Loyalty programs and personal data sharing preferences in the Czech Republic

187 1, XX, 2017 Ma ke ing and T ade DOI: 10.15240/ ul/001/2017-1-013 In oduc ion Loyal cus ome s a e a key ac o o success ul e ail ac i i ies. Cus ome loyal y can be de i ned as a highe p obabili y o making new and epea ed pu chases, spon aneously ecommending a pa icula e aile and sp eading he posi i e wo d-o -mou h. “Loyal cus ome s a e less likely o swi ch o a compe i o due o p ice inducemen , and hese cus ome s make mo e pu chases compa ed o less loyal cus ome s” (Dhal, 2015; Mohelska & Sokolo a, 2016) Fo he e aile s, cus ome loyal y is a key ac o in eaching long- e m comme cial success and p o i abili y. I is much cheape o e ain and cu a e exis ing cus ome s han o acqui e new ones. Simila ly, cus ome loyal y has been inc easingly s udied in heal h and social se ices o e he pas ew yea s – pa icula ly in economies whe e he p i a e sec o domina es his i eld. Many such s udies ocus on he opic o demand egula ion wi hin he heal h and social se ices sec o , which is s ongly mo i a ed by popula ion aging and ela ed ac o s occu ing in de eloped coun ies (Ga u o á e al., 2014; Šol és e al., 2014). In mode n cus ome -o ien ed ma ke ing, loyal y p og ams (de i ned as ma ke ing p og ams ha ewa d membe s wi h pu chase incen i es) a e pe cei ed as he s a egic ins umen o c ea ing and main aining e ec i e con ac wi h cus ome s (Bacik e al., 2015). “In he e ail scene, loyal y p og ams in ol e a concen a ed e o by e aile s o build s o e a i c, inc ease baske size and inc ease equency which c ea es deepe ela ionship ies wi h i s cus ome base” (Oma , Wel, Musa, & Naz i, 2010). An impo an pa in c ea ing consume s´ sa is ac ion and inducing consume loyal y is played by he human ac o (e.g. Wasan and T ipa hi (2015)). I is o en claimed ha e ail pe sonnel should be well ins uc ed and mo i a ed owa ds inc easing he numbe o loyal cus ome s and c ea ing ha mony be ween co po a e s a egic aims and he cus ome s’ demand. As loyal y p og ams (LPs) a e implemen ed, managed and e alua ed, e aile s need app op ia e da a and means o iden i y indi idual pa icipa ing cus ome s. Such da a allow o e ec i e e alua ion o consume s’ pu chasing beha iou and habi s. Speci i cally, o he pu pose o egis e ing, uniquely iden i ying and p ope ly managing cus ome s in a LP, a ious iden i i ca ion da a a e equi ed. In he Czech Republic, es ablishing and managing any such consume / LP da abase is bound by legal amewo k, implemen ed o consume and pe sonal da a p o ec ion. Besides legisla u e, cus ome s o en ea ha hei pe sonal da a – once passed o LP-ope a o – migh be misused (e.g. sold o hi d pa ies and used in an in usi e manne ). Gene ally speaking, consume s wo y abou hei p i acy and a e a aid o losing con ol o e hei pe sonal da a – o some ex en . Impo an ly, consume s’ p i acy conce ns a e no uni o m. Indi idual p e e ences, sociodemog aphic and li es yle ac o s play a signi i can ole. F om a good- ai h LP o ganize poin o iew, he quali y o pe sonal da a and con ac in o ma ion collec ed h ough he LP has an eno mous e ec on hei abili y o manage and e alua e LPs p ope ly. This s udy p o ides a s uc u ed quan i a i e analysis o cus ome s´ willingness o sha e a ious ypes o pe sonal and con ac da a wi h LP o ganize s. 1. Li e a u e Re iew The use o LP- ela ed pe sonal and con ac da a o he bene i o ma ke e s and businessmen is a delica e opic, discussed by academic esea che s and e ail ma ke e s, as well as by ins i u ions supe ising legal aspec s o such ac i i ies (see e.g. Alb ech (2006) o Ma ine and Cannella (2015)). LOYALTY PROGRAMS AND PERSONAL DATA SHARING PREFERENCES IN THE CZECH REPUBLIC Radek Tahal, Tomáš Fo mánek, Hana Mohelská EM_1_2017.indd 187EM_1_2017.indd 187 13.3.2017 16:59:1213.3.2017 16:59:12 188 2017, XX, 1 Ma ke ing a obchod “Loyal y p og ammes, app op ia ely managed, a e conside ed o allow s uc u ed and e ec i e ac ions o manage, selec , ela e, and con ol cus ome s’ buying beha iou ” (La a & De Mada iaga, 2007). In o de o mee hei objec i es, LPs need o be bene i cial o bo h he e aile and he cus ome s. A new s udy by Nielsen (a co po a e p o ide o in o ma ion and insigh s in o wha consume s wa ch and buy), e ealed ha nea ly 60 pe cen o global esponden s epo ed ha LPs we e a ailable o hem h ough local e aile s. F om hose consume s wi h access o LP-o ganizing e aile s, 84 pe cen epo being mo e likely