The primary objects of this research were applying the innovation diffusion model to internet hosts and internet subscribers . The results of this study may be a useful reference to the further concept building of the relationship of innovative and adopters

Huang, Hsiang-Chi

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Association for Information Systems AIS Electronic Library (AISeL) ICEB 2002 Proceedings International Conference on Electronic Business (ICEB) Winter 12-10-2002 Taiwan Internet Diffusion: An Empirical Study of Internet Interactive Effects Hsiang-Chi Huang Follow this and additional works at: https://aisel.aisnet.org/iceb2002 This material is brought to you by the International Conference on Electronic Business (ICEB) at AIS Electronic Library (AISeL). It has been accepted for inclusion in ICEB 2002 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org. Taiwan Internet Diffusion: An Empirical Study of Internet Interactive Effects  Hsiang-Chi Huang FIND-ECRC Institute for Information Industry Taipei, Taiwan illusion@iii.org.tw   Abstract The primary objects of this research were applying the innovation diffusion model to internet hosts and internet subscribers . The results of this study may be a useful reference to the further concept building of the relationship of innovative and adopters.  1. Introduction  During the last decade, there has been a lot of optimism regarding Internet Development. Reports and news showed that Internet growing at an exponential pace with no predictable upper bound or saturation limit. However, in March 2000, with NASDAQ Internet stocks falling, no one thought about it but rather than returned to the reality. [4][6]  Internet hosts’ and Internet subscribers’ growth at an exponential pace happened in early stage of Internet development. Common customer frustrations are concluded because of slow response time, inaccessible online service…etc. These problems can follow inadequate capacity planning caused by ignorance of the Internet growth pattern. [2]   After a six-year growth boom, Internet growth rate slow down. Analysts are claiming that Internet adoption by consumers has essentially saturated. Since Internet stock fell, stockholders are eager to see the real profit than gold in the heaven.   Thus for Internet companies accessing Internet subscribers and hosts accurately is a daunting but increasing important task. The purpose of this study is to understand Taiwan internet diffusion models of internet subscribers and internet hosts and the relation between internet subscribers and internet hosts.  2. Internet Diffusion  2.1. Internet Diffusion Model  The diffusion model is one classical model used to study the impact of a specific technology. The model is informed by the diffusion of innovation and various economics theories and assumes that non-adopters of an innovation are increasingly likely to imitate adopters.   In other words, one person adopted because he or she observes the success of another person who has already adopted.   However, the number of adopters in a social system has its upper limit. Thus growth rates increases first and then decreases over time to achieve a finite saturation level. We call the point at the maximum growth rate of the growth models “the inflection point”. Some growth curves are called “S-Curve” because of its shape.  Empirical studies shows the model have been used in many disparate fields such as social study ,public health ,management, sociology, marketing, communication networks to model diffusion through a population of adopters or subscribers.[7]  2.2. Mathematics form of growth model  There are three common growth models –the exponential model, the logistic model and the Gompertz model. [3]  2.2.1. The exponential model The exponential model assumes a constant rate of growth and thus does not involve either an inflection point in the growth rate or a definite saturation limit. Some studies shows in the early stages of its diffusion, the exponential model is suitable for understanding the growth. And the form of the exponential model  Y=A*exp( B*T )           (1)  In equation (1), Y is the cumulative number of existing adopters. A、B are constants. T is the period.  