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 Science, Kyung Hee University, Seoul 02447, Korea; [email protected] 2 School of Management, Kyung Hee University, Seoul 02447, Korea; [email protected] * Correspondence: [email protected]

 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), catching up with Myspace in the USA (Case 3), and Facebook catching up with 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 services like WhatsApp, , 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 , 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 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 [1]. 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 [2]. 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 [3]. The second one focuses on NateOn, after achieving dominance, being overtaken by KakaoTalk, which was initially a mobile messenger service [4]. The third focuses on when Myspace was overtaken by Facebook [5]. The fourth investigates Facebook overtaking Cyworld, which was once the largest social network in Korea [6].

2. Literature Review The network effect has traditionally been used to explain the competitive advantage possessed by -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 [9]. The company can then enjoy monopolistic profits [10]. 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 [11]. 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 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 as the “ that a user derives from consumption of ” which “increases with the number of other agents consuming the (same) good” [13]. 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” [14]. Church et al. emphasize that “network effect exists if consumption benefits depend positively on the total number of consumers who purchase compatible products” [15]. 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 . 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 , 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 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 [16], such as integrating portal services [17], collaborating with partners on an API [18,19], utilizing a CEO’s ability [20], adapting to environmental change, or providing user satisfaction [21], 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 , 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 [28]. 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, Sustainability 2020, 12, x FOR PEER REVIEW 4 of 22

However, even in the above studies, we could not figure out why the first SNS, which enjoyed the , 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, such as rapid environmental change or technological change, which are relatively difficult to control, or superficial factors like . Additionally, little research has been undertaken to investigate why this exceptional phenomenon (i.e., latecomers overtaking the older Sustainabilityones) transpires,2020, 12 ,and 502 currently, available research only superficially touches upon the nature of4 of the 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 [25], which claims that network effects do not work well in comprehensive cases, including available research only superficially touches upon the nature of [29,30]. the marketplace, riding apps like , 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 [25], 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 networkOur effstudyects stillis based work well,on a especiallypractice-oriented within thecase SNS study industry, methodology. and that onlyA practitioner those who properlyrequires understandknowledge to the define nature and of thesolve social problems network in itselfan iden cantified sustain practice, its network and this eff methodologyect. is intended to provide the practitioner with useful knowledge. Dul and Hak divided the types of case study 3.methods Research into Method descriptive research, hypothesis-building research, and hypothesis-testing research [31]. OurPractice-oriented study is based research on a practice-oriented is a case study to caseprovide study knowledge methodology. to industrial A practitioner practitioners. requires We knowledgebuilt hypothesis to define by andconducting solve problems case study. in an identifiedFigure 1 practice,shows our and hypothesis this methodology building is intendedresearch toframework. provide the practitioner with useful knowledge. Dul and Hak divided the types of case study methodsWe intodefined descriptive the problem research, or topic hypothesis-building we want to study research, and search and hypothesis-testingfor relevant theories. research Then, [ 31we]. Practice-orientedbuilt the hypothesis research from isfour a case cases. study We tofound provide same knowledge problem and to industrial issue from practitioners. context of four We cases built hypothesisthat prior theory by conducting could not case explain. study. By Figure analyzing1 shows each our cases, hypothesis we drew building the hypothesis research framework.that could the context of four cases.

Figure 1. Hypothesis building research Figure 1. Hypothesis building research.

WeA case defined study the can problem describe or actual topic society we want and to prov studyide and the searchopportunity for relevant to establish theories. theories Then, based we builton experience the hypothesis [32]. fromTo enhance four cases. this Wecase found study’s same validity problem and and credibility, issue from a research context of framework four cases thatwas priordesigned, theory as could shown not in explain. Table 1, By and analyzing suggested each by cases, Yin [1]. we In drew each the case, hypothesis the pattern-matching that could the method context ofand four theory cases. triangulation were performed to verify the hypotheses. Additionally, to reinforce constructA case validity study can and describe provide actual a clear society evidence and provide chain, theour opportunity researchers to collected establish and theories compared based ondifferent experience data and [32]. presented To enhance their this sources. case study’s Furtherm validityore, we and tried credibility, to generalize a research Bandara’s framework hypotheses was designed,by conducting as shown multiple in Table case1 studies, and suggested to secure byexternal Yin [ 1 validity]. In each [33]. case, the pattern-matching method and1. theoryThe purpose triangulation of the werecase study performed in this to paper verify is the to hypotheses.determine which Additionally, factors enabled to reinforce latecomers to validityoutperform and provide earlier a clear entrants evidence despite chain, the ourfact researchersthat the latter collected group andwas compared producing di thefferent network data and presentedeffect. We their investigated sources. Furthermore,cases where this we phenom tried to generalizeenon was observed Bandara’s with hypotheses later participants. by conducting We multiplehave case limited studies the toresearch secure externalscope to validitySNS, one [33 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 [32], 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 Figure2, 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 Figure3) 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 Figure3. 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 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 power of the monthly 100 free text messages for its 20 million cellular network subscribers was a Sustainability 2020, 12, x FOR PEER REVIEW 6 of 22 SustainabilitySustainability 20202020,, 1212,, xx FORFOR PEERPEER REVIEWREVIEW 66 ofof 2222 theSustainability number2020 one, 12 cellular, 502 carrier company with a market share of over 50% at that time. The marketing6 of 23 powerthethe numbernumber of the oneone monthly cellularcellular 100 carriercarrier free company companytext messages withwith aafor markmark its etet20 shareshare million ofof over overcellular 50%50% network atat thatthat time.time. subscribers TheThe marketingmarketing was a powerfulpowerpower ofof weaponthethe monthlymonthly for outpacing 100100 freefree text textthe messagesnumbermessages of forfor MSN itsits 20Messenger20 millionmillion cellularcellularusers (about networknetwork 6 million subscriberssubscribers at the wastime).was aa powerful weapon for outpacing the number of MSN Messenger users (about 6 million at the time). Cyworld,powerfulpowerful weaponatweapon that time, forfor outpacingoutpacinghad 15 million thethe numbernumber users and ofof wasMSNMSN still MessengerMessenger growing. usersusers (about(about 66 millionmillion atat thethe time).time). Cyworld, at that time, had 15 million users and was still growing. Cyworld,Cyworld, atat thatthat time,time, hadhad 1515 millionmillion usersusers andand waswas stillstill growing.growing.

Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. FigureFigure 2. 2. MSNMSN IM IM and and NateOn NateOn IM IM offline oofflineffline Soci SocialSocialal Network Network Occupation Occupation map map in in 2003. 2003.

. [Before[Before 2003:2003: MSN hadhad biggerbigger LPSNLPSN thanthan NateOn.]NateOn.] .. MSN_SNMSN_SN[Before[Before 2003:2003: (local,(local, MSNMSN PC)PC) hadhad >> NateOn_SN biggerbigger LPSNLPSN (local, thanthan PC)NateOn.]NateOn.] or (MSN = 10M10M users users > NateOnNateOn == 0)0) .. MSN_SNMSN_SN (local,(local, PC)PC) >> NateOn_SNNateOn_SN (local,(local, PC)PC) oror (MSN(MSN == 10M10M usersusers >> NateOnNateOn == 0)0)

