Consumers’ resistance to digital innovations: A systematic review and framework development

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ContentslistsavailableatScienceDirect

Australasian Marketing Journal

journalhomepage:www.elsevier.com/locate/ausmj

Consumers’ resistance to digital innovations: A systematic review and framework development

Shalini Talwar

^a

, Manish Talwar

^b

, Puneet Kaur

^c^,^e

, Amandeep Dhir

^d^,^e^,^f^,^∗

aK. J. Somaiya Institute of Management, Mumbai, India

bAlkesh Dinesh Mody Institute for Financial and Management Studies, Mumbai, India

cDepartment of Psychosocial Science, University of Bergen, Bergen, Norway

dNorwegian School of Hotel Management, University of Stavanger, Stavanger, Norway

eOptentia Research Focus Area, North-West University, Vanderbijlpark, South Africa

fSchool of Business and Management, Lappeenranta University of Technology, Lappeenranta, Finland

a rt i c l e i nf o

Article history:

Received 22 December 2019 Revised 8 June 2020 Accepted 28 June 2020 Available online 6 July 2020 Keywords:

Consumer resistance Barriers

Digital innovations Innovation resistance Non-adoption

Passive and active resistance

a b s t ra c t

Consumerresistanceisoneofthemajorcausesoffailureofanyinnovation.Despiterisingacademicin- terest,thenon-adoptionofdigitalinnovationorconsumerresistancehasreceivedlessscholarlyattention ascomparedtothefactors drivingtheadoptionofdigitalproductsandservices.The existingresearch onconsumerresistanceisalsoinsiloes,runningacrossmultipleverticals, spanningfromresistanceto greenproductstotheInternetofthings(IoT).Thecurrentstudyprovidesasystematicreviewoftheex- tantliteratureonconsumerresistancetodigitalinnovationsbyutilisingthesystematicliteraturereview (SLR)methodology.Atotalof54studieswereselectedforcontentanalysistoisolatethematicfoci,iden- tifyresearchgaps,recommendfutureresearchavenuesanddevelopaframework.Ouranalysisrevealed thatthe extant literaturecould begroupedunder broadresearchthemes,namelyresistancetodigital innovations,organisationalresistancetotechnologicalinnovations,resistancetotechnologicalhealthcare innovationsandconsumerresistancetoinnovations(oﬄine).TheresultsofthisSLRstudyareexpectedto galvanisefutureresearchinthisareafromthetheoreticalaswellasfromapractice-orientedperspective byprovidingvariousactionableinputstocombatconsumerresistancetodigitalinnovations.

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1. Introduction

Consumer resistance towards innovation is an aspect of con- sumerbehaviour thatis asimportantasacceptanceandadoption

∗ Corresponding author.

E-mail addresses: shalini.t@somaiya.edu (S. Talwar), talwars25@gmail.com (M.

Talwar), puneet.kaur@uib.no (P. Kaur), amandeep.dhir@uis.no (A. Dhir).

(Seth etal., 2020).Inits simplestform, consumerresistancemay beseenastheunwillingnessamongconsumerstotrynewerinno- vationsinthemarket(Tansuhajetal.,1991).Consumerresistance toinnovationisoneofthemaincausesbehindthemarket failure of innovations (Talke andHeidenreich, 2014). It is also a signiﬁ- cantfactorthat canimpedeordelaytheadoptionofanyinnova- tion(Laukkanen etal., 2008).Empirical studies havedocumented ahighfailurerateofinnovations,indicatingthatmanyinnovations

https://doi.org/10.1016/j.ausmj.2020.06.014

1441-3582/© 2020 The Author(s). Published by Elsevier Ltd on behalf of Australian and New Zealand Marketing Academy. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ )

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S. Talwar, M. Talwar and P. Kaur et al. / Australasian Marketing Journal 28 (2020) 286–299 287

fail duetoconsumerresistance (Heidenreich andKraemer, 2016).

Consumerresistancehasremainedacriticalproblemfacedbyor- ganisations,anditwillcontinuetobeathreatinthefutureaswell (Abbasetal.,2017). Scholarsarguethatﬁrmsneedtounderstand the causesthat leadto productfailuresforthe effectivemanage- ment ofinnovationactivities (Joachimetal., 2018).Consequently, consumerresistanceisanimportantareaofinterestthatcannotbe ignoredbyscholarsandpractitionerswhoareinterestedinensur- ingthefastdiffusionandadoptionofnewinnovations.

Despiteitscriticalimpactandimportance,consumerresistance to innovations has received relatively little attention inthe past;

forexample,mobilewallets(Kauretal.,2020;Leongetal.,2020).

Moreover,regardlessofthefactthatthedecisionnottobuyisalso a realconsumptionchoice(Laukkanen andKiviniemi,2010),early research onconsumerinnovationsfocused mainlyon themotives and factors related to their adoption with a distinct pro-change bias (Hew etal., 2019). Incontrast,the factors that inhibitedthe diffusionofinnovation oraclearstatusquobiashavebeenquite neglectedbypaststudies(HeidenreichandSpieth,2013).However, scholarsnowrealisethatthemotivatorscatalysingtheadoptionof an innovationarenot veryusefulwhen itcomestoanalysingthe reasons behindnon-adoptionandresistance (Claudy etal.,2015).

Thestudyofadoption,aswell asdiffusionofinnovation,isuseful only in understanding thespread ofinnovation, whereas innova- tionresistanceneedstobeexploredtoexplainwhyconsumersare not willing to adopt a possibly useful newoffering (Groß, 2015; NelandBoshoff,2019).Asaresult,studiesexaminingconsumerre- sistancetoinnovationsarenowgrowing.Forinstance,someschol- arshaveexaminedconsumerresistancetoinnovationsincontexts suchasorganicfood, mobilepaymentsandsoon(e.g.Kauretal., 2020).

