content of the set of existing users, that is, the more people who use the mobile Internet service, the more that will want to join that network. The joining of each new user not only increases the utility of the system but also leads to non-users' imitation and interest. Such findings may explain why Taiwan s mobile Internet services diffusion is slow, despite its very high mobile phone penetration rate. In the past, mobile service providers in Taiwan facilitated mobile phone penetration mainly via reduced within-network tariffs. This strategy, which is consistent with the internal influence model and network externalities theory, took full advantage of acquaintance s influence, and contributed to Taiwan having the highest mobile phone penetration rate in Asia and the world. However, a different approach, i.e. the advertising approach, was employed when providers planned to promote mobile Internet services. Although this approach has increased the penetration rate of expensive 3G handsets, it has not led to the successful diffusion of mobile Internet services. Based on this study s findings, the reason for this becomes somewhat clearer. Possessing the feature of network externalities, acquaintances' communication is likely to be the dominant channel for the diffusion of mobile Internet services after the initial stage. Marketing strategies should thus be developed to facilitate such diffusion. For example, existing users of mobile Internet services who have successfully persuaded another mobile phone user to adopt these services should be rewarded with free online minutes or premium tariffs. Further, the more people s(he) has recommended successfully, the more free minutes or improved tariffs s(he) should receive. Any group registering with a mobile Internet service should also receive premium tariffs or services, e.g. personalized ring tones, icons or the allotment of a special online chat room for its use only. At the same time, a company package should be designed and promoted. It is increasingly common for companies to offer their managers and salespersons a mobile phone for work purposes. As long as the package meets business benefit, it will not be too difficult to persuade a company's president to sign a company use contract and to offer all other essential employees convenient mobile Internet services. The price premium may be a key issue, but other value-added services can attract the interest of such company president/managers. For example, the company's logo can be attached free of charge to each short message or sent out. Further, data transmission from company mobile handsets to the company's Website can be provided free (e.g. access, search and download from the company's Website or FTP site). Further, mobile Internet services' user interfaces and contents can be 7

8 customized to meet business requests and satisfy the working needs of managers and salespeople (e.g. a special organizer, an e-card organizer to enhance the customer relationships, and an industry news organizer which collects world-wide related economic, financial and social news or repor. The aim of these value-added services is to meet the needs of busy managers and salespeople and enable them to deal with their work without time and location limitations. Once this business model is set up and running well, the imitation effect is no longer limited to the personal level, but will speed up mobile Internet services' diffusion at the company level. Mobile service providers can also seek alliance opportunities with mobile s, mobile messengers, famous Webblogs and Web communities built for intensive user communication and interaction. For example, Wretch Webblog ( famous for its photos and articles in Taiwan, has reached 48, 471, 061 viewings per day, which was recently reported to be the top seven globally. This outstanding number of viewings is second only to that of the well-known search engine 'Yahoo!Taiwan'. It would be a useful promotion strategy for mobile service providers to develop an Interface for mobile phone users to easily transmit pictures shot by them, store them in their mobile handsets attached to the Webblog, and then share them with all the community. The convenience of taking pictures at any time and anywhere and being able to share them instantly with the community should provide a strong motivation for existing users to adopt the mobile Internet service, and the community's influence in terms of imitation and interactivity will be wisely leveraged. As regards the influence of advertising, this study's findings indicated that it contributed positively to the explanatory ability of the mixed influence model. Therefore, practitioners should not ignore this influence when designing strategies to market mobile Internet services. Advertising influence would double if a popular pop group, e.g. Boyzone in the United Kingdom, were to appear in advertisements extolling the benefits of mobile Internet services and describing them as a 'must' for today's younger generation, making new friends and communicating globally. Weaving the influences of acquaintances and advertising together, promising results can be expected. To conclude, identification of the reasons for Taiwan's present inadequate diffusion of mobile Internet and how the service providers can design a fitting marketing to facilitate further diffusion are of vital importance to other countries. Indeed, successful diffusion of mobile Internet services will not only influence the future development of mobile service providers and Internet content providers world-wide, but also indicate the future business trend for mobile phone related industries, e.g. the design and manufacture of 3G handsets and accessories. 7. Conclusion The amazing speed of technological advances today has made the marketing strategies of innovations a key issue for any technology-oriented or manufacturing business. Apple s ipod may be one of the best examples. Its extraordinary success was not only due to the delicate design and functions, but also a result from the appropriate marketing strategies. Product managers now realize that to build a top-selling product, a appropriate marketing strategy has played an equal importance as the new and fantastic technology. For international practitioners and researchers, the innovation diffusion models offer a unique lens through which influential sources can be examined and fitting marketing strategies can be developed. In this study, through which communication media do the mobile Internet services diffuse in Taiwan was examined, and suggestions to and revisions of current marketing strategies were also proposed. The study has successfully demonstrated how the influence source of an innovation diffusion should be linked to its marketing strategies, and how these strategies should be designed and implemented according to the features of the innovation its own. That is, if the marketing strategy does not fit the influential source of the innovation diffusion properly, various resources including time, labor and money invested by the company or government are wasted without any significant achievement. In the contrary, if managers realize that different innovation product may require its unique marketing strategies to take off its diffusion, an instant market response may be found once the adequate marketing strategies are undertaken. In sum, for manufacturers developing innovation products at different categories, it is essential that managers are able to discover the influential source of new product s diffusion before its market release. The innovation diffusion model is an effective technique to help marketers investigating the influential sources, and from which, unique marketing strategies could be developed accordingly. For example, if the 8

