Why Are the Largest Social Networking Services Sometimes Unable to Sustain Themselves? - MDPI
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sustainability Article Why Are the Largest Social Networking Services Sometimes Unable to Sustain Themselves? Yong Joon Hyoung 1, * , Arum Park 2 and Kyoung Jun Lee 1,2 1 Department of Social Network Science, Kyung Hee University, Seoul 02447, Korea; email@example.com 2 School of Management, Kyung Hee University, Seoul 02447, Korea; firstname.lastname@example.org * Correspondence: email@example.com Received: 11 November 2019; Accepted: 13 December 2019; Published: 9 January 2020 Abstract: The sustainability of SNSs (social networking services) is a major issue for both business strategists and those who are simply academically curious. The “network effect” is one of the most important theories used to explain the competitive advantage and sustainability of the largest SNSs in the face of the emergence of multiple competitive followers. However, as numerous cases can be observed when a follower manages to overcome the previously largest SNS, we propose the following research question: Why are the largest social networking services sometimes unable to sustain themselves? This question can also be paraphrased as follows: When (under what conditions) do the largest SNSs collapse? Although the network effect generally enables larger networks to survive and thrive, exceptional cases have been observed, such as NateOn Messenger catching up with MSN Messenger in Korea (Case 1), KakaoTalk catching up with NateOn in Korea (Case 2), Facebook catching up with Myspace in the USA (Case 3), and Facebook catching up with Cyworld in Korea (Case 4). To explain these cases, hypothesis-building and practice-oriented methods were chosen. While developing our hypothesis, we coined the concept of a “larger population social network” (LPSN) and proposed an “LPSN effect hypothesis” as follows: The largest SNS in one area can collapse when a new SNS grows in another larger population’s social network. For the validity and reliability of our case studies, we used an evidence chain and case study protocol with a publicly-accessible LPSN index to determine which SNS is better for participating in or adding offline social networks to their platform. Keywords: network effect; larger population social network; social network; business sustainability; network sustainability 1. Introduction Some of the most powerful companies in the world are social networking services (SNSs) such as Facebook or mobile instant messaging services like WhatsApp, Line, WeChat, and KakaoTalk. They dominate countries or a wide range of continents. In Korea, KakaoTalk is so strong that even the most dominant portal, Naver, never tries to compete with it, while Facebook or LinkedIn are so strong on a global scale that nobody tries to defeat them. However, what if we can provide meaningful information with which one can decide whether it is worth trying? We chose practice (rather than theory)-oriented research for our methodology because the motivation for this study comes from working in the social networking service industry. Among the three options for practice-oriented research, we chose the hypothesis-building (instead of the descriptive or hypothesis-testing) method because we want to determine why and how the largest SNS can collapse even with their network effects advantage. There are three traditional case study methods: exploratory, descriptive, and explanatory. We chose explanatory for the purposes of this research, which includes a case study (or case studies) aimed at explaining how or why certain phenomena came to be. Explanation Sustainability 2020, 12, 502; doi:10.3390/su12020502 www.mdpi.com/journal/sustainability
Sustainability 2020, 12, 502 2 of 23 models and multiple case study methods are strongly recommended by Yin, which was a factor in our decision to utilize them . These research results can provide valuable clues as to whether Facebook, LinkedIn, Line, or KakaoTalk can be destroyed by a competitor in the future. Facebook, an SNS that was developed relatively recently, defeated Myspace in the USA and Cyworld in Korea, which were the first SNSs in those countries. Under what circumstances can a dominant SNS be destroyed by a newer entrant and competitor? To answer this question, we developed an LPSN effect hypothesis as follows: If a leading SNS does not have the majority of the offline social network universe using its platform, then it can be defeated by a newer competitor who can obtain a larger social network population more quickly. In other words, if the LPSN index (width rate × depth rate) of the first SNS becomes smaller than the newer SNS’s index, the newer SNS can easily overtake the older one, where the width rate = the number of users/local population, and the depth rate = the average number of first-degree friends in