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Diffusion and Network Effects - Essay Example

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The object of analysis for the purpose of this paper "Diffusion and Network Effects" is Facebook which is known for regularly coming up with innovations such as timelines, in-built chat services, ads, and common interest groups, which enhance the user experience…
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Diffusion and Network Effects
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Extract of sample "Diffusion and Network Effects"

?Topic: The Diffusion of Facebook and Network Effects Introduction Facebook, which currently has a user base of over a Billion active users, was founded in 2004 by March Zuckerberg (Fowler, 2012). In 2012, the annual revenue of the organization was $5.1 billion and during the initial public offering which took place in the same year, the organization was valued at $104 billion (Sengupta, 2012 ). Facebook is known for regularly coming up with innovations such as timelines, in-built chat services, ads and common interest groups, which enhance the user experience. 2. The Diffusion Process for Facebook According to Rogers (2005) Diffusion of Innovation takes place through a heterogeneous type of adopters, who are characterised by their own specific social, personal and psychological profiles. It starts with Innovators, who are the educated, risk oriented, information seeking people and who like to try new experiences. These roughly comprise of 5% of the population (Davidoff and Kleiner, 1991; Dewett, Whittier and Williams,, 2007). Next, are the early adopters, who are educated young and popular people and may be opinion leaders in their groups (David, 1985). Next are the early majority who are cautious and wait out to see how new products are reviewed by the early adopters. The early majority consist of a large number of people and these are also instrumental in propelling the late majority (who are older, conservative and low on social activity) into adopting the technology. The Laggards are the remaining people who missed out on adoption and may adopt the product toward the maturity of the product (Farrell and Saloner, 1985). Figure 1: Diffusion of Innovation Curve (Source: Rogers, 2003) In the case of Facebook, the adoption process started with ‘‘Innovators’, but these e Innovators were the people who already had adopted older social networking versions like MySpace and Orkut. With the creation of Facebook, these ‘Innovators’ simply converted to Facebook and encouraged the early adopters to choose Facebook when they were ready to embrace the online social-networking concept (Fernandes, 2011). However, the diffusion of Facebook during the initial phases was also constrained by the diffusion of the Internet. Innovators and early adopters were therefore largely confined to the USA, Western Europe, Australia, and parts of India and China. As the Internet penetration increased with the development of broadband technology and liberalisation in other parts of the world, it spread to Middle East and then to the Latin America, Africa and Eastern Europe and Russia (Fowler, 2012). So, while regions like the Middle East, Brazil, South Africa and Russia were now in the ‘Innovators’ and ‘Early Adopters’ phases, at the same time, the USA had and Western Europe had moved on to ‘Late Majority’ and the ‘Laggards’ phase as shown in the following figure. Figure 2: Stage of Adoption (Facebook) Source: CheckFacebook.com, 2010 3. Five Factors that Determine Success or Failure of Adoption Adoption, which is an individual process that depends on several attributes of the innovation like the relative advantage, compatibility, complexity, trialability and observability. Figure 3: Factors Determining Success of Adoption (Facebook) Relative Advantage Relative advantage, as the phrase suggests, is the perceived value added or additional advantage that a person thinks he can derive from adopting the new product (Haggman, 2009). In the context of Facebook, it offered the relative advantage over sites like MySpace and Orkut because of its higher level of security, the ability to limit who views the profile and ease in uploading and managing photographs. The user interface of Facebook is also much more user friendly. Also, the spam prevention mechanism on Facebook is much stronger when compared to other social networking sites such as Orkut. So, users perceive it to be a more secure and private option than the existing ones. Compatibility Compatibility is the level of ‘fit’ with