o isi such e aile s (Nielsen, 2013). La a and De Mada iaga (2007) discuss he impo ance o i nding a desi able balance be ween he olume o da a equi ed by he e aile and cus ome s’ willingness o sha ing hem. As consume s a e disinclined o acili a e access o ce ain da a ypes o he companies (LP o ganize s), his can be a ac o ha mode a es consume willingness o pa icipa e in such p og amme. “I onically, e en hough loyal y p og am membe s c a e a mo e pe sonalized, ele an expe ience, hey also show conce n abou sha ing he in o ma ion equi ed o enable he e aile o deli e on his desi e.” (PR Newswi e, 2013). Consume incen i es, along wi h us and p i acy assu ances a e he key s a egic ools in LP consume engagemen . Nume ous s udies poin ou ha people a e o en a aid o hei pe sonal da a becoming an objec o ading among companies (see e.g. Wade (2010), Spieke mann, Acquis i, Böhme and Hui (2015)). “Compounding his p oblem is he common p ac ice o businesses selling cus ome in o ma ion o o he businesses, ma ke ing companies, mailing lis s and so o h, he eby u he inc easing he amoun o unwan ed o e s and ad e ising” (Ma ke ing Weekly News, 2013). In hei co po a e esea ch, Aimia (2011) desc ibe he young gene a ion (gene a ion Y o he Millennials, bo n app oxima ely be ween 1977 and 1994) as being less conce ned when sha ing pe sonal da a wi h a e aile o shopping easons. O all named ma ke ing channels in hei su ey, loyal y and ewa d p og ams a e pe cei ed as he mos p i acy- iendly by Millennials: ewe han 20% o Millennial loyal y p og am membe s a e conce ned abou sha ing pe sonal in o ma ion wi h loyal y p og am o ganize s. As Koponen and Mangia acina (2014) s a e, a good way o illus a e he inc easing comme cial alue o pe sonal da a is by conside ing he ecen g ow h o he online ad e ising sec o . “Ou cul u e places a high alue on p i acy. Pu ing con ol o he da a – wha may be collec ed and e ained and wha mus be dele ed ( he igh o be o go en) – in each o ou hands h ough a con ac ual a angemen wi h he collec o o selle , p omo es indi idual choice and con ol” (Ande son, 2015). I is e ail ma ke e s’ ask o communica e pe sonal da a equi emen s o consume s as well as he ad an ages o LPs. Bo h LP-o ganize s and consume s may be iewed as being in a p ocess o de eloping a new unde s anding o he ules o consume engagemen p og ams (such as LPs) ha no only ma ch consume p e e ences bu help consume s accomplish goals in eal- ime – see Documen News (2013) o de ailed discussion. Cus ome s ough o be assu ed ha hei da a will be u ilized e hically, law ully and o imp o ing e ail o e s. “Imagine a membe o a cus ome loyal y p og am who is comple ely in o med and ag ees ha da a abou his shopping habi s a e used o op imize business p ocesses and o p o ide ad e ising ma e ial and special o e s” (Ma zne , 2014). 2. Resea ch Focus This esea ch is ocused on cus ome s´ beha iou , p i acy and da a-sha ing p e e ences ela ed o LP pa icipa ion in he Czech Republic. Acco ding o ou p e ious esea ch, Czech cus ome s a e less inclined o sha ing hei pe sonal and con ac da a as compa ed o cus ome s in selec ed EU coun ies (see e.g. Tahal (2015)). We aim o help ma ke e s wi h a pa icula ly impo an ask: o i nd he app op ia e balance be ween he amoun o pe sonal da a equi ed om consume s pa icipa ing in a gi en LP and he ex en o which consume s a e willing o sha e such da a. In his pape , we use he p ima y su ey da a o answe wo main esea ch ques ions: Resea ch ques ion 1: Wha de e mines consume willingness o p o ide di e en ypes o con ac and pe sonal in o ma ion o o ganize s o LPs wi hin he FMCG – a e he e any signi i can di e ences ha would be de e mined by socio-demog aphic and li es yle ac o s? EM_1_2017.indd 188EM_1_2017.indd 188 13.3.2017 16:59:1213.3.2017 16:59:12 189 1, XX, 2017 Ma ke ing and T ade Resea ch ques ion 2: Does he ex en o indi idual pa icipa ion in LPs a ec consume ’s willingness o p o ide con ac and pe sonal in o ma ion? I should be explici ly no ed ha ou esea ch ac i i ies (and conclusions made) aim owa ds ai LP schemes, i.e. LPs whe e cus ome s may ealis ically eel ha in exchange o he pe sonal in o ma ion p o ided hey a e ewa ded by adequa e pu chase incen i es and/o imp o ed and mo e pe sonalized se ices by he e aile . Ou esea ch o consume willingness o p o ide con ac and pe sonal in o ma ion o LP o ganize s ocuses on he ollowing 6 ypes o pe sonal da a: (a) Name & Su name, (b) Email, (c) Add