DIFFERENCES IN THE CAPACITY OF ADOPTION OF THE ENABLING ICTs FOR INDUSTRY 4.0 IN CHILE

180 2022, XXV, 4 In o ma ion Managemen 10.15240/ ul/001/2022-4-012 DIFFERENCES IN THE CAPACITY OF ADOPTION OF THE ENABLING ICTS FOR INDUSTRY 4.0 IN CHILE F ancisco Ga ica-Nei a1, Ma io Ramos-Maldonado2 1 Uni e si y o Bío-Bío, Facul y o Business Sciences, Depa men o Economics and Finance, Chile, ORCID: 0000-0002-1968-9384, [email p o ec ed]; 2 Uni e si y o Bío-Bío, Facul y o Enginee ing, Depa men o Wood Enginee ing, Chile, ORCID: 0000-0001-9498-6373, [email p o ec ed]. Abs ac : In he con ex o he Fou h Indus ial Re olu ion his pape analyzes he ac o s ha explain he deg ee o di usion o some In o ma ion Technologies (ICTs) enabling Indus ies 4.0 in Chilean companies. In his g oup we ind echnologies such as: Big da a, RIFD (Radio equency iden i ica ion), Cloud compu ing, ERP (En e p ise equi emen s planning), CRM (Cus ome ela ionship managemen ), SCM (Supply chain managemen ) and Compu e secu i y. Th ough he analysis o clus e s, o de ly logis ic eg ession and decision ee, based on 2,081 companies epo ed in he Su ey o Access and Use o In o ma ion Communica ion Technology (ICT) in Companies 2018 (MINECON, 2020). I is concluded ha he e is an impo an di e ence in echnological adop ion based on size om he olume o sales and he amoun o di ec labo . I is also no ed ha companies ha subcon ac and a he same ime ha e ICT p o essionals a e mo e likely o in es in his ype o echnology. We de ec ed a “ echnological s agge ing” whe e companies begin by inco po a ing Cloud Compu ing and ERP and hen inc ease in he numbe and complexi y o he echnologies used, achie ing g ea e syne gies and bene i s in digi al ans o ma ion. I is necessa y o implemen mechanisms o moni o ing echnical change o gene a e public policies aimed a le eling echnological adop ion in small and medium-sized en e p ises. This wo k p o ides a global and in e sec o al iew o he p ocess o di usion o enabling echnologies o Indus y 4.0 h ough mul i a ia e analysis echniques and da a science, being a con ibu ion o wha is cu en ly wo ked on ocused on he s udy o business cases, on he moni o ing o a speci ic echnology o on an analysis o a speci ic p oduc i e sec o . Keywo ds: Technology adop ion, ICT, Indus y 4.0, echnology p omo ion policy, digi al ans o ma ion, echnological syne gy, o de ed logi , s a is ical clus e s, decision ee. JEL Classi ica ion: O33, O14. APA S yle Ci a ion: Ga ica-Nei a, F., & Ramos-Maldonado, M. (2022). Di e ences in he Capaci y o Adop ion o he Enabling ICTs o Indus y 4.0 in Chile. E&M Economics and Managemen , 25(4), 180–195. h ps://doi.o g/10.15240/ ul/001/2022-4-012 In oduc ion This wo k seeks o iden i y he deg ee o di usion o some enabling ICTs o indus ies in he Chilean p oduc i e ab ic. Some echnologies included he e a e: Big da a, RIFD, Cloud compu ing, ERP, CRM, SCM and he a ea o IT secu i y, which a e basic elemen s o he Fou h Indus ial Re olu ion. Da a om he Su ey on Access and Use o In o ma ion and Communica ion (ICT) in Companies 2018, which we e published by he Minis y o Economy o Chile (MINECON, 2020), we e used. I should be no ed ha a pa o he o al enabling echnologies o Indus y 4.0 is measu ed, which a e no only ocused on so wa e, bu also include inno a ion in elec onics, op ics and mecha onics. We cu en ly a e lacking he EM_4_2022.indd 180 7.12.2022 10:56:11 181 4, XXV, 2022 In o ma ion Managemen su icien ly comp ehensi e na ional s a is ics ha allow us o ully ollow he phenomenon o adop ion o his ype o echnology. This s udy begins wi h a concep ual e iew o he echnology adop ion model. Subsequen ly, he