the SNS/the Dunbar Number . This hypothesis means that a company that eventually gains a larger social network population or effectively replicates an offline real social network will enjoy the benefits of the network effect. To verify this hypothesis in line with the explanation research method, we chose multiple case studies. In addition, in line with our case study protocol, we chose four cases in which the newer SNS overtook the older, dominant SNS, as follows: The first case study involves MSN messenger, which was once Korea’s largest PC instant messenger service, being overtaken by NateOn in Korea . The second one focuses on NateOn, after achieving dominance, being overtaken by KakaoTalk, which was initially a mobile messenger service . The third focuses on when Myspace was overtaken by Facebook . The fourth investigates Facebook overtaking Cyworld, which was once the largest social network in Korea . 2. Literature Review The network effect has traditionally been used to explain the competitive advantage possessed by market-leading companies, including SNSs. As a company’s size crosses the network effect threshold, its network effect increases exponentially. As a result, the company can completely overtake its competitors [7,8] and increase the switching cost of consumers . The company can then enjoy monopolistic profits . If a company with market dominance enjoys the benefits of the network effect, then late market entrants or established competitors will find it difficult to beat that company . However, sometimes online SNSs that already dominate the market have been defeated by later entrants and competitors. Therefore, it is natural to ask why this happens in spite of the network effect [3–6,12]. There is a dearth of research on this question, especially in the online SNS industry. As a methodology for this study, we adopt a multiple-case study methodology, including four cases. In each case, the first SNS is repulsed by the later one. There is a copious amount of literature on existing network effects, but there is no specific and direct research on how a late SNS can defeat the existing, largest one. The network effect means that a network’s value increases as its size increases. There are several other definitions for network effect in economics literature. Katz and Shapiro describe the source of positive consumption externalities as the “utility that a user derives from consumption of goods” which “increases with the number of other agents consuming the (same) good” . Katz and Shapiro newly define network effects as “the value of (a) membership to one user (which) is positively affected when another user joins and enlarges the network” . Church et al. emphasize that “network effect exists if consumption benefits depend positively on the total number of consumers who purchase compatible products” . Literally, all kinds of consumption with positive feedback on the supply and consumer sides could be subsumed under the terms of the network effect and network externalities. Here we can briefly summarize the famous three Laws of Network Effect: - Sarnoff’s Law: David Sarnoff led the Radio Corporation of America (which created NBC) from 1919 until 1970. It was one of the largest networks in the world during those years. Sarnoff observed
Sustainability 2020, 12, 502 3 of 23 that the value of his network seemed to increase in direct proportion to its size—proportional to N, where N is the total number of users on the network. Sarnoff’s description of network value ended up being an underestimate for SNS types of networks, although it was an accurate description of broadcast networks with a few central nodes broadcasting to many marginal nodes (a radio or television audience). - Metcalfe’s Law: Metcalfe’s Law states that the value of a communications network grows in proportion to the square of the number of users on the network (N2 , where N is the total number of users on the network). The formulation of this concept is attributed to Robert Metcalfe, one of the inventors of the Ethernet standard. Metcalfe’s Law seems to hold because the number of links between nodes on a network increase mathematically at a rate of N2 , where N is the number of nodes. Although originally formulated to describe communication networks like the ethernet, faxing or phones, with the arrival of the internet, it has evolved to describe SNSs as well. - Reed’s Law: Reed’s Law was published by David P. Reed of MIT in 1999. While Reed acknowledged that “many kinds of value grow proportionally to network size” and that some grow as a proportion to the square of network size, he suggested that “group-forming networks” that allow for the formation of clusters (as described above) scale value even faster than other networks. Group-forming networks, according to Reed, increase value at a rate of 2N , where N is the total number of nodes on the network. Reed suggested