The increase in academicinterest notwithstanding, our exten- sivereviewoftheliteraturehasrevealedthatstudiesonconsumer resistancearefewandfarbetween.Furthermore,ourexamination ofthebackgroundliteraturehasalsoshedlightonthefactthatthe existing studies are spreadacross avariety ofareas andcontexts in which resistance has been examined. Such fragmented litera- turemakesitchallengingforresearcherstobuildupontheextant learningandtaketheresearch inthearea forward.Tohelpschol- arsovercomethischallenge,weproposetosystematicallyorganise theliteratureintheareaandcriticallysynthesiseitforfutureref- erence.Towardsthisend,thecurrentstudyproposestoemploythe systematic literature review (SLR) methodology, which offers one waytheextensiveevaluationoftherelatedresearch,yieldingmul- tiplebeneﬁts asdiscussed byprior SLRstudies(e.g.Beheraetal., 2019;Sethetal.,2020;Sahuetal.,2020).

Notably,ourpreliminarysearchoftheconsumerresistancelit- erature has revealed that the extant studies can be categorised into four broadareas basedon the underlying products andser- vicesinvestigated.Theseareasare:(a)resistancetodigitalinnovations (e.g.Hong, 2020), (b)organisational resistancetotechnolog- ical innovations (e.g.Chen andKuo, 2017), (c)resistanceto technological healthcareinnovations (e.g.Gurtner, 2014) and(d) consumerresistance toinnovations (oﬄine)(e.g.Claudyetal., 2010).

Withintheseareas,a widevariety ofproducts andservices,such as smartwatches (e.g. Mani and Chouk, 2017), organic food (e.g.

Kushwah et al., 2019a), Internet banking (e.g. Laukkanen, 2016), green products (Claudy et al., 2010), mobile sales assistants (e.g.

ChoandChang,2008)andsoonhavebeeninvestigatedtounder- stand consumerresistance.Areview oftheliterature revealsthat each of theseareas and products offers insights that are unique andinteresting.Therefore,webelievethattheliteraturerelatedto eachofthesefourareasneedstobe reviewedseparately toguide futureacademicresearch.Consequently,thisSLRproposestofocus onstudiesrelatedtooneofthefourbroadareasmentionedabove, namelyconsumerresistancetodigitalinnovations.Digitalinnova-

tionsincludeproductsandservices,suchasmobilebanking,online shopping,e-books,smartwatchesandsoon.

Thereasons behindthechoice ofdigitalinnovations to exam- ineconsumerresistanceare:(a)Theseinnovationsarerevolution- isingthelivesofindividualsinmanyways(ManiandChouk,2019), (b)digitalproductsandservicesofferimmensepotentialforinno- vationsbut atthe same time are difficult to manage (Nylén and Holmström, 2015), (c)innovations in the field of digital technol- ogyhavebeenagile,whichhasshortenedtheinnovationlifecycle ofexistinginnovationsandcreatedconfusioninthemindsofcon- sumersaboutthefrequentchangesthatchallengetheir statusquo (Laukkanen,2016),and(d)digitalinnovations,suchasinformation and communications technology (ICT) applications, have a short shelf life, which requires firmsto ensure quick diffusion oftheir productsbyovercomingresistance(Sun,2016).However,fastdiffu- sionoftheseinnovationsmayfaceimpediments,suchasthenega- tiveattitudeandresistanceofconsumers,leadingtodelayedadop- tionorcomplete rejection.Due tothis, inputsfromacademic re- searchrelatedtoovercomingresistanceareessentialtokeeppace with the digital innovations. However, prior scholars have noted thatthisareahasremainedunder-presented,withlimitedresearch ontheresistance towearables, smartservices,convergenceprod- ucts,e-books,mobile socialcommerceandsoon(e.g.Hew etal., 2019).Thereisalsoalackofunderstandingabouttheissueofslow diffusion and late adoption of digital innovations (Jahanmir and Cavadas,2018),whichiserodingtheprofitsoffirmsandimpeding theirgrowth.Hence,itisessentialtoevolveabetterunderstanding ofthecausesanddeterminantsofslowdiffusionoroutrightrejec- tionofdigitalinnovationstoaidthefirmstoovercomeconsumer resistance.Accordingly,theinvestigationofresistantbehavioursto- wardsdigitalinnovationscanbeofgreatvaluetomanagersandre- searchers(Caoetal.,2015;Kauretal.,2020;Talwaretal.,2020a).

Therefore,ourSLRintendstomotivateandsupportfutureresearch inthearea.

Thecurrentstudyaimstoaddressthefourmainresearchques- tions:RQ1. What is the research proﬁle of the extant studies in theareaofconsumerresistancetodigitalinnovations?RQ2.What arethe key themesofresearch on consumerresistanceto digital innovations? RQ3. What are the gaps and limitations in the ex- tantliterature that needtobe addressed?RQ4.What arethe avenuesoffutureresearch?Weproposetoanswertheresearchques- tionsthroughacriticalsynthesisofstudiesonconsumerresistance todigital innovations identiﬁedthrough a robustsearch protocol.

Consequently, thisSLR presents a deep insight into two decades ofrelatedliterature toserveasa platformtoencourageacademic researchintheareaofconsumerresistancetodigitalinnovations.

Thestudymakestwonovelcontributions:First,thestudyclas- siﬁes the consumerresistance literature into four distinct heads:

resistancetodigitalinnovations,organisational resistancetotech- nologicalinnovations, resistanceto technologicalhealthcare innovations andconsumer resistance to innovations (oﬄine). Second, thestudygoesbeyondthenarrativeoftheexistingbodyofknowl- edgeonconsumerresistancetodevelopaframeworktoguidefu- tureresearchandpractice.

2. Theconceptualboundaryofthisreview

Clarityabouttheconceptofconsumerresistancetoinnovations ingeneralanddigitalinnovations,inparticular,isrequiredbefore thesearchprotocolforidentifyingstudiescanbedefinedandexe- cuted.Thisisessential becauseconsumerresistancehasremained side-linedasaconceptforquitesometime(HeidenreichandSpi- eth,2013). Due to this, the concept of consumerresistance con- tinuestobeinits infancy,anditlacks well-articulateddefinitions (Claudyetal., 2010). However,fewdefinitionsare availableinthe seminalliterature.Tobeginwith,RamandSheth(1989)described

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itasresistance towardsanyinnovation that arisesfrompotential threatsto the status quo and the existing belief system of consumers.Inadditiontothis,scholarshavedeﬁneditasatendency amongconsumers tomaintaintheir statusquoandavoidtheuse ofnewtechnology (Sagaand Zmud,1994), combined withresis- tance to change (Mani and Chouk, 2018). Similarly, the seminal literature has used diverse descriptions for consumer resistance, suchasunwillingnesstotryinnovations,negativeresponseto innovations,lackofmotivationtouseinnovationandcompletenon- acceptance(e.g.AntiocoandKleijnen,2010;Tansuhajetal.,1991).