gap in his adop ion among companies o a ying sizes and in ela ion o he OECD a e age is es ima ed. Rega ding he me hodology, some da a algo i hms con ained in he Weka so wa e o da a sc ubbing (In e qua ile Range), s a is ical clus e s (K-means wi h Manha an dis ance) and decision ees (J48). Some adi ional mul i a ia e analysis models a e also p esen ed: o de ed logi and ma ginal e ec s calcula ions. Among he main conclusions o he s udy, i is iden i ied ha in la ge companies, depending on he sales olume and he numbe o wo ke s, hose ha ha e ou sou cing p ocesses and ha e IT p o essionals ha e a g ea e p obabili y o achie ing a syne gis ic echnological de elopmen , coun ing wi h mo e han ou enabling ICTs o Indus y 4.0 simul aneously. The con ibu ion o his pape is ha we add ess he phenomenon o he adop ion o enabling echnologies in a wide g oup o companies h ough da a ools and mul i a ia e analysis, illing a space in he bibliog aphic e iew as i is a ecen esea ch opic. Ou app oach is di e en om he adi ional ones associa ed wi h case s udies o he moni o ing o a ce ain echnology, p o iding inpu s o he de elopmen o new public policies ha make i possible o b idge gaps in smalle companies. 1. Theo e ical Backg ound The Fou h Indus ial Re olu ion (Schwab, 2016), which implies he digi iza ion o he di e en links in he alue chain, will b ing wi h i he bi h o new business models (Bo ha, 2019; Dean & Spoeh , 2018), he eo ganiza ion o he indus y and a he same ime, an inc ease in unemploymen , especially in a low-skilled and highly ou ine job (Nedelkoska & Quin ini, 2018). In his con ex , i is essen ial o analyze he deg ee o adop ion and di usion o some enabling ICTs o Indus y 4.0. This in ol es e iewing he models o echnology adop ion, being he T.O.E. he bes -known amewo k, which iden i ies ac o s a he Technological, O ganiza ional and En i onmen al le el o explain he adop ion o echnologies in a company (Roge s, 1995). The ollowing ac o s ha explain he adop ion o enabling echnologies o Indus y 4.0 a e iden i ied below h ough biblio g aphical e iew: a) The quali ica ion o labo a ec s he capaci y o echnological adop ion on he pa o he companies, as well as he abili y o sea ch o and co ec ly e alua e he echnological complexi y, adjus ing he pe cep ions o he challenges imposed by adap ing he new echnologies o he business eali y (P ause & Gün he , 2019; Reyes e al., 2016). On he o he hand, quali ied human esou ces allow he company o ha e a mo e lexible o ganiza ional cul u e ocused on con inuous imp o emen and he c ea ion o new business models, which a o s he adop ion o enabling echnologies o Indus y 4.0 (Chege e al., 2019; Ki az e al., 2020; Mülle e al., 2018; Vowles e al., 2011). b) Ha ing p o essionals wi h digi al skills is essen ial o he adop ion o enabling ICTs (Almeida e al., 2020; Cab e a-Sánchez & Villa ejo-Ramos, 2019). The di usion a e o ICTs an icipa es a high di usion o 4.0 echnologies a he le el o companies and coun ies (Nhamo e al., 2020). To make a echnological leap, a base o co e echnological compe encies al eady acqui ed mus be coun ed, de e mining he adop ion capaci y o companies (Maggi e al., 2020; Mo a e al., 2019). c) The size o he company posi i ely a ec s echnology adop ion p ocesses due o he signi ican inancial and adminis a i e e o in ol ed in in es men decisions and ein es men o esou ces in his ype o enabling echnologies (A nold e al., 2018; B ambilla, 2018; Dalenoga ea e al., 2018; Ga ica-Nei a, 2022; Ho á h & Szabo, 2019; Ingaldi & Ulewicz, 2020). d) The highe he p oduc i i y pe wo ke , he g ea e he p obabili y o adop ing a new echnology, inc easing he