a formula of 2N instead of N2 because the number of possible groups within a network that “supports easy group communication” is much higher than 1 so that the total number of connections in the network (the network density) is not just a function of the total number of nodes (N). In reality, it is a function of the total number of nodes plus the total number of possible sub-groupings or clusters, which scales at a much faster rate with the addition of more users to the network. Since most online networks allow for the formation of clusters, they will likely behave at least somewhat as Reed’s Law suggests and grow in value at a much faster rate than either Metcalfe’s Law or Sarnoff’s Law suggests. The range of networks effect that we mainly want to deal with in this paper is produced by SNS, such as Myspace, Cyworld, Facebook, Whatsapp, Line, KakaoTalk, and WeChat. The effect is related to Metcalfe’s Law or Reed’s Law. All of these services assume initial consent for connecting new people online by using the real names used in daily life. This paper concentrates only on the SNSs that have accumulated offline social network- and offline relationships-related data. This study focuses on cases in which network effect did not seem to be working, as each case only included the largest SNS defeated by a newer one. All of the literature dealing with network effects is based on the assumption that network effects have already been achieved and companies use various tactics to firmly maintain it by maximizing the switching cost of the carrier or employing a complementary strategy , such as integrating portal services , collaborating with partners on an API [18,19], utilizing a CEO’s ability , adapting to environmental change, or providing user satisfaction , none of which are pertinent to why network effects fail or continue to succeed. These articles point out that network effects are extremely important, hard to break, and require a variety of tactics to continue. But nowadays, there are more and more reports questioning network effects and providing examples of the downfall of services that once had the largest networks [22–25]. There is a misbelief that the emergence of a new technology paradigm means that the existing network effects are almost inoperable. As the PC era transforms into the mobile era, with disruptive innovations, those who once enjoyed such network effects have changed. There is also a new argument that network effects alone are not enough. In the case of online marketplaces, it is difficult to grow too quickly; or, if an SNS fails to build trust, network effects cannot be sustained [26,27]. Also, as an existing network becomes saturated, it collapses due to leakage of personal information and stalking from an overflow of content and excess of people . However, even in the above studies, we could not figure out why the first SNS, which enjoyed the network effect, could fall even if they followed recommendations or overcame problems by providing new service features. We later realized that this was because they only considered either external factors,
providing new service features. We later realized that this was because they only considered either external factors, such as rapid environmental change or technological change, which are relatively difficult to control, or superficial factors like user interface. Additionally, little research has been undertaken to investigate why this exceptional phenomenon (i.e., latecomers overtaking the older ones) transpires, Sustainability 2020, 12, and 502 currently, available research only superficially touches upon the nature of 4 ofthe 23 social network [29,30]. On the other hand, this study focuses on the verification of the hypothesis that, if any online SNS such as rapid environmental change or technological change, which are relatively difficult to control, cannot occupy most of the offline social network’s “large set”, it can fail at any time if another SNS or superficial factors like user interface. Additionally, little research has been undertaken to investigate continues to occupy the “large set” faster than the first. This is different from Tucker’s central why this exceptional phenomenon (i.e., latecomers overtaking the older ones) transpires, and currently, argument , which claims that network effects do not work well in comprehensive cases, including available research only superficially touches upon the nature of the social network [29,30]. the marketplace, riding apps like Uber, SNSs, and music services. Rather, our study shows that On the other hand, this study focuses on the verification of the hypothesis that, if any online network effects still work well, especially within the SNS industry, and that only those who properly SNS cannot occupy most of the offline social network’s “large set”, it can fail at any time if another understand the nature of the social network