On the whole, resistance has been argued to take varied forms and have different degrees of manifestations, depending on the innovation (rejection, postponement, opposition). To be- gin with, resistance can take the form of rejection, which is a straightforward refusal to accept the product, as contended by Kleijnenetal.(2009).Anotherformofresistanceispostponement, whichindicates a delayed decisionon the acceptance ofinnova- tionand, ﬁnally,thereisopposition,whichrepresentsstrongneg- ativefeelingstowards theinnovation(Kleijnenetal., 2009).From theperspectiveofdegreesofresistance,innovationresistancecan beexpressedbytheconsumerintheformofinertia(adherenceto thestatusquo),active resistance(negativeresponsetoinnovation on account of being perceived as beingrisky) andstrong, active resistance(strong opposition to innovation asbeing perceived as beinginappropriate)(RamandSheth,1989).

Adding another dimension tothe debate, theliterature on innovation resistance hasbroadly divided resistance to innovations intotwogroups,namely,activeandpassive(HeidenreichandKrae- mer,2015).Activeinnovationresistance(AIR)maybedescribedas thenegative attitudetowards a newproduct after its evaluation, andpassive innovation resistance (PIR) may be described asthe predispositionofconsumerstoresistinnovationevenbeforeeval- uating it (Heidenreich and Spieth, 2013). Active resistance has a moreovertconnotation,resulting innegativeattitudes causedby psychologicalandfunctionalbarrierstotheinnovationattheeval- uation stage itself (Heidenreich andHandrich,2015). Incompari- son,passiveresistance stems fromaninclination toresist change andmaintainthestatusquothatarisesratherunconsciously,even beforebeginningtoevaluatetheinnovation(HeidenreichandHan- drich,2015).

All the available definitions and descriptions have a general context and can be applied equally for digital as well asoffline innovations. However, to clarify the conceptual boundary of the currentstudy,wedrawupon theseminal literaturetodefinecon- sumerresistancetodigital innovations as:‘Consumer resistanceto digital innovations represents barriers to theadoption of any inno- vationderivedfromtheadvancesin informationandcommunication technology, a resistance driven by varied personal,situational, con- textual,regulatory, andproduct-related factors,such asage, innova- tiveness,pre-disposition to maintainthestatus quo,cultural aspects, governmentalsurveillance, innovationcharacteristics,and manifested invaryingdegreessuchasrejection,oppositionorpostponement.’ 3. Methodology

3.1.Searchprotocol

A robustsearch protocolwasdevised toidentifystudies tobe reviewed,which aligns with the SLR methodology used by prior scholars (Kushwah et al., 2019b; Sahu et al., 2020; Seth et al., 2020).Twowell-knowndatabases,WebofScience(WOS)andSco- pus,wereusedforsearchingrelevantstudiesforthisreview.Both thesedatabasesareconsideredreliableandfrequentlyusedinre- cent SLR studies (Mongeon and Paul-Hus, 2016). For the search protocol, first, the keywords were specified based on the con- ceptualboundariesofthe SLR. Thesekeywordswere searchedon Google Scholar, and the first 100 results were screened to up-

datethe keywords list.Next,theleadingmarketing andinforma- tion system journals were searched to see if the list is exhaus- tive.Finally,theexpertteamoffive(twoprofessorsandthreere- searchers), well informed about consumer resistance, were con- sultedto finalisethelistof keywordsthat were usedonthe two leading databases. In addition to keywords, inclusion and exclu- sion criteria were specified, along with quality evaluation questions. To ensure extensive and thorough coverage, articles were alsoincludedbasedonfull textwithcitationchainingsearch, us- ing both backward and forward approaches. A search was exe- cutedusingthefollowingkeywords:‘consumerresistance^∗’OR‘innovation resistance^∗’OR‘new productresistance^∗’OR‘technology resistance^∗’ OR ‘consumer non-adoption’ OR ‘service resistance^∗’ OR‘resistancetoinnovation^∗’OR‘userresistance^∗’.

Studies were shortlisted based on the following inclusion criteria (IC):IC1,articles publishedinpeer-reviewedjournals;IC2, articles publishedin theEnglish language from January 2000 till March 2020;and IC3, articlespublished inquantitative, qualita- tiveandconceptualjournals.Theexclusioncriteria(EC):EC1,rel- evance(consumerresistancetodigitalinnovations);EC2,duplicate studies withmatching titlesand/ordigital objectidentiﬁer(DOI);

EC3,thesis,reviews, conferenceproceedings,editorialsandshort communicationitems;andEC4,low-qualityevaluationquestions.

To ensure that the results of the study are relevant and un- biased, fourquality evaluation (QEfromnow on) questions were formulated toevaluatetherigourofthecandidatestudies,inline with the recommendations of prior studies (e.g. Behera et al., 2019). QE 1: The study contains evidence that is quantitatively and/orqualitatively analysed.Thepossibleanswers are:‘quantita- tiveresearch (+2)’and‘bothquantitativeandqualitativeresearch’

(+3.5).QE2:Thestudyexplicitlyexamines thebeneﬁtsandlimi- tations.Thepossibleanswers are:‘yes(+2)’,‘no(0)’and‘partially (+1)’. The score is partial when only one and not both are re- ported.QE 3: The output ofthe studyis justiﬁable. The possible answersare:‘yes’(+2),‘no’(0)and‘partial’(+1).Thescoreispar- tialwhenonlythetechniquesusedareexplainedinaverylimited way,oroneofthetechniquesusedisnotdetailed.QE4:Thestudy hasbeen publishedina recognisedandstablesourceof publication.The possible answersare asfollows: (+2) ifthesummation of a number of citations and HIndex exceeds 100, (+1.5) ifthe numberliesbetween50and99,(+1.0)ifthenumberliesbetween 1and49and(+0)ifthenumberis0ordataarenotavailable.A randomscore torepresenttherelative importanceofeach aspect of QE is assigned asused by Behera etal. (2019) and added up acrossallQEs.

