Appendix
NFC
NFC is one of the promising technologies in connection with mobile payments. Under the umbrella NFC Forum, several big industry players have been engaging in promoting NFC standards since 2004 (Lerner, 2013).
Figure 19. Payment at the POS using NFC. From Isaac (2014).
Potential security gaps associated with NFC include data corruption or modification and eavesdropping. The threat of hackers attacking sensitive payment data is critical. To limit such risks, tokens can be saved on the smart phone. They authorize the payment app for a limited number of payments for a limited amount of time only, e.g. a token may be valid for one transaction only. This means that in case the payment process was somehow corrupted, the hacked information would only be valid for as long as the token (Sims 2014).
Furthermore, establishing a secure channel can provide protection against various forms of data manipulation (Lerner 2013, p. 55). Fraudulent transactions via a hidden NFC readers, can be prevented when the smart phone operating system turns off the NFC units automatically when the smart phone screen is locked (Sims 2014).
The Mobile Wallet
A mobile wallet application consists of databases and software placed on the secure element of the smart phone. The databases can include information relevant for identification, shipment and payment (e.g. security and expiry date for credit cards). The software then connects the critical information with a payment application (Kemp, 2013).
Appendix B. Mobile Payments Solutions
Softcard
Softcard is a joint venture between three US American mobile networking operators (Verizon, T-Mobile, and AT&T). The mobile wallet solution enables mobile POS payments through NFC. It is possible to add payment cards, loyalty, membership, and reward cards. Money-saving offers by merchants can also be integrated in the app.
Parkmobile
Parkmobile offers mobile parking. Consumers can pay-by-phone and avoid issues such as not having exact change or extending the parking time when away. Depending on the implementation, mobile parking is an example for the stationary-merchant-automat scenario or remote payments (mobile commerce or line skipping apps).
Apple Pay
In October 2014, Apple Pay went live in the United States supported by several credit and debit cards including MasterCard, VISA, and American Express, as well as some US banks.
Apple Pay enables contactless payments and focuses primarily on offline commerce at the POS. The payment process can be authorized by the user’s fingerprint. Apple Pay relies on NFC technology and will so far only be available for 6th generation iPhones and Apple’s soon to be released smart watch. Through a combination of tokenization of credit card details and several steps of authentication, the payment process via Apple Pay is thought to be more secure than comparable mobile payments solutions and regular card readers (Tung, 2014).
Nevertheless, information on the connected Apple account may still be subject to data corruption. Apple reported one million activated credit cards within 72 hours of availability (Eadicicco, 2014). However, specific merchants are blocking the Apple approach in favor of their own solution Current C (see below).
Starbucks
Starbucks’ mobile payments app is one of the few that can in fact be called successful: 15%
of POS purchases are currently made via the app (“Bribing the users,” 2014). It should be
Appendix
highlighted though, that the app promises added value by awarding loyalty points and cutting the wait at the POS. In December 2014, Starbucks announced to add a feature, which allowed customers to skip the line by ordering ahead of time; the full rollout of the update is scheduled for 2015.
Google Wallet
The Google Wallet launched in the US only in 2011. Functions include contactless in-store and P2P payments (for gmail users), as well as typical wallet functions such as storing payment and loyalty cards, and redeeming sales promotions. The app has so far been downloaded more than 10 million times (Eadicicco, 2014). While the Google Wallet has fallen behind expectations it has nevertheless been a first attempt to wake the payments industry. It was speculated whether Google’s intrusive data tracking approach might have caused banks and other potential stakeholders to hold back off.
Venmo
Launched in January 2013, Venmo is owned by a subsidiary of PayPal that allows sending money to friends using money directly in Venmo, a bank account, or debit card.
Snapcash
In November 2014, Snapchat introduced the possibility to send money to their friends via their mobile picture-messaging app. The payments are an attempt to approach consumer P2P payments.
PayPal
PayPal’s main business is online payments. The company’s app offers a mobile wallet function and supports the possibility to claim offers, pay in store, order ahead, as well as send or request payments. Thus PayPal is not bound to POS payments as opposed to, for instance, Apple Pay. Furthermore, PayPal partnered with Samsung and allows users to link their account to fingerprint scanner of the 5th generation Galaxy phones (Colt, 2014). Its noteworthy, that initiating and authorizing a money transfer between two email accounts does not use any special security means, other than the password needed to login to the app.
Netto App
Netto is a German grocery discount store, which offers a proprietary mobile payments solution including a couponing and loyalty system. In order to use the mobile payments function, the phone has to be connected to the internet. A 4-digit code is generated, which has to be presented at the checkout.
MVG
The MVG (Münchner Verkehrsgesellschaft) operates the public transport in Munich Germany. The MVG is run by the city of Munich. Since 2013 the company offers a mobile app that, in addition to routing services, offers mobile ticketing. While it is impossible to use a mobile phone for payment at the ticket automat, the app allows purchasing selected transport tickets through the app.
mPass
mPass is a joint venture between the three German MNOs O2, Vodafone, and Telekom. The companies agreed to start a cooperative mobile payments solution called mPass. NFC is the underlying technology that enables mobile payments at the POS. The solution is open and can be used by customers of all MNOs. mPass also allows paying in selected online stores.
kesh
The biw Bank offers kesh since May 2013. kesh offers the possibility to pay at the POS, as well as to send and request money using the mobile phone number. So far, one online store offers payments via kesh.
Yapital
Yapital exists since 2011 as subsidiary of the Otto Group. The Otto Group used to be the biggest German mail order company and is now exploring the electronic commerce.
Opentabs
Opentabs allows ordering in advance to avoid waiting time. In addition, the order can be paid via the mobile app.
Appendix
Current C
In order to avoid high processing fees, a consortium of big US retailers, including Walmart and Target, released their own mobile-payment service, called Current C. Due to the app relying on QR codes, Current C has been criticized for being clumsy and prone to data corruption (Constine, 2014). The app is rumored for the first half of 2015.