itself can sustain its network effect. SNS continues to occupy the “large set” faster than the first. This is different from Tucker’s central argument , which claims that network effects do not work well in comprehensive cases, including 3. Research Method the marketplace, riding apps like Uber, SNSs, and music services. Rather, our study shows that networkOureffects studystill is based on a especially work well, practice-oriented within thecase SNS study methodology. industry, A practitioner and that only requires those who properly knowledge to define and solve problems in an identified practice, understand the nature of the social network itself can sustain its network effect.and this methodology is intended to provide the practitioner with useful knowledge. Dul and Hak divided the types of case study 3. Research methods Method into descriptive research, hypothesis-building research, and hypothesis-testing research . Our Practice-oriented study is based research is a case study tocase on a practice-oriented provide studyknowledge to industrial methodology. practitioners. A practitioner We requires built hypothesis knowledge to define by and conducting case study. solve problems Figure 1 practice, in an identified shows our andhypothesis building this methodology research is intended framework. to provide the practitioner with useful knowledge. Dul and Hak divided the types of case study methodsWe into defined the problem descriptive or topic research, we want to study hypothesis-building and search research, for relevant theories. and hypothesis-testing Then,. research we built the hypothesis Practice-oriented from is research four cases. a case We to study found same provide problem and knowledge issue from to industrial context of four practitioners. We cases built that prior theory could not explain. By analyzing each cases, we drew the hypothesis hypothesis by conducting case study. Figure 1 shows our hypothesis building research framework. that could the context of four cases. Figure 1. Hypothesis building research Figure 1. Hypothesis building research. A case We studythe defined canproblem describeoractual topicsociety we want andtoprovide study andthe search opportunity to establish for relevant theories theories. based Then, we on experience built . from the hypothesis To enhance this We four cases. casefound study’s validity same problemandand credibility, issue from a research context offramework was four cases that designed, prior theoryascould shown notinexplain. Table 1,Byand suggested analyzing bycases, each Yin . weIn eachthe drew case, the pattern-matching hypothesis that could the method context and theory of four cases. triangulation were performed to verify the hypotheses. Additionally, to reinforce construct A casevalidity study can and provide describe a clear actual evidence society chain, the and provide ouropportunity researcherstocollected establish and compared theories based different data and presented their sources. Furthermore, we tried to generalize Bandara’s on experience . To enhance this case study’s validity and credibility, a research framework was hypotheses by conducting designed, multiple as shown case 1, in Table studies to secure by and suggested external validity Yin . In each. case, the pattern-matching method and 1. theory triangulation The purpose of the were performed case study in thistopaper verifyisthe to hypotheses. Additionally, determine which to reinforce factors enabled construct latecomers to validity and provide a clear evidence chain, our researchers collected and compared outperform earlier entrants despite the fact that the latter group was producing the networkdifferent data and presented effect. Wetheir sources. Furthermore, investigated cases where thiswe phenomenon tried to generalize Bandara’swith was observed hypotheses by conducting later participants. We multiple havecase studies limited theto secure external research scope to validity SNS, one. of the industries where the network effect occurs. 2. We performed literature studies on factors that cause the network effect as well as those which 1. The purpose of the case study in this paper is to determine which factors enabled latecomers to enable later participants to overtake first entrants because of the network effect. outperform earlier entrants despite the fact that the latter group was producing the network effect. We investigated cases where this phenomenon was observed with later participants. We have limited the research scope to SNS, one of the industries where the network effect occurs. 2. We performed literature studies on factors that cause the network effect as well as those which enable later participants to overtake first entrants because of the network effect. 3. Case Selection: The four cases analyzed for this study were chosen because they were examples of later participants outperforming first entrants.