Figure 3. MSNMSN IM IM and and NateOn NateOn IM IM offline offline Soci Socialal Network Occupation map in 2005. FigureFigure 3.3. MSNMSN IMIM andand NateOnNateOn IMIM offlineoffline SociSocialal NetworkNetwork OccupationOccupation mapmap inin 2005.2005. . [After[After 2005:2005: NateOn mademade biggerbigger LPSNLPSN thanthan MSNMSN atat KoreaKorea PCPC space.]space.] . MSN_SN[After 2005: (local, NateOn PC) made< [NateOn_SN bigger LPSN (local, than PC) MSN NateOn_SNat Korea PC (local,space.] Cyworld) NateOn_SN .. MSN_SN[After 2005: (local, NateOn PC) made< [NateOn_SN bigger LPSN (local, than PC) MSN ∪∪ NateOn_SN at Korea PC (local,space.] Cyworld) ∪∪ NateOn_SN .. (local,(local,MSN_SNMSN_SN Cellular)]Cellular)] (local,(local, PC)PC) << [NateOn_SN[NateOn_SN (local,(local, PC)PC) ∪∪ NateOn_SNNateOn_SN (local,(local, Cyworld)Cyworld) ∪∪ NateOn_SNNateOn_SN . Or(local, (MSN Cellular)]= 6M users < NateOn users 10M Cyworld users 15M cell phone users 20M) Or(local, (MSN Cellular)] = 6M users < NateOn users 10M ∪∪ Cyworld users 15M ∪∪ cell phone users 20M) .. OrOr (MSN(MSN == 6M6M usersusers << NateOnNateOn usersusers 10M10M ∪∪ CyworldCyworld usersusers 15M15M ∪∪ cellcell phonephone usersusers 20M)20M) As a result, whereas MSN only occupied the Korea PC-space social network, which was much smallerAsAs thanathana result,result, thethe o whereaswhereasffloffline,ine, real realMSNMSN social social onlyonly network occupiednetworkoccupied (40 million (40 thethe millionKoreaKorea Korean PC-space PC-spaceKorean population population socialsocial in network,network, total), in NateOn,total), whichwhich NateOn, by waswas utilizing muchmuch by utilizingsmallerthesmaller cell phonethan thanthe cell thethe address phone offline,offline, address book realreal (which socialsocial book networknetwork included(which included (40(40 over millionmillion 20 over million KoreanKorean 20 million people, populationpopulation people, more than inmorein total),total), 50% than ofNateOn,NateOn, 50% the of entire the byby entireutilizingpopulation)utilizing population) thethe and cellcell Cyworld’sphonephone and Cyworld’s addressaddress first-degree bookbook firs t-degree (which(which friend includ includfriend lists (theeded lists over onlyover (the 20 one,20 only millionmillion and one, the people, people,and largest the moremore largest SNS thanthan in SNS Korea 50%50% in atKoreaofof that thethe atentiretime,entire that with population)population) time, 15 millionwith andand15 users million Cyworld’sCyworld’s and users growing), first-degreefirst-degree and wasgrowing) able friendfriend to, was overtakelistslists able(the(the MSNonlyonlyto overtake one,one, Messenger’s andand MSN thethe largestlargest No.Messenger’s 1 position SNSSNS inin KoreaNo.Korea in the 1 positionatKoreanat thatthat time, PCtime, in the messenger withwith Korean 1515 million market.millionPC messenger usersusers and andmarket. growing),growing), waswas ableable toto overtakeovertake MSNMSN Messenger’sMessenger’s No.No. 11 positionposition inin thethe KoreanKorean PCPC messengermessenger market.market. Sustainability 2020, 12, 502 7 of 23 Sustainability 2020, 12, x FOR PEER REVIEW 76 ofof 22 the4.2. numberNateOn NateOn vs.one vs. KakaoTalk KakaoTalkcellular 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 In 2006, NateOn completely outperformed MSN MSN Messenger, Messenger, the the previous No. No. 1 1 market market player, player, powerful weapon for outpacing the number of MSN Messenger users (about 6 million at the time). in terms ofof thethe numbernumber ofof installersinstallers andand monthlymonthly visitors.visitors. Before 2010, NateOn, represented as Cyworld, at that time, had 15 million users and was still growing. NateOn_SN (Korean,(Korean, PC) PC) in Figurein Figure4, had 4, ahad bigger a bigger LPSN inLPSN the PC in space the thanPC space KakaoTalk’s, than KakaoTalk’s, represented representedas KakaoTalk_SN as KakaoTalk_SN (local, mobile), (loc whichal, mobile), was growing which inwas the growing mobile space.in the Themobile number space. of The NateOn_SN number (Korean,of NateOn_SN PC) users (Korean, was 12PC) million users andwas the12 million mobile and IM, KakaoTalk,the mobile IM, had KakaoTalk, just started. had just started.

Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. Figure 4. NateOn and KakaoTalk offline Social Network Occupation map in 2010. Figure 4. NateOn and KakaoTalk offline Social Network Occupation map in 2010.

. . [Before[Before 2010:2003:2010: NateOnMSNNateOn had hadhad bigger biggerbigger LPSN LPSNLPSN than thanthan NateOn.] KakaoTalkKakaoTalk atat KoreaKorea PCPC space.]space.] . . NateOn_SNMSN_SNNateOn_SN (local, (local,(local, PC) PC)PC) > NateOn_SN >> [KakaoTalk_SN (local, (local,(local,PC) or PC) PC)(MSN ∪ KakaoTalk_SN = 10M users > NateOn (local,(local, mobile)]mobile)] = 0) . ∪ (NateOn(NateOn == 12M (but locally saturated in PC) > [(0[(0 = KakaoTalkKakaoTalk PC PC IM) IM) ++ mobilemobile IM IM growing] growing] Since 2010, KakaoTalk has steadily defeated several competing mobile messengers in Korea, gainingSince more 2010, than KakaoTalk 42 million has mobile steadily users defeated in 2012, several which competing accounts mobilefor a majority messengers of the in Korean Korea, populationgaining more (reflecting than 42 most million of the mobile offline users social in network). 2012, which accounts for a majority of the Korean populationKakaoTalk (reflecting occupied most the of mobile the offl messengerine social network). market in a short period. On the other hand, the once No. 1KakaoTalk PC messenger occupied NateOn, the which mobile beat messenger out MSN market messenger, in a shorthas been period. saturated On the in the other smaller- hand, populationthe once No. social 1 PC network messenger that NateOn,is Korea PC which space. beat out MSN messenger, has been saturated in the smaller-populationKakaoTalk is a socialmobile network messenger that app is Korea that allows PC space. users to send free text/photo messages to any of theirKakaoTalk friends. Because is a mobile of this messenger strong benefit, app that users allows were users willing to send to send free text“invitations”/photo messages to their to friends, any of whotheir accepted friends. Becausethem. Thus, of this it spread strong benefit,quickly usersin such were a viral willing . to send Similar “invitations” to the 100 to free their texts friends, per whomonth accepted of NateOn, them. the Thus, free text it spread messaging quickly feature in such between a viral friends manner. has Similar played to a themajor 100 role free in texts quickly per creatingmonth of an NateOn, enormous the freeinstallation text messaging base. On feature the other between hand, friends NateOn has PC played Messenger a major also role launched in quickly its mobilecreating messenger an enormous quite installation late. Since base. its parent On the company other hand, SKT, NateOn Korea’s PC largest Messenger also launched its mobile messenger quite late. Since its parent company SKT, Korea’s largest telecommunications company, made close to 1 billion USD per in short text messaging sales at that time, it hesitated tocompany, strengthen made its close mobile to 1presence. billion USD In June per year 2013, in KakaoTalk, short text messaging which already sales athad that 40 time, million it hesitated mobile users,to strengthen started a its new mobile PC version presence. of KakaoTalk In June 2013, IM KakaoTalk,and overwhelmed which alreadyNateOn hadmessenger 40 million within mobile just nineusers, months. startedFigure a new 3. PCMSN version IM and of NateOn KakaoTalk IM offline IM andSocial overwhelmed Network Occupation NateOn map messenger in 2005. within just nineA months. is always connected online and in real-time, unlike a PC. 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(local, whereas In PC) other a∪ PCNateOn_SN words, is utilized a PC (local, heavilyis a tool Cyworld) bythat only exists some∪ NateOn_SN apart workers from people,and temporarily(local, whereas Cellular)] a by smartphone students and is equivalent most people. to the In entity other of words, a person. a PC Mobile is a tool messenger that exists users apart use from the . people,PC versionOr whereas(MSN of the= 6M a same smartphone users messenger < NateOn is equivalent service users 10Mfor to reason the∪ Cyworld entitys such of users as a person. the 15M convenience Mobile∪ cell phone messenger of continuous users 20M) users use use of the a PC version of the same messenger service for reasons such as the convenience of continuous use of a mobileAs messenger’sa result, whereas contacts, MSN typing only longoccupied texts theusing Korea a big PC-space keyboard, social and network,sending files. which was much mobile messenger’s contacts, typing long texts using a big keyboard, and sending files. smallerIn previousthan the sections,offline, real we socialhypothesized network that (40 anmillion SNS whichKorean occupies population most in of total), the offline NateOn, social by utilizingnetwork universethe cell phone would address be the final book winner (which in included exercising over the 20 everlasting million people, network more effect. than The 50% of the entirethat better population) captures and offline Cyworld’s social firs networkst-degree friendcan change lists (the as onlytechnology one, and advances. the largest As SNS technology in Korea atinnovation that time, migrates with 15 from million PCs usersto cellphones and growing) and th, enwas , able to overtake innovative MSN mediaMessenger’s which No.better 1 positioncaptures inoffline the Korean social networks PC messenger is essential market. in order to maximize or sustain network effects. However, Sustainability 2020, 12, 502 8 of 23