pe cep ion o ela i e ad an ages, as a esul o he expec ed leaps in p oduc i i y (B ambilla, 2018). Depending on he p oduc i i y le els o he companies, he inco po a ion o enabling echnologies will p oduce di e en impac s on unskilled employmen h ough subs i u ion and complemen a i y e ec s (Almeida e al., 2020). e) The exis ence o ou sou cing p ocesses pu s p essu e on he capaci y o coo dina e EM_4_2022.indd 181 7.12.2022 10:56:11 182 2022, XXV, 4 In o ma ion Managemen and measu e he en i e alue chain in eal ime, inc easing he p obabili y o adop ing 4.0 echnologies (Ho á h & Szabo, 2019). The s imulus o inc ease he e iciency o he alue chain, h ough digi al echnologies, will be g ea e when he company has s a egies ocused on cos (Dalenoga ea e al., 2018). The p esence o hese ac o s will condi ion he amoun o enabling ICTs ha can ope a e simul aneously in a company, signi ican ly a ec ing p o i abili y and asse u no e (Be ge , 2016). The companies ha achie e g ea e echnological syne gy will dis ance hemsel es om he es o he na ional p oduc i e ab ic, inc easing he exis ing gaps h ough g ea e e iciency o hei ope a ions and he implemen a ion o new businesses. Some s udies in La in Ame ica a e highligh ing he impo ance o adop ion ac o s. In his ega d, Mo a e al. (2019) in A gen ina and Maggi e al. (2020) in Chile ha e made i possible o deepen he in e nal adop ion p ocess wi h an emphasis on echnological managemen , highligh ing he igu e o he business leade , he impo ance o local supplie s and he pull o a la ge company usually in ensi e in na u al esou ces. Ga ica Nei a and Ramos Maldonado (2020) con i ms he de elopmen o enabling echnologies o Indus y 4.0 in expo ac i i ies in ensi e in na u al esou ces. This s udy is complemen a y o he case analysis and allows a wide iew o he di usion p ocess in he na ional economy, iden i ying he echnology clus e s and he explana o y ac o s o he di e en le els o adop ion. Clea ly, he s a e mus play an ac i e ole in he c ea ion o echnology ma ke s (Mazzuca o & McPhe son, 2019), which s imula e di usion and inno a ion, especially in he SMEs segmen . Acco dingly, public policy ini ia i es ha dis inguish he a ie y o si ua ions in he echnology adop ion in he na ional p oduc i e ab ic, will be be e o ien ed o gene a e inno a i e impulses in he na ional economy. In gene al, La in Ame ican coun ies do no ha e global policies aimed a s imula ing digi al ans o ma ion in SMEs. The emphasis has been on p omo ing aining p og ams, accompanimen and he p omo ion o esea ch and de elopmen . Suppo o echnology adop ion is s ill sca ce. Ini ia i es a e agmen ed ac oss minis ies, co po a ions and egional go e nmen s, bu he e is no global policy. Coun ies o en o mula e ‘digi al agendas’ whe e he ocus is on access, educa ion, and e-go e nmen , de o ing li le a en ion o p oduc i e issues (Dini e al., 2021). In he Chilean case, he mos ecen p eceden is he launch o he p oposal “Digi al T ans o ma ion S a egy: Chile 2035”, which o da e is no ans o med in o a public policy, which adds o he A i icial In elligence Policy 2021–2030 o he Minis y o Science and Technology. All hese ini ia i es a e e y ecen , which p e en s hei e alua ion. A he La in Ame ican le el in some egions we begin o see mesoeco nomic wo k ini ia i es aimed a he de elopmen o indus ies 4.0, he ollowing s and ou : he cases o Medellín in Colombia, Co doba 4.0 in A gen ina, o name a ew. In e na ional compa a i e s udies in La in Ame ica use da a on in e ne connec i i y (quali y and co e age) and he use o e-comme ce, bu he e a e s ill ew s udies whe e echnological adop ion p ocesses in companies a e massi