Thesearchresultedinaninitialdatasetof1421articles,butnot allwerecongruentwiththetopicathand.Inclusionandexclusion criteriawereappliedtoensurethatthestudies’short-listconforms to the conceptual boundary of thisSLR. Thereby,conference and other types of articles, articles in other languages and duplicate articles were excluded, resultingin a combinedpool of536 articles.Toensurethat onlythearticlesthat arerelevanttothe area of focus are selected, analysis of the articles was undertaken by athoroughreadingoftheabstracts.As aresult,154articles were excluded as not immediatelyrelevant for consumerresistance to digitalinnovations.Thesearticlesweredeletedastheywerebased on resistance in a medical andpolitical context aswell as anti- consumptionandsustainability.Abalanceof382werecoded,and thekeythemesonwhichthearticlesfocused weretheresistance of consumersto oﬄine products, resistance in the organisational context, resistance to digital innovation, resistance to healthcare innovations, resistanceto socialchangeandso on.Inconsonance withourconceptual boundary,we selected studiesrelatedto the resistanceofconsumerstodigitalinnovations,resultinginan ini- tialpoolof89articles.Theseweretakenforwardfordetailedanal- ysis,wherethearticleswereanalysedthroughathoroughreading offullarticlesandgenerationofqualityscores.Thirty-ﬁvearticles

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Fig. 1. Year-wise publications.

were furtherexcluded onthegroundsofnotbeingrelatedtothe topicathandorscoringlowonQEs. Hence,aﬁnallistof54arti- cleswasselectedforthisreview.

3.2. Researchprofiling

Theresearchproﬁleoftheselectedstudiesispresentedthrough the year ofpublication, thepublication source title, geographical scope, research methods and the digital products/services investigated. Such a descriptive summary of thereviewed studies can providean overviewofthemomentumofpublicationandthefo- cus ofthe extant studies forthe reference of futureresearchers.

Fig. 1 reveals that duringthe initial decade of the new century, onlyeight studieswereconductedthat investigatedconsumerre- sistancetowardsdigitalinnovation,butthemomentumofpublica- tion hasincreasedinthecurrentdecade(2011–2020), conﬁrming therisingacademicinterestinthearea. Notonlyarepublications rising,butthereisalsoabroadacceptanceofconsumerresistance as an area of research. This is evident fromthe wide variety of journalsinwhichtheselectedstudieshavebeenpublished(Fig.2).

The geographicalscope (Fig.3) showsthat 45% ofthestudiesare relatedtoAsiaand30%to Europerevealingtwo keypatterns:(a) resistancetodigitalinnovationsisprobablyhigherinAsia,thereby attractingtheattentionofscholars,and(b)thereisaskew inthe geographicalcoverageglobally,withlimitedornostudiesfocussing oncountriesinAfrica,SouthAmericaandsoon.Amongstindivid- ualcountries,theUnitedStateshasthehighestnumberofstudies.

Intermsofdigitalproducts/servicesinvestigated,studieshaveex- aminedresistance todigitalpayments,e-commerce,social media, smart products andsoon, but50% havefocussedon onlydigital payments,e-commerceandm-commerce(Fig.4).

4. Researchthemes

Content analysis was undertaken to distil the key themes of thereviewedstudies. Thecontentanalysisofthe selectedstudies suggests that research on consumerresistance to digital innovations canbe categorisedintoseven broaddimensions:theoretical underpinnings, barriers against digital innovations, characteristics inhibiting orstimulatingresistance, non-adoption (postponement, oppositionandrejection),socio-demographicaspects,methodolog- icalperspectivesandoutcomevariablesofinterest.Anoverviewof

theseareas ofresearch andthe key variables in consumerresis- tancetodigitalinnovationstudiesispresentedinFig.5.

4.1. Theoreticalunderpinnings

Most studies on consumer resistance towards digital innova- tionshaveutilisedvariousconsumerbehaviourtheoriestoexplain theresistanceandnon-adoptionofsuchinnovationsbyconsumers.

Innovation resistance theory (IRT) wasthe mostfrequently used theoryinthe reviewedliterature,andcloseto 55%ofthe studies fromthecurrentreview alsoutilised IRTasthebasis fortheem- piricalevaluationofconsumerresistancetoinnovations.

IRT was first proposed by Ram (1987) and later modified by Ram and Sheth (1989), and it describes consumer resistance through differentbarriers that obstructthe adoption of an innovation. IRTprovides crucialinsights into howconsumers react to innovations.AccordingtoIRT,usage,valueandriskbarriersrepre- sentfunctionalbarriers,whereastraditionandimagebarriersrefer topsychological barriers to innovation.A usage barrieris related to the usability of the service and the changes that consumers needtoundergotouseit;avalue barrierrepresentsthecompar- ativeperformance of thesubstitutes interms ofperformance-to- pricevalue; a risk barrier representsthe consumers’ perceptions ofthe risk in innovation; atradition barrier isrelated toa habit ofhowthingshavebeendone sofar;andanimage barrierisre- latedto the ease-of-usage (Laukkanen etal., 2007). Furthermore, inthedigitalcontext,ausagebarrierrepresentstimeeffort,anda risk barrierrepresentsfinancial burdenanduncertaintyinchoice (Heinzeetal.,2017).

Notably,morethanhalf(53%)oftheselectedstudiesthathave employed IRT have actually used it in conjunction with other popular theoretical frameworks,such as the technologyadoption model (TAM) and the Unified Theory of Acceptance and Use of Technology(UTAUT)(e.g.Ohetal.,2019;Sohetal.,2020).Forin- stance,two importantmeasures ofTAM,perceivedusefulnessand perceived ease of use, have a significant association with resistance; for example, perceived usefulness exerts significant influ- enceonresistancetowards smartTVwhile perceivedease ofuse is influential inreducing resistance (Imet al., 2014). Similarly, a model based on the integration of UTAUT andIRT revealed that performanceexpectation andsocialinfluenceimpactonlineshop-

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Fig. 2. Publication source title.