SustainabilityIn previous 2020, 12 sections,, x FOR PEER we hypothesizedREVIEW that an SNS which occupies most of the offline social network6 of 22 universe would be the final winner in exercising the everlasting network effect. The medium that thebetter number captures one o cellularffline social carrier networks company can with change a mark as technologyet share of over advances. 50% at As that technology time. The innovationmarketing powermigrates of fromthe monthly PCs to cellphones 100 free andtext thenmessages smartphones, for its 20 innovative million cellular whichnetwork better subscribers captures owasffline a Sustainability 2020, 12, x FOR PEER REVIEW 8 of 22 powerfulsocial networks weapon is essentialfor outpacing in order the to number maximize of orMSN sustain Messenger network users effects. (about However, 6 million merely at the migration time). Cyworld, at that time, had 15 million users and was still growing. tomerely the mobile migration world to orthe new mobile tech world platform or new will tech not beplatform sufficient. will Keepingnot be sufficient. the focus Keeping on accumulating the focus oonffl ineaccumulating social networks offline should social benetworks a higher should priority. be a higher priority. AsAs seen in Figure5 5,, afterafter 2013,2013, KakaoTalkKakaoTalk achieved achieved a a bigger bigger LPSN LPSN than than NateOn, NateOn, even even in in the the PC PC space,space, letlet alonealone thethe mobilemobile space.space.

Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. Figure 5. NateOn and KakaoTalk oofflineffline socialsocial networknetwork occupationoccupation mapmap inin 2013.2013.

.. [After[Before[After 2013:2013: 2003: KakaoTalkKakaoTalk MSN had had biggerhad bigger bigger LPSN LPSN LPSN than than thanNateOn.] NateOn NateOn at at Korea Korea PC PC space.] space.] .. [NateOn_SNMSN_SN[NateOn_SN (local, (local,(local, PC) PC)] PC)]> NateOn_SN< < [KakaoTalk_SN[KakaoTalk_SN (local, PC) (local,(local, or (MSN mobile)mobile) = 10M includesincludes users KakaoTalk_SN KakaoTalk_SN > NateOn = 0) (local, (local, PC)] PC)] . (NateOn(NateOn= =6M 6M (but (but decreasing) decreasing)< [40M < [40M mobile mobile users users includes includes PC IM usersPC IM 5M users (but rapidly5M (but growing)] rapidly growing)] 4.3. Myspace vs. Facebook 4.3. Myspace vs. Facebook In 2007, Myspace was the largest SNS in the , as can be seen in Figure6. However,In 2007, in Myspace 2013, its positionwas the largest was overtaken SNS in the by United latecomer States, Facebook. as can be seen Unlike in Figure Korea, 6. the However, United Statesin 2013, still its had position a bigger was LPSN overtaken in the by PC latecomer space in 2013. Facebook. Myspace Unlike did Korea, not occupy the United more than States 30% still of had the oaffl biggerine social LPSN network in the PC space space and in failed 2013. toMyspace continue did building not occupy up its more offl inethan social 30% networkof the offline databases. social Innetwork the end, space even and within failed the to PC continue space, Myspace building was up defeated its offline by social Facebook. network Additionally, databases. as In the the mobile end, spaceeven within provides the larger PC space, containers Myspace for fetching was defeated offline socialby Facebook. networks, Additionally, Facebook steadily as the collected mobile space more ofprovides the offl inelarger social containers networks for using fetching its mobile offline app. social networks, Facebook steadily collected more of the offlineIn 2013 social in the networks United using States, its most mobile users app. accessed their online SNSs through a PC rather than a mobileIn 2013 phone in the because United the States, mobile most penetration users access rateed was their not online yet that SNSs high through in the US.a PC Facebook rather than had a alreadymobile phone surpassed because Myspace the mobile in the USpenetration in terms ofrate monthly was not active yet that users, high largely in the by US. occupying Facebook offl hadine socialalready networks surpassed consisting Myspace of universityin the US in and terms high-school of monthly students. active Facebookusers, largely also hadby occupying a strong presence offline insocial foreign networks countries consisting through universityof university and and college high-school students abroad.students. Facebook also had a strong presenceIn Figure in foreign7, Facebook_SN countries through (Global, un PCiversity+ mobile) and college means Facebookstudents abroad. used the global network and Figure 3. MSN IM and NateOn IM offline Social Network Occupation map in 2005. usedIn PC Figure+ mobile, 7, Facebook_SN Myspace_SN (Global, (local, PC)PC means+ mobile that) means Myspace Facebook focused used on domesticthe global (i.e., network the USA) and socialused PC networks + mobile, rather Myspace_SN than global (local, ones PC) and means it also focusedthat Myspace on PC focused networks on rather domestic than (i.e., mobile the ones.USA) . [After 2005: NateOn made bigger LPSN than MSN at Korea PC space.] social networks rather than global ones and it also focused on PC networks rather than mobile ones. . MSN_SN (local, PC) < [NateOn_SN (local, PC) ∪ NateOn_SN (local, Cyworld) ∪ NateOn_SN (local, Cellular)] . Or (MSN = 6M users < NateOn users 10M ∪ Cyworld users 15M ∪ cell phone users 20M) As a result, whereas MSN only occupied the Korea PC-space social network, which was much smaller than the offline, real social network (40 million Korean population in total), NateOn, by utilizing the cell phone address book (which included over 20 million people, more than 50% of the entire population) and Cyworld’s first-degree friend lists (the only one, and the largest SNS in Korea at that time, with 15 million users and growing), was able to overtake MSN Messenger’s No. 1 position in the Korean PC messenger market.

Figure 6. Myspace and Facebook offline social network occupation map in 2007. Sustainability 2020, 12, x FOR PEER REVIEW 8 of 22 merely migration to the mobile world or new tech platform will not be sufficient. Keeping the focus on accumulating offline social networks should be a higher priority. As seen in Figure 5, after 2013, KakaoTalk achieved a bigger LPSN than NateOn, even in the PC space, let alone the mobile space.

Figure 5. NateOn and KakaoTalk offline social network occupation map in 2013.