ely add essed. In his ega d, Dini e al. (2021) con i ms he exis ence o na ional su eys o companies whe e he inco po a ion o echnology is analyzed, we ind he cases o B azil (2019), Ecuado (2018) and Mexico (2019). The e is s ill he e ogenei y in he de ini ion o business sizes and he e a e di e ences in he b ead h o he echnologies analyzed. The esul s in gene al sugges ha companies ha e in e ne connec i i y, bu make unsophis ica ed use o i . When mo e complex echnologies a e analyzed, he gap be ween companies acco ding o size ends o inc ease, which is consis en wi h wha was iden i ied in he Chilean case. 2. P io Da a Re iew As can be seen below, wi h da a om he Su ey on Access and Use o In o ma ion Technology and Communica ion (ICT) in Companies 2018 (MINECON, 2020), ou s udy analyzes he adop ion gaps acco ding o size, he gap in he na ional a e age compa ed o he OECD a e age and he exis ence o se e al echnologies ac ing simul aneously. This backg ound se es he con ex o he ield s udy. 2.1 Gaps Acco ding o Size and in Rela ion o Technological F on ie Tab. 1 shows ha on a e age 31.5% o la ge companies ha e o ha e used enabling ICTs EM_4_2022.indd 182 7.12.2022 10:56:12 183 4, XXV, 2022 In o ma ion Managemen o Indus y 4.0. In con as , only 8.8% o SMEs ha e succeeded in adop ing hem. The e is cu en ly a 3.6 imes gap in adop ion le els based on size. This i s esul shows how ele an i is o ha e speci ic public policies ha allow suppo ing adop ion in smalle companies. Looking a he speci ic echnologies, i is ound ha he bigges gaps a e in he implemen a ion o he IT secu i y a ea wi h a di e ence o 6.2 imes be ween la ge companies and SMEs. In a second o de we ha e he implemen a ion o Radio F equency Iden i ica ion senso s (RFID) in which he gap is 5.5 imes. In an in e media e ange, in which he gap be ween SMEs and la ge companies mo es be ween 3 o 4 imes, he e is he use o Big da a, En e p ise esou ce planning (ERP) and Cus ome ela ionship managemen (CRM). Finally, we ha e a g oup o echnologies in which he gaps be ween SMEs and la ge companies a e ela i ely smalle , highligh ing Cloud se ices (2.8) and supply chain managemen (Supply Chain Managemen SCM) wi h a gap o 2.0 imes. In he Chilean case, he a e age o enabling echnologies is 10.6%, while in he OECD coun ies i is 23.3%. The gap is 2.2 imes, which shows he leap ha he na ional economic ab ic mus make in ela ion o on ie pe o mance. Th ee echnologies a e no ed o hei g ea es lag. In p inciple, he e is Big da a, in which he gap be ween na ional companies wi h he OECD a e age is 6.5 imes. Fu he back we ha e he CRM in which he dis ance is 4.8 imes and inally he exis ence o an IT secu i y a ea in which dis ance is 4.1 imes. In echnologies in which he gap wi h OECD coun ies is smalle , i is in he use o Cloud se ices (1.4 imes) and ERP sys ems (1.3 imes). 2.2 Technological Syne gy Fo he pu poses o ou analysis, Fig. 1 is p esen ed in which he p esence o a ious echnologies is ela ed o he size o he o ganiza ion. In 59% o small companies he e is no p esence o any ype o enabling ICTs o Indus y 4.0. This si ua ion con i ms how ele an he size a iable is when explaining he adop ion o echnologies. In his ein, 24% o medium-sized companies and 8% o la ge companies do no p esen enabling ICTs. Key dimension A) B) C) D) E) F) La ge companies (%) SMEs (%) Sho ening o gaps be ween SMEs and la ge companies* To al o Chilean companies (%) OECD a e age (%) Sho ening o gaps wi h OECD** Big da a 7.2 1.7 4.2 2.0 13.0 6.5 Radio- equency iden i ica ion (RFID) 22.0 4.0 5.5 6.0 14.0 2.3 Cloud compu ing 50.0 18.0 2.8 21.0 30.0 1.4 IT secu i y a ea 31.0 