Appendix C. Trend Analysis Fit Parameters
Indicator M α t0 MAD
EFTPOS Machines, Germany 753.2586 0.2698 2001 9.5%
EFTPOS Machines, USA 5469.7869 0.4197 2000 5.2%
Indicator M α t0 MAD
Online Shoppers (in %), Germany 73.8924 0.2105 2006 3.8%
Online Shoppers, USA 203655.6810 0.3432 2005 1.0%
Indicator M α t0 MAD
Internet Users (per 100 people), Germany 83.4884 0.4631 2002 4.2%
Internet Users (per 100 people), USA 83.2341 0.2726 2001 10.4%
Indicator M α t0 MAD
Mobile Cellular Subscriptions (per 100 people), Germany
121.5173 0.3509 2002 11.8%
Mobile Cellular Subscriptions (per 100 people), USA 99.9039 0.2969 2002 2.7%
Indicator M α t0 MAD
Sales of Smart Phones (Cumulated Volume), Germany 103107.1350 0.8777 2012 2.8%
Sales of Smart Phones (Cumulated Volume), USA 640517.1352 0.7257 2012 2.2%
Appendix
EFTPOS'Machines'(in''000'units) Germany VariablesCompletionExpected'Parameter'Uncertainty α"=0.26980.98723.0%alpha'"(+)"=0.2779alpha'"(,)"=0.2618 t0"=20010.064t0'"(+)"=2002t0'"(,)"=2000 M"=753.25864.7%M'"(+)"=788.6617M'"(,)"=717.8554 e"=2.7183 Historical"Mean"=375.9587 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 645.51769.5%(+)(,)(+)(,) 1198910.928010.92800.014728.55547.41350.039411.310317.627417.546045.13037.64941.9204 2199023.152012.22400.031736.96679.48580.05165.429113.814722.987857.64649.79302.4501 3199134.673011.52100.048347.692612.05480.06763.794513.019630.045173.291512.45423.1024 4199251.806017.13300.073961.264515.18740.08851.58969.458539.146892.644615.70523.8912 5199328.0000-23.80600.038678.270118.92630.116036.030650.270150.8021116.277319.59554.8234 6199462.500034.50000.090599.318523.26700.151915.722036.818565.5923144.685224.12845.8928 7199570.04897.54890.1025124.979328.12960.198928.945354.930484.1452178.194329.23227.0744 81996115.001044.95210.1802155.691433.32910.260513.405340.6904107.0843216.850734.73058.3189 91997162.795147.79410.2757191.648638.55790.34125.826428.8535134.9452260.317140.32309.5505 101998230.881168.08600.4420232.676643.39250.44700.02001.7955168.0604307.806045.591010.6715 111999300.683269.80210.6644278.138047.34090.58542.897322.5452206.4241358.086750.042611.5743 122000591.2800290.59683.6504326.904049.93030.7667377.7435264.3760249.5700409.584853.198312.1603 132001435.6800-155.60001.3719377.428150.81581.004318.019358.2519296.5057460.567054.699812.3595 142002460.609024.92901.5739427.923449.87351.31535.780432.6856345.7512509.368054.402812.1475 152003495.790035.18101.9256476.607247.23521.72282.102219.1828395.4984554.599352.414811.5503 162004520.020024.23002.2296521.943943.25132.25640.02311.9239443.8653595.286349.061310.6390 172005569.527049.50703.0998562.819638.39702.95540.31626.7074489.1713630.912444.79589.5129 182006578.42008.89303.3083598.612333.16333.87083.317720.1923530.1537661.378540.09368.2795 192007566.0370-12.38303.0234629.161127.97035.069938.442463.1241566.0706686.909535.36617.0360 202008592.994026.95703.7001654.669223.12206.640444.392961.6752596.6855707.944530.91475.8573 212009645.427052.43305.9855675.581518.79948.697313.052030.1545622.1692725.033726.92334.7919 222010678.180032.75309.0329692.469815.079811.39143.654014.2898642.9661738.759923.47483.8642 232011710.912032.732016.7879705.943511.965814.92010.55674.9685659.6656749.684220.57953.0799 242012720.00009.088021.6486716.58899.413519.54170.33353.4111672.9016758.315218.20142.4317 252013743.624023.624077.1829724.93537.355525.595112.813218.6887683.2847765.095116.28101.9056 262014731.43985.717233.5235691.3640770.396714.75041.4846 272015736.48514.425543.9078697.6111774.527713.54261.1512 282016740.38433.414757.5088702.4179777.737712.59690.8895 292017743.38922.628375.3230706.1027780.226511.86080.6854 302018745.69992.019298.6553708.9191782.153111.29040.5270 312019747.47381.5490129.2151711.0670783.642510.85000.4046 322020748.83391.1870169.2411712.7025784.792710.51080.3102 332021749.87570.9088221.6659713.9461785.680410.25010.2376 342022750.67300.6953290.3298714.8908786.365010.05010.1818 352023751.28290.5317380.2633715.6079786.89279.89670.1391 362024751.74920.4065498.0550716.1520787.29949.77930.1063 372025752.10560.3106652.3342716.5646787.61279.68950.0813 382026752.37800.2373854.4034716.8774787.85419.62080.0621 392027752.58610.18131119.0662717.1145788.03999.56830.0474 402028752.74500.13851465.7119717.2941788.18309.52810.0362 412029752.86640.10581919.7357717.4303788.29329.49750.0277 422030752.95910.08082514.3995717.5334788.37819.47400.0211
Appendix
EFTPOS'Machines'(in''000'units) USA VariablesCompletionExpected'Parameter'Uncertainty α"=0.41970.94613.0%alpha'"(+)"=0.4323alpha'"(,)"=0.4071 t0"=20000.064t0'"(+)"=2001t0'"(,)"=1999 M"=5469.78694.7%M'"(+)"=5726.8669M'"(,)"=5212.7069 e"=2.7183 Historical"Mean"=2318.3071 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 642.35465.2%(+)(,)(+)(,) 1198951.000051.00000.009451.472321.39900.00950.00440.472324.6458103.438822.05048.6757 2199060.00009.00000.011177.932032.24110.01454.185717.932037.8766154.013933.229913.0441 3199188.000028.00000.0164117.698048.33350.02207.658329.698058.1305228.313149.832619.4940 41992115.042027.04200.0215177.088771.91570.033522.466762.046789.0284336.292774.184528.8728 51993196.000080.95800.0372264.9645105.81560.050918.863868.9645135.9165490.7825109.239142.2028 61994375.5000179.50000.0737393.2069153.16100.07750.859117.7069206.5036706.9362158.304460.5194 71995528.7000153.20000.1070576.6418216.49610.11784.455647.9418311.50021000.1063224.170784.4745 81996875.4000346.70000.1905831.6323295.95960.17932.716543.7677464.92431381.3990307.2920113.6436 919971300.0000424.60000.31181172.3636386.56840.272817.6868127.6364683.37861851.1300403.0416145.7005 1019981700.0000400.00000.45101604.4068475.84190.41518.059795.5932983.22132392.6415499.2493176.0307 1119992350.0000650.00000.75332117.2255544.62780.631541.7539232.77451374.69462971.1262576.8696198.5513 1220002800.0000450.00001.04882680.2981573.67200.960910.4825119.70191853.72043540.9396616.3687207.8385 1320013100.0000300.00001.30813248.0394553.69671.461916.6115148.03942395.48424059.0685607.7348201.4653 1420023500.0000400.00001.77683773.3643491.15522.224363.8544273.36432956.23274497.0403556.4027181.1083 1520033890.0000390.00002.46244222.1884404.17403.3843114.5848332.18843485.82894844.9866479.5433151.7804 1620044900.00001010.00008.59974580.2597312.61175.1491137.2511319.74033944.45295108.0373396.4161119.6312 1720055032.0000132.000011.49424850.6319230.43787.834359.9093181.36814312.72285299.5447320.822989.6635 