. [After 2013: KakaoTalk had bigger LPSN than NateOn at Korea PC space.] . [NateOn_SN (local, PC)] < [KakaoTalk_SN (local, mobile) includes KakaoTalk_SN (local, PC)] . (NateOn = 6M (but decreasing) < [40M mobile users includes PC IM users 5M (but rapidly growing)]

4.3. Myspace vs. Facebook In 2007, Myspace was the largest SNS in the United States, as can be seen in Figure 6. However, in 2013, its position was overtaken by latecomer Facebook. Unlike Korea, the United States still had a bigger LPSN in the PC space in 2013. Myspace did not occupy more than 30% of the offline social network space and failed to continue building up its offline social network databases. In the end, even within the PC space, Myspace was defeated by Facebook. Additionally, as the mobile space Sustainability 2020, 12, x FOR PEER REVIEW 6 of 22 provides larger containers for fetching offline social networks, Facebook steadily collected more of thethe numberoffline social one cellular networks carrier using company its mobile with app. a mark et share of over 50% at that time. The marketing powerIn of2013 the in monthly the United 100 States,free text most messages users access for itsed 20 their million online cellular SNSs networkthrough asubscribers PC rather thanwas a powerfulmobile phone weapon because for outpacing the mobile the penetration number of rateMSN was Messenger not yet thatusers high (about in the6 million US. Facebook at the time). had Cyworld,already surpassed at that time, Myspace had 15 in million the US users in terms and ofwas mo stillnthly growing. , largely by occupying offline social networks consisting of university and high-school students. Facebook also had a strong presence in foreign countries through university and college students abroad. In Figure 7, Facebook_SN (Global, PC + mobile) means Facebook used the global network and Sustainabilityused PC + 2020mobile,, 12, 502 Myspace_SN (local, PC) means that Myspace focused on domestic (i.e., the USA)9 of 23 social networks rather than global ones and it also focused on PC networks rather than mobile ones.

Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. Sustainability 2020Figure, 12, x FOR 6. Myspace PEER REVIEW and Facebook offline offline social network occupation mapmap inin 2007.2007. 9 of 22

.. [Before[Before[Before 2007: 2003:2007: MyspaceMSNMyspace had had hadbigger biggerbigger LPSN LPSNLPSN than thanthan NateOn.] FB FB at at USA USA PC PC space] space] .. #MSN_SN# ofof UsersUsers (local, ofof Myspace_SNMyspace_SN PC) > NateOn_SN (USA,(USA, PC) PC) (local,> > ## of ofPC) UsersUsers or (MSN ofof Facebook_SNFace = 10Mbook_SN users (USA,(USA, > NateOn PC)]PC)] = 0) . WhereWhere # # of of Users Users of of Myspace_SN Myspace_SN (USA, (USA, PC) PC)= 110M = 110M and and # of Users# of Users of Facebook_SN of Facebook_SN (USA, PC)](USA,= 30MPC)] = 30M

FigureFigure 7.7. Myspace and Facebook oofflineffline socialsocial networknetwork occupationoccupation mapmap inin 2013.2013.

. [After[After 2013: 2013:Figure FBFB 3. mademade MSN biggerIMbigger and LPSNNateOnLPSN thanthan IM offline MyspaceMyspace Soci atalat Network USAUSA PC PC Occupation space.] space.] map in 2005. . Myspace_SNMyspace_SN (USA,(USA, PC) PC)< < [Facebook_SN[Facebook_SN (Global,(Global, PC)PC) ∪ Facebook_SN (Global, mobile)] . ∪ . Where[AfterWhere 2005: ## of Users NateOn of of Myspace_SNmade Myspace_SN bigger LPSN(USA, (USA, than PC) PC) =MSN =2020MM at and Korea and # of # PCUsers of space.] Users of Facebook_SN of Facebook_SN (Global, (Global, PC) . PC)MSN_SN= 1.3B= 1.3B and (local, and# of #Users ofPC) Users

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 Figure8. In 2013, the LPSN shifted from PC to mobile, but before that, even in the PC space, Cyworld had been overwhelmed by Facebook (Figure9). Cyworld was attacked from two directions as follows: First, Facebook, which had built up a steady network of offline social networks, connected to the global network. Cyworld, on the other hand, simultaneously established branch offices in , 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 [29]. 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. Sustainability 2020, 12, 502 11 of 23

Second, although Cyworld had an opportunity to occupy a bigger LPSN in the mobile sphere in 2013, the company could not take advantage of it because its parent company, SKT, was reluctant to quickly move to mobile. Cyworld was hesitant to develop a free mobile messaging service because SKT would not give up sales (about 2 billion USD in 2011) in cellular-based short text messaging (SMS). As users became closer with their mobile smartphones than with PCs, they naturally began using alternatives, like Facebook mobile rather than Cyworld online-only, especially because most of their friends were migrating, almost at the same time, to this more flexible choice. Migration occurred because Facebook was fully ready with mobile and global versions to make building an offline social Sustainability 2020, 12, x FOR PEER REVIEW 6 of 22 network (by fetching existing online PC users and adding new users through phone address books) theeasier number and faster, one cellular resulting carrier in the company creation with of aa globalmarket social share networkof over 50% based at that on localtime. socialThe marketing network. powerCyworld, of the by contrast,monthly was100 stuckfree text in a messages small, PC for market its 20 in million Korea. cellular network subscribers was a powerfulAs a result,weapon in for 2013, outpacing Facebook the built number a global of MSN network Messenger by occupying users (about local networks 6 million (o atffl theine time). social Cyworld,networks) at in that each time, country, had 15 acquired million users bigger and LPSN was still in the growing. growing mobile sector, and completely defeated Cyworld. In Figure9, Cyworld_SN (the US, PC) means Cyworld used US social networks separately from Korean or other countries, and it used the PC space only except for mobile. Sustainability 2020, 12, x FOR PEER REVIEW 11 of 22 Sustainability 2020, 12, x FOR PEER REVIEW 11 of 22

Figure 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. FigureFigure 8. 8.Cyworld Cyworld and and Facebook Facebook oofflineffline social network occupation occupation map map in in 2009. 2009. Figure 8. Cyworld and Facebook offline social network occupation map in 2009. . . [Before[Before[Before 2009:2003: 2009: CyworldMSN Cyworld had had bigger had bigger bigger LPSN LPSN LPSN than than than NateOn.] FacebookFacebook atat Korea PC PC space.] space.] . [Before 2009: Cyworld had bigger LPSN than Facebook at Korea PC space.] . . Cyworld_SNMSN_SNCyworld_SN (local, (Korea, (Korea, PC) > PC) NateOn_SN PC)> >Facebook_SN Facebook_SN (local, PC) (Korea,(Korea, or (MSN PC) where where = 10M # # usersof of Users Users > NateOn of of Cyworld_SN Cyworld_SN = 0) (Korea, (Korea, . Cyworld_SN (Korea, PC) > Facebook_SN (Korea, PC) where # of Users of Cyworld_SN (Korea, PC)PC)= 25M = 25M PC) = 25M

FigureFigure 3. MSN 9. Cyworld IM and and NateOn Facebook IM offline offline Socisocial networkNetwork occupation Occupation map map in 2013.in 2005. FigureFigure 9. 9.Cyworld Cyworld and and Facebook Facebook oofflineffline socialsocial network occupation occupation map map in in 2013. 2013. . [After 2012: Facebook made bigger LPSN than Cyworld at Korea PC space, let alone mobile . . [After[After 2005: 2012: NateOn Facebook made made bigger bigger LPSN LPSN than than MSN Cyworld at Korea at PCKorea space.] PC space, let alone mobile space.] . MSN_SNspace.] (local, PC) < [NateOn_SN (local, PC) ∪ NateOn_SN (local, Cyworld) ∪ NateOn_SN . Cyworld_SN (Korea, PC) < Facebook_SN(Korea, PC) ∪ Facebook_SN (global, mobile) where # of . (local,Cyworld_SN Cellular)] (Korea, PC) < Facebook_SN(Korea, PC) ∪ Facebook_SN (global, mobile) where # of Users of Cyworld_SN (Korea, PC) = 9M unique visitors/month and # of Users of Facebook_SN . OrUsers (MSN of = Cyworld_SN6M users < NateOn (Korea, usersPC) = 10M9M unique ∪ Cyworld visitors/month users 15M and ∪ cell # of phone Users users of Facebook_SN 20M) (Korea, mobile) = 13M unique visitors/month and # of users of Facebook_SN (global, mobile) = (Korea, mobile) = 13M unique visitors/month and # of users of Facebook_SN (global, mobile) = As0.9B a result, whereas MSN only occupied the Korea PC-space social network, which was much 0.9B smaller than the offline, real social network (40 million Korean population in total), NateOn, by 5. Construct Definition and Hypothesis Building utilizing5. Construct the cell Definition phone address and Hypothesis book (which Building included over 20 million people, more than 50% of the entire population) and Cyworld’s first-degree friend lists (the only one, and the largest SNS in Korea 5.1. Construct Definition at that5.1. Construct time, with Definition 15 million users and growing), was able to overtake MSN Messenger’s No. 1 positionA in network the Korean is defined PC messenger as a set of market. nodes and links. A social network is a set of nodes and links, A network is defined as a set of nodes and links. A social network is a set of nodes and links, where each node represents a person, and a link represents a connection between two persons. Social where each node represents a person, and a link represents a connection between two persons. Social networking is a behavior of creating, reinforcing, or deleting the relationship between nodes (people). networking is a behavior of creating, reinforcing, or deleting the relationship between nodes (people). The social relationship between two persons usually, in the real world, starts with some behavior The social relationship between two persons usually, in the real world, starts with some behavior with or without artificial tools, i.e., technologies, such as shaking hands, name card exchange, with or without artificial tools, i.e., technologies, such as shaking hands, name card exchange, exchanging eye contact. The relationship can be reinforced, also with or without technology, exchanging eye contact. The relationship can be reinforced, also with or without technology, 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 power of the monthly 100 free text messages for its 20 million cellular network subscribers was a powerful weapon for outpacing the number of MSN Messenger users (about 6 million at the time). Cyworld, at that time, had 15 million users and was still growing.