Fig. 3. The geographical scope of studies.

ping for both younger aswell asolder adults (Soh etal., 2020).

Additionally,studies have also referred to seminal theories, such asstatusquobias(SQB)(e.g.ManiandChouk,2018), behavioural reasoningtheory (BRT) (e.g. Gupta and Arora, 2017) andthe Big Fivepersonalitymodel (e.g.LissitsaandKol,2019) toprovide insights into the reasons for as well as reasons against the adoption of digital innovations. The remaining 45% of the studies of thereview utilisedvarious othertheories todiscussintentionsto adopt or resist innovations. These theories include the diffusion of innovation (DOI) (Jahanmir and Lages, 2016), means-end ap-

proach (Kuisma et al., 2007), dual-factorperspective (Chouk and Mani, 2019), Foucauldiantheory (Humphreys, 2006), activitythe- ory (Sun, 2016) and generational cohort theory (Lissitsa and Kol,2019).

4.2. Barriersagainstdigitalinnovations

As mentioned above, more than half of the selected studies usedIRTasthetheoreticallens.Withinthisgroup,nearlyhalfused all ﬁve generic IRT barriers as antecedents to explain variations

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Fig. 4. Products investigated.

Fig. 5. Overview of emerging themes and key variables in consumer resistance to digital innovations research.

in the dependant variable. The accumulated ﬁndings reveal that the impact of barriers on resistance or intentions to adopt may vary withthe type ofdigitalinnovation.Forinstance,inthe case ofmobilebanking,psychologicalbarriers,representedbytradition andimagebarriers,werefoundtobesigniﬁcant(Laukkanen,2016; GuptaandArora,2017).Onthefunctionalside,value barriershad themostdominanteffectontheadoptionofboththeInternetand mobile banking (Laukkanen,2016). Notably,mature mobile bank- ingconsumershadhigherriskperceptionascomparedtoyounger ones, whereas value barriers were intense in both age groups (Laukkanen etal., 2007). Studiesalsoexamined theimpactof in- formationandguidanceaboutinnovationontheresistanceinmo- bilebanking andrevealedthatpersonalcommunicationandguid- ance through one-to-one contact could vastly reduce usage barriers (Laukkanen andKiviniemi,2010). Incomparison,impersonal communicationthroughmassmediacanbeusedeffectivelyinde- creasing the value barrier (e.g. Laukkanen and Kiviniemi, 2010; Laukkanen etal., 2009). Chemingui and Lallouna(2013) revealed

the negativeimpact of tradition barriers andtrust on use inten- tionstowardsmobileﬁnancialservices.

Image,valueandtraditionbarrierswerefoundtobecriticalfor online shopping by consumers with reference to different prod- uctcategories(LianandYen,2013).Furthermore,thepossibilityof thecoexistenceofresistancewithintentionwasconﬁrmed,along withthe ‘privacy paradox’ phenomenon, which refers to a situation whereprivacy concerns do not hinder usage intentions (e.g.

Hewetal., 2019).Incomparison,psychologicalbarriershavebeen foundtobesigniﬁcantinthecaseofsmarthomes(Paletal.,2019).

Inthecaseofmobile apps,such asbrandmobile appsofservice organisations,image,usageandvalue,representingconsumers’ac- tiveresistancearesigniﬁcantantecedentsofresistance(Chenetal., 2019).Notably,inthecontextofdigitalinnovations,perceivedrisk, including security risk, has been found to increase resistance to digitalinnovations(ChoukandMani,2019;ManiandChouk,2018).

Attempts have also been made to adapt the Ram and Sheth (1989) model to accommodate digital technologies by in-

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cluding new categoriesof barriers, such as technological vulner- ability,ideological andindividual (inertia) barriersand mediators suchasscepticism(ManiandChouk,2018).Similarly,extendedIRT models were proposed by considering the perceived cost barrier (Moorthyetal., 2017) andindividual mobiquity, technological in- novativenessandgovernmentsurveillance(ChoukandMani,2019).

4.3.Characteristicsinhibitingorstimulatingresistance

Scholarshaveinvestigateddigitalinnovationresistanceinterms of innovation and consumer characteristics. Innovation characteristics represent features of innovation as perceived by consumers. Perceived price, complexity, perceived enjoyment, social influence,perceived usefulness,economic benefit, perceived novelty and intrusiveness are examples of innovation characteristics (Abbas etal.,2017; Achadinhaetal., 2014;Antónetal., 2013). In comparison, emotions, innovativeness, motivation, self-congruity, self-efficacy and dependence are examples of consumer characteristics (Abbas et al., 2017; Cha, 2011; Chouk and Mani, 2019; ManiandChouk,2017;Matsuoetal.,2018).

Around 51% of the studies included in the current review usedthisapproachtoapply innovationcharacteristicsand/orcon- sumercharacteristics tothe examination ofresistance andinten- tionsto adoptdigitalinnovations.The key constructsusedinthe related studies include compatibility, perceived usefulness, complexity, perceived beneﬁts, self-eﬃcacy and innovativeness (e.g.

Cha, 2011; Chouk and Mani, 2019). Jahanmir et al. (2018) discussed the role of innovativeness in the late adoption of digital innovations, whereas Pal et al. (2019) discussed innovativeness in the case of IoT-enabled smart homes. Similarly, the role of consumer characteristics in the form of self-efficacy was revealed to have a significant influence on risk perceptions about Internet banking (Laukkanen etal., 2009). Similarly, opennessto change has a substantial influence on reasons for adopting mobile banking (Gupta and Arora, 2017), whereas personality traits drivem-shopping intentions (Lissitsa andKol, 2019), while scep- ticismresultsin apreferenceforsimpler products (Jahanmir and Lages,2016).Additionally,generalInternetapprehensiveness(GIA) and transactional Internet apprehensiveness (TIA) can be used to capture the effect of consumer characteristics on information searchingandonlinebuying(Susskindetal.,2003).