the users current life, lifestyle or the technology that he or she uses (Hochbaum, 2011; Rogers, 2003). Innovations which do not ‘fit in’ or which cannot get assimilated into the lives of the users are likely to fail. For Facebook, the biggest advantage was that it was based on the concept of social networking which in itself is nothing new; instead it caters to an inherent need of humans. The innovation related to the conversion of the same physical social networking to online with the added benefit of mammoth expansion in terms of an individuals’ reach and potential to make contacts and friends. So, Facebook was easily assimilated in the lives and lifestyle of the adopters. Complexity Complexity determines the difficulty of use or access, and it can seriously restrict adoption by an individual (Leibenstein, 1950; MacVaugh and Schiavone, 2010). On the other hand, Facebook began as a user friendly platform which was very simple and easy to use. Trialability This is the attribute related to the ease of testing the innovation (Love and Roper, 1999; Malik, 2013). If an individual who wants to try out an innovation is faced with a long procedure of sign up, lengthy instructions or manuals, or has to buy costly accessories or hardware software as a prerequisite, he may be overwhelmed with the process and give up. In the case of Facebook, the site was made very easy to try and anyone can build a quick short profile for starters and become functional on it. It ran on the readily available operating systems and machines and no additional effort or costs were incurred by the users. Observability People are impacted by the observability of the product. The more the product is advertised and made visible, the more it will catch the attention of the user and encourage adoption (Meade and Islam, 2006). 4. Factors that Determine Success or Failure of Diffusion While ‘adoption’ of innovation is an individual decision and factors related to the perceived advantages, simplicity, ease of trying and compatibility play a role in the decision process, the process of diffusion or the spread of the innovation is dependent on several additional factors that potential users’ gauge and which enables (or prevents) the diffusion of an innovation. Figure 4: Factors Impacting Success of Diffusion (Facebook) Type of Innovation-Decision The diffusion of innovation depends on the types of decisions related to its adoption. For example, the decision to adopt or not may be individual’s choice, a groups’ consensus, or an authoritative command (Nooteboom, 1994; Meade and Islam, 2006). In the first case, diffusion is more rapid as individuals can decide based on the utility and value of the decision to them (even though they are influenced by interpersonal communications and recommendations of the friends) (Parker and Alstyne, 2005). Facebook, being an individual adoption decision is easy and quick to make, and this is one of the reasons for the rapid rate of diffusion. Communication Channels The diffusion rate also depends on the availability of communication channels, both interpersonal and mass media channels through which information about the innovation can be conveyed to people (Rogers, 2005; Rohlfs, 1974). In the case of Facebook, the biggest communication channel is the interpersonal one where user generated awareness and recommendations created interest and curiosity for the new site. There was very little mass media or sender/inventor generated communication channels employed by Facebook, and till date the site invests very less into public media advertising. However, Facebook uses additional means for spreading the word which include providing novel apps, allowing Facebook users to log into multiple sites, creating additional features that enhance user experience and privacy. However, these activities are used to communicate about the site direct to the users. Nature of the Social System The diffusion of innovation depends on the acceptability of that innovation within the socio-cultural norms as understood and followed by the target population (Shin, 2010). For example, in a community, where it is taboo for men and women to date prior to marriage, any innovation (for example, a new user friendly, authentic dating site), that encourages dating between men and women is bound to fail. In the case of Facebook, it gained acceptance starting with the high school and college community to wider friendship and professional networks and communities (Sengupta, 2012.). People across socio-cultural dimensions found the service useful and fit in with their needs. Extent