Sustainability 2020Figure, 12, 502 2. MSN IM and NateOn IM offline Social Network Occupation map in 2003. 12 of 23

. [After[Before 2012: 2003: Facebook MSN had made bigger bigger LPSN LPSN than than NateOn.] Cyworld at Korea PC space, let alone mobile space.] . Cyworld_SNMSN_SN (local, (Korea, PC) > PC) NateOn_SN< Facebook_SN(Korea, (local, PC) or PC)(MSN Facebook_SN= 10M users > (global,NateOn mobile) = 0) where # of ∪ Users of Cyworld_SN (Korea, PC) = 9M unique visitors/month and # of Users of Facebook_SN (Korea, mobile) = 13M unique visitors/month and # of users of Facebook_SN (global, mobile) = 0.9B

5. Construct Definition and Hypothesis Building

5.1. Construct Definition A network is defined as a set of nodes and links. A social network is a set of nodes and links, where each node represents a person, and a link represents a connection between two persons. Social networking is a behavior of creating, reinforcing, or deleting the relationship between nodes (people). The social relationship between two persons usually, in the real world, starts with some behavior with or without artificial tools, i.e., technologies, such as shaking hands, name card exchange, exchanging eye contact. The relationship can be reinforced, also with or without technology, sometimes called “media”, by face-to-face meetings, phone conversations, or texting. Social networking builds a trust-based information-sharing network. SNS refers to a technology-based service that supports social networking.

Its examples include PC-based online or phone-based mobile service. In SNS, social networking starts with an or SMS message to “Invite/Add” friends and “Accept Invitation”. In the case of using mobile instantFigure messenger 3. MSN services, IM and NateOn automatic IM offline retrieval Soci ofal existing Network phone Occupation address map books in 2005. can be a starting point of the SNS. . [AfterAll persons 2005: NateOn builda made first-degree bigger LPSN relationship than MSN with at Korea parents PC or space.] guardians as they are born. .They MSN_SN accumulate (local, other PC) first-degree < [NateOn_SN relationships (local, PC) in town ∪ NateOn_SN and school, (local, and stillCyworld) more as ∪ theyNateOn_SN become involved(local, in Cellular)] social and working lives. These relationships begin to be included in the email lists on PCs, .in cell-phoneOr (MSN address= 6M users books, < NateOn and, nowadays, users 10M in∪ Cyworld mobile messenger users 15M apps ∪ cell or phone SNSs users such 20M) as Facebook. The conceptAs a result, of social whereas networking MSN only has occupied existed fromthe Korea an early PC-space age of humankind.social network, However, which was this much study smallerdoes not than deal the with offline, chronological real social social network networks. (40 million It only considersKorean population the period sincein total), the InternetNateOn, and by utilizingmobile technology the cell phone and address services book became (which popular included in 1999 over when 20 million Cyworld people, became more a corporationthan 50% of andthe entirestarted population) its business and as the Cyworld’s first successful first-degree social friend network lists service (the only in theone, world. and the largest SNS in Korea at thatWe time, differentiate with 15 SNSmillion with users social and media growing) as follows:, was able Social to overtake media includes MSN Messenger’s , , No. 1 positionand , in the Korean companies PC messenger that mainly market. treat content but not people, whereas SNS includes Facebook, LinkedIn, Myspace, Cyworld, and instant messenger services that mainly treat friends or contacts first, before contents. In the latter situation, trust-building or social networking itself is important; sharing information is merely a tool for reinforcing the relationship. Usually, the term “” includes “SNS”, but not vice versa. Social networks are a social construct that has always existed in offline society long before PCs and smartphones appeared. A social network is a basis for trust-based information sharing and trust-based information distribution, such as job introduction and partner introduction. Our daily lives are based on social interaction, for private or business purposes, with those we know. An SNS is a necessary, everyday utility like electricity and water. This is why Mark Zuckerberg termed a “social networking service” as a social utility. This study does not deal with the network effects of all kinds of platforms or all kinds of social media. Instead, it concentrates only on those services that have replicated the offline social network into PC or smartphone-based online platforms. In our study, online/mobile messenger services are regarded as an SNS because they also replicate offline social networks. A messenger service contains lists which cannot be browsed by each friend, and which makes it an optimized tool for one-to-one (or closed-group) communication. Considering the importance of the network effect of SNSs, we can understand the sustainability of an SNS depends on the occupation and management of the offline social network by the SNS. Every SNS occupies, represents, or replicates a snapshot and a stream of offline social networks using state-of-the-art technologies. An SNS is born based on the technology at a time. The technology on which the SNS is based creates and enlarges the potential population of the social network it aims to Sustainability 2020, 12, 502 13 of 23 occupy. We define an LPSN as the social network with a population larger than what existing SNS aims to occupy. The biggest LPSN is the offline social network universe itself. In an individual’s view, the LPSN exists in his/her brain than in an offline Rolodex, email, or cell-phone number. The size comparison of the LPSN can be represented by the sign of inequalities as below. Offline social network > an individual’s memory about people he/she once met > Rolodex or digital address book he/she keeps (“container”) The LPSN not only considers the population of one country or world but also relationship information between two selected persons. Even if a mobile space or container (e.g., Rolodex, mobile devices, friends list, or new technologies like VR, AR, or smart glasses) can include a broader or bigger population of one country or world, that is not enough to create an LPSN. Creating an LPSN also requires links or connection information between two persons. As we observed in the aforementioned cases, Facebook outwitted Cyworld and Myspace even before the mobile space era fully arrived, because Facebook kept fetching offline social networks by dragging not only nodes but also links

Sustainabilitybetween them, 2020, whereas12, x FOR PEER Cyworld REVIEW and Myspace were negligent in this pursuit. For , Cyworld 13 of 22 used celebrity marketing in China without carefully considering fetching real relationships between databaseusers. And of NateOnan LPSN fetched which o fflhadine already social networks been created by utilizing by Cyworld the existing and the nodes SKT and cellular links network. database KakaoTalkof an LPSN fetched which hadoffline already social been networks created by by utilizing Cyworld the and existing the SKT node-and-link cellular network. database KakaoTalk of an LPSNfetched which offline had social already networks been bycreated utilizing on a the smartphone existing node-and-link phone address database book. of an LPSN which had alreadyWe beenregard created the PC-based on a smartphone social network phone or address mobile-b book.ased social network as a subset of the offline socialWe network regard theuniverse. PC-based For socialexample, network some or messen mobile-basedger networks social networkin the PC as space a subset have of thereplicated offline offlinesocial networksocial networks, universe. and For some example, mobile some messenger messenger networks networks have done in the so PC in the space smartphone have replicated space. Inoffl 2009,ine social when networks, the smartphone and some era mobile had messengerjust started, networks the PC have space done had so a in social the smartphone network with space. a populationIn 2009, when larger the smartphonethan that of erathe had mobile just started,space. However, the PC space in 2019, had athe social mobile network space with had a a population far larger populationlarger than thatsocial of network the mobile than space. that of However, the PC space. in 2019, In theKorea, mobile the spaceLPSN hadmoved a far from larger PC population to mobile insocial 2013. network Before than2013, that the ofPC the space PC space.was a better In Korea, plac thee to LPSN build moved an SNS. from That PC year, to mobile however, in 2013. the mobile Before space2013, thebecame PC space the waslarger a better space place upon to which build anto SNS.build That social year, networks however, that the mobilereplicated space offline became social the networkslarger space (Figure upon 10). which to build social networks that replicated offline social networks (Figure 10).