1820065183.0000151.000018.07275046.4205163.929011.919747.7575136.57954590.91335435.1711259.193364.6363 1920075146.5000-36.500015.91935183.9454113.695118.13575.175937.44544791.51165529.3559212.443745.2754 2020085175.000028.500017.55515278.490677.476927.593358.0171103.49064931.36035593.8737178.648331.0718 2120095342.531752.165041.98285026.57915637.6565154.990021.0237 2220105385.475834.838963.87635090.37155667.1794138.778714.0880 2320115414.079023.141897.18695132.64835687.0012127.82659.3791 2420125433.044515.3168147.86855160.46505700.2712120.49666.2171 2520135445.582210.1135224.97995178.68075709.1378115.62114.1092 2620145453.85416.6673342.30395190.57235715.0546112.39142.7108 2720155459.30464.3909520.81075198.31955718.9994110.25771.7860 2820165462.89292.8897792.40645203.36005721.6281108.85051.1757 2920175465.25381.90091205.63565206.63675723.3790107.92360.7736 3020185466.80671.25011834.35835208.76565724.5449107.31340.5088 3120195467.82780.82192790.95135210.14825725.3212106.91200.3346 3220205468.49910.54034246.39475211.04605725.8381106.64810.2200 3320215468.94050.35526460.83205211.62885726.1821106.47450.1446 3420225469.23060.23359830.06855212.00715726.4111106.36040.0950 3520235469.42120.153514956.31615212.25275726.5635106.28540.0625 3620245469.54660.100922755.83265212.41215726.6650106.23610.0411 3720255469.62900.066334622.69135212.51565726.7325106.20360.0270 3820265469.68310.043652677.95635212.58285726.7775106.18230.0177 3920275469.71870.028680148.79775212.62635726.8074106.16830.0117 4020285469.74210.0188121945.31105212.65465726.8273106.15910.0077 4120295469.75740.0124185538.14045212.67305726.8405106.15310.0050 4220305469.76760.0081282293.76975212.68495726.8494106.14910.0033
Appendix
Online&Shoppers&(in&%) Germany VariablesCompletionExpected&Parameter&Uncertainty α"=0.21050.81201.5%alpha'"(+)"=0.2136alpha'"(,)"=0.2073 t0"=20060.034t0'"(+)"=2007t0'"(,)"=2006 M"=73.89242.5%M'"(+)"=75.7397M'"(,)"=72.0451 e"=2.7183 Historical"Mean"=40.4167 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 2.44623.8%(+)(,)(+)(,) 120021717.00000.298820.68883.13520.38890.91353.688817.939023.65343.21240.6217 22003247.00000.481023.96353.40790.48000.00010.036520.967927.14913.49950.6731 32004295.00000.646027.48883.63320.59240.13231.511224.278830.85543.74100.7152 42005323.00000.763931.20853.79420.73120.03480.791527.827634.70633.91980.7452 52006386.00001.058735.05133.87780.90240.47192.948731.552438.62424.02260.7606 62007413.00001.246538.93573.87671.11380.23132.064335.377042.52604.04180.7604 72008421.00001.316942.77653.79121.37470.03350.776539.216146.33003.97710.7446 82009453.00001.557546.49223.62841.69680.12921.492242.983649.96243.83530.7143 92010483.00001.853850.01203.40172.09430.25052.012046.599253.36283.62910.6719 102011546.00002.714653.28003.12812.58490.03490.720049.995756.48753.37520.6203 112012551.00002.911256.25862.82573.19040.11801.258653.122959.31033.09120.5628 122013605.00004.318958.92762.51173.93770.09641.072455.949461.82112.79430.5026 13201461.28312.20104.860258.461764.02402.49880.4423 14201563.33441.90465.998760.661665.93362.21600.3844 15201665.09981.63047.403962.563067.57161.95360.3303 16201766.60401.38279.138364.188068.96411.71620.2811 17201867.87461.163411.279065.563370.13891.50570.2373 18201968.94020.972413.921166.717971.12371.32210.1989 19202069.82840.808317.182267.680571.94461.16420.1657 20202170.56500.668821.207268.478572.62601.02990.1374 21202271.17330.551226.175069.136873.18940.91670.1134 22202371.67390.452932.306769.677873.65370.82190.0933 23202472.08470.371239.874670.121074.03550.74310.0766 24202572.42090.303549.215570.483174.34870.67790.0627 25202672.69570.247860.744470.778374.60530.62410.0512 26202772.91980.202074.974071.018574.81510.58000.0418 27202873.10250.164592.537071.213774.98650.54380.0340 28202973.25110.1338114.214371.372175.12640.51420.0277 29203073.37200.1088140.969571.500675.24050.49000.0225
Appendix
Online&Shoppers&(in&'000) United&States VariablesCompletionExpected&Parameter&Uncertainty α"=0.34320.93831.5%alpha'"(+)"=0.3484alpha'"(,)"=0.3381 t0"=20050.034t0'"(+)"=2006t0'"(,)"=2005 M"=203655.68102.5%M'"(+)"=208747.0730M'"(,)"=198564.2889 e"=2.7183 Historical"Mean"=172133.3333 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 833.45261.0%(+)(,)(+)(,) 12008144200144200.00002.4253147007.969614035.35322.5951192.82272807.9696134037.3712159275.310615147.58764409.8065 2200916310018900.00004.0216159932.526811785.51003.6578292.19593167.4732148205.3912170893.729313029.51743748.0694 320101723009200.00005.4950170571.88529510.92775.1558107.77521728.1148160154.0268180271.391310867.53153061.5334 420111783006000.00007.0320179021.09407432.75117.267124.0121721.0940169817.5167187613.16438881.42492418.8763 520121838005500.00009.2568185541.62785664.441510.2430183.80201741.6278177371.5208193225.02287185.77381860.4615 620131911007300.000015.2202190463.42524234.779014.437532.8447636.5748183121.0215197436.55545811.88101401.2035 72014194116.66683120.803420.3498187408.8108200553.84384739.81601038.5307 82015196794.68402275.625028.6831190558.1538202837.69223925.6390760.5497 92016198739.90441646.559140.4290192845.5254204498.40843319.2393552.0696 102017200143.46131184.740356.9849194493.3506205699.42512873.8525398.1595 112018201151.3233849.022880.3205195673.4830206564.56062549.9743285.8191 122019201872.5472606.6822113.2122196515.1090207185.97392316.1265204.4868 132020202387.3772432.6202159.5733197113.5216207631.41172148.1373145.9461 142021202754.2282308.0442224.9194197538.0951207950.23892027.8939103.9852 152022203015.3050219.1109317.0252197838.8725208178.20311942.046673.9975 162023203200.9389155.7367446.8488198051.7209208341.07691880.868152.6118 172024203332.8463110.6338629.8358198202.2305208457.38311837.326137.3834 182025203426.534478.5636887.7571198308.6018208540.40391806.364926.5511 192026203493.055555.77481251.2986198383.7501208599.64901784.363718.8516 202027203540.276539.58891763.7124198436.8260208641.91911768.736913.3819 212028203573.791628.09632485.9625198474.3054208672.07371757.64139.4977 222029203597.576319.93813503.9781198500.7678208693.58321749.76486.7402 232030203614.454114.14784938.8767198519.4499208708.92491744.17444.7829
Appendix