of Change Agents’ Promotional Efforts In the case of early adopters, some are expected to be vocal opinion leaders and expend voluntary time on promoting the innovation and convincing others to adopt too (Sundbo, 1998; Trusov, Bucklin and Pauwels, 2008). This is a crucial aspect of diffusion as these change agents give it an initial fillip. In the case of Facebook, this change agents did not really had to get involved to a great extent as the network effect set in early on. 5. Network Effects and Impacts on Facebook Diffusion The Network Effects is a phrase used to understand how consumers perceive value of a product or an innovation. According to Sundararajan (2007), the innovation has a degree of inherent and intrinsic value for the customers, but the customers also tend to attach more value to the innovation if it is being adopted and used by a large number of others. So, while in the initial phases, the innovation is adopted b innovators or earl adopters, the later phase sees more and more people joining in on the recommendations of these earl adopters or on perceiving the value that the early adopters are driving from the product (Katz, and Carl, 1994; Wejnert, 2002). Network effects are gauged by directly assessing the number of people using the innovation and also by assessing the social and communication reach of these people (Katz and Shapiro, 1985; Liebowitz and Margolis, 1994). In the case of Facebook diffusion, its adoption by a large number of users created ‘positive externalities’ (sent out the signal that it is extremely useful and essentially needed by everyone) and added to its value. The more the number of users on Facebook, the more the others too would like to join in. This created what is called a ‘bandwagon effect’ (Kirkpatrick, 2010). Facebook is no longer a place where you could just look up friends or hang out on chat; it is now a part of your social existence and it has the potential of impacting your professional and personal life. And this perception is brought about by the fact that ‘everybody‘ who could possibly matter to your professional growth (or personal life) is on Facebook and can access some information on you. This perception added tremendous value to Facebook and created further network effects as more people want to join in and not miss out on what’s happening (Woodside and Biemans, 2005). Another way network effects have played a role in the case of diffusion of Facebook is the diffusion of online advertising. Since, online advertising was already in a growth stage with Google and Yahoo ads, the merchants and advertisers were already aware of the immense potential and reach of online advertising (Woodside and Biemans, 2005). This readymade market comprising of the businesses who wanted to advertise online and create an online presence realized the tremendous scope for using Facebook as an advertising platform. This again led to the diffusion of Facebook across corporate and business networks where companies started adopting Facebook and used Facebook pages as official platforms for reaching out to their own customers. Additionally, Facebook ads further added to the diffusion of the site across more users. It can therefore be seen that the Network Effects have positively impacted the rapid diffusion of Facebook. Conclusions The above discussion has highlighted the fact that diffusion of innovation is a process that occurred in a heterogeneous manner in the case of Facebook, with people from Internet enabled countries leading in Innovators and Early adopters. The different parts of the world are therefore witnessing different stages of diffusion for Facebook, with the US and West Europe currently in the last laggard stages, followed by India, China in the early majority stage, and the Middle East, Latin America in Innovative stages. The factors that impact on adoption include the relative advantage, compatibility, complexity, triability and observability while the factors that impact on the rate of diffusion include the type of innovation-adoption decision (individual, collective or authoritative), availability of communication channels, types of social systems, and presence of change agents and promoters. In addition, the diffusion is affected by network effects that include both direct and indirect network effects. The direct network effects include the perceived enhancement in value for an innovation based on the assessment that a large number of people are already using it. The indirect network effects include the increase in diffusion of innovation due to the diffusion of another related product, like Internet penetration positively impacted on the diffusion of Facebook. References Besen, S. M., and Farrell, J. 1994. Choosing How to Compete: Strategies and Tactics in Standardization, Journal of Economic Perspectives, 8(2), pp.117-131. CheckFacebook.com, 2010. [Online]. CheckFacebook.com Available at: http://www.slideshare.net/limbyk/the-diffusion-of-facebook Accessed March 11, 2013. David, P. A. 1985. Clio and the Economics of QWERTY. American Economic Review, 75(2), pp. 332-337.  