In their study on resistance to smartphone usage, Abbas et al. (2017) found that innovation characteristics such as price, complexity and social inﬂuence were the dominant predictors of resistance to smartphones. Similarly, Mani and Chouk (2017) found that innovation characteristics such as perceived uselessness, perceived price, intrusiveness and perceived novelty had a noticeable impact on the resistance to smart products. Additionally, privacy concerns inﬂuenced intrusiveness.

Furthermore, perceived ease of use has the effect of reducing resistanceinthecaseofsmartproducts(e.g.Imetal.,2014).

4.4.Non-adoption(postponement,oppositionandrejection)

About 10% of the articles included in this review discussed digital innovation resistance in terms of postponement, opposition and rejection, which represent varying degrees of resistance(Laukkanen etal., 2008). Inthe caseofbrandmobile apps, Chenetal.(2019)foundthattheeffectsofmarketcompetitionand cross-channelfactorsweredifferentforresistancebehaviour,mea- suredintermsofthreedifferentdegreesofresistance(postponement,oppositionandrejection).Differences intheantecedentsof thesethree were alsonoted by Laukkanen (2016) in the case of mobileandInternet banking.Laukkanen etal.(2008)studiedthe differenceintheresponse ofpostponers,opponents andrejectors tothe ﬁvebarriers in thecontext ofInternet banking. The study revealedthattheintensityandnatureofinnovationresistanceim-

pactthedecisiontoreject,postponeorrejectanyinnovation.The threecategories ofnon-adoptersdiffered signiﬁcantly intermsof allﬁvebarriers.Herein,postponershadlowerresistanceandrejec- tors hadhighresistanceon accountofall barriers. Inthecaseof mobilebankingaswell,thethreenon-adoptersdifferedintermsof image,valueandusagebarriers(ElbadrawyandAbdelAziz,2011).

ParkandKoh(2017)conﬁrmedthedifferencesindegreesofre- sistanceinthecaseofconvergenceproductssuchassmartwatches.

Theyrevealedthatrejectionwasdrivenbyexpectationsrelatedto betterorhigherqualityaswellaslowerprice,whereaspostpone- mentwasaffectedbyexpectationsrelatedtolowerpriceonly.Sim- ilarly, while investigatingthesethree non-adoptergroups foron- line shopping, Lian and Yen (2013) revealed that opponents and rejectorshadahigherlevelofbarrierscomparedtopostponers.

4.5. Socio-demographicaspects

A large part of the prior literature discussed consumer resistancetodigitalinnovations inthecontextofconsumercharacter- isticsandtheirimpactonintentionstoadoptorrejectanewdig- ital productor service. Withinthis discussion, some studies pre- sentedﬁndingsrelatedtotheimpact ofdemographic factorssuch asage,gender,incomeandeducationonanindividual’sresistance, which needs to be delved into separately (e.g. Laukkanen, 2016; Leonget al., 2020). Similarly, Elbadrawy andAziz Aziz (2011) argued that resistance to digital innovations remained a less ad- vancedmodel,particularly inthecontext ofdevelopingcountries andculturaldimensions.

Gender and social norms have been found to predict intentions to buy virtual items (Cha, 2011). Similarly, age and gender drive adoption as well as rejection decisions related to mobile and Internet banking (Laukkanen, 2016), whereas gender and education inﬂuence the image, risk and usage barriers in the case of mobile banking (Elbadrawy and Aziz Aziz, 2011).

Similarly, Leong et al. (2020) revealed the inﬂuence of the effects of education and income on resistance to m-wallets.

Laukkanenetal.(2007)emphasisedtheimportanceofage-related factorsinresistanceto digitalinnovations.Theyfounddifferences intheperceptionsofmatureandyoungerconsumerstowardsmo- bile banking, where ageing was related to the risk barrier. Fur- thermore,psychologicalbarrierswerealsohigheramongtheolder group compared to the younger group. These results were also confirmedinthecaseofonlineshopping,whereolderadultswere found to have higher risk and tradition barriers as compared to younger adults, though gender was not found to play any role (Lian and Yen, 2014). The influence of age on the use of digital innovationswasalsoconfirmedbyLissitsaandKol(2019),whore- vealeddifferencesinthe mobileshopping intentionsoffourgen- erationalcohorts,namelybabyboomersandgenerationsX,Yand Z.

Religion-relatedaspectshavealsobeenarguedtoinfluencethe response to digital innovations. For instance,religiosity has been revealedtoinfluenceinnovationresistance(Hong,2020).Similarly, in one of the few studies associating Internet and online shop- pingadoptionwithreligiosity,LissitsaandCohen(2018)confirmed that the chancesof theadoption of thetwo were higher forthe ultra-OrthodoxcommunityincomparisontootherJewishreligios- itygroups(secular,traditionalandreligious).Furthermore,gender and locality impacted the online shopping pattern of the ultra- Orthodoxgroup,withmenmorelikelytoindulgeinit.

4.6. Methodologicalperspectives

Nearly 65% of the studies reviewed inthis SLR employed the cross-sectional approach of data collection, and the remaining studies are either conceptual or used a mixed-method approach tocollectdata(e.g.Paletal.,2019;Shi,2011)(Fig.6).Theselected

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Fig. 6. Research methods.

studiesfocusingonempiricalanalysisutilisedavarietyoftoolsfor statisticsandeconometricsformulti-variateanalysis.Themethods rangedfromanalysisofvariance(ANOVA)(e.g.ElbadrawyandAziz Aziz, 2011; Laukkanen et al., 2009), t-test (e.g. Laukkanen et al., 2007), discriminantanalysis (e.g.Lian andYen,2013),logistic re- gression (e.g. Lissitsaand Cohen,2018), exploratoryfactor analysis and confirmatoryfactor analysis (e.g.Chemingui andBen Ial- louna, 2013) and structural equation modelling, both the parametric covariance-based (CB) (e.g. Matsuo et al., 2018) and non- parametric, partial least square (PLS) (e.g. Chang et al., 2019; Nel and Boshoff, 2019) forms. Thus, most of the empirical studies haveutilised commonbutpopularmethods ofanalysis.Some scholars have also applied less common methods, like the hier- archical value map (Kuisma etal., 2007; Heinze et al., 2017), artificial neural network (Hew et al., 2019) and two-staged structural equation modelling-artificial neuralnetwork (SEM-ANN) approaches (Leongetal.,2020).Inaddition,oneofthestudiesused the method of post-analysis to examine the reaction of users to the implementation of algorithmic personalisation by Instagram (Skrubbeltrangetal.,2017).