Sustainability 2020, 12, 502 5 of 23 4. Case analysis tool: The unit of a case analysis is the number of users or unique visitors, the market share of each social network service, and the network effect strategy of each company. 5. For the validity of case analysis and hypothesis-building, three researchers collected literary data on each case, compared this to determine the facts, and analyzed all information based on the verified data. In the analysis process, one researcher presented the analysis process and results of one case, and the other two researchers followed the same process and verified the triangulation method to determine whether the same result was reached. 6. Implications: This study proves the hypothesis about which factors enable latecomers to outwit the first entrants. This knowledge can be used as a strategy for a later entrant to increase its market share when the earlier entrant’s network effect has occurred. Table 1. Case study framework. Case Study Research Sequence 1. Case study purpose Presenting the research purpose for the case study 2. Draw a hypothesis Drawing a hypothesis through literature research and data collection 3. Select cases Selecting cases that meet the set study objectives 4. Case analysis tools Presenting tools and protocols for case analysis 5. Case analysis and hypothesis building Analyzing the selected cases 6. Present implications Drawing implications for case studies from an integrative perspective Among the methods presented by Bent , extreme and deviant case selection methods were applied. This method is a useful method for acquiring information about specific cases. 4. Analysis of the Four Cases 4.1. MSN vs. NateOn From 2000 to 2004, MSN Messenger was the No. 1 online messenger in Korea and had hundreds of millions of installers around the world. Most PC users installed MSN Messenger and used it for business purposes as well as among friends. However, MSN’s market dominance was overturned by latecomer NateOn as early as 2005. As seen in Figure 2, before 2003, MSN had a bigger LPSN than NateOn in the same PC space in Korea. The number of PC-based MSN social network users in Korea was about 6 million, which was much larger than that of PC-based NateOn social network users in Korea. Since 2005, NateOn integrated both the social network of Cyworld, i.e., NateOn_SN (Cyworld; represented in Figure 3) and the cellular phone address book social network, i.e., NateOn_SN (Cellular), into its PC-based social network, i.e., existing NateOn messenger, NateOn_SN (PC). With the integration, NateOn constructed a bigger LPSN than MSN in the PC space in Korea, as depicted in Figure 3. In addition, NateOn replicated offline social networks more deeply. Although MSN’s global social network was bigger than NateOn’s, MSN could not occupy the majority of the offline local social networks in Korea. Therefore, it could not keep its No. 1 position in Korea. In August 2005, NateOn provided 100 free SMS messages every month and the button for “one- step login” to Cyworld. The free SMS function was a great help in securing the installation base, and the login linkage with Cyworld and the Cyworld notification functionality provided the main driving force for continuous use of the NateOn messenger. By installing the NateOn Messenger, users could download their cell phone address books into the PC messenger and then easily send text messages to any friend in their address book with just one click in Messenger, which provided a strong incentive to install the NateOn Messenger. SK Telecom (SKT), the NateOn Messenger operator, was the number one cellular carrier company with a market share of over 50% at that time. The marketing power of the monthly 100 free text messages for its 20 million cellular network subscribers was a
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Sustainability 2020, 12, 502 8 of 23 Sustainability 2020, 12, In previous x FOR PEER sections, REVIEW we hypothesized 6 of 22 that an SNS which occupies most of the offline social network universe would Sustainability 2020, 12,be the PEER x FOR final REVIEW winner in exercising the everlasting network effect. The medium6 that of 22 the number better captures Sustainability one cellular offline 2020, 12, carrier x FORsocial company with a market share of over 50% at that time. networks can change as technology advances. As technology innovation PEER REVIEW The marketing 6 of 22 power the migrates offrom number the monthly one PCscellular to 100 freecompany carrier cellphones text then and messages with afor its 20innovative market smartphones, million share cellular of over network 50%which media subscribers at thatbetter