Internet&Users&(per&100&people) GER ParameterCompletionExp.&Parameter&Uncertainty α"=0.46311.00571.5%alpha'"(+)"=0.4701alpha'"(,)"=0.4562 t0"=20020.034t0'"(+)"=2002t0'"(,)"=2001 M"=83.48842.5%M'"(+)"=85.5756M'"(,)"=81.4012 e"=2.7183 Historical"Mean"=40.7689 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 14.03074.2%(+)(,)(+)(,) 119900.12590.12590.00150.35490.16370.00430.14840.22900.23190.53730.16610.0745 219910.25030.12440.00300.56250.25880.00680.17450.31230.37050.84480.26270.1177 319920.43530.18500.00520.89040.40800.01080.23510.45510.59121.32560.41420.1852 419930.46360.02830.00561.40600.64020.01710.64240.94240.94192.07330.65010.2898 519940.92250.45890.01122.21230.99740.02720.77241.28971.49673.22651.01310.4494 619951.83770.91520.02253.46141.53660.04330.79461.62372.36884.98301.56130.6878 719963.05481.21710.03805.36922.32680.06871.06622.31443.72547.60742.36561.0311 819976.71113.65630.08748.22063.43240.10920.30741.50955.801811.41823.49321.4998 919989.87793.16680.134212.34684.87260.17360.57942.46908.902816.72824.96672.0898 10199920.846010.96810.332818.04766.55170.27580.55362.798413.368223.71806.69472.7483 11200030.21639.37040.567225.43928.19190.43821.29024.777119.471832.25988.40373.3610 12200131.65091.43460.610634.27289.35700.69640.34022.621927.246041.79919.65873.7779 13200248.820017.16911.408243.85639.64191.10661.18354.963736.304751.437910.05073.8776 14200355.90007.08002.026253.22178.93591.75840.37182.678345.826560.24069.45893.6290 15200464.73008.83003.450761.48447.50502.79420.65013.245654.810367.56828.13673.1081 16200568.71003.98004.649468.14185.80114.44020.02580.568262.463073.21146.52582.4578 17200672.16003.45006.369873.12454.20417.05570.10250.964568.434477.30265.00151.8188 18200775.16003.00009.024576.65172.907011.21190.35451.491772.782780.14063.75751.2788 19200878.00002.840014.211879.05141.945817.81620.26311.051475.792382.04952.83310.8664 20200979.00001.000017.600980.64001.274228.31090.97761.640077.803083.30692.18630.5722 21201082.00003.000055.092781.67290.822544.98760.06020.327179.114684.12381.75090.3714 22201181.2700-0.730036.634582.33660.526171.48771.00161.066679.957084.64991.46500.2384 23201282.35001.080072.338282.75990.3345113.59770.23260.409980.492684.98661.28010.1520 24201383.96141.6114-177.508483.02840.2119180.51281.90290.933080.831085.20141.16170.0964 25201483.19840.1339286.844481.043985.33811.08650.0610 26201583.30560.0845455.811181.177585.42501.03880.0385 27201683.37330.0532724.308081.261385.48011.00860.0243 28201783.41590.03351150.964081.313785.51510.98960.0153 29201883.44280.02111828.943181.346585.53720.97760.0096 30201983.45970.01332906.288081.367085.55130.97010.0061 31202083.47030.00844618.246381.379885.56020.96530.0038 32202183.47700.00537338.639381.387885.56580.96230.0024 33202283.48120.003311661.488481.392885.56940.96040.0015 34202383.48390.002118530.725981.396085.57170.95930.0010 35202483.48560.001329446.309981.397985.57310.95850.0006 36202583.48660.000846791.753981.399285.57400.95800.0004 37202683.48730.000574354.587781.399985.57460.95770.0002 38202783.48770.0003118153.397981.400485.57500.95760.0001 39202883.48800.0002187752.038881.400785.57520.95740.0001 40202983.48810.0001298347.984081.400985.57540.95740.0001 41203083.48820.0001474090.828281.401085.57540.95730.0000
Appendix
Internet&Users&(per&100&people) USA VariablesCompletionExpected&Parameter&Uncertainty α"=0.27261.01163.0%alpha'"(+)"=0.2808alpha'"(,)"=0.2645 t0"=20010.064t0'"(+)"=2002t0'"(,)"=1999 M"=83.23414.7%M'"(+)"=87.1461M'"(,)"=79.3221 e"=2.7183 Historical"Mean"=44.3017 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 71.534510.4%(+)(,)(+)(,) 119900.78470.78470.00954.22531.09350.05352.95143.44052.62256.59781.12900.2852 219911.16320.37850.01425.46261.39150.07023.62164.29943.43598.40251.43780.3615 319921.72420.56100.02127.03011.75470.09234.37405.30594.486810.63561.81490.4538 419932.27170.54750.02818.99532.18740.12125.63466.72365.834413.36162.26520.5625 519944.86282.59110.062011.42752.68780.15914.37146.56477.546016.63432.78820.6867 619959.23714.37430.124814.38993.24490.20902.23085.15289.693520.48613.37360.8230 7199616.41947.18230.245717.92853.83500.27450.16191.509112.347024.91463.99880.9652 8199721.61645.19700.350822.05844.42010.36060.01210.442015.564529.87154.62641.1038 9199830.09328.47680.566326.75004.94910.47360.61573.343219.377935.25595.20591.2271 10199935.84875.75550.756531.91895.36500.62200.78483.929823.777140.91745.68031.3228 11200043.07927.23041.072837.42475.61550.81701.55235.654428.696946.67045.99611.3799 12200149.08086.00171.437143.08295.66601.07301.73105.997934.011452.31686.11711.3914 13200258.78549.70462.404448.68745.50931.40935.046010.098039.541357.67316.03361.3558 14200361.69712.91172.864754.03975.16761.85103.09367.657545.075962.59225.76491.2776 15200464.75833.06113.505058.97594.68612.43121.94535.782450.403766.97745.35341.1660 16200567.96813.20984.452263.38404.12113.19311.39014.584055.343570.78414.85321.0332 17200668.93120.96314.819467.20883.52784.19390.22931.722459.766874.01334.31780.8915 18200775.00006.06889.108570.44532.95095.50841.91664.554763.605976.69923.79100.7513 19200874.0000-1.00008.013873.12652.42107.23480.08590.873566.848778.89703.30350.6207 20200971.0000-3.00005.803575.30881.95509.50232.58914.308869.525380.67142.87250.5043 21201071.69000.69006.210177.05971.558512.48055.04405.369771.693282.08872.50460.4040 22201169.7295-1.96055.163478.44831.229716.392116.85358.718973.422083.21082.19870.3202 23201279.30009.570520.157279.53970.962521.52970.01630.239774.783984.09321.94950.2515 24201384.20004.9000-87.171480.39110.748628.27745.28343.808975.846384.78341.74980.1961 25201481.05180.579437.140176.668885.32091.59170.1521 26201581.56210.446748.780477.301985.73821.46760.1175 27201681.95490.343464.069077.786986.06131.37090.0904 28201782.25660.263484.149378.157286.31101.29600.0694 29201882.48770.2017110.523278.439386.50361.23820.0532 30201982.66460.1542145.163178.653686.65201.19370.0407 31202082.79980.1178190.659778.816286.76631.15960.0311 32202182.90300.0899250.415878.939586.85431.13350.0237 33202282.98180.0686328.900479.032886.92191.11350.0181 34202383.04190.0523431.983579.103486.97391.09820.0138 35202483.08760.0399567.374679.156887.01391.08660.0105 36202583.12250.0304745.199679.197287.04461.07770.0080 37202683.14910.0231978.758179.227887.06811.07090.0061 38202783.16940.01761285.517879.250887.08621.06570.0047 39202883.18480.01341688.421479.268387.10011.06180.0035 40202983.19660.01022217.601979.281487.11081.05880.0027 41203083.20550.00782912.636779.291487.11901.05650.0021
Appendix