5.0 6.2 7.0 29.0 4.1 En e p ise esou ce planning (ERP) 77.0 22.0 3.5 26.0 33.0 1.3 Cus ome ela ionship managemen (CRM) 21.0 5.0 4.2 6.0 29.0 4.8 Supply chain managemen (SCM) 12.0 6.0 2.0 6.0 15.0 2.5 Linea a e age o echnologies 31.5 8.8 3.6 10.6 23.3 2.2 Sou ce: own No e: * imes = A/B; ** imes = E/D. Tab. 1: A compa a i e iew o new echnologies acco ding o he su ey on access and use o in o ma ion echnology and communica ion (ICT) in companies 2018 (MINECON 2020) EM_4_2022.indd 183 7.12.2022 10:56:12 184 2022, XXV, 4 In o ma ion Managemen When e iewing he g aph da a, we ound ha 25% o small companies ha e only one echnology, some hing simila happens wi h medium-sized companies. Meanwhile, 15% o la ge companies ha e only one echnology. To his ex en we can say ha companies ha e no de eloped echnological syne gies by no expe iencing he combined e ec s o hese. F om wo o ou echnologies, he i s syne gies began o be expe ienced in he la ge company segmen and o a lesse ex en in he medium-sized segmen . On a e age 20% o la ge companies ha e combined bene i s o wo o ou echnologies. A di e en ial e ec on g ow h and p o i abili y a es will p obably be obse ed in his g oup o companies. In he sec ion anging be ween 5 and 7 enabling ICTs, we mainly see la ge companies, in which 4% o hese a e al eady aking ad an age o echnological syne gies. In summa y, om his i s e iew, we ind ha na ional companies p esen a gap in ela ion o he OECD coun ies while a he same ime he e is a di e ence in adop ion le els acco ding o size. I is e iden ha la ge companies will be mo e likely o de elop echnological syne gies. This condi ion o asymme y will inc ease o e ime, a ec ing smalle companies. 3. Me hodology In he ield s udy, he ac o s ha explain he le el o echnological syne gy o he i ms will be iden i ied and he echnological combina ions will be analyzed in o de o isualize a echnological s agge ing. To his end, some da a science algo i hms (clus e s and decision ee) and mul i a ia e analysis models (o de ed logi and ma ginal e ec s) we e applied. This in ol ed debugging he ini ial da abase, elimina ing companies wi h incomple e da a and ex eme cases, he la e using Weka’s unsupe ised In e qua ile Range algo i hm. The numbe o companies was educed om 3,344 o 2,081, which implied a 37.7% d op in he o al da a p ocessed. The elimina ion o hese da a did no condi ion he explana o y capaci y o he me hodologies used in his s udy. 3.1 Iden i ica ion o Technological Clus e s The clus e s we e ex ac ed using he K-means algo i hm and he Manha an dis ance on a bina y ma ix o occu ence om Weka (Sha ma e al., 2012). I should be no ed ha an adequa e dis ance o his da a co esponds o he Hamming dis ance, i.e., XOR (Kuba , 2017). Ne e heless, in his case, i is easible o glimpse ha bo h dis ances – Manha an and Hamming – a e equi alen due o dis ances be ween wo elemen s o ally di e en p o ide 0; mean ime, o he same elemen , he dis ance is 1. The K-means algo i hm uns he ollowing s eps: Fig. 1: Pe cen age dis ibu ion o enabling ICTs o Indus y 4.0 by business segmen Sou ce: own EM_4_2022.indd 184 7.12.2022 10:56:12 185 4, XXV, 2022 In o ma ion Managemen  The k poin s a e placed in space ep e- sen ing he objec s o be g ouped. These poin s ep esen he cen oids o he ini ial g oups;  each objec is assigned o a g oup, which has he closes cen oid;  he posi ions o k cen oids a e ecalcula ed;  s eps 2 and 3 a e epea ed un il all poin s belong o a g oup;  his p oduces a sepa a ion o objec s in o g oups. The isualiza ion o hese clus e s has an explo a o y and analy ical emphasis. I does no a ec he use o he o he ins umen s o his wo k (o de ed logi and decision ee), which use he numbe o echnologies adop ed by companies as an explana o y a iable. On he e ined da abase, we wo ked wi h companies ha al eady ha e enabling ICTs o Indus y 4.0. This implied educing he da abase om 2,081 o 1,348 companies. In his case, companies wi h no echnologies a e excluded so as no o dis o he isualiza ion o echnology clus e s, allowing be e cons uc ions o subg oups wi hin he g oup o companies al eady adop ing (basic and syne gis ic). 