ess/ esidence, (d) Bi hda e, (e) Phone numbe , ( ) Pe sonal ID numbe . O e all, his classi i ca ion e l ec s common ma ke ing p ac ice, as LP-based pe sonal da a a e used o e ail segmen a ion, analysis, audi ing, o ecas ing, e c. In his pape , he i s da a ype (Name & Su name) and he las da a ype (Pe sonal ID numbe ) ha e a limi ed, benchma k- ype usage: P o iding one’s name in o de o pa icipa e in a LP is he e y minimum equi emen . Besides Czech language-speci i c gende segmen a ion, name bea s li le usable in o ma ion and i is o en seen as he leas sensi i e da a ype. The e o e, Name/Su name da a may se e as a benchma k in measu ing he basic p opensi y o sha e non-sensi i e pe sonal da a. In con as , he Pe sonal ID numbe is a e y sensi i e ype o in o ma ion wi h a non-negligible misuse po en ial. Ac ually, LP o ganize s in he Czech Republic a e no allowed o legally collec and use his ype da a unde mos p ac ical ci cums ances (excep ions may apply). Also, Pe sonal ID bea s li le usable in o ma ion, once da a ypes (b) o (e) a e con olled o . The e o e, we use he in o ma ion on consume willingness o sha e hei ID numbe s as a second benchma k, o he o he end o he pe sonal da a sensi i i y spec um. 3. Da a and Resea ch Me hodology Ou esea ch is based on p ima y su ey da a om he Czech Republic. A complex, anonymized and s a i i ed (quo a) sampling was pe o med du ing he pe iod om No embe 2015 o Ap il 2016, ga he ing da a o a sample o 411 esponden s om he popula ion o FMCG consume s aged 15+. The s a i i ed quo a sampling was based on h ee ac o s: loca ion, age and gende o he esponden s. Hence, ou me hodology ensu es consis ency and ele ance o he esul s – conclusions may be d awn wi h espec o he popula ion o 15+ consume s. A combina ion o pe sonal and online da a collec ion was used o ga he socio- demog aphic in o ma ion, li es yle p e e ences, a i udes owa ds di e en ypes o wo k and ee ime ac i i ies o he esponden s. Bo h quan i a i e (in e al based) and quali a i e (Yes/No, Like scale-based) ques ions we e used in he su ey. The su ey was o ganized and pe o med by a esea ch eam a he Uni e si y o Economics, P ague. This eam is led by uni e si y employees and eache s who supe ise and coo dina e he asks pe o med by s uden s specializing in ma ke ing esea ch. This s udy is pa o a sys ema ic long- e m p ojec o specialized ma ke ing analyses (see e.g. Tahal and S ří eský (2014)). Ou empi ical analysis (logis ic eg ession and ela ed s a is ical in e ence) is adjus ed o con ol o he quo a sampling, hus ensu ing p ope conclusions a e made owa ds he FMCG consume popula ion based on he esul s and hei in e p e a ion. Also, o su ey da a alida ion, he Wald- Wol owi z “Runs” es was used o es he H0 o o de o obse a ions being a ibu able o chance agains he H1 o po en ial da a collec ion mishandling (Wacke ly e al., 2008). All he su eyed da a (in e al-based, Yes/ No, Like scale) need o be con enien ly s o ed o subsequen quan i a i e analysis. Gi en he na u e o ou ques ionnai es, he ga he ed da a may be con enien ly eco ded as logical (bina y) a iables. The ans o ma ion o Yes/ No answe s (e.g. o ques ions ela ed o willingness o p o ide pe sonal da a) is s aigh - o wa d. Fo in e al-based quan i a i e opics such as age o income, we use bina ies o eco d esponden ’s app op ia e in e al en y. Fo example, he a iable Age_65_plus equals 1 o all he esponden s aged 65 and olde and is ze o o he wise. Answe s o Like scale- based ques ions a e also eco ded as bina y a iables. Fo he sake o ou analysis, o de ed mul inomial da a may be app oached in a way simila o he in e al-based quan i a i e a iables. Fo example, esponden s a e asked o posi ion hemsel es owa ds a s a emen “I like eading books” using a i e deg ee Like scale (“1” = his s a emen desc ibes me e y EM_1_2017.indd 189EM_1_2017.indd 189 13.3.2017 16:59:1213.3.2017 16:59:12 190 2017, XX, 1 Ma ke ing a obchod well, …, “5” = his s a emen does no desc ibe me a all). Subjec i ely pe cei ed impo ance o indi idual li es yle is add essed by his ques ion, a he han some ac ual measu e o eading ime (o page olume coun ). Also, he ac ha “1” is a be e a ing han “2” (in e ms o ag eeing wi h he s a emen e alua ed) bea s o dinal meaning only, i.e. we canno say ha he di e ence be ween “2” and “4” is somehow wice as impo an as he di e ence be ween “4” and “5”. The e o e, he su eyed answe s o his s a emen we e used o p oduce wo bina y a