2.2.2. The Logistic model Unlike the exponential model, the logistic model assume variable growth rate whereby the growth rate first increase and then decrease over time. The form of the Logistic model (also known as Pearl model)  Y=A/ (1+B*exp(-C*T))       (2)  In equation (2) , Y is the cumulative number of existing adopters. B、C are constants. T is the period. A is the upper limit of Y.  2.2.3. The Gompertz model In this model, the rate of diffusion is a function of existing adopters and the difference between the logarithms of the number of adopters at the saturation level and the existing number of adopters. The form of the Gompertz model   Y=A*exp(B*exp(C*T))            (3)  In equation (3),Y is the cumulative number of existing adopters. B、C are constants. T is the period. A is the upper limit of Y.  2.3. Interactive Process of innovations and adopters  The Internet is a communication mesh of networked computers with their associated resources, including but not limited to email, ftp, gopher and the web. Organizations and individuals may adopt and use varying subsets of the Internet. [5]  In other words, Internet users could communicate with each other through “Internet” such as email, news groups…etc and interact with internet resources (webs, mail servers, ftp servers).   This interactive process first studied in the 1980s by diffusion researchers. Researchers found that interactive innovations (telephone, fax, email) possess different characteristics from innovations spread in the normal diffusion networks (mass media, interpersonal networks).   The main feature of the interactive process of innovations and adopters is its two-way communication process. In the case of non-interactive innovations, the earlier adopters have a sequential interdependence effect on later adopters. But in the case of interactive innovations, not only earlier adopters influence later adopters but later adopters also influence earlier adopters.[9]   Based on interactive innovations, one researcher showed internet client-server architecture may cause a new many-to-many communication model. .   In dual acceptance theory, diffusion of internet adopters depends upon internet servers whereas the diffusion of the web server depends upon internet users.  Chen’s study indicated that if there are only a few web servers, using the web is of the little value to users. Similarly, if there are only a few web users on the internet, creating a web server is of little value to organizations or business. That is the rate of adoption of web servers depends upon the number of users, not the number of web servers.[1]  3. Research Method  This study attempts to use diffusion model to access diffusion models of Taiwan internet users and internet hosts ,but also to fit the dual acceptance model.   The data of Taiwan internet hosts comes from Internet Software Consortium. The number of Taiwan Internet hosts equals to the sum of the number of .tw domain name and the number of  hinet.net domain name. Hinet is the largest ISP in Taiwan and other ISP domain name is including in .tw domain name.  The data of Taiwan internet users comes from Taiwan Directorate of Telecommunications.[8]  At the beginning this study, we use the data of internet users and hosts in Taiwan to fit the diffusion model . Secondly, we apply Taiwan Internet diffusion model for dual acceptance model.     Table 1 Taiwan Internet hosts Time Taiwan Internet Hosts Jan-95 14618 Jul-95 16166 Jan-96 25273 Jul-96 30645 Jan-97 34650 Jul-97 40533 Jan-98 176836 Jul-98 353523 Jan-99 519597 Jul-99 676623 Jan-00 849652 Jul-00 1156573 Jan-01 1349261 Jul-01 2293024 Jan-02 3433129 Jul-02 3544102 Source: Internet Software Consortium   Table 2 Taiwan Internet Users Time Taiwan Internet Users (thousands) Jul-96 440 Jan-97 600 Jul-97 1260 Jan-98 1660 Jul-98 2170 Jan-99 3010 Jul-99 4020 Jan-00 4800 Jul-00 5600 Jan-01 6273 Jul-01 7214 Jan-02 7824 Jul-02 8080 Source：Taiwan Directorate of Telecommunications  4. Analysis and Discussions  4.1. Analysis of Taiwan Internet Hosts We use the data regarding the number of Taiwan Internet hosts from Jan-95-Jul-00 at half-year intervals, based on reports publish by Internet Software Consortium to fit three growth models. Which model is the best? For R2, these three growth models are acceptable. （Table3） Table.3 Regression Models of Taiwan Internet hosts Model R2 Model Logistic 0.9813 Y=6794521/(1+844.38*exp(-0.4339*t))Gompertz 0.9760 Y=65693134*exp(-10.70*exp(-0.0826*t)) Exponential 0.9737 Y=33328*exp(0.2976*t)  4.2. Analysis of Taiwan Internet Users We use the data regarding the number of Taiwan Internet hosts from Jan-95-Jul-00 at half-year intervals, based on reports publish by Internet Software Consortium to fit three growth models. Which model is the best?  For R2, these three growth models are acceptable. （Table 4） But if we look at the trend of the three model, the exponential model seems not unacceptable.   