Mobile'Cellular'Subscriptions'(per'100'people) Germany VariablesCompletionExpected'Parameter'Uncertainty α"=0.35090.97953.0%alpha'"(+)"=0.3614alpha'"(,)"=0.3404 t0"=20020.064t0'"(+)"=2003t0'"(,)"=2001 M"=121.51734.7%M'"(+)"=127.2286M'"(,)"=115.8060 e"=2.7183 Historical"Mean"=49.7577 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 144.722311.8%(+)(,)(+)(,) 119850.00140.00140.00000.32380.11330.00270.32190.32240.16280.62460.11670.0385 219860.03010.02870.00020.45940.16060.00380.40260.42930.23360.87620.16540.0546 319870.06150.03140.00050.65140.22740.00540.53710.58990.33501.22800.23420.0772 419880.12400.06260.00100.92320.32150.00770.69700.79910.48031.71920.33120.1091 519890.20450.08040.00171.30700.45370.01090.94021.10260.68812.40310.46750.1538 619900.33870.13420.00281.84810.63860.01541.25181.50940.98523.35180.65830.2162 719910.65690.31820.00542.60830.89560.02191.49191.95141.40894.66110.92340.3025 819921.19070.53380.00993.67151.24940.03121.72852.48082.01166.45521.28870.4207 919932.15790.96730.01815.14941.73040.04431.81482.99152.86578.88981.78580.5802 1019943.00970.85170.02547.18602.37250.06292.57984.17644.069612.15022.45060.7910 1119954.48001.47030.03839.95913.20830.08933.28355.47915.753416.44023.31781.0618 1219966.61002.13000.057513.67434.25840.12684.11237.06448.083421.95594.41111.3962 1319979.91253.30250.088818.54525.51440.18014.74228.632711.260628.84055.72581.7876 14199816.66216.74970.158924.75276.91660.25583.32098.090615.506837.12267.20592.2136 15199928.082111.42000.300632.38468.33540.36330.77934.302521.032146.65528.72552.6330 16200057.718329.63620.904741.36389.57400.51619.803316.354527.976957.084910.08972.9894 17200167.14969.43131.235151.397010.40720.73308.366915.752636.335767.881811.07213.2240 18200270.65533.50571.389261.981910.65591.04112.47738.673445.887178.437311.48853.2933 19200377.33766.68231.750572.493110.26261.47870.80254.844556.174588.194911.27223.1836 20200485.06027.72262.333282.32209.31752.10030.28242.738266.572396.759210.50192.9163 21200594.55499.49473.506991.00968.01772.98320.55013.545376.4280103.94069.36412.5401 222006102.28297.72805.317798.31436.58744.23710.83903.968685.2173109.73548.07552.1146 232007115.140412.857518.0557104.20285.21006.01828.057310.937692.6394114.26806.81611.6936 242008126.557511.4171-25.1096108.79033.99828.548027.705117.767298.6238117.72795.69841.3139 252009126.2281-0.3294-26.7957112.27022.997912.141222.803713.9578103.2714120.32004.77080.9940 262010106.4837-19.74447.0830114.85692.209117.244711.13698.3732106.7770122.23494.03670.7376 272011109.65963.17599.2479116.75071.607024.493610.98017.0912109.3633123.63493.47520.5394 282012111.59401.934511.2457118.12201.158134.789512.91156.5279111.2405124.65073.05580.3903 292013119.03067.436647.8668119.10690.829049.41340.00250.0763112.5868125.38362.74810.2802 302014119.81020.590670.1843113.5442125.91042.52490.2000 312015120.31040.419399.6864114.2208126.28792.36450.1422 322016120.66510.2969141.5898114.6971126.55782.24990.1008 332017120.91610.2099201.1073115.0312126.75062.16840.0713 342018121.09340.1482285.6430115.2651126.88822.11050.0504 352019121.21850.1046405.7135115.4286126.98622.06960.0356 362020121.30680.0737576.2558115.5428127.05602.04070.0251 372021121.36900.0520818.4859115.6225127.10582.02030.0177 382022121.41290.03661162.5377115.6781127.14122.00590.0125 392023121.44380.02581651.2123115.7169127.16641.99580.0088 402024121.46550.01822345.3020115.7439127.18431.98860.0062 412025121.48080.01283331.1534115.7627127.19711.98360.0044 422026121.49160.00904731.4090115.7758127.20621.98000.0031 432027121.49920.00636720.2642115.7850127.21271.97750.0022 442028121.50460.00459545.1379115.7914127.21731.97580.0015 452029121.50830.003113557.4517115.7958127.22051.97450.0011 462030121.51100.002219256.3479115.7989127.22291.97370.0008
Appendix
Mobile'Cellular'Subscriptions'(per'100'people) USA VariablesCompletionExpected'Parameter'Uncertainty α"=0.29690.95621.5%alpha'"(+)"=0.3014alpha'"(,)"=0.2924 t0"=20020.034t0'"(+)"=2003t0'"(,)"=2001 M"=99.90392.5%M'"(+)"=102.4015M'"(,)"=97.4063 e"=2.7183 Historical"Mean"=38.6426 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 5.35692.7%(+)(,)(+)(,) 119840.03830.03830.00040.47820.14130.00480.40670.44000.35110.64690.14340.0413 219850.14070.10240.00140.64250.18950.00650.39450.50180.47390.86480.19240.0553 319860.27910.13840.00280.86260.25390.00870.39820.58360.63951.15520.25780.0741 419870.49870.21960.00501.15740.33960.01170.37930.65870.86241.54170.34480.0990 519880.82990.33120.00841.55130.45340.01580.34080.72141.16212.05490.46040.1320 619891.39280.56300.01412.07630.60370.02120.22980.68351.56422.73430.61300.1754 719902.07580.68300.02122.77420.80080.02860.18080.69842.10253.63030.81340.2323 819912.93960.86380.03033.69761.05720.03840.16140.75802.82054.80561.07410.3058 919924.24901.30940.04444.91301.38690.05170.09440.66393.77406.33701.40950.3998 1019936.10371.85470.06516.50081.80450.06960.02590.39705.03268.31491.83470.5179 1119949.10493.00120.10038.55552.32260.09370.03860.54946.681110.84042.36280.6630 12199512.60473.49980.144411.18192.94830.12600.20391.42288.818114.01883.00170.8361 13199616.23823.63340.194114.48683.67740.16960.24761.751411.551517.94643.74801.0348 14199720.14243.90420.252518.56404.48750.22820.16481.578314.988822.69184.58011.2519 15199824.89064.74830.331823.47345.33170.30710.11181.417219.220128.27205.45231.4739 16199930.57615.68550.441029.21496.13740.41330.08961.361224.293634.62836.29221.6818 17200038.46817.89200.626235.70466.81210.55620.33292.763530.189441.61147.00811.8528 18200144.69066.22250.809442.76387.26180.74840.15181.926836.795948.98507.50521.9653 19200248.85104.16050.956950.12897.41521.00710.06541.277843.903756.45187.71132.0035 20200354.84685.99581.217357.48637.24661.35520.28542.639451.224363.69957.59851.9616 21200462.54727.70041.674364.52376.78431.82370.17101.976558.432370.45107.19241.8458 22200568.31775.77052.162970.98106.10112.45410.34522.663365.222276.50366.56331.6725 23200676.29357.97583.231476.68395.29173.30250.00850.390371.356581.74575.80371.4635 24200782.06415.77064.600181.55304.44764.44410.01740.511176.692986.15225.00361.2413 25200885.20923.14505.798685.59173.64055.98030.01190.382581.184789.76394.23411.0249 26200988.62363.41457.856688.86182.91608.04760.00580.238284.861792.66363.54070.8272 27201091.31172.688010.627391.45852.295410.82950.00280.146987.803894.95322.94530.6555 28201194.39723.085617.142593.48871.782414.57300.13750.908690.115296.73732.45210.5117 29201296.02391.626724.748895.05661.369319.61060.20290.967391.905198.11342.05460.3948 