iables: LS_books_yes equals 1 o hose who espond “1” on he Like scale and ze o o he wise, LS_books_no equals 1 o esponden s who dissocia e hemsel es om he s a emen by answe ing “5” (and ze o o he wise). Al hough bina y a iable may be p oduced o each o he Like scale answe s, we i nd i empi ically con enien o combine s a emen s “2” o “4” – i.e. no a e y s ong posi ion o he esponden – in o a single e e ence ca ego y. Ou app oach has h ee ad an ages: Fi s , we e ie e all cases whe e esponden s ha e s ong pe sonal posi ions on gi en li es yle ac i i ies and opics such as eading books, doing spo s, ea ing o ganic ood, e c. Second, he combined base answe s “2” o “4” may s ill be included implici ly in he analysis as a e e ence ca ego y, necessa y o in e p e a ion o he es ima ed eg ession models. Thi d, using such e e ence base de-couples he LS_book_yes and LS_book_no bina ies ha a e no linea ly dependen and may be bo h used as explana o y a iables in he same eg ession model. Using he abo e desc ibed app oach, we ha e ans o med he su eyed ma e ial in o a 402- ow da ase (9 esponden s we e dis ega ded due o missing da a issues) wi h 106 a iables. Six o he a iables desc ibe consume willingness o sha e pe sonal da a wi h LP o ganize s. The emaining one hund ed a iables o m a pool o po en ial/concei able eg esso s bea ing socio-demog aphic, li es yle and o he ele an in o ma ion ha may be used o model consume s’ pe sonal da a sha ing p e e ences. Fo ou da ase , an exhaus i e (b u e- o ce) sea ch o a uly op imal pa ame ic model speci i ca ion is compu a ionally inaccessible, as i would equi e an es ima ion and e alua ion o 6×2100 models. Hence, in o de o iden i y a small, in o ma i e and consis en se o explana o y a iables, we combine a o wa d-s epwise selec ion me hod ( his is a po en ially subop imal algo i hm ha p oduces nes ed sequences o models) wi h he non-pa ame ic andom o es app oach. Di e ences in ou pu s om he wo me hods a e analysed in o de o de ec any po en ial sub-op imali y in ou pu om he s epwise me hod. This app oach allows o e i cien and compu a ionally easible e alua ion o indi idual explana o y a iables wi h espec o p edic ion accu acy o he model, as only 6×1002 models and 6×5,000 andom o es s a e p oduced and e alua ed ( as and au oma ed e alua ion p ocedu es a e a ailable in R and o he so wa e packages). Al hough ou me hodology does no gua an ee a uly op imal model speci i ca ion, i may be ega ded as an accep able app oxima ion wi h a ela i ely low po en ial o sub op imali y). Fo de ailed discussion o model selec ion me hodology, see James e al. (2013). Hence, in o de o answe he esea ch ques ions (RQs) 1 and 2, he abo e desc ibed model selec ion p ocess was used o gene a e a consis en model speci i ca ion as ou lined by equa ion (1): yi = β0 + β1 Femalei + + β2 Age_15_24i + β3 Age_65_ plusi + + β4 Mo a iai +β5 Ea nings_highi + + β6 LS_TV_noi + β7 LS_books_noi + + β8 LS_In e ne use_noi + + β9 LS_Payca d_yesi + + β10 LS_exo ics_yesi + + β11 LS_cooking_noi + + β12 LP_Memb_1_2i + + β13 LP_Memb_3_plusi+ui (1) whe e yi is a bina y dependen a iable – six di e en dependen a iables a e used wi h he le hand side o he equa ion and he e o e six di e en equa ions a e es ima ed using he model (1) – Yes/No answe s we e eco ded o he ques ion “Would you p o ide he ollowing ype o pe sonal in o ma ion in o de o become a membe o a loyal y p og am?” o each esponden and da a ca ego y (a) o ( ) as de i ned abo e. Please no e ha such ques ion applies iden ically o consume s who al eady a e membe s and/o ac i e use s o a LP as well as o indi iduals no pa icipa ing in LPs. On he igh hand side o (1), βj a e he coe i cien s o be es ima ed h ough logis ic eg ession (see Da idson, MacKinnon (2009, p. 454-465)). Femalei is a bina y explana o y a iable dis inguishing be ween emale and EM_1_2017.indd 190EM_1_2017.indd 190 13.3.2017 16:59:1213.3.2017 16:59:12 191 1, XX, 2017 Ma ke ing and T ade male esponden s, Age_15_24i, is a bina y indica ing he 15-24 age g oup while Age_65_ plusi depic s indi iduals aged 65 and olde (upon a iable impo ance e alua ion as desc ibed abo e, age anges 25-34, 35-49 and 50-64 a e combined in o a single base ca ego y). Mo a iai desc ibes he esidence o esponden s and Bohemia se es as i s e e ence ca ego y. Responden s wi h high ea nings (de i ned by a mon hly household income o e 80 housand CZK) a e disce ned using Ea nings_highi. Like scale-based