time. was a The marketing captures offline Sustainability 2020, 12, x FOR PEER REVIEW 8 of 22 powerful power the of weapon number social the networks monthly one for outpacing cellular is 100 infree carrier essential the text company order number withof tomessages maximize MSN afor market or Messenger its 20 sustain million share users cellular of over network 50%(about effects. at 6 time. network that However,million Theatmarketing merely the subscribers time). was migration a Cyworld, powerful power to of the mobile at thethat weapon time, monthly world for had 100 15 outpacing million free the text users number messagesand of was MSN for still its 20 growing. Messenger million users cellular(about 6 network million at the subscribers time). was a merely migration to or thenew mobiletechworld platform will tech or new not be sufficient. platform willKeeping the focusKeeping not be sufficient. on accumulating the focus Cyworld, powerful offline at that weapon social time, networks had 15 for outpacing should million the users number and was of MSN still growing. Messenger users (about 6 million at the time). on accumulating offline social be a higher networks priority. should be a higher priority. Cyworld, As As seenat that seen in time, in Figure Figure 5,had 15 5, after million after 2013, users 2013, KakaoTalkand was KakaoTalk achieved still growing. achieved aa bigger bigger LPSN LPSN than than NateOn, NateOn, even even in in the the PC PC space, space, let let alone alone the the mobile mobile space. space. Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. NateOn Figure2.5.MSN Figure andNateOn IM and KakaoTalk offline Social offline IM offline social network social network occupation Network occupation map Occupationmap inin2013. mapin 2013. 2003. Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. 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In 2007, However, in Myspace was the largest 2013, its position SNS in thebyUnited was overtaken States, latecomer as can be seen Facebook. Unlikein Figure Korea,6.the However, United in 2013, States itshad still position wasLPSN a bigger overtaken in the by PClatecomer space in 2013. Facebook. MyspaceUnlikedid Korea, the United not occupy States more than 30% still ofhad the a bigger offline LPSN social in the PC network space space andinfailed 2013. to Myspace continue did not occupy building up itsmore than offline 30% network social of the offline social databases. network In the end,space evenand withinfailed the toPCcontinue building space, Myspace wasupdefeated its offline bysocial network Facebook. databases. Additionally, as In thethe end, mobile even within space provides thelarger PC space, Myspace containers was defeated for fetching by Facebook. offline social networks,Additionally, Facebook steadilyas thecollected mobile spacemore provides of the offlinelarger containers social networks for fetching using offlineapp. its mobile social networks, Facebook steadily collected more of the offline In 2013 social in the networks United usingStates,itsmost mobileusersapp. accessed their online SNSs through a PC rather than In 2013 a mobile phone in the United because the States, mobile most users accessed penetration rate wastheir notonline SNSs yet that highthrough a PCFacebook in the US. rather than hada mobile phone already surpassed because the mobile Myspace in the US penetration in terms of rate was not monthly yet that active high users, in the largely byUS. Facebook occupying had offline already social surpassed networks Myspace consisting in the US in of university andterms of monthly high-school activeFacebook students. users, largely by occupying also had offline a strong presence social in networks foreign countries consisting of university through university andand college high-school students. Facebook also had a strong students abroad. presence in foreign In Figure countries through 7, Facebook_SN PC + mobile) (Global,university and college students abroad. means Facebook used the global network and Figure 3. MSN IM and NateOn IM offline Social Network Occupation map in 2005. used In + mobile, PCFigure 7, Facebook_SN Myspace_SN(Global, PC means (local, PC) + mobile) thatmeans MyspaceFacebook focused used the global(i.e., on domestic network the USA)and Figure 3. MSN IM and NateOn IM offline Social Network Occupation map in 2005. used PC social + mobile, networks Myspace_SN rather than global(local,ones PC) and means that Myspace it also focused on PC focused networks onrather domestic than(i.e., mobilethe ones. USA) [After 2005: Figure NateOn 3. MSN made IMglobal andbigger NateOnLPSN than MSN at IM offline Korea PC space.] social networks rather than ones and it also Social focused Network on PCOccupation networks map in than rather 2005. mobile ones. MSN_SN [After 2005: (local, NateOn PC)made < [NateOn_SN bigger LPSN (local, thanPC) MSN ∪ NateOn_SN (local, Cyworld) ∪ NateOn_SN at Korea PC space.] (local, 2005: MSN_SN [After Cellular)] (local, NateOn PC)made < [NateOn_SN bigger LPSN (local, thanPC) MSN ∪ NateOn_SN (local, Cyworld) ∪ NateOn_SN at Korea PC space.] Or (MSN (local, MSN_SN =(local, 6M users Cellular)] PC)