Figure 10. OccupationOccupation of larger population social network change in Korea.

5.2. Hypothesis Hypothesis Building Here isis ourour hypothesis: hypothesis: If anyIf any SNS SNS cannot cannot occupy occupy a majority a majority of the oofffl inethe socialoffline network social universe,network universe,it can fail atit can any fail time at ifany another time if SNS another continues SNS co tontinues occupy to the occupy LPSN the faster LPSN than faster the first. than the first. Contact information in a cell phone, contact lists on messenger apps, and friends lists in SNS all reflectreflect or replicate a subset of thethe oofflineffline social network universe. An email address on a PC, a phone number on a smartphone, and a messenger ID ID are are recognized as as unique unique reflections reflections of of real real people. people. Because some people (whether they be illiterate, t toooo young or poor, blind or simply tech-averse) still do not use a mobile phone or e-mail, the offline offline socialsocial network universe is much larger than merely collections ofof emailemail addressesaddresses or or phone phone numbers. numbers. Users Users in in mobile mobile messenger messenger apps apps or or Facebook Facebook are are all all subsets of the universal set. Now, smartphone-space has a bigger LPSN than the PC space, in terms of the number of users connected and instantaneous accessibility. Our hypothesis can be represented in the functional format below.

Hypothesis 1. An SNS that can make the value of “width rate × depth rate (= LPSN index)” bigger than a competitor, will win the market, where width rate = # of active users/offline population, depth rate = average # of connected friends in the SNS/150 (= Dunbar Number)

• width: # of (active) users in an SNS, cannot be bigger than the offline social network (or population). • depth: # of friends of each person (node) in an SNS, the average number of each depth cannot be greater than 150 (Dunbar number). • Dunbar Number: A theoretical limit to the number of people’s names which individuals can memorize for trust-based social relationships (usually considered to be roughly 150) [2]. Let’s say our real-world has only eight people, and they are connected to each other like Figure 11. For example, person A has B, C, tag">D and F as friends, and so forth. Sustainability 2020, 12, 502 14 of 23

subsets of the universal set. Now, smartphone-space has a bigger LPSN than the PC space, in terms of the number of users connected and instantaneous accessibility. Our hypothesis can be represented in the functional format below.

Hypothesis 1. An SNS that can make the value of “width rate depth rate (= LPSN index)” bigger than a × competitor, will win the market, where width rate = # of active users/offline population, depth rate = average # of connected friends in the SNS/150 (= Dunbar Number)

width: # of (active) users in an SNS, cannot be bigger than the offline social network (or population). • depth: # of friends of each person (node) in an SNS, the average number of each depth cannot be • greater than 150 (Dunbar number). Dunbar Number: A theoretical limit to the number of people’s names which individuals can • memorize for trust-based social relationships (usually considered to be roughly 150) [2].

Let’s say our real-world has only eight people, and they are connected to each other like Figure 11. SustainabilitySustainabilityFor example, 20202020 person,, 1212,, xx FORFOR A PEER hasPEER B, REVIEWREVIEW C, D and F as friends, and so forth. 14 14 of of 22 22

FigureFigure 11.11. RealRealReal WorldWorld World representation.representation.

NowNow let let usus saysay thatthatthat an anan SNS SNSSNS is isis replicated replicatedreplicated in inin the thethe real-world real-worldreal-world like likelike in in Figurein FigureFigure 12 . 12.12. This ThisThis means meansmeans A has AA hashas not notnotor could oror couldcould not not replicatenot replicatereplicate friend friendfriend B yet BB andyetyet andand F has FF has nothas ornotnot could oror couldcould not replicatenotnot replicatereplicate D or DD G oror yet, GG yet, either.yet, either.either. (Even (Even(Even if B orifif BBG oror becomes GG becomesbecomes a member aa membermember of this ofof SNS, thisthis it SNS,SNS, happens itit happenshappens that A isthatthat not AA yet isis connectednotnot yetyet connectedconnected to B or F to isto notBB oror yet FF connectedisis notnot yetyet connectedconnectedto G. Let us toto say G.G. Let theyLet usus were saysay enemiestheythey werewere before.) enemiesenemies before.)before.)

FigureFigure 12.12. AnAnAn SNSSNS SNS representationrepresentation representation ofof thethe realreal world.world.

TheThe aboveabove nodenode andand linkslinks cancan bebe representedrepresented byby matrixmatrix formform suchsuch asas TablesTables 22 andand 3.3. •• TheThe offlineoffline socialsocial networknetwork universeuniverse cancan bebe represenrepresentedted byby aa matrixmatrix tabletable likelike this.this. AA isis aa person,person, BB isis anotheranother person,person, CC isis anotheranother personperson …… livingliving inin thisthis society.society. “1”“1” meansmeans therethere isis aa relationshiprelationship betweenbetween twotwo people.people. “0”“0” meansmeans therethere isis nono relationshiprelationship betweenbetween twotwo people.people.

TableTable 2.2. Real-worldReal-world matrix.matrix.

AA BB CC DD EE FF GG HH

AA 00 11 11 11 00 11 00 00

BB 11 00 00 00 00 00 00 00

CC 11 00 00 00 00 00 00 00

DD 11 00 00 00 00 11 00 00

EE 00 00 00 00 00 11 00 11

FF 11 00 00 11 00 00 11 11

GG 00 00 00 00 00 11 00 00

HH 00 00 00 00 11 11 00 00 Sustainability 2020, 12, 502 15 of 23

The above node and links can be represented by matrix form such as Tables2 and3.

The offline social network universe can be represented by a matrix table like this. A is a person, B • is another person, C is another person ... living in this society. “1” means there is a relationship between two people. “0” means there is no relationship between two people.

Table 2. Real-world matrix.

A B C D E F G H A 0 1 1 1 0 1 0 0 B 1 0 0 0 0 0 0 0 C 1 0 0 0 0 0 0 0 D 1 0 0 0 0 1 0 0 E 0 0 0 0 0 1 0 1 F 1 0 0 1 0 0 1 1 G 0 0 0 0 0 1 0 0 H 0 0 0 0 1 1 0 0

Table 3. Sample SNS matrix.

A B C D E F G H A 0 0 1 1 0 1 0 0 B 0 0 0 0 0 0 0 0 C 1 0 0 0 0 0 0 0 D 1 0 0 0 0 0 0 0 E 0 0 0 0 0 0 0 0 F 1 0 0 0 0 0 0 1 G 0 0 0 0 0 0 0 0 H 0 0 0 0 0 1 0 0

In this SNS, this platform can not replicate the offline social network 100% because this SNS has lost its real relationship information between persons A and B, D and F, G and F, E and H. From one SNS matrix table, if we can sum up each cell’s value, we can get an “occupation or replication rate” of the real-world social network universe.

Sum of each cell value of Table2 = 17 or 9 (if counted symmetrically) Sum of each cell value of Table3 = 8 or 4 (if counted symmetrically) then the occupation rate is 47.1% (8*100/17) or 44.4% (4*100/9).