li es yle a iables LS_TV_noi, LS_books_noi and LS_In e ne use_noi ma k esponden s who dissocia e hemsel es om wa ching TV, eading books and using he in e ne . LS_Payca d_yesi indica es whe he he i- h esponden uses pay ca ds (c edi and debi ) equen ly and LS_exo ics_yesi de e mines whe he esponden s a e keen on spending hei holidays a exo ic des ina ions o by he sea (as he Czech Republic is a landlocked coun y). LS_cooking_noi disce ns people who dissocia e hemsel es om cooking (again, subjec i ely pe cei ed impo ance is add essed he e). LP_Memb_1_2i iden i i es indi iduals (consume s) who ac i ely pa icipa e in 1 o 2 LPs. Simila ly, LP_Memb_3_plusi desc ibes esponden s who ac i ely ake pa in 3 o mo e LPs. Finally, ui is he po en ially he e oskedas ic andom elemen . The in e p e a ion o mos o he li es yle a iables included in equa ion (1) is ela i ely s aigh - o wa d, gi en he in ui ion on Like scale da a ans o ma ion as p o ided abo e. Howe e , he las wo a iables (LP_Memb_1_2 and LP_Memb_3_plus) equi e some addi ional explana ion: In he ques ionnai e, esponden s we e asked wo ques ions ela ed o he quan i y o LPs. Fi s , esponden s p o ided he o al numbe o LPs hey a e membe s o . The i s ques ion se ed mos ly as a lead-in o he nex ques ion, whe e indi iduals epo ed hei ac ual (ac i e) LP pa icipa ion. In his a icle, we ocus on ac i e LP usage a he han a simple LP membe ship as we i nd ac i e LP pa icipan s o be a a mo e in e es ing g oup as a as LP o ganize s a e conce ned. Al hough he esponden s we e choosing om i e op ions (0 LPs ac i ely used, 1-2, 3-4, 5-9, 10+), low obse ed quan i ies in he las wo ca ego ies led us o combine he las h ee LP ca ego ies in o a single ca ego y o 3+ LPs ac i ely used. Hence, we include LP_Memb_1_2 and LP_Memb_3_plus a iables in equa ion (1) o desc ibe he e ec o ac i e LP pa icipa ion, while consume s using ze o LPs o m a base ( e e ence) ca ego y. The wo LP- ocused a iables discussed in his pa ag aph a e indispensable o answe ing he esea ch ques ion 2. In his con ex , i is wo h men ioning ha he inclusion o bo h a iables is based on he a iable selec ion ( eg esso impo ance e alua ion) p ocess desc ibed abo e – i.e. we didn’ ha e o “ o ce” he wo a iables in o equa ion (1) in o de o allow o answe ing esea ch ques ion 2. The logis ic unc ion used o es ima ion o he βj coe i cien s in equa ion (1) may be exp essed as P(yi = 1 | xi T) = G(xi Tβ) = = exp (xi Tβ)/[1 + exp(xi Tβ)], (2) whe e P(yi = 1 | xi T) is he p obabili y o success (i.e. consume willingness o p o ide he speci i c ype o his/he pe sonal da a), gi en he obse ed ow ec o o explana o y a iables xi T. The exp ession G(xi Tβ) is a simpli i ed no a ion o he logis ic unc ion: exp (xi Tβ)/ [1 + exp(xi Tβ)] which gua an ees ha all i ed alues o he dependen a iable lie wi hin he 0, 1 in e al. In model (2), he di ec ion o he e ec o change in he explana o y a iable xj on he p obabili y o “success” in he dependen a iable is always de e mined by he sign o he co esponding βj coe i cien . Howe e , he magni udes o he indi idual βj coe i cien s a e no su i cien ly in o ma i e, gi en he nonlinea na u e o he logis ic unc ion. The e ec o a change in xj on he p obabili y o “success” o he i- h esponden mus be calcula ed indi idually: as can be seen om equa ion (2), a change in condi ional p obabili y o success is calcula ed om a compound unc ion ha depends on he ollowing a gumen s: βj, all he emaining coe i cien s in ec o β and all he obse ed alues o he explana o y a iables o he i- h esponden (xi T). Hence, o he i- h esponden and a chosen bina y explana o y a iable, say xk, he pa ial e ec om changing xk om 0 o 1 (while holding all o he explana o y a iables unchanged) may be simply calcula ed as ∆G ( . ) = G (β0+ β1x1,i + ... + βk–1,ixk–1,i + βk ) –G (β0+ β1x1,i + ... + βk–1,ixk–1,i ) (3) EM_1_2017.indd 191EM_1_2017.indd 191 13.3.2017 16:59:1313.3.2017 16:59:13 192 2017, XX, 1 Ma ke ing a obchod In exp ession (3), we may no e ha he βk coe i cien is p esen when G(.) is e alua ed o xk = 1 and omi ed o xk = 0. Fo each indi idual consume , he exp ession (3) may be used o e alua ion o changes in condi ional success p obabili ies. Howe e , o e ec i e model in e p e a ion, we need o summa ize he indi idual in o ma ion ob ained om equa ion (3) ac oss all indi iduals. This is done h ough he a e age pa ial e ec (APE) s a is ics: ] , G(^ β0 + ^ β1x1,i + ... + –G(^ β0 + ^ β1x1,i + ... ∑ [ APE(xk ) = n–1 + ^ βk–1,ixk–1,i + ^ βk ) ... + ^ βk–1,ixk–1,i ) n i–1 (4) whe e ^ βj a e he sample es ima es o βj coe i cien s. In equa ion (4), he expec ed pa ial e ec o changing a gi en bina y eg esso xk om 0 o 1 (ce e is pa ibus) is ob ained o each o he su ey esponden s and hen a e aged ac oss indi iduals. Using his app oach, APE(xk) alues a e usually epo ed along wi h hei co esponding s anda d e o s and signi i cance s a is ics. Using exp ession (4), consis en APEs may be calcula ed o all bina y eg esso s xj in model (1). Al hough all eg esso s in model (1) a e bina y, he speci i ca ion chosen p o ides enough con ol o di e se obse ed ac o s ha i allows o a s aigh o wa d in e p e a ion o indi idual APEs; a si ua ion ha is analogous o he Igno abili y o ea men assump ion (as in Woold idge (2010, p. 908)). 4. Empi ical Resul s Indi idual willingness o disclose pe sonal in o ma ion o LP o ganize s is app oached using di e se da a e alua ion me hods in o de answe RQs 1 and 2 and o quan i y impo an sociodemog aphic and li es yle aspec s o his ype o consume beha iou . Fi s , able 1 summa izes he o e all consume eadiness o sha e pe sonal da a. Rows a e o ganized in descending o de and we may see ha he e a e p ominen di e ences in pe sonal in o ma ion sha ing p e e ences ela ed o he ype o da a. The willingness o sha e pe sonal da a anges om 91.3% (Name & Su name) o 3.7% (Pe sonal ID numbe ). This ange be ween he wo benchma k da a ypes (as desc ibed abo e) se s an in e p e a ion amewo k o he emaining da a ypes. In ac , as we es o s a is ically signi i can di e ences in obse ed means ( ela i e “success” a ios, i.e. he a e age willingness o sha e a speci i c ype o da a), we i nd ha all mean pai s a e s a is ically di e en (6 ca ego ies make o 15 possible ca ego y-pai s), wi h he excep ion o Bi hda e and Phone numbe – means o hose wo ca ego ies a e no s a is ically di e en a any easonable signi i cance le el. Such e alua ion is based on he Wilcoxon signed ank es ( o ma ched/co ela ed pai s). Fo de ailed desc ip ion o he es , see Wacke ly e al. (2008). The ow o de ing and he posi i e ou come a ios shown in able 1 p o ide an in e es ing insigh in o indi idual p i acy p e e ences and he gene al a i ude o consume s o pe sonal da a disclosu e. Fo example, we may poin ou he qui e low le el o p epa edness o sha e phone numbe s, ha Pe sonal in o ma ion ype & consume willingness o sha e i No. o „Successes“ (ou o 402 esponden s) Posi i e ou come a io Va iance Name & Su name 367 0.913 0.080 Email 260 0.647 0.229 Add ess/ esidence 227 0.565 0.246 Bi hda e 156 0.388 0.238 Phone numbe 149 0.371 0.234 Pe sonal ID numbe 15 0.037 0.036 Sou ce: own Tab. 1: Obse ed willingness o sha e di e en ypes o pe sonal in o ma ion EM_1_2017.indd 192EM_1_2017.indd 192 13.3.2017 16:59:1313.3.2017 16:59:13 193 1, XX, 2017 Ma ke ing and T ade consume s p e e o keep undisclosed mo e han o he con ac in o ma ion such as Email and Add ess/ esidence. Mos p obably, his a i ude is ela ed o he a oidance o ad e isemen oice calls and ex messages, which may be pe cei ed as a mo e in usi e when compa ed o emails o pape -based lea l e s. Dele ing i ele an e-mails is less bo he ing han dealing wi h unsolici ed phone calls and ex messages (e.g. Blackbu n (2015)). The a iance in o ma ion in able 1 is p o ided mos ly o eade s’ con enience. Gi en he binomial na u e o he unde lying dummy a iable ep esen ing willingness o unwillingness o sha e pe sonal da a, a iance equals p(1 – p), whe e p is he Posi i e ou come a io. Table 1 p o ides easonable o e all insigh in o consume s’ pe sonal da a sha ing p e e ences. Ye , in o de o answe RQ1 and RQ2, we need o ocus on he sociodemog aphic and li es yle aspec s. A he indi idual le el, many andom elemen s and ac o s play a signi i can ole in de i ning he ex en o pe sonal da a sha ing. Howe e , by means o logis ic eg ession, we a e o en able o quan i y s a is ically signi i can di e ences in pe sonal da a sha ing habi s be ween speci i c g oups o esponden s (ei he sociodemog aphic o li es yle-based). Such esul s may be p esen ed in an in o ma i e, easily accessible and o en ac ionable o m ha may be used o di e se ma ke ing and LP-managemen pu poses. Nex , we u n ou a en ion o he RQ1. Based on he explana o y a iables (sociodemog aphic and li es yle ac o s), model (1) was es ima ed o each o he six dependen a iables (willingness o sha e di e en pe sonal da a ypes). All