the number one cellular carrier company with a market share of over 50% at that time. The marketing already surpassed Cyworld, at that Myspace time, had inmillion the theUS in terms of monthly active users users,(about largely6 by occupying at the offline powerful power of weapon the for monthly 100 15 outpacing free text users number messagesand was offor MSNitsstill growing. Messenger 20 million cellular network million subscribers time).a was social networks Cyworld, weapon consisting at that time, of university had 15 million and users and high-school students. Facebook also had a strong powerful for outpacing the number of was MSNstill growing.users (about 6 million at the Messenger time). presence in foreign countries through university and college students abroad. Cyworld, at that time, had 15 million users and was still growing. In Figure 7, Facebook_SN (Global, PC + mobile) means Facebook used the global network and used PC + 2020, Sustainability mobile, Myspace_SN (local, PC) means that Myspace focused on domestic (i.e., the USA) 12, 502 9 of 23 social networks rather than global ones and it also focused on PC networks rather than mobile ones. Sustainability 2020, 12, x FOR PEER REVIEW 6 of 22 Sustainability 2020, 12, x FOR PEER REVIEW 6 of 22 the number one cellular carrier company with a market share of over 50% at that time. The marketing Sustainability 2020, 12, x FOR PEER REVIEW 6 of 22 power the of the number monthly one cellular100 freecompany carrier text messages with afor its 20share market million cellular of over 50% network subscribers at that time. was a The marketing Figure 2. 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Cyworld, powerful [Before [Before [Beforeat 2007: that 2007:time, weapon 2003: for had Myspace MSN Myspace had15 outpacing hadmillion bigger bigger had biggerusers theLPSN number LPSN and than LPSN of thanwas MSNFB NateOn.] than FBstill at growing. Messenger atUSA USAPC users space](about 6 million at the time). PCspace] Cyworld, #MSN_SN #of [Before at that ofUsers Users oftime, of (local, 2003: MSN had Myspace_SN PC) Myspace_SN> 15 had million (USA, NateOn_SN (USA, bigger users PC) LPSN >>and ## of (local, PC) than was PC) of orstill Users Users NateOn.] ofgrowing. of (MSN Facebook_SN = 10M users(USA, Facebook_SN PC)] > NateOn (USA, PC)]= 0) Where Where# #2003: MSN_SN [Before ofof Users Users (local, MSN of PC)of Myspace_SN Myspace_SN > NateOn_SN had (USA, (USA, bigger LPSN (local, than =PC) PC)PC) =or 110M 110M NateOn.] and and (MSN # of # of Users = Users 10M >ofNateOn Facebook_SN of Facebook_SN users =(USA, (USA, 0) PC)] = 30M PC)] = 30M MSN_SN (local, PC) > NateOn_SN (local, PC) or (MSN = 10M users > NateOn = 0) Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. Figure Figure Figure 7. 2.7. MSNMyspace Myspace and and IM and Facebook Facebook NateOn offline Social IM offline social network social network occupation Network occupation map Occupationmap inin2013. mapin 2013. 2003. Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. [After [After2013: [Before Figure 2013: 2003: FB FB3.made MSN MSN made hadIM and LPSN bigger bigger bigger NateOn LPSN LPSN IM than than thanoffline Myspace Myspace Social NateOn.] atNetwork at USA USAPC PCOccupation space.] map in 2005. space.] Myspace_SN Myspace_SN MSN_SN [Before Figure 2003: (USA, 3. MSN (USA, (local, MSN PC) >IM PC) PC)had NateOn mobile)] map in 2005. =mobile)] 0) Where [After Where 2005: # of NateOn Users of made bigger Myspace_SN LPSN (USA, than PC) MSN = = (MSN 20M at Korea and # of PC space.] Users of Facebook_SN MSN_SN [Before Figure #2003: (local,3. MSN MSN of PC) hadIM and NateOn >Myspace_SN NateOn_SN bigger IM (USA, LPSN(local,thanoffline PC)Social PC) or NateOn.] 20MNetwork and= 10M# Occupation ofusers Users ofmap = 0) (Global, in 2005. Facebook_SN > NateOn PC) (Global, MSN_SN [After = 1.3B 2005: = 1.3B (local, NateOn and(local, # of #UsersPC) < made[NateOn_SN bigger LPSN (local, than PC) MSN ∪ NateOn_SN at Korea PC = 0.9B(local, space.] Cyworld) ∪ NateOn_SN PC) MSN_SN and PC) >of of Users Facebook_SN of Facebook_SN NateOn_SN (Global, (local, mobile) (Global, PC) or mobile) (MSN == 0.9B 10M users > NateOn = 0) (local, 2005: MSN_SN [After Cellular)] (local, NateOn PC)made < [NateOn_SN bigger LPSN (local, thanPC)MSN ∪ NateOn_SN at Korea PC space.] (local, Cyworld) ∪ NateOn_SN Before 2013, Myspace was thethe representative representative SNS in inusers theUnited United States. Myspace was acquired Before Or (MSN (local, MSN_SN 2013, Cellular)] Myspace =(local, 6M PC)
