Hyelim Oh. School of Computing, National University of Singapore, 13 Computing Drive, Singapore SINGAPORE

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1 RESEARCH ARTICLE FREE VERSUS FOR-A-FEE: THE IMPACT OF A PAYWALL ON THE PATTERN AND EFFECTIVENESS OF WORD-OF-MOUTH VIA SOCIAL MEDIA Hyelim Oh School of Compuing, Naional Universiy of Singapore, 13 Compuing Drive, Singapore SINGAPORE {hyelim.oh@nus.edu.sg} Animesh Animesh and Alain Pinsonneaul Desauels Faculy of Managemen, McGill Universiy, Monréal, Québec, H3A 1G5 CANADA {animesh.animesh@mcgill.ca} {alain.pinsonneaul@mcgill.ca} Appendix A Summary of Underlying Theoreical Mechanisms A. Impac of Paywall on WOM Paern Alernaive Mechanisms A1. Based on A1a WTP and Exposure Theory A2. Sraegic User Behavior (by users wih low WTP) A1b A2a A2b Raionale/Argumens/ Assumpions Ligh user segmens (who are likely o have low WTP) are more likely o reduce (or even disconinue in exreme cases) heir consumpion of conen afer he paywall implemenaion. Ligh user segmens are more likely o consume popular aricles whereas he heavy user segmen is more likely o consume a mix of niche aricles and popular conen. Ligh user segmens (who are likely o have low WTP) are more likely o reduce (or even disconinue in exreme cases) heir consumpion of conen afer he paywall implemenaion. Search cos for finding popular conen is lower han he search cos for finding niche conen. Conneced Lieraure/Theory Resuling (Possible) Effec Suppor for/agains Uiliy heory WTP (Danaher 2002) Exposure heory (McPhee 1963) Uiliy heory WTP (Danaher 2002) Search Cos Theory (differenial search cos for popular and niche conen on alernaive websie) Juxaposing A1a and A1b leads o long ail effec due o he disproporionae reducion of a resuls of reducion of conen consumpion by ligh users). Juxaposing A2a and A2b, users who have lower WTP and are forced o curail heir consumpion due o paywall are more likely o curail heir consumpion of popular conen on. This leads o long ail effec due o disproporionae reducion of popular conen consumpion (as a resul of sraegic reducion of popular conen consumed by ligh users). A1a. Suppored (see he descripive saisics in Table 11). A1b. Suppored (see resuls from he posesimaion of finie mixure model in Table 9) Since he resuling effecs as well as boh he assumpions (A1a and A1b) are suppored, we sugges ha here is suppor for his mechanism. A2a is suppored (as i is he same as A1a). A2b was no empirically esed in his paper bu can be argued heoreically. To he exen ha A2b is rue, i is possible ha his mechanism may also be playing a role in creaing he long ail effec afer he paywall. A1

2 A3. Sraegic User Behavior (by users wih high WTP) A4. User Resenmen (sysemaically relaed o reading behavior) A3a A3b A4a A segmen of heavy users decide o curail heir consumpion of in order o avoid paying subscripion fee. Search cos for finding popular conen is lower han he search cos for finding niche conen. Due o negaive emoional response oward he paywall, a proporion of readers may boyco he. Search Cos Theory (differenial search cos for popular and niche conen on alernaive websie) Campbell (1999); Xia e al. (2004) Leads o long ail effec due o he disproporionae reducion of a resul of sraegic reducion of popular conen consumpion by heavy users). This mechanism can coexis wih exposure mechanism as well as he sraegic user behavior by low WTP users. Leads o long ail effec due o disproporionae reducion of a resul of dropping ou of users who feel resenmen). Similar o he mechanism based on he sraegic behavior of ligh users, assumpion A3b was no empirically esed. However, our finding ha he ligh users who read mosly popular conen are more likely o reduce heir conen consumpion afer he paywall (see Table 11) suggess ha his mechanism would have weak impac, if any. This mechanism does no sugges ha here will be a differenial impac of paywall on he conen consumpion of ligh and heavy users. A4b Users who feel resenmen are more likely o read popular han niche conen B. Impac of Paywall on WOM Effeciveness Alernaive Mechanisms B1. Bypass B1a effec Raionale/Argumens/ Assumpions allows visiors who come from links on social media o bypass is paywall. This bypass effec (i.e., increase in non-subscribers likelihood o click on a conen available hrough social media as hey aemp o maximize he number of aricles ha can be accessed wihou paying subscripion fee) may be dominan in websie raffic generaion. B2. Viraliy effec B2a Given ha popular aricles are he conen ha has a greaer demand from a larger audience (Zenner e al. 2013), we expec ha a decrease in he proporion of WOM abou popular conen will in urn lower he average clicks per link shared hrough social WOM. B3. User Resenmen B3a Due o negaive emoional response oward he paywall, a proporion of readers may boyco he and sysemaically do no click on and/or share link on social media. No heory (suggesed by an anonymous reviewer) Though we do no es assumpion A4b, however, we find ha consisen wih exposure based mechanism ligh users reduce heir conen consumpion more han heavy users afer paywall (see Table 10), which canno be explained by his mechanism. So we infer ha his mechanism may have relaively weak role, if any. Underlying Theory/Raionale Resuling (Possible) Effec Suppor for/agains Relaed o he Design of he Paywall mechanism Conen characerisics play a significan role in deermining he viraliy of online conen (Berger and Milkman 2012) No heory (suggesed by an anonymous reviewer) The relaive srengh of he posiive relaionship beween raffic may increase afer a The relaive srengh of he posiive relaionship beween raffic may decrease afer a The relaive srengh of he posiive relaionship beween social media WOM and websie raffic may decrease afer a paywall implemenaion. Though his resenmen based mechanism may be an alernaive o he viraliy based logic suggesing negaive impac of paywall on WOM effeciveness, boh he mechanisms may coexis. Our sudy does no direcly measure he bypass effec bu given he resuls ha show decrease in he srengh of he posiive relaionship beween rafficafer a paywall implemenaion, we can assume ha role of such mechanism, if any, is small. Resuls show ha he paywall may lead o a disproporionae decrease in he WOM abou popular conen. Resuls also show decrease in he srengh of he posiive relaionship beween social media WOM and websie raffic afer a Our paper does no measure user resenmen and canno isolae he relaive impac of viraliy vs. resenmen mechanism on he relaionship beween social media WOM and websie raffic. However, we believe ha such a segmen of users is likely o be relaively small. A2