Davidoff, L. and Kleiner, B. H.1991. New Developments in Innovation Diffusion. Work Study, 40 (6), pp.6 – 9 Dewett, T., Whittier, N. C. and Williams, S. D. (2007). Internal diffusion: the conceptualizing innovation implementation. Competitiveness Review: An International Business Journal incorporating Journal of Global Competitiveness, 17(1/2), pp.8 – 2 Farrell, J. and G. Saloner. 1985. Standardization, Compatibility, and Innovation. Rand Journal of Economics, 2(5), PP. 34-56 Fernandes, R. 2011. Facebook second most accessed site, behind Google in the US. tech2.in.com. [Online]. Available at: Accessed March 11, 2013. Fowler, G. 2012. Facebook: One Billion and Counting. online.wsj.com. [Online] Available at: Accessed March 11, 2013. Haggman, S. K. 2009. Functional actors and perceptions of innovation attributes: influence on innovation adoption. European Journal of Innovation Management,12(3), pp.386 – 407 Hochbaum, D. S., Moreno-Centeno, E., Yelland, P. and Catena, R.A. 2011. Rating Customers According to Their Promptness to Adopt New Products. Operations Research 59(5), pp. 1171-1183 Katz, M. L. and Carl, S. 1994, Systems Competition and Network Effects. Journal of Economic Perspectives,8(2), pp. 93-115.   Katz, M. L. and Shapiro, C.1985. Network Externalities, Competition, and Compatibility. American Economic Review, 4(2), pp. 1985-87. Kirkpatrick, D. 2010. The Facebook Effect: The Inside Story of the Company That Is Connecting the World. New York: Simon and Schuster Liebowitz, S. J., and Margolis, S. E. 1994. Network Externality: An Uncommon Tragedy. Journal of Economic Perspectives, 8 (1), pp. 133-150. Leibenstein, H. 1950. Bandwagon, Snob, and Veblen Effects in the Theory of Consumers’ Demand. The Quarterly Journal of Economics, 2, pp. 115-116. Love, J. H., & Roper, S. 1999. The determinants of innovation: R & D, technology transfer and networking effects. Review of Industrial Organization, 15(1), 43-64. MacVaugh, J. and Schiavone, F. 2010. Limits to the diffusion of innovation: A literature review and integrative model. European Journal of Innovation Management, 13 (2), pp.197 – 221 Malik, T. H. 2013. Institutional barriers in the diffusion of a high technology: A case of biotechnological innovation from a Chinese university. Journal of Chinese Entrepreneurship, 5(1), pp.4 – 25 Meade, N., and Islam, T. 2006. Modelling and forecasting the diffusion of innovation–A 25- year review. International Journal of Forecasting, 22(3), 519-545. Nooteboom, B. (1994). Innovation and diffusion in small firms: theory and evidence. Small Business Economics, 6(5), 327-347. Parker, G and Alstyne, M. 2005. Two Sided Networks: A Theory of Information Product Design. Management Science, 5(10), 1494–1504 Rogers, E. M. 2003. Diffusion of innovations. New York: Free Press. Rogers, E. M. 2005. Diffusion of Innovations. Glencoe: Free Press Rohlfs, J. 1974.A Theory of Interdependent Demand for a Communications Service. Bell Journal of Economics and Management Science (4), p. 16. Sengupta, S. 2012. Facebook’s Prospects May Rest on Trove of Data. nytimes.com. [Online] Avaiable at: Accessed March 11, 2013. Shin, D. H. 2010. The effects of trust, security and privacy in social networking: A security- based approach to understand the pattern of adoption. Interacting with Computers, 22(5), 428-438. Sundbo, J. 1998. The theory of innovation: entrepreneurs, technology and strategy. NY: Edward Elgar Publishing. Sundararajan, A. 2007. Local network effects and complex network structure. The B.E. Journal of Theoretical Economics, 7(1), 46 Trusov, M., Bucklin, R. E., & Pauwels, K. (2008). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. NY: Robert H. Smith School Research Paper No. RHS, 06-065. Wejnert, B. 2002. Integrating Models of Diffusion of Innovations: A Conceptual Framework. Annual Review of Sociology 28, pp. 297–306. Woodside, A. G. and Biemans, W. G.. 2005. Modeling innovation, manufacturing, diffusion and adoption/rejection processes. Journal of Business & Industrial Marketing, 20(7), pp.380 - 393     Read More
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