Notably,mostoftheempiricalstudiesevaluatedthedirectpath, with only a limited number of studies (about 30%) considering the third variablesin termsof moderating,mediating orcontrol- ling influences. Some key moderating influences considered by priorscholarsincludeconsumerinnovativeness(Abbasetal.,2017), satisfaction with offline service (Chen et al., 2019), variety seek- ing (Kim et al., 2017), gender (Lian and Yen, 2014), experience (Matsuoetal.,2018),stickinesstocashpayment(Sivathanu,2019), attitude (Siyal et al., 2019), mobile shopping-service experience (NelandBoshoff,2019),voluntarinessofuse(Sohetal.,2020),task situations(Sun,2016)ande-lifestyle(Yuetal.,2015).

The key mediatingvariables examined by theselected studies areattitude(Antónetal.,2013),innovationresistance(Hong,2020; Im et al., 2014), perceived value (Kim et al., 2017), complexity barriers, performance risk barriers and existing usage patterns (Matsuoetal.,2018),scepticism(ManiandChouk,2018),perceived usefulness and perceived ease of use (Siyal et al., 2019). On the whole,thestudiesofutilised moderatorsfocusonusercharacter- isticsandexperienceandmediatorsthatcapturebarriersandresis- tance.Withregardto controlvariables,demographic factors,such as age, gender, education, social class and work situation, have beenutilised(e.g.LissitsaandKol,2019;WagnerMainardesetal., 2019).

Adding methodological variety to the area, Skrubbeltrang et al. (2017) analysed comments posted on In-

stagram and Twitter in response to the implementation of algorithmic personalisation by Instagram. Finally, contributing to methodological enrichment in the area, two studies have also developed scales to help future researchers. Out of these, the late-adopter scale comprises three dimensions, namely, slowness ofadoption,resistancetoinnovationandscepticism(Jahanmirand Lages, 2016). Another developed scale consisted of two dimensions: general Internet apprehensiveness (GIA) and transactional Internetapprehensiveness(TIA)(Susskindetal.,2003).

4.7.Outcomevariablesofinterest

All selected studies have been shortlisted on the basis of the fact that they havediscussed consumer resistancein some other digitalcontexts.However,ontheevaluationofthearticles,weﬁnd thatabout40%ofthepreviousliterature ondigitalinnovationre- sistancehasexaminedresistanceastheoutcomevariable(Fig.7).

Within this limited number, a few used generic IRT barriers to representresistance(e.g.LaukkanenandKiviniemi,2010) ormea- sured it through the three degrees represented by rejection, op- positionandpostponement (e.g.Park andKoh, 2017; Chenetal., 2019). Some other studies haveinvestigated resistance to change asaconsequence(e.g.Changetal.,2019;Zhou,2014).Incompari- son,moststudiesmeasuredresistanceasawhole(Ohetal.,2019; Paletal.,2019).

Notably,otherempiricalstudieshavefocussedonoutcomevari- ables, such as adoption, attitude, intention to use, late adoption andactual usage, revealinga continued adherence toa positivist agendaofacceptance(e.g.LissitsaandCohen,2018;Patsiotisetal., 2013; Sun, 2016; Goyal et al., 2013). Afew studies havealso in- vestigatedresistanceasaninterveningvariabledrivenbyhypothe- sisedantecedentsand,inturn,drivingintentions(e.g.Hong,2020; Sivathanu,2019).

5. Researchgapsandfutureresearchavenues

Asystematicreviewofthe selectedstudies revealedsixmajor gapsintheprior literature.Thesegapsandassociatedavenuesof futureresearcharediscussedhere.

5.1. Limitedtheoreticaladvancement

Theextantliteraturehasexhibitedlimitedtheoreticaladvance- mentas well astheacknowledgement ofconsumerresistance as an area that requiresindividual focus.This gaphaspersisted de-

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Fig. 7. Outcome variables of interest.

spiteprior scholarsnotingadearth ofrelatedstudiesandaneed forintensifying theinvestigations (e.g.Hew etal., 2019).For dis- cussionin theresearch themes,about55% ofthe studiesutilised IRT to model resistance, and within this, more than half of the studies used IRT in conjunction with one or the other theories ofadoption/acceptance (e.g. TAM). This adherence to a positivist agendaforinvestigatingthefactorsincreasingadoptionhaslimited theaccumulatedknowledge availabletoassist decision-makingin thefaceofresistancetodigitalinnovations.

Avenues of future research: We recommend that in future research,scholarsextendtheclassictenetsofIRTandidentifynewer barriersthatmayincrease consumers’resistance,therebyslowing down the diffusion of innovations or leading to their complete rejection.Scholars should draw newer insights from SQB theory, whichprovidesaninteresting wayofmodellingpost-adoptionbe- haviourthroughinertia,asarguedbySeth etal.(2020).Similarly, scholarshaveutilised behaviouralreasoningtheoriessuch asBRT (Sahuetal.,2020)tostudyconsumerresistancetowardsdigitalin- novations. Prior research also contended that inertia, which also representsadherencetothestatusquo,likeresistance,inhibitson- linebuying(NelandBoshoff,2019).

5.2.Anexcessofaction-orientedconceptualisationsinresearch design

The currentreviewsuggeststhatmorethan60%ofthestudies haverelied on the cross-sectional data collection method,which indicates an excess of action-oriented conceptualisationswithout deeper investigation of the social and psychological aspects of consumerresistance.The cross-sectionalstudydesignalsosuffers frommethodological issues, which may limit the generalisability androbustnessofﬁndings,asarguedbyseveralpriorscholars(e.g.

Talwaretal.,2020b).