30201395.5295-0.494421.838896.25631.043426.38960.15030.726893.275799.16631.74070.3019 31201497.16770.790135.511994.316399.96721.49660.2292 32201597.85610.595547.787695.1012100.57341.30890.1731 33201698.37410.447264.306895.6904101.03071.16590.1302 34201798.76260.335086.536396.1310101.37481.05760.0976 35201899.05320.2504116.450196.4596101.63320.97600.0730 36201999.27040.1869156.704596.7041101.82690.91470.0545 37202099.43230.1393210.873996.8858101.97200.86880.0407 38202199.55300.1038283.768697.0207102.08050.83450.0303 39202299.64290.0773381.861497.1207102.16170.80890.0226 40202399.70980.0575513.862997.1948102.22240.78980.0168 41202499.75960.0428691.494497.2498102.26770.77560.0125 42202599.79660.0318930.529597.2904102.30160.76500.0093 43202699.82410.02361252.194097.3205102.32690.75710.0069 44202799.84460.01761685.051197.3428102.34580.75130.0051 45202899.85980.01312267.537897.3593102.35990.74690.0038 46202999.87110.00973051.378097.3715102.37040.74370.0028 47203099.87950.00724106.175197.3806102.37830.74130.0021
Appendix
Sales&of&Smart&Phone&(Volume) Germany VariablesCompletionExpected&Parameter&Uncertainty α"=0.90980.72042.0%alpha'"(+)"=0.9279alpha'"(,)"=0.8916 t0"=20110.067t0'"(+)"=2012t0'"(,)"=2010 M"=25564.07583.3%M'"(+)"=26407.6903M'"(,)"=24720.4613 e"=2.7183 Historical"Mean"=7755.6000 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 626.33456.3%(+)(,)(+)(,) 120071038.31038.30000.0423732.4305647.24090.0295131.5014305.8695194.42812499.0118660.8078561.9450 220081730.5692.20000.07261744.96901479.13300.07330.128814.4690485.92555364.44441512.24661239.8291 320092976.51246.00000.13183935.43523029.11700.1820276.1764958.93521193.190810124.17243107.65882369.7153 420107780.54804.00000.43757957.03434985.76350.45195.6866176.53432810.498315913.40985158.89813548.5899 5201114592.36811.80001.330013519.45635794.90561.1224180.69691072.84376055.732620787.07846122.75023955.3841 6201218415.53823.20002.576118815.08614518.96552.787832.1444399.586111142.503723772.20645019.85153292.4274 7201322337.99372564.56456.924216682.955225259.49373194.47952049.2579 8201424159.27771207.791317.197720765.154525924.51691908.77111024.2064 9201524979.2743519.855142.714122989.189226207.41961253.8004453.7349 10201625325.3590215.1457106.089624005.713326325.2071963.1850190.1457 11201725467.423787.5972263.496024432.847926373.8084841.453177.8213 12201825525.073335.4285654.448224605.943826393.7876791.649931.5413 13201925548.358214.29041625.461124675.058126401.9882771.468012.7333 14202025557.74525.75794037.177824702.490526405.3520763.32125.1323 15202125561.52662.318910027.188624713.353226406.7315760.03762.0673 16202225563.04930.933824904.652524717.650526407.2972758.71500.8325 17202325563.66250.376061855.994124719.349926407.5291758.18240.3352 18202425563.90940.1514153632.498924720.021926407.6242757.96800.1350 19202525564.00880.0609381578.941124720.287526407.6632757.88160.0543 20202625564.04880.0245947732.343624720.392626407.6792757.84690.0219 21202725564.06490.00992353894.564324720.434126407.6857757.83290.0088 22202825564.07140.00405846397.097424720.450526407.6884757.82720.0035 23202925564.07400.001614520768.897324720.457026407.6895757.82500.0014 23203025564.07510.000636065413.522924720.459626407.6900757.82410.0006
Appendix
Sales&of&Smart&Phone&(Volume) USA VariablesCompletionExpected&Parameter&Uncertainty α"=0.50040.64140.7%alpha'"(+)"=0.5039alpha'"(,)"=0.4969 t0"=20120.027t0'"(+)"=2012t0'"(,)"=2011 M"=191827.94311.5%M'"(+)"=194705.3622M'"(,)"=188950.5239 e"=2.7183 Historical"Mean"=69940.4833 Confidence"Bands tY(t)ΔY(t)Y'(t)X(t)ΔX(t)X'(t)χ2"=MAD"=Growth"CurveLife"Cycle 542.62302.2%(+)(,)(+)(,) 1200825879.525879.50000.155924920.525110849.63230.149342.4125958.974919067.021632162.913710949.68725081.3174 220093866012780.50000.252437904.841615218.80970.246318.7495755.158429601.375747782.273015381.11466952.7242 320105156912909.00000.367755408.508219716.73040.4062374.11953839.508244433.588567821.898819973.92388774.3954 4201177959.726390.70000.684676953.986523058.70410.669921.94861005.713563725.598591057.244623449.993910059.0063 52012102527.724568.00001.1481100693.497923936.80461.104970.32701834.202186386.8035115035.872124497.948410386.7921 6201312304720519.30001.7890123860.126921959.22611.822315.0659813.1269110026.0826136983.639622708.46569642.8649 72014143938.529017980.37633.0056131820.1404154973.431718910.48888083.7761 82015159627.561913407.60134.9573149740.7115168431.832314490.58296189.1303 92016170923.20089320.26008.1763163140.6150177827.941110519.56944402.5343 102017178585.14376168.894213.4855172465.2738184075.81767450.09612965.0709 112018183574.45763952.135622.2421178634.1321188096.73865287.96221922.6268 122019186737.59402479.485236.6846182579.8450190630.30673850.47291215.8167 132020188709.05911535.232560.5053185049.3971192205.48212928.3431756.6141 142021189924.7680942.849999.7936186574.1036193176.67042349.6821466.1367 152022190669.5150576.1460164.5933187507.5445193772.38941991.4142285.3971 162023191123.9093350.9867271.4698188076.0550194136.64491771.4133174.0711 172024191400.4667213.4211447.7451188421.2138194358.94041636.9912105.9229 182025191568.5347129.6255738.4824188630.3680194494.44121555.107664.3629 192026191670.578978.67621218.0062188756.9609194576.97681505.319839.0756 202027191732.501747.73262008.9024188833.5287194627.22831475.081123.7109 212028191770.065228.95183313.3565188879.8199194657.81561456.728114.3831 222029191792.847317.55785464.8406188907.7994194676.43061445.59358.7231 232030191806.662810.64699013.3624188924.7081194687.75831438.84005.2898
Appendix
!! GER! USA! AVG$
ΔT# 18$ 11$ 14.5$
T(0.1)# 1992$ 1995$ $$
T(10)# 2010$ 2006$ $$
!! GER! USA! AVG$
ΔT# 10$ 17$ 13.5$
T(0.1)# 1997$ 1992$ $$
T(10)# 2007$ 2009$ $$
Appendix
!! GER! USA! AVG$
ΔT# 13$ 17$ 15$
T(0.1)# 1995$ 1994$ $$
T(10)# 2008$ 2011$ $$
!! GER! USA! AVG$
ΔT# 13$ 17$ 15$
T(0.1)# 1995$ 1994$ $$
T(10)# 2008$ 2011$ $$
Appendix
!! GER! USA! AVG$
ΔT# 5$ 10$ 7.5$
T(0.1)# 2009$ 2007$ $$
T(10)# 2014$ 2017$ $$
Appendix D. Interview Guide
(1) Academic literature repeatedly argues that the following factors influence the dissemination of mobile payments: security, usefulness, ease of use, and costs. How important are these factors? Is one of them more important than the others? Do you think problems regarding these factors (e.g. security, ease of use) are going to be long-term problems or can they be solved in the short term?