Sustainability 2020, 12, 502 10 of 23 the SNS was used for illegal activities such as prostitution. This is an inevitable consequence of emphasizing anonymous activities. It should be noted here that transforming an offline social network into an online one requires strategy. The largest offline social network is society itself, and the best place to clone a society, if you can weave a contact list to another contact list that follows an overlapped person, is the address book on cell phone networks or mobile phones. Thus, with a mobile messenger service, it is relatively easy to build a first-degree (friends) network by absorbing the mobile phone address book. However, it was not easy to replicate the offline social network in the online or PC world, where the anonymous culture is strong and true user identity is difficult to verify. Accordingly, Facebook founder Mark Zuckerberg initially sent e-mail invitations to Harvard students whom he already knew. Because of this, Facebook first consisted of mostly Harvard students and then expanded its invitations to other Ivy League students. Later, it expanded again to the remaining college or university students in the world. In other words, the SNS has grown by successfully transforming offline social networks into an online social network. After taking control of most US college offline social networks and expanding the invitation to international students, Facebook naturally entered the global market. The culture and habit of using a real name might have initially been awkward for the online PC world’s anonymous name culture, but after users were invited, they became used to the requirement. After this, Facebook opened to the general public without requiring an invitation-only, so that follow-up subscribers naturally behaved according to the existing culture and inertia. 4.4. Cyworld vs. Facebook Cyworld was officially launched in August 1999 after changing its name from PeopleSquare, which was founded in 1998. SKT took over Cyworld in 2003 and invested in server infrastructure. Cyworld grew rapidly, absorbing more than 51% of the Korea population, more than 25 million users. It has grown to generate revenue of 100 million USD annually, mostly with digital item selling. By 2009, Cyworld was Korea’s largest SNS, as can be seen in Figure 8. In 2013, the LPSN shifted from PC to mobile, but before that, even in the PC space, Cyworld had been overwhelmed by Facebook (Figure 9). Cyworld was attacked from two directions as follows: First, Facebook, which had built up a steady network of offline social networks, connected Koreans to the global network. Cyworld, on the other hand, simultaneously established branch offices in China, Germany, the USA, and Japan, and built a separate social network database for each country without connecting them. Ignoring the basic global expandability of offline social networks, Cyworld Korea was isolated from the Cyworld China membership database, US users, and all other country users. As a result, Korean Cyworld users living in Korea had to sign up to use American Cyworld to meet foreign friends living in the US, and if a relative who had lived in Korea went to the United States to study, he/she had to register again with Cyworld USA and had to be invited again. Korean Cyworld users could not invite foreign friends who only spoke English to use their SNS because only Korean menus were provided. This meant that it lost an enormous opportunity to acquire global social networks utilizing Korea’s local network, which was already built into Cyworld at the time. More than 51% of the population were using Cyworld, including most Korean celebrities. It also meant losing the opportunity to save on significant marketing costs globally. How Facebook built up the largest social network to include 50% of the world population should be summarized. As seen in interviews with the former Myspace CEO, Myspace introduced an SNS, but only Facebook mastered it . In short, Facebook insisted on the real-name system and never looked back. This requirement reflects offline society, where we call our friends and family by their real names. On this basis, Facebook executed the open API policy to more quickly acquire offline social networks from each country.
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