3.2 Iden i ica ion o Explana o y Fac o s Explana o y ac o s a e iden i ied o companies ha a e in a null, basic and syne gis ic phase o adop ion. Fo hese pu poses, we wo ked wi h an o de ed logis ic eg ession model (o de ed logi ). This analysis is done on he o al numbe o he e ined da abase (n = 2,081). To his end, a ee econome ic so wa e called G e l is used (h ps://g e l.sou ce o ge.ne /). To gene a e he p obabilis ic model, a dependen a iable called ‘dep h index’ is buil , explained by: Dep h index = Le el o p esence (0/1) in: ERP + CRM + SCM + Big da a + RIFD + + Cloud compu ing + IT Secu i y a ea (1) whe e he index anges be ween 0 and 7 in each company (n = 2,081). When e iewing he dis ibu ion o he dep h indica o , a disc e e a iable was gene a ed in which he ollowing g oups a e gene a ed om a Weka disc e iza ion algo i hm:  G oup 0: ‘no de elopmen ’ – consis s o companies ha do no ha e mo e ad anced ICTs echnologies;  g oup 1: ‘basic le el’ – g ouping hose o ganiza ions ha ha e 1, 2 and 3 echnologies;  g oup 2: ‘syne gic le el’ – in eg a ed by companies ha ha e 4, 5, 6 and 7 echnologies. 3.3 Cons uc ion o Decision T ee By using wo a ibu e selec ion algo i hms, C sSubse E al and Bes Fi s , a ailable in Weka so wa e, he main a iables ha can explain he le el o dep h in adop ion a e iden i ied. F om his selec ion o a ibu es, on a sample o 2,081 companies, a decision ee is buil by applying he J48 algo i hm o Weka so wa e. The quali y o he p oposed ee is e alua ed om he numbe o co ec ly p edic ed cases. Fo hese pu poses, he con usion ma ix is p esen ed la e in Tab. 6. The decision ee has a se ies o analy ical ad an ages: i does no equi e he assump ions o p obabili y dis ibu ion, i is as , i acili a es he in e p e a ion o esul s, obus esul s a e deli e ed and he co ela ion be ween a ibu es does no al e i s p ecision (Rojas- Có do a e al., 2020). The J48 o Weka so wa e is based on he C 4.5 algo i hm, de ised by J. Ross Quinlan (Wi en e al., 2011), and as he decision pa ame e , i chooses he a ibu e wi h he highes in o ma ion gain measu ed by he en opy di e ence. I s s ages a e:  Inco po a e base cases;  calcula e he en opy pool;  o each a ibu e, calcula e he in o ma ion gain;  ind he a ibu e ha gi es he highes no malized in o ma ion gain;  epea he p ocess un il he in o ma ion gain is ze o in he whole ee. (2) whe e each pi is a ac ion = class i cases/ o al cases. To a oid o e i ing in he decision ee, he Weka so wa e applies a ‘p uning’, wi h a con idence ac o = 0.25 ( he smalles alue incu s mo e p uning) and a minimum o wo ins ances pe lea , which is wha is sugges ed in Weka’s J48 algo i hm (Wi en e al., 2011). Tab. 2 syn hesizes he main a iables ha we e examined in he ield s udy and ha a e based on he heo e ical amewo k and a ailable da a collec ed in he ICTs Su ey o he Chilean Minis y o Economy. EM_4_2022.indd 185 7.12.2022 10:56:13 186 2022, XXV, 4 In o ma ion Managemen The a iables jus p esen ed do no ha e collinea i y p oblems, showing a iance in la ion ac o s (VIF) below 10 in all pa ame e s. 4. Field Resea ch The ollowing p o ides a clus e analysis, an o de ed logi , and a decision