However, in reality, summarizing the real data of each cell’s values of each SNS is difficult. Therefore, rather than summarizing each of the cell values of each of the SNSs within the matrix, we chose to use the average number of a friends list in each SNS, which comparatively easy to acquire. In this way, we could formulate the value of an LPSN by using the variables of width rate and depth rate, where width rate means the number of service users among real population, and depth rate means the average number of friends per SNS compared to the Dunbar Number (an official average number of friends in real offline society) [2]. LPSN index = width rate depth rate × With this formula, we were able to produce LPSN values for each SNS, compare them to each other, as in Tables4–7, and discover why the index value of the LPSN is so important in order to defeat the first entrant or to newcomers. Sustainability 2020, 12, 502 16 of 23

We compared each pair by considering when the first entrant had its peak value as an LPSN and when it was surpassed by a newcomer. Table4 shows that NateOn’s LPSN far exceeded that of MSN.

Table 4. LPSN index comparison between MSN vs. NateOn.

MSN (2003) NateOn (2008) Nation usage rate 13% 27% (= width rate) Average Num. of friends/150 33/150 = 22% 40/150 = 27% (= depth rate) LPSN index = width rate*depth rate 2.86% 7.29%

Table 5. LPSN index comparison between NateOn vs. KakaoTalk.

NateOn (2008) KakaoTalk (2013) Nation usage rate 27% 34% (= width rate) Average Num. of friends/150 40/150 = 27% 65/150 = 43% (= depth rate) LPSN index = width rate*depth rate 7.29% 15%

In Table5, we can see KakaoTalk’s LPSN far exceeded that of NateOn. In Table6, we can see Facebook’s LPSN far exceeded that of Myspace.

Table 6. LPSN index comparison between Myspace vs. Facebook.

Myspace (2011) Facebook US. (2013) Nation usage rate 32% 58% (= width rate) Average Num. of friends/150 68/150 = 45% 120/150% = 80% (= depth rate) LPSN index = width rate*depth rate 14% 50%

In Table7, we can see Facebook’s LPSN exceeded that of Cyworld.

Table 7. LPSN index comparison between Cyworld vs. Facebook.

Cyworld (2011) Facebook Korea (2013) Nation usage rate 53% 26% (= width rate) Average Num. of friends/150 50/150 = 33% 120/150% = 80% (= depth rate) LPSN index = width rate*depth rate 17% 21%

We can see below in Table8, the summary of national usage rate, the average number of friends per each SNS in the related year. Sustainability 2020, 12, 502 17 of 23

Table 8. Comparison of the number of subscribers/unique visitor/average friends lists per social network cases within the research period.

2003 2005 2007 2008 2009 2010 2013 2018 Population of Korea (1000) 47,890 48,180 49,050 49,550 50,430 51,000 Internet users of Korea (1000) 28,000 (58%) 33,000 (72.8%) 35,000 (76.5%) 40,000 (82.1%) 46,000 (90%) Cellphone users of Korea (1000) 34,000 46,000 48,000 50,000 Smartphone users of Korea (1000) 0 0 (30%) 37,500 (74%) 50,000 (98%) Population of US (1000) about 300,000 about 300,000 300,000 about 300,000 about 300,000 Internet users of US 61% of adults 74% 84% 89% Cellphone users of US 63% of adults 77% 89% 95% Smartphone users of US 0 0 56% of adults 77% MSN (Korea) (1000) 6000 4000 4000 0 0 Nation usage rate 13% 8% 8% Average # of friends/150 (Dunbar#) = depth rate 33/150 = 22% 39/150 = 26% LPSN index = width*depth 2.86% 2% NateOn (1000) 0 10,000 13,000 4500 1000 Nation usage rate 20% 27% 9% Average # of friends/150 (Dunbar#) = depth rate 12/150 = 8% 40/150 = 27% LPSN index = width*depth 1.6% 7.29% KakaoTalk (PC/Mobile) (1000) 0 0 17,000/25,000 /50,000 Nation usage rate 34% 97% Average # of friends/150 (Dunbar#) = depth rate 65/150 = 43% 100/150 = 67% LPSN index = width*depth 15% 65% Cyworld (1000) 3500 20,000 26,000 9000 UV/month 3000 Nation usage rate 52.4% 18% Average # of friends/150 (Dunbar#) = depth rate 40/150 = 27% 50/150 = 33% 50 (33%) LPSN index = width*depth 26% 6% Sustainability 2020, 12, 502 18 of 23

Table 8. Cont.

2003 2005 2007 2008 2009 2010 2013 2018 Myspace(US/Global) (1000) 1000 115,000/200,000 95,000 20,000 10,000 US nation usage rate 32% 7% Average # of friends/150 (Dunbar#) = depth rate 68/150 = 45% 68/150 = 45% LPSN index = width*depth 14% 3% Facebook (PC/Mobile), Korea (1000) 0 (100/0) 13,000 UV/month Nation usage rate 26% Average # of friends/150 (Dunbar#) = depth rate 120/150 = 80% LPSN index = width*depth 21% (100,000/0) global (global (global 220,0000/global Facebook (PC/Mobile), US (1000) 0 200,000 1,300,000/900,000) 1,400,000) US nation usage rate 58% 68% Average # of friends/150 (Dunbar#) = depth rate 80% LPSN index = width*depth 50% Sustainability 2020, 12, 502 19 of 23

6. Discussion

6.1. Disproof of “Open API is the Most Important Factor Explaining Why Facebook Defeated All Competitors” In our arguments, we did not agree that the API alone is the main reason for network effect failure, especially in the SNS industry [19]. We learned, through our multiple case studies, that even before Facebook executed its API strategy, it had already conquered Myspace. Thus, although an API can be a necessary condition to make it stronger, by itself, it cannot cause the network effect to arise or be sustained. Moreover, Cyworld copied the API strategy after Facebook, but it did not work.

6.2. Disproof of “New Technology or Mobile is the Most Important Factor to Explain Network Effect is Reversed” In 2007, Myspace was the largest SNS in the United States. However, in 2013, its position was reversed by latecomer Facebook. Unlike Korea, the United States still had an bigger LPSN in the PC space in 2013. Myspace did not have more than 30% of the offline social network as its users and failed to keep building up the offline social network databases. In the end, even within the PC space, Myspace was defeated by Facebook. In addition, as the mobile space provided a larger social network container, Facebook steadily collected more offline social networks using its [5]. In 2013, in the US, most users accessed SNSs through their PC rather than their mobile device because the mobile penetration rate was not that high yet in the US. Facebook had already surpassed Myspace in the US in terms of monthly active users, especially by occupying offline social networks, which had been accumulated with most of the university students and high-schoolers in the US. Facebook had also a strong presence in foreign countries through university and college students abroad [5]. These facts show that, even before the mobile era arrived, Myspace was thoroughly defeated by Facebook. This was not because of mobile or technology change, but because of a lack of effort to incorporate real offline social networks into their existing service. Myspace killed and destroyed existing precious relationship links built among real friends. Before 2013, Myspace was the representative SNS in the United States. Myspace was acquired by ’s at about 700 million USD. At this time, Mark Zuckerberg of Facebook said that it was a matter of time before Myspace was overtaken by Facebook because the new owners did not understand the nature of the social network. Mike Jones, former president of Myspace, said in an interview, “I believe that Facebook was able to keep up with Myspace because it made the social network perfect, whereas Myspace just introduced the social network to people” [5]. Myspace encouraged anonymous systems, which increasingly hindered the enjoyment of social networks. On the other hand, Facebook continued to encourage people to use their real names. This important difference in the two SNSs will serve as a game-changer in the future. Using an individual’s real name meets the basic requirements for bringing offline social networks online.

7. Conclusions

7.1. Summary The four cases above are illustrated in this graph in Figure 13, which shows a shift of the LPSN from PC to mobile. Even before the mobile era began, Facebook had outpaced Cyworld and Myspace. As the mobile age emerged, KakaoTalk easily defeated NateOn. The winners focused on their occupation of the LPSN, wherever it exists. That means that we can now answer the research question, “How can the largest SNS sometimes collapse?” through hypothesis-building. Sustainability 2020, 12, 502 20 of 23 Sustainability 2020, 12, x FOR PEER REVIEW 19 of 22

Figure 13. LPSN shift from PC to mobile in 2013 in Korea. Figure 13. LPSN shift from PC to mobile in 2013 in Korea.