es ima ed models p o ide easonable es ima ion accu acy and a e s a is ically signi i can a he 5% signi i cance le el – wi h he excep ion o he model es ima ed o Pe sonal ID numbe , which is only signi i can a α = 10%. In logis ic eg ession, he indi idual coe i cien es ima es a e no pa icula ly in o ma i e, excep o hei signs and s a is ical signi i cance. The e o e, we skip he eg ession ou pu om equa ion (1) and ocus on he APE alues as de i ned in (4). In ac , all signs and s a is ical signi i cances o he βc,j coe i cien s a e unambiguously e l ec ed in he co esponding APEc(xj), whe e xj is he j- h explana o y a iable and subsc ip c deno es he c- h ype o pe sonal in o ma ion – a dependen a iable in (1). All logis ic es ima ion ou pu s omi ed om his a icle a e a ailable om he au ho s upon eques , along wi h p ima y da a and he R-code used. In able 2, all APEc(xj) alues a e epo ed along wi h hei s anda d e o s (he e oscedas ici y co ec ed alues) and p- alues. Columns in able 2 a e o ganized by he o e all consume willingness o sha e pe sonal da a in he same descending o de as in able 1 – hus allowing o a simple compa ison. Fo in e p e a ion o he sociodemog aphic and li es yle ac o s ela ed o RQ1 – as shown in able 2 – we shall s a wi h he a iable Female as an example: Women a e oughly 10% less likely o sha e hei email add ess and phone numbe wi h LP o ganize s when compa ed o he e e ence ca ego y (i.e. men). Fo he emaining ou pe sonal da a ypes, gende plays no signi i can ole. Such in e p e a ion is made in a ce e is pa ibus con ex – we con ol o all he emaining a iables explici ly included in model (1). In e es ingly, ou i ndings somewha con adic a common s e eo ype ha ega ds women as mo e likely o conceal hei age. This conclusion is implied h ough he dependen a iable Bi hda e, o which esponden ’s gende is no a signi i can eg esso . Fo illus a ion and eade s’ con enience, he ce e is pa ibus e ec s on willingness o sha e pe sonal da a ela ed o he eg esso Female a e included in i gu e 1 (along wi h co esponding ba s showing 90% signi i cance in e als). Fo example, he le mos ba (wi hin he Female g oup) shows ha women a e 3.5% less likely o sha e hei name wi h LP o ganize s. A he same ime, he co esponding 90% signi i cance in e al includes ze o and he e o e his pa icula e ec is no s a is ically signi i can a α = 0.1 (signi i cance le el o 10%). The emaining sociodemog aphic ac o s in l uencing consume willingness o sha e pe sonal da a wi h LP o ganize s may be b ie l y summa ized as ollows: F om able 2 we can see ha people aged 15 o 24 a e 21% mo e likely o sha e hei bi hda e compa ed o he age- e e ence g oup o people aged 25 o 64. Fo o he pe sonal da a ypes, signi i can in l uence o he a iable Age_15_24 is no iden i i ed. In con as , esponden s aged 65 and olde di e ge om he age- e e ence g oup a he signi i can ly. They a e almos 28% less likely o sha e hei email add esses (no e ha he non-use o he In e ne is con olled o EM_1_2017.indd 193EM_1_2017.indd 193 13.3.2017 16:59:1313.3.2017 16:59:13 194 2017, XX, 1 Ma ke ing a obchod Name & Su name Email Add ess Bi hda e Phone numbe Pe sonal ID numbe Female ( s.e. ) [ p- alue ] -0.0352 ( 0.0269 ) [ 0.1904 ] -0.1032 * ( 0.0407 ) [ 0.0112 ] -0.0014 ( 0.0490 ) [ 0.9779 ] 0.0464 ( 0.0483 ) [ 0.3371 ] -0.1015 * ( 0.0489 ) [ 0.0378 ] 0.0047 ( 0.0236 ) [ 0.8428 ] Age_15_24 -0.0144 ( 0.0600 ) [ 0.8108 ] 0.0841 ( 0.0675 ) [ 0.2130 ] 0.0393 ( 0.0743 ) [ 0.5970 ] 0.2106 * ( 0.0751 ) [ 0.0050 ] 0.0820 ( 0.0747 ) [ 0.2724 ] 0.0281 ( 0.0361 ) [ 0.4364 ] Age_65_plus -0.0582 ˙ ( 0.0310 ) [ 0.0606 ] -0.2792 * ( 0.0628 ) [ 0.0000 ] -0.1520 * ( 0.0624 ) [ 0.0148 ] -0.0306 ( 0.0627 ) [ 0.6261 ] 0.1186 ˙ ( 0.0629 ) [ 0.0593 ] 0.0085 ( 0.0283 ) [ 0.7629 ] Mo a ia -0.0179 ( 0.0299 ) [ 0.5503 ] -0.0839 ˙ ( 0.0446 ) [ 0.0600 ] -0.0431 ( 0.0489 ) [ 0.3774 ] -0.0865 ˙ ( 0.0476 ) [ 0.0693 ] -0.0094 ( 0.0475 ) [ 0.8436 ] 0.0300 ( 0.0244 ) [ 0.2181 ] Ea nings_high -0.1349 ˙ ( 0.0689 ) [ 0.0503 ] -0.0443 ( 0.1065 ) [ 0.6775 ] -0.2708 * ( 0.1085 ) [ 0.0126 ] -0.0050 ( 0.1006 ) [ 0.9607 ] -0.1118 ( 0.0938 ) [ 0.2333 ] -0.0383 * ( 0.0093 ) [ 0.0000 ] LS_TV_no -0.0863 ( 0.0586 ) [ 0.1409 ] -0.0839 ( 0.1038 ) [ 0.4187 ] 0.0423 ( 0.1037 ) [ 0.6833 ] 0.0203 ( 0.1255 ) [ 0.8716 ] 0.0019 ( 0.1118 ) [ 0.9866 ] -0.0385 * ( 0.0094 ) [ 0.0000 ] LS_books_no 0.0949 * ( 0.0131 ) [ 0.0000 ] 0.1755 * ( 0.0700 ) [ 0.0121 ] 0.1460 ˙ ( 0.0873 ) [ 0.0943 ] 0.3214 * ( 0.0836 ) [ 0.0001 ] 0.1028 ( 0.0960 ) [ 0.2844 ] 0.0475 ( 0.0406 ) [ 0.2418 ] LS_In e ne _use_no -0.0828 ( 0.0529 ) [ 0.1174 ] -0.3343 * ( 0.1320 ) [ 0.0113 ] -0.0191 ( 0.0886 ) [ 0.8290 ] -0.0393 ( 0.0915 ) [ 0.6671 ] -0.2538 * ( 0.0782 ) [ 0.0012 ] -0.0155 ( 0.0207 ) [ 0.4535 ] LS_Payca d_yes -0.0361 ( 0.0311 ) [ 0.2464 ] 0.1170 * ( 0.0439 ) [ 0.0076 ] -0.0158 ( 0.0508 ) [ 0.7555 ] -0.0069 ( 0.0502 ) [ 0.8908 ] 0.1083 * ( 0.0514 ) [ 0.0353 ] -0.0276 ( 0.0224 ) [ 0.2193 ] LS_exo ics_yes 0.0424 ( 0.0292 ) [ 0.1466 ] 0.0337 ( 0.0525 ) [ 0.5205 ] 0.1086 ˙ ( 0.0583 ) [ 0.0628 ] 0.1023 ˙ ( 0.0581 ) [ 0.0786 ] 0.0647 ( 0.0575 ) [ 0.2608 ] -0.0206 ( 0.0198 ) [ 0.2962 ] LS_cooking_no 0.0103 ( 0.0287 ) [ 0.7187 ] -0.1707 * ( 0.0805 ) [ 0.0339 ] -0.0290 ( 0.0725 ) [ 0.6886 ] -0.1575 * ( 0.0768 ) [ 0.0404 ] 0.0776 ( 0.0808 ) [ 0.3371 ] 0.0073 ( 0.0274 ) [ 0.7890 ] LP_Memb_1_2 0.1254 * ( 0.0230 ) [ 0.0000 ] 0.1015 * ( 0.0422 ) [ 0.0162 ] 0.1450 * ( 0.0511 ) [ 0.0046 ] 0.0428 ( 0.0583 ) [ 0.4629 ] 0.1807 * ( 0.0534 ) [ 0.0007 ] -0.0294 ( 0.0199 ) [ 0.1405 ] LP_Memb_3_plus 0.1555 * ( 0.0237 ) [ 0.0000 ] 0.2412 * ( 0.0484 ) [ 0.0000 ] 0.3267 * ( 0.0548 ) [ 0.0000 ] 0.1735 * ( 0.0643 ) [ 0.0069 ] 0.3548 * ( 0.0603 ) [ 0.0000 ] 0.0577 ( 0.0418 ) [ 0.1680 ] Sou ce: own No e: * – coe i cien signi i can a α = 0.05; ˙ – coe i cien signi i can a α = 0.1. Tab. 2: APEs o selec ed ypes o pe sonal da a EM_1_2017.indd 194EM_1_2017.indd 194 13.3.2017 16:59:1313.3.2017 16:59:13 195 1, XX, 2017 Ma ke ing and T ade by a sepa a e li es yle explana o y a iable LS_In e ne use_no), 15% less likely o di ulge hei add ess/ esidence and also 6% less likely o p o ide hei names. On he o he hand, membe s o he age g oup 65+ a e abou 12% mo e likely o hand o e hei phone numbe s when compa ed o he e e ence. The e e ence g oup exhibi s a a he uni o m beha io in e ms o pe sonal da a sha ing p e e ences. Hence, i is no con enien o s udy he age anges 25-34, 35-49 and 50-64 indi idually: we combine hem in o a single 25-64 e e ence and s udy how he younge and olde consume s di e . Responden domiciled in Mo a ia a e oughly 8.5% less likely o sha e hei email and bi hda e compa ed o he e e ence g oup (Bohemia combined wi h he sepa a ely su eyed egion o P ague). Consume s wi h high ea nings a e 27% less likely o p o ide LP o ganize s wi h hei add ess/ esidence, mos p obably as a secu i y p ecau ion. Simila ly, hey a e 13.5% less likely o p o ide hei name. Educa ion (su eyed as p ima y/seconda y/ uni e si y deg ee) does no ha e a signi i can e ec on indi idual willingness o sha e di e en ypes o pe sonal da a, once he o he ac o s in model (1) a e con olled o . The e o e, educa ion- ela ed a iables a e omi ed om equa ion (1) and om able 2. Nex , we b ie l y summa ize he RQ1- ela ed li es yle ac o s ha in l uence consume s’ willingness o sha e da a mos p ominen ly. Gene ally speaking, people who dissocia e hemsel es om eading books (LS_books_ no equals 1) a e mo e likely o p o ide hei pe sonal da a o LP o ganize s when compa ed o he e e ence g oup (ac i e book eade s combined wi h people wi hou a s ong posi ion on his opic). The di e ence is mos p ominen o he Bi hda e dependen a iable – we obse e a 32% inc ease in p obabili y. Responden s who dissocia e hemsel es om using he In e ne (LS_In e ne use_no) a e less likely o sha e hei pe sonal da a: we obse e a dec ease o 33% in willingness o sha e email ( o ob ious easons, people who dissocia e hemsel es om using he In e ne a e less likely o ac ually ha e an email add ess), a 25 % dec ease in likelihood o sha ing phone numbe and e en an 8 % dec ease in willingness o sha e name (howe e , his esul na owly misses s a is ical signi i cance a α = 0.1). In con as , a s a is ically signi i can e ec o LS_TV_no is obse ed only o he Pe sonal ID numbe (a dec ease o 3%). Consume s who epo being ac i e payca d use s (42% o he esponden s s ongly iden i y hemsel es wi h using a c edi o debi ca d as measu ed by he Like scale-based a iable LS_Payca d_yes) a e 12% mo e likely o sha e email add ess and 11% mo e likely o sha e phone numbe wi h LP o ganize s, while he e ec o his li es yle ac o is no s a is ically signi i can o he o he ou ypes o pe sonal da a. Fo illus a ion, he a iable LS_Payca d_yes is also included in i gu e 1. Fig. 1: Illus a ion o selec ed esul s om able 2 Sou ce: own EM_1_2017.indd 195EM_1_2017.indd 195 13.3.2017 16:59:1313.3.2017 16:59:13