ee in o de o iden i y how echnological syne gies beha e and he ac o s ha explain why a company has a ce ain le el o echnology adop ion. 4.1 Technology Clus e s Th ough he analysis o s a is ical clus e s, nine clus e s a e iden i ied in which all he echnologies s udied appea (Tab. 3). Va iables and au ho s Explana ion Hypo he ical linkage Sales (A nold e al., 2018; Dalenoga ea e al., 2018; Ingaldi & Ulewicz, 2020; Ho á h & Szabo, 2019; B ambilla, 2018) Annual sales income o each company excluding axes (Sou ce: da a ob ained om he su ey) A posi i e hypo he ical linkage is expec ed. Pu chases Annual pu chase cos o each company wi hou axes (Sou ce: da a ob ained om he su ey) A nega i e linkage is expec ed; i pu chases a e high, con ibu ion ma gins a e lowe , making adop ion mo e di icul . Di ec labo (A nold e al., 2018; Dalenoga ea e al., 2018; Ingaldi & Ulewicz e al., 2020; Ho á h & Szabo e al., 2019; B ambilla, 2018). S a di ec ly hi ed by he company (Sou ce: da a ob ained om he su ey) A posi i e linkage is expec ed; mo e wo ke s mean la ge size and g ea e inancial s eng h o adop mo e complex echnology. Added alue on sales (A nold e al., 2018; Dalenoga ea e al., 2018; Ingaldi & Ulewicz e al., 2020; Ho á h & Szabo e al., 2019; B ambilla, 2018) Resul o = (sale − pu chase)/ sale (Sou ce: calcula ed om su ey) A posi i e linkage is expec ed; he highe he ma gin on sale, he company will ha e a g ea e inancial slack o adop echnology. P oduc i i y (sale/labo ) (Almeida e al., 2020; B ambilla, 2018). Resul o = sale/labo Sou ce: calcula ed om su ey A posi i e linkage is expec ed; companies wi h highe p oduc i i y a e mo e likely o in es in echnologies. Ou sou cing (bina y) (Dalenoga ea e al., 2018; Ho á h e al., 2019 ; Ho á h & Szabo, 2019) A bina y is buil om he numbe o subcon ac ed wo ke s in he company; ou sou cing is unde s ood as he comme cial ela ionship wi h ano he company o speci ic asks ha may in ol e labo . (Sou ce: calcula ed om su ey) A posi i e linkage is expec ed be ween he adop ion o echnologies and he p esence o ou sou cing; subcon ac ing companies ha e g ea e o ganiza ional complexi y which jus i ies adop ion as a managemen ool. ICTs specialis s (bina y) (Mo a e al., 2019; Maggi e al., 2020; Almeida e al., 2020; Cab e a-Sánchez & Villa ejo- Ramos, 2019) A bina y is buil om he numbe o ICT specialis s a ailable in he company du ing he yea ; hey a e employees who a e able o de elop, ope a e and main ain he company’s in o ma ion and communica ion sys ems (Sou ce: calcula ed om su ey) A posi i e linkage is expec ed be ween he p esence o skilled labo and he possibili y o adop ing echnologies; companies ha ha e ICT specialis s ha e he capaci y o abso b new echnologies. Sou ce: own Tab. 2: Explana o y a iables o he dep h in he adop ion o enabling ICTs echnologies o Indus y 4.0 EM_4_2022.indd 186 7.12.2022 10:56:13 187 4, XXV, 2022 In o ma ion Managemen Based on he g ouping (Fig. 2), we ind wo la ge g oups o clus e s which allow us o acili a e eading o da a. I should be no ed ha his analysis excludes companies wi h no echnologies which we p e iously classi ied as ‘ze o de elopmen ’, ep esen ing 35% o he o al companies analyzed. Two le els o adop ion a e iden i ied in addi ion o he null condi ion o adop ion. Basic De elopmen Clus e s 0, 4 and 7 explain 71% o he clus e ed cases (NC = 1,348) and ep esen 46% o he o al companies analyzed (NA = 2,081). In hese subg oups, he syne gy be ween he ERP and CLOUD is con i med, which c osses he o he conglome a es ans e sally. These echnologies make i possible o imp o e he e iciency o in o ma ion managemen wi hin companies and