Hypothesis: “If any SNS cannot occupy a majority of the oofflineffline social network universe, it can fail at any any time time if if another another continues continues to to occupy occupy the the LPSN LPSN faster faster than than the the first.” first.” Or “The Or “The SNS SNS which which can canmake make the thevalue value of “width of “width rate rate × depthdepth rate rate (=( =LPSNLPSN index)” index)” larg largerer than than its its competitor competitor wins the × market, where width rate == # of active usersusers/off/offllineine population,population, depthdepth raterate == average # of connected friendsfriends/150/150 ((== Dunbar Number)”. Although Facebook enjoys exclusive worldwide networknetwork eeffects,ffects, moremore thanthan 51%51% of the 7.5 billion people on oofflineffline socialsocial networksnetworks areare notnot yetyet registeredregistered toto Facebook.Facebook. In addition, many Facebook users are leaving. According According to to our our hypothesis, hypothesis, there there may may still still be be opportunities opportunities for for a a latecomer, particularly if it can get ahead by occupyingoccupying (fetching not only nodes but also links) the remaining 51% of the oofflineffline social network market in response to thethe paradigmparadigm shifts from future hardwarehardware changes or innovative technologies. If new medium mediumss or technologies emerge which can be far more closely or heavily used to suit each individual’s daily behaviors, and they can reflectreflect more realistic social networks, there there is is a a theoretical theoretical plausibility plausibility th thatat Facebook Facebook can can collapse. collapse. In previous In previous sections, sections, we wehypothesized hypothesized that that an anSNS SNS which which occupies occupies the the majority majority of of offline offline social social network network universe universe would would be the last winnerwinner inin exercisingexercising thethe everlastingeverlasting networknetwork eeffect.ffect. As thethe technologytechnology innovationinnovation migrates from PCs to cellularcellular phones, and from cellcell phonesphones to smartphones,smartphones, in turn, the use of anan innovativeinnovative media toto betterbetter capturecapture o fflofflineine social social networks networks is essentialis essential to maximizeto maximize or sustain or sustain network network effects. effects. Still, merelyStill, merely migration migration to the to mobile the mobile world world or new or technew platformtech platform is not is enough. not enough. The focusThe focus should should be kept be onkept accumulating on accumulating offline offline social soci networksal networks (which (which consist consist of nodes of andnodes links). and Forlinks). example, For example, a service a whichservice enables which peopleenables to people share theirto share implicit their experience implicit experience data (tacit data)data with(tacit others data) onwith a “trust-based” others on a and“trust-based” “social process and “social re-engineering” process re-engineering” basis, such as basis, that which such as was that somehow which was executed somehow by Activenet executed (acquiredby Activenet by Oracle)(acquired in theby Oracle) B2B market in the first B2B [34 market], could first defeat [34], Facebook. could defeat Facebook.

7.2. Contribution According toto thethe results results of of this this research, research, the the network network effect effect is never is never dead dead [35]. The[35]. network The network effect, ateffect, least at in least terms in of terms the SNS of the industry SNS industry (including (inc bothluding Facebook both andFacebook messenger and messenger services), in services), order to bein maximizedorder to be needs maximized to occupy needs most to of occupy the offl inemost social of the network. offline Paradoxically, social network. the contributionParadoxically, of thisthe studycontribution is that the of first-moverthis study company’sis that the failure,first-mov despiteer company’s the existing failure, network despite effect, the did notexisting occur network because thereeffect, wasdid nonot longeroccur because a “network there eff wasect”. no However, longer a “network it strengthened effect”. the However, importance it strengthened of the network the eimportanceffect in the correctof the network way, especially effect in in the the correct online /wamobiley, especially SNS industry. in the Mostonline/mobile of all, we SNS answered industry. the question,Most of all, “Why we andanswered how are the the question, largest SNSs “Why sometimes and how unable are the to sustainlargest themselves?”SNSs sometimes by supporting unable to oursustain hypothesis. themselves?” According by supporting to Dul and our Hak, hypothesis hypothesis-building. According to without Dul and hypothesis-testing Hak, hypothesis-building has also valuablewithout hypothesis-testing research significance has [31 also]. valuable research significance [31]. There areare two two more more contributing contributing points. points. First, weFirst, can we argue can the argue API strategy the API for strategy the sustainability for the ofsustainability an existing of SNS an isexisting not suffi SNScient is [not19]. sufficient Facebook [19]. executed Facebook the APIexecuted strategy the onlyAPI strategy after they only became after successfulthey became at replicatingsuccessful oatffl inereplicating social networks offline social first (interweaving networks first local (interweaving networks to local naturally networks produce to globalnaturally social produce networks global on theirsocial platform.). networks Furthermore, on their platform.). even when Furthermore, Cyworld copied even thewhen API Cyworld strategy ofcopied Facebook, the API they strategy were unsuccessful of Facebook, because they theywere didunsuccessful not continue because to accumulate they did pure not ocontinueffline social to accumulate pure offline social networks into their existing platform first. Second, a timely accepting mobile or technology paradigm shift is not sufficient for sustaining existing network effects [20]. Sustainability 2020, 12, 502 21 of 23 networks into their existing platform first. Second, a timely accepting mobile or technology paradigm shift is not sufficient for sustaining existing network effects [20]. Facebook outwitted Cyworld and Myspace even before the mobile era had fully emerged. Cyworld or Myspace lost control because they did not continue to accumulate precious offline social network databases. This also means they did not raise the value of the LPSN index, depth rate (average number of friends on each SNS per Dunbar Number 150) or width rate (number of active users of SNS per local real population). Considering the exact value of the LPSN index, we still cannot provide an exact standard value for an LPSN to outwit any competitor, whether the competitor is a newcomer or not. However, one certainty is that an LPSN index lower than 30% is dangerous and may cause an SNS to lose its control of the market, as can be seen in Table8. Future research is needed. What factor will be the main force for network effect sustainability on other online platforms like Uber, , Netflix, and ?

7.3. Implications First, our hypothesis can be utilized by practitioners in the SNS industry to decide if a new venture should be undertaken to defeat, for example, Facebook, WeChat, or if their existing SNS system is sufficient for maintaining their No. 1 position. Second, in academia, most articles are written with the assumption that all platforms are equal in terms of analyzing network effects. However, there are actually major differences among platforms. Amazon (a commerce platform), Airbnb, Uber (online to offline platforms), Facebook, LinkedIn (social network platforms), Pinterest and Twitter (content network platforms) have their inherent principles. For example, users of Amazon and Airbnb are not connected. Users of Pinterest do not have to know each other in Facebook or LinkedIn. This means the basic structure, content type, and user relation type of each service are different, which makes the network effect quite different. For example, you can use Amazon without your friends, but not Facebook. Nevertheless, Amazon and Facebook have their inherent network effects. In the SNS industry, the network effect occurs earlier, meaning that first entrants are not safe from latecomers. Myspace and Cyworld had a huge, rapid growth with their network effects. However, because they did not replicate most of their offline social networks (which are a kind of “mother set” or large set) sufficiently, they eventually could not maintain their first-entrant advantage.

Author Contributions: Conceptualization, Y.J.H. and K.J.L.; Case Study Methodology, A.P.; Validation, Y.J.H. and K.J.L.; Formal Analysis, Y.J.H. and K.J.L.; Investigation, Y.J.H.; Resources, Y.J.H.; Writing—Original Draft Preparation, Y.J.H.; Writing—Review & Editing, K.J.L.; Supervision, K.J.L. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A5B8059804). Acknowledgments: The authors give special thanks to Neena Bucks( A researcher in MIT)’s friendly review and editing help. Conflicts of Interest: The authors declare no conflict of interest.

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