Table.4 Regression Models of Taiwan Internet Users Model R2 Model Logistic 0.9982 Y=9044.2858/(1+80.38*exp(-0.4089*t))Gompertz 0.9982 Y=11366.30*exp(-7.7977*exp(-0.1997*t)) Exponential 0.9378 Y=701.3346*exp(0.1607*t)    Diffusion of Taiwan Internet hosts0100000020000003000000400000050000006000000Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02 Jan-04Hitorical data Logistic ModelGompertz Model Exponential Model Figure 1 .Diffusion of Taiwan Internet hosts   Diffusion of Taiwan Internet Users010002000300040005000600070008000900010000Jul-96Nov-96Mar-97Jul-97Nov-97Mar-98Jul-98Nov-98Mar-99Jul-99Nov-99Mar-00Jul-00Nov-00Mar-01Jul-01Nov-01Mar-02Jul-02Historical data Logistic ModelGompertz Model Exponential Model Figure 2 .Diffusion of Taiwan Internet Users  Figure 3.Taiwan Internet Users Per hosts  4.3. Analysis of Dual Acceptance Model Dual acceptance model implied that internet hosts is related to internet users and the relation of internet hosts and internet users follows a linear relationship.   Though the Correlation coeefficient of Taiwan internet users and Taiwan Internet hosts appears to be a high relationship ( r=0.9193), the total number of internet hosts doesn’t follow a stable linear relationship with the total number of internet users , but follows a decreasing  relationship.(Table 5, Figure 3).  Dual acceptance theory indicated the rate of adoption of web servers depends upon the number of users, not the number of internet hosts, which means more internet users, more internet hosts. But the growth rates of Taiwan internet hosts and internet users changes every period and don’t follow the dual acceptance theory. In short ,the data of Taiwan Internet users and hosts doesn’t support. dual acceptance theory.   Table 5.Taiwan Internet Users per host Time Internet Users Per host Jul-96 14.36 Jan-97 17.32 Jul-97 31.09 Jan-98 9.39 Jul-98 6.14 Jan-99 5.79 Jul-99 5.94 Jan-00 5.65 Jul-00 4.84 Jan-01 4.65 Jul-01 3.15 Jan-02 2.28 Jul-02 2.28  5. Conclusion  Although the Internet is still developing now, we couldn’t get the whole picture how Taiwan internet users interact with Taiwan internet hosts. However the results of this study still prove some useful finding to the concept of the diffusion process of innovative and adopters. They are summarized as following:  5.1. Models of diffusion of innovations  Developing models that explain the growth process is critical for policy formulation, capacity planning and introduction new network hardware and software. Growth projection informs providers of Internet products and service of the potential consumer base.    This paper has presented that Gompertz and Logistic models could be used for forecasting regional Internet diffusion.  The exponential model is only useful at early stage of diffusion of innovations. For a long-term planning, the general S curve regression model could be better fitting models.  5.2. Dual acceptance theory in Taiwan  In dual acceptance theory, diffusion of internet adopters depends upon internet servers whereas the diffusion of the web server depends upon internet users. The rate of adoption of web servers depends upon the number of users, not the number of internet hosts.  The Correlation coeefficient of Taiwan internet users and Taiwan Internet hosts supports the relationship of Internet hosts and Internet Users.   However , the total number of internet hosts  follows a decreasing relationship with the total number of internet users. The data indicated the rate of adoption of internet hosts increased faster than the rate of adoption of internet users. And this doesn’t follow the study of Chen’s research.  This finding implied that dual acceptance model should be modified. The dual acceptance model is a new concept which has not been sufficiently explored. Further research on the dual acceptance model is needed to expand the knowledge about on the relationship of innovative and adopters and the consequence of innovative.    References [1]Chen, Hsiang, Dual Acceptance of Web Diffusion: A case of Clients and Servers.26th Annual conference of the Canadian Association for information Science.  [2]Goodman, S.E., Press, L.I., Ruth, S.R. and Rutkowski, A.M., The global diffusion of the Internet patterns and problems. Communications of the ACM 37, 8 (Aug. 1994), 27-31.  [3] Martino, P.Joseph.Technological Forecasting for Decision-making. McGraw-Hill Press (1994) [4]Press, L.Tracking the global diffusion of the Internet.Comm.ACM 40,11 (1997),11-18  [5]Prescott, M..B , Slyke ,Craig Van, The Internet as an Innovation, the Second Americas Conference on Information Systems(1996).  [6]Rai, A., Ravichanran, T., and Samddar, S., How to anticipate the Internet’s global diffusion. Association for computing Machinery. Communications of the ACM 41, 12 （1998） 97-106.  [7]Rogers, E. Diffusion of Innovations. Free Press, New York, N.Y., 1983.   [8]Taiwan 2001 Internet Almanac (Chinese Edition), MOEA press, (2001) ISBN-957-01-0228-4  [9]Williams, F.R., Rice,R.E.,and Roger,E.M., Research Methods and the New Media, New York, Free Press.  

Taiwan Internet Diffusion: An Empirical Study of Internet Interactive Effects

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Taiwan Internet Diffusion: An Empirical Study of Internet Interactive Effects

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