allow ull use o he esou ces a ailable in he Cloud. These companies a e a a basic le el o de elopmen and can ake a leap by adop ing a new enabling ICTs o Indus y 4.0. Syne gis ic De elopmen Clus e s 1, 2, 3, 5, 6 and 8 explain 29% o he clus e ed cases (NC = 1,348) and ep esen only 19% o he companies analyzed (NA = 2,081). These subg oups combine a g ea e numbe o echnologies ac ing simul aneously. When e iewing Fig. 2, he clus e s on he le o he g aph a e highligh ed. In he case o clus e 8, his accoun s o 5% o he o al numbe o clus e ed companies among which s and ou he companies ha combine a basic ERP and Cloud pla o m wi h RIFD echnology and compu e secu i y. In he same ein, we ha e clus e s 6 and 2, which explain 9% o he clus e ed cases, p esen ing a base o ERP, Cloud, CRM. Addi ionally, he compu e secu i y unc ion is de ec ed in he i s subg oup and, in he second, we ind he supplie managemen sys ems (SCM). Clus e s Numbe o companies Dis ibu ion o e he clus e ed g oup (%; N = 1,348) Dis ibu ion o e he o al analyzed (%; N = 2,081) Technologies Clus e 0 525 39 25 ERP Clus e 4 233 17 11 Cloud Clus e 7 200 15 10 ERP, Cloud Clus e 6 119 9 6 ERP, CRM, Cloud, SEGTIC Clus e 2 120 9 6 ERP, CRM, SCM, Cloud Clus e 8 67 5 3ERP, RIFD, Cloud, SEGTIC Clus e 1 45 3 2 ERP, CRM, SCM, Big cloud, SEGTIC Clus e 3 25 21 ERP, Big cloud Clus e 5 14 1 1 ERP, CRM, Big cloud To al numbe o clus e ed companies 1,348 65 Companies wi h no echnologies 4.0 733 35 To al numbe o companies 2,081 100 Sou ce: own based on he use o Weka so wa e Tab. 3: Clus e ing esul s – clus e dis ibu ion o he enabling ICTs o Indus y 4.0 EM_4_2022.indd 187 7.12.2022 10:56:13 188 2022, XXV, 4 In o ma ion Managemen In his con ex , clus e 1, loca ed a he lowe end o Fig. 2, can be desc ibed as ‘ i s echnological mo e s’. This g oup ep esen s 3% o clus e ed companies and ep esen s only 2% o he o al analyzed. I comp ises 45 companies ha p esen a wide ange o echnologies, highligh ing ERP, CRM, SCM, Big da a, Cloud and IT secu i y. By e iewing Fig. 2, i is possible o see wha he logical pa h should be o a digi al ans o ma ion in he company. I begins wi h a Cloud and ERP base → mo es o wa d wi h pla o ms SCM and CRM → inally, i inco po a es Big da a, IT secu i y and RIFD echnologies. 4.2 Explana o y Fac o s Analysis o he Le el o Dep h in Technological Adop ion Two complemen a y analyses a e p esen ed. On he one hand, an o de ed logi model is de eloped, which includes he analysis o ma ginal e ec s and, secondly, a decision ee is p esen ed, which allows he ac o s ha explain he le el o echnological adop ion o be ela ed in a hie a chical way. O de ed Logi Model When e iewing Tab. 4, we ind ha he model allows 73.4% o he cases o be answe ed co ec ly, p esen ing a good explana o y capaci y based on he likelihood es . I should be no ed ha ou objec i e is o analyze he slopes o each explana o y a iable a he han he magni ude o he coe icien . The i s esul is he cu -o poin s calcula ed by he model. The i s poin (Cu = 0.41***) indica es ha he null g oup goes om 0 o 0.41 echnologies, he e o e, he e will be he companies ha ha e no adop ed ICT 4.0. The second cu -o poin (Cu = 3.012***) dis inguishes be ween he second and hi d g oup. Be ween 0.41 and 3.01 echnologies we will ha e he basic g oup unde s ood as ha which has be ween 1, 2 and 3 echnologies. The sec ion goes om 3.01 echnologies onwa ds a e desc ibed as syne gis ic companies. These esul s con i m he disc e iza ion ca ied ou in he Weka so wa e algo i hm, explained in he me hodology, and which will be used la e in he decision ee. 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