3 Appendix B Online Survey Quesionnaires 1. Do you share online news aricles (URLs) on social media (e.g., Facebook, Twier)? 2. How many online newspaper aricles (e.g., The New York Times, The Los Angeles Times) do you READ per week on average? 3. Please specify an approximae number of online news aricles you READ in a normal week? 4. How many online newspaper aricles (URL links) do you SHARE on social media (e.g., Facebook, Twier)? 5. Please specify an approximae number of news aricles (URL links) you SHARE on Facebook in a normal week. 6. Please specify an approximae number of news aricles (URL links) you SHARE on Twier in a normal week. Appendix C Supplemenary Informaion of Difference-in-Difference Seup Table C1. Key Demographics of and LTAT Websie Visiors Household income < $30, % 16.09% $30,000 $59, % 26.97% $60,000 $99, % 25.50% $100,000 $149, % 21.20% > $150, % 10.24% Age % 8.62% % 11.49% % 18.82% % 21.50% % 37.13% Gender Female 43.56% 34.18% Male 56.44% 65.82% Appendix C2. Comparison of and News Even Coverage Before Paywall Rollou: Top 200 Mos-Shared News Aricles # of News Aricles # of Sharing # of News Aricles # of Sharing Overlapped news evens , ,439 Region-specific local news evens 5 2, ,340 Before Paywall Rollou: Top 200 Mos-Shared News Aricles # of News Aricles # of Sharing # of News Aricles # of Sharing Overlapped news evens 89 50, ,765 Region-specific local news evens 8 3, ,681 Major news evens covered across and in pre-paywall sample include Japan sunami & nuclear acciden, Libya rebels & miliary acions, Bahrain & Arab proess, Wisconsin bale on union, U.S. governmen budge debaes, and U.S. pacific sunami. Major news evens covered across and in pos-paywall sample include U.S. governmen shudown & budge defici, Japan nuclear disaser, Libya rebels & miliary acions, and elecion campaigns. A3

4 Appendix D Supplemenary Informaion of Finie Mixure Model Esimaion Table D1. Model Fi for Alernaive Numbers of Segmens Number of Laen Segmens LL AIC BIC R² Noe: In erms of he BIC crierion, he models wih greaer numbers of segmens improve model performance. However, an inerpreaion wih a wo-segmen model is more suiable for our hypohesis esing, and adding an addiional segmen marginally improves he model fi indices afer he hree-segmen model. We herefore op o repor he model esimaes for boh he wo- and hree-segmen models. Table D2. Parameer Esimaes of Finie Mixure Models (Three-Segmen Model) Dependen Variable: ln(average Rank of Conen Shared) Segmen 1 Segmen 2 Segmen 3 Inercep (.139) ln(user rank j ) (0.15) (.096).004 (.001) Proporion 46% 51% 2% Noe: Sandard errors in parenheses. *p < 0.1, **p < 005, ***p < (.186).478 (.020) Table D3. Posesimaion of Finie Mixure Model (Three-Segmen Model) ln(avg. Rank of Conen Shared j ) (.008) ln(user rank j ) (.005) Noe. Sandard errors are in parenheses. Segmen 1 Segmen 2 Segmen 3 inerval] [1.765, 1.799] (.006) [9.053, 9.075] (.007) inerval] [5.148, 5.174] (.007) [8.938, 8.966] (.013) inerval] [7.360,7.391] [9.351, 9.402] A4

5 Appendix E Supplemenary Informaion on Robusness Checks We esimae he following VARX model: ln( Websie visi ) p = ln( Twees ) α + α w ln( Websie visis ) X p + ln( Twees ) β 1 + ε β X where ln(websie visis ) denoes he daily gross sie raffic a day, and is one-day lagged variable is defined as Websie visis -1. Similarly, ln(twees ) represens he oal number of wees ha conain he link a day. The dummy variables, Saurday and Sunday, are included in all equaions o conrol for variaions due o differences in he ype of news aricles published on he weekends as well as he differences in he reading habis of consumers during he weekend. We firs conduced he uni roo ess. The Dickey-Fuller es resuls confirm ha he variables are saionary raher han evolving in 95% confidence inervals. The resuls of he VARX model are repored in Table E1. Table E1. Esimaion Resuls for WOM and Websie Traffic (VARX Model) Before Paywall Afer Paywall Sie Traffic Equaion: ln(websie visis ) ln(twees -1 ) (0.1399*) (0.0848) ln(websie visis -1 ) (0.2648) Saurday (0.0548) Sunday (0.0548) Consan (2.6188)*** Twees Equaion: ln(twees ) ln(websie visis -1 ) (0.5866) ln(twees -1 ) (0.3100) Saurday (0.1203)*** Sunday (0.1214) Consan (5.800)*** Sandard errors are in parenheses. *p < 0.1, **p < 0.05, ***p < (0.2025) (0.037)*** (0.037)*** (2.4953)*** (0.3063) (0.1284) (0.0475)*** (0.0560)*** (3.7767)*** A5