(2) Who are the most important players in the mobile payments industry?
How important was Apple’s move to introduce Apple Pay?
(3) When I recently signed up for a mobile payments solution (with my supermarket), it actually felt somewhat sketchy (a) to key in my personal data into some beta-version app and (b) to know that my supermarket can now track all my purchases. Do you think that are typical German thoughts and fears?
(4) When do you expect mobile payments to take off?
(5) What technology is going to come out on top? (NFC, BLE, QR codes...)
(6) What is going to happen with the companies that miss out on mobile payments (e.g. the banking industry)?
(7) In the very long run: what is possible (related to mobile payments)? Any futuristic scenarios?
Appendix
Appendix E. Expert Interview #1
Thank you very much for taking your time for the interview. I am looking forward to the conversation and I am curious to hear your perspective. I would like to mention the formalities that guide this interview: I will not record the conversation, but will take notes of your statements. Your statements will be included anonymously in my thesis. The interview is structured as follows: First, I will ask you some specific questions regarding the results I have come to find as part of research for my thesis. Subsequently, I will ask you some open questions in respect to the future of mobile payments.
(1) Academic literature repeatedly argues that the following factors influence the dissemination of mobile payments: security, usefulness, ease of use, and costs.
How important are these factors? Is one of them more important than the others?
Expert #1: Security is a must. Even when it is secure, it does not meant that it will be used.
Either high usefulness (parking ticket cannot be paid with card ! mobile payments are useful) or higher ease of use (than already existing solutions, such as payment card or cash at POS ! has to be easier, less important than usefulness
How important is the registration process?
It is also very important " barrier. High user numbers only possible with easy registration process. Usefulness is one of the factors, that bring the most added value, since they are enablers (for customers).
Do you think problems regarding these factors (e.g. security, ease of use) are going to be long-term problems or can they be solved in the short term?
Ease of use: will be solved, Apple Pay is a nice example.
Security: Always an essential point. Solution can be sufficiently secure, nevertheless race against hackers.
Usefulness: Has to be given. What people can already pay for with cash is not that exciting in the future.
Costs: Ads may take the cost burden of merchants/customers.
(2) Literature found the fact that the players are extremely intertwined to be one of the essential factors.
He agrees.
Who do you think are the most important players in the mobile payments industry?
Depends on the technical implementation. Key players: Customer and retail " two-sided market, acceptance of both is necessary ! added value for the merchant is necessary
How can the chicken-and-egg problem be solved?
First, fall back on the existing infrastructure (NFC + VISA, MasterCard), upgrading the existing infrastructure is easy. Second, don’t focus on the whole market, but on a specific use case, z.B. mobile parking. Mobile payments only used for parking.
How important do you think was Apple’s move to introduce Apple Pay?
Not a revolution, but will influence the dissemination permanently. Extreme media and coverage (also banks ! need to consider how to react)
# it is open whether it will prevail. In the USA, 3% of the location accept Apple Pay. Either the users lose interest or merchants expand the network.
" vs. Google Wallet = was unsuccessful, nevertheless the MNOs started to introduce their own wallet solutions
(3) When I recently signed up for a mobile payments solution (with my supermarket), it actually felt somewhat sketchy (a) to key in my personal data into some beta-version app and (b) to know that my supermarket can now track all my purchases. Do you think that are typical German thoughts and fears?
Appendix
More of a German consideration, in the USA different relationship to data security
USA still uses magnetic stripe cards. Switching causes costs for merchants ! inhibition threshold is lower in the USA (also in respect to NFC)
Do you think that is going to change in the future?
It will increase. More critically questioning of data sharing.
I compared the development of indicators that represent the purchase and payment practices.
That showed that structures changed slower in Germany. What do you expect: Is Germany going to be slower to adapt to mobile payments than the USA?
Germans struggle with electronic payment solutions, struggle more than US Americans, cash still very high.
! Yes, we need more time for such an adaption.
! There is movement in the market, players are active, but customer acceptance will not be as quick as in the USA ! providers need to be more patient
Furthermore, I analyzed how different technologies diffused in both countries. This showed that the technologies spread faster and faster, or else, their growthcurves are steeper and steeper. Consequentially, mobile payments could take two years or less to reach 90% of their final diffusion. Do you think that is realistic?
That timeframe is too short. Mobile payments have different dynamics: Facebook can grow fast, because it only depends on users. Mobile payments depend on the merchants acceptance
" infrastructure is slower " similar to cash, decreases by 1% per year
In five years, 20% of the payment market are ambitioned but realistic. Limited users: 20% of the locations offer mobile payments, 50% percent of consumers use it, 20% of the time…
Payments are a habit ! customer habits need more time to change " why forecasts have failed " unreasonably high expectations
Mobile payments need a technical enabler but are a process. Process:
Technological infrastructure is in place (terminals, NFC) All components are ready
Now, bring together the stakeholders
(5) How important is technology? What technology is going to come out on top? (NFC, BLE, QR codes...)
Does not have to be NFC. But usability is hard to beat. Apple Pay is very convenient. Mobile parking.
(6) Is it a possibility that mobile payments will not take off?
It will take off. Some processes will exist where the use of cash is not longer possible.
Still question mark behind ‘will mobile payments replace cash at the POS?’.