9 interpersonal communication is the dominant factor, liaisons and imitation between customers should be highlighted. Webblog or recommend-for-free-gift discussed previously is a useful means for this type of innovation. If it is the mass media communication, advertisings on TV, radios, Internet and magazines will be the most efficient ways to promote this type of innovation. Further, if mixed-influence source is revealed, then a hybrid of both methods, e.g. advertising using popular idol group to emphasize the interactions within the group as discussed previously may achieve the best effect. It is vital for managers to be aware of that the consumer behaviors change all the time, and sometimes these changes may be the result from particular market incidents. Thus, managers should monitor the market changes in terms of competitors actions or government new laws so as to revise their influence source analysis accordingly or develop new marketing strategies instantly. By doing so, the company has built its competitive advantages for the innovation product via using the innovation diffusion model to fight against the endless price competition and the shorten product life cycle. 8. References [1]. Brancheau, J. C. and J.C. Wetherbe, The Adoption of Spreadsheet Software: Testing Innovation Diffusion Theory in the Context of End-User Computing, Information Systems Research, 2 (1990), pp [2]. Chaddha, R.L. and S.S. Chitgopekar, A Generalization of the Logistic Curves and Long-range Forecasts ( ) of Residence Telephones, The Bell Journal of Economics and Management Science, 2 (1971), pp [3]. Davidson, R. and J.G. MacKinnon, Several Tests for Model Specification in the Presence of Alternative Hypotheses, Econometrica, 49, 3 (1981), pp [4]. Dos Santos B. L. and Peffers, K., "Competitor and Vendor Influence on the Adoption of Innovative Applications in Electronic Commerce", Information and Management, 34 (1998), pp [5]. Find, How Many - Worldwide Mobile Internet Survey (WMIS), 2006, available: p=1 (accessed June 2006). [6]. Gatignon, H. and T. S. Robertson, Technology Diffusion: An Empirical Test of Competitive Effects, Journal of Marketing, 53(1989), pp [7]. Gurbaxani, V., Diffusion in Computing Networks: The Case of BITNET, Communications of the ACM, 33, 12 (1990), pp [8]. Ipsos-Insight, Wireless Internet access set to flourish, 2004, available: (accessed May 2005). [9]. Kauffman, R., and A. Techatassanasoontorn, International Diffusion of Digital Mobile Technology: A Coupled-Hazard State-based Approach, Information Technology and Management, 6, 2 (2005), pp [10]. Loh, L. and N. Venkatraman, Diffusion of Information Technology Outsourcing: Influence Sources and the Kodak Effect, Information Systems Research, 3, 4 (1992), pp [11]. Mahajan, V. and R. A. Perterson, Innovation Diffusion in a Dynamic Potential Adopter Populaton, Management Science, 24 (1978), pp [12]. Mahajan, V. and R. A. Perterson, Integrating Time and Space in Technological Substitution Models, Technological Forecasting and Social Change, 14 (1979), pp [13]. Mahajan, V. and R. A. Peterson, Models For Innovation Diffusion, Beverly Hill, CA: Sage Publication Inc., [14]. Mahajan, V., E. Muller, and F. M. Bass, Diffusion of New Products: Empirical Generalizations and Managerial Uses, Marketing Science, 14(1995), pp [15]. Mahajan, V., S. Sharma, and R.A. Bettis, The Adoption of the M-Form Organizational Structure: A Test of Imitation Hypothesis, Management Science, 34, 10 (1988), pp [16]. Okazaki, S., What do we Know about Mobile Internet Adopters? Information and Management, June, [17]. Ovum, 2006, available: (accessed June 2006) [18]. Prescott, M.B. and Slyke, C.V., The Internet as an Innovation, Proceedings of the Association of Information Systems Conference, [19]. Probe Group, Mobile Internet usage to surge, May 2, 2004, available: mobile_internet_usage_to_surge.htm (accessed November 2005). 9

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