Just the smart phone without the physical wallet = very high added value proposition Next or in 10 years, no prognosis possible.
(7) What is going to happen with the companies that miss out on mobile payments (e.g. the banking industry)?
Banks will continue to exist. They will cover the payment. Question: Are they just going to be in the background?
Similar to transformations in music industry
# only few payment solutions simultaneously
(8) In the very long run: what do you think is possible (related to mobile payments)? Any futuristic scenarios?
Process for the future: you replace the POS ! NFC becomes irrelevant; Pre-ordering;
Payments with NFC at the table in the restaurant
! we are headed towards this but are just at the beginning.
Appendix
Appendix F. Expert Interview #2
Expert #2: Before we jump in, I’m going to lay out the different types of mobile payments that are currently out there and briefly how they work.
First is the traditional credit card terminals. Most businesses, websites, and mobile applications with no dedicated payment system implement online transaction channels that request credit card information as input and make contact with the designated banking firms via credit card services (VISA, Mastercard, Discover, American Express, etc). In this majority case, these credit card services (ex. VISA) take cut (either a fixed amount or a percentage) of each transaction made from the businesses.
Then there are mobile payments platforms built on top of the credit card terminals, such as Apple Pay, Square, Intuit QuickBooks GoPayment, Amazon Local Register, etc., that make the implementation described above much easier, for extra transaction cut, of course. For example, the mobile credit card readers (ex. Square) take around 3% of each transaction, typically. Apple is reported to receive 0.15% cut of purchases made with Apple Pay (in conduction with or on top of the registered credit card services such as VISA).
Now, some businesses (with large resources) may provide their own payment platforms. A big known is Starbucks. You load a balance in Starbucks mobile app using a credit card, and you pay with their provided interface (bar code).
Second is the rising form (new era) of payments, as known as peer-to-peer (P2P) payments.
PayPal broke the ground a few years ago as a leading service, and the tech giants such as Amazon Payments and Google Wallet are catching up. Small start-ups such as Venmo and Snapchat (Snapcash) are also implementing P2P payment systems. The key advantage of P2P payments is no man in the middle (credit card services such as VISA and Mastercard) that take cut of all transactions. P2P systems allow direct transactions from a firm to another firm or from an individual to another. Most P2P transactions happen on a direct bank-to-bank electronic funds transfer basis, which obviously incur no credit card processing fees.
There are new comers such as Coin (https://onlycoin.com) and Bitcoin entering the two markets, respectively. Coin is a new high-tech hybrid mobile payments system with digitalized credit cards, which falls under the first aforementioned market, the traditional credit card terminals. Bitcoin is a P2P-based digital crypto-currency that falls under the second category. I’m not going to go into details on these, but they’re at least worth mentioning as they will be key players in the future mobile payments industry.
Pardon my long background introduction. I’m sure you knew most of these already. Just wanted to make sure we were on the same page. So to answer your questions:
(1) Academic literature repeatedly argues that the following factors influence the dissemination of mobile payments: security, usefulness, ease of use, and costs.
How important are these factors? Is one of them more important than the others? Do you think problems regarding these factors (e.g. security, ease of use) are going to be long-term problems or can they be solved in the short term?
All of the above are equally important. If you haven’t watched, I think you’d appreciate this video: http://www.youtube.com/watch?v=5ExcCyS1ZH8
They do a very good job in the presentation of addressing these very questions.
In order to understand the security aspect of Apple Pay, you might wanna pay attention to the following components: NFC, Touch ID, Secure Element, and dynamic security code. Read up on each topic. I think that they nailed both usability and security with the Touch ID two-factor authentication. In case you didn’t know, Apple does not store the user’s finger prints. They are stored inside the CPU chip deep inside the phone. Secure Element works a similar way.
I’d like to point out that it is safer to lose your phone than to lose your wallet because you can remotely wipe your phone and credit card data in case you lost your phone. Plus, nobody would be able to make payments with your phone without your fingers. On the other hand, if you lost your wallet, you risk an unknown making purchases with the credit cards in your wallet in the grace period of realizing that you’d lost your wallet and calling every credit card
Appendix
company to replace your cards. Another advantage is that you won’t need to get new credit cards if you just lost your phone.
(2) Who do you think are the most important players in the mobile payments industry? (e.g.
Consumers, merchants, providers (MNOs, banks, independent players), device manufacturers, the government)
How important do you think was Apple’s move to introduce ApplePay?
There are four important players. We call it “A Four-Party Network” as opposed to the traditional three-party network. Read this:
http://arstechnica.com/gadgets/2014/10/how-mobile-payments-really-work/
I believe that Apple could not have come with Apple Pay at any better time. Apple Pay had remained an extremely confidential project for years (part due to non-trivial work in building partnership with the existing credit card services and banks). The concept of mobile payments has been around for quite a few years, but no single company succeeded in implementing anything successfully, due to many reasons: lack of user accountability (too much focus on business), lack of technology, firm trust issues, just to name a few. With the rise of NFC, this was the perfect time for Apple to kick off the new payment system. Aside from the brand name (in hands of millions of people) that people trust and love, Apple Pay’s implementation was impressive in the way that was to minimize the amount of user data the company holds in their security schemes.
(3) When I recently signed up for a mobile payments solution (with my supermarket), it actually felt somewhat sketchy (a) to key in my personal data into some beta-version app and (b) to know that my supermarket can now track all my purchases.
Do you think that are typical German thoughts and fears? Or is it the same in the US?
I have not come across such mobile payments solution, if any, in the US. Aside from Starbucks, the most attempted I’d seen at restaurants was LevelUp (https://www.thelevelup.com). It did seem a bit dodgy at the time, but maybe not as bad as you described.
(4) When do you expect mobile payments to take off?
To simply say, it has already taken off (at least in the US). But first, I’d like to point out there are two different mobile payments methods within Apple Pay or Google Wallet. One is to physically “shop in stores” and the other is to “buy online”.
1 million credit cards were registered with Apple Pay in the first 3 days it came out. If you think about how few people have the iPhone 6 already, that’s a very impressive number. God knows how many credit cards are registered now.
I’ve personally registered all of my debit and credit cards (10+) with Apple Pay on my phone, and have used Apply Pay with different credit cards in different surroundings, stores, and spaces. I’ve made an Apply Pay purchase at a vending machine. I’ve made purchases at Bartell’s, McDonald’s, and many many more. I’ve made purchases in Groupon app, Uber app, on various websites, etc, all with Apple Pay.
Strictly speaking from my experience in the US, you can use Apply Pay virtually anywhere with a Chip-and-PIN reader, in majority of shopping apps, and on any website. Apple Pay is very prevalent here.
This could be, of course, a biased observation since I’m a tech savvy, power user. But I doubt it’ll take much longer for other people to adapt Apply Pay and Google Wallet.
(5) What technology is going to come out on top? (NFC, BLE, QR codes…)
All the technologies are here already. NFC is probably the most important and prevalent one when it comes to making physical transactions. BLE and QR codes are outdated, inefficient, and unnecessary. The only thing that NFC puts on is the fact that it’s so new that it’s not available on many personal devices yet, hence why it’s only available on iPhone 6, Apple Watch, and select few Android devices.