Avenuesoffutureresearch:Werecommendthatfutureresearch be driven by experiment-based studies that can provide a better perspective and interesting results on the thought processes ofconsumers.Furthermore, longitudinalstudies can also be con- ductedtocapturethechangeresponseofconsumerswhoarecon- stantlyexposed to stimuliin the formof social andpromotional inﬂuences,which mayalter their resistant response todigital innovations.

5.3. Narrowrangeofdigitalinnovationsinvestigated

As describedin Fig. 5,most ofthe existing studies relatedto consumerresistancearelargelyconﬁnedtotheInternetormobile banking(ElbadrawyandAzizAziz,2011;Patsiotisetal.,2013),on- lineshopping(LianandYen,2013) andmobile shopping(Neland Boshoff, 2019; Lissitsa and Kol, 2019). With existing resistance studies heavily skewed in favour of innovations in banking and shopping,gapsinthestudyofresistanceexistintermsofthevari- etyofdigitalinnovationsexaminedandsectorscovered.Thisopens upareasforfutureresearch.

Avenues of future research: Scholars should explore resistance tonewandupcoming innovations, suchaswearableslike Google glasses, artiﬁcial intelligence-driven solutions, smart services and soon. Suchinnovations representa changeinthe existinghabits anddeviations from experiences of consumers.Due to this, they arelikelytoencounterconsumerresistanceandneedtobeexam- inedinvaryingcontexts.

5.4. Passiveresistanceremainsunder-explored

Thereviewedstudieshavelargelyfocused ontheﬁvebarriers, namely,usage,value,risk,traditionandimagebarriers,asbothin- dependentanddependant variables.Thesebarriers captureactive resistance,thoughonlyonestudyincludedinthereviewmaderef- erence to active resistance (Chen et al., 2019), and no study ex- aminedpassiveresistance.We feelthatthelackofknowledgere- latedtopassiveresistancetodigitalinnovations,whichmayman- ifestevenbeforeevaluatingit(HeidenreichandSpieth,2013),isa criticalgapinlearningasitlimitsthestrategicinputsavailablefor managerstoplanforsuchacovertresponse.

Avenues offuture research: We recommend that future studies in the area measure the inclination to resist change and main- tain thestatusquoinprospectivebuyers.Inthiscontext, switch- ing behaviour that examines impediments to moving from sys- temsbeingused tonewinnovations/improvementscan be evalu- atedthroughexperimentalstudiesthatcompareuserresponsesto experiences obtained from different alternatives (e.g. Politesand Karahanna,2012).The focusshould beon evaluating thesubcon- sciousrejectionofadigitalinnovationevenbeforeevaluation.

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S. Talwar, M. Talwar and P. Kaur et al. / Australasian Marketing Journal 28 (2020) 286–299 295 5.5. Limitedfindingsondegreesofresistance

Thereisapaucityofstudiesintermsofinvestigatingthethree degreesofresistance,namely,rejection,oppositionandpostpone- ment. Since prior studies have noted differences in the barriers for consumers exhibiting these three degrees of resistance (e.g.

Laukkanen,2016;LianandYen,2013),thescarcityofperspectives isagapthatneedstobeaddressed.Thisisparticularlyimportant inthecaseofoppositionorpostponement,whichcan ﬁnallylead toeitherrejectionoradoption.

Avenuesoffutureresearch:Webelievethatthethreedegreesof resistance present a clearer picture of the resistant behaviour of consumers.Therefore,theyneedtobe conceptualisedinfarmore detailthanhasbeendoneintheextantliterature.Werecommend that future studies measure differences in the degrees of resistance by considering the impact of not only the classic IRT bar- riersbutalsoother variables.Thesevariables could includeprod- uct knowledge, technology readiness,governmental support, regulatory surveillance, service attributes, channel features and fre- quencyofpurchase.Inaddition,futureresearcherscanalsoutilise various context-speciﬁc factors that can make a situation unique by enhancing the predictability of behaviour (Sahu et al., 2020).

Furthermore,sinceopposition canturneitherway,postponement orrejection,investigationofthedynamicsthroughwhichonede- greeofresistanceleadstoanotherneedstobeconductedthrough longitudinalstudies.

5.6. Lackofsocio-demographicinsights

Despite the manifestation of consumer resistance as consumer behaviour,few studies haveexplored the impact ofsocio- demographic, geographic and cultural factors in highlighting the individual differences in consumers’ resistance to digital innovations. Some of the selected studies have suggested the influential roleof socio-demographicvariables(age, gender, income,ed- ucationandculture)indrivingconsumerresistance(e.g.Lianand Yen, 2014; LissitsaandKol,2019). However, thefindings are lim- itedandcoveranarrowspectrumofdigitalproducts/services.Sim- ilarly,thereisavisibleskewinthefindingsofthepriorliterature relatedtoculture-andcountry-relatedfindingsinthepriorlitera- ture.MostofthestudieswererelatedtoAsia,EuropeandtheUSA (Fig.3).

Avenues of future research: Models accommodating socio- demographic factors can provide improvedinsight intothe resis- tantbehaviour ofconsumerstowardsdigitalinnovations.Priorre- searchers have argued that the pattern and degree of resistance can vary from country to country (Joachim et al., 2018). Due to this, we recommend that scholars interested in the area should seek toincorporate socio-demographicfactors, such asreligiosity andcollectivistversus individualisticculture, andpoliticalfactors, suchastheextentofthegovernmentalpromotionofdigitalinno- vations.ScholarsshouldconsiderusingHofstede’sculturaldimensions(Hofstede,2001)toassessresistantbehaviourssincetheindi- vidualresponseandattitudetowardsanyinnovationcanbedriven bytraitssuchasrisk-taking,andfutureresearchersshouldconsider usingHofstede’sculturaldimensions(Hofstede,1983)toassessre- sistant behaviours.Furthermore, studiesshould also measuredif- ferencesinresistanceacrossvariousgenerationalcohortstomake availablegeneration-speciﬁcﬁndingsthatcanserveasthebasisfor the personalisation ofpromotional campaigns, especially through socialmediaplatforms.

6. Frameworkdevelopment

Our systematic review revealed a dearth of comprehensive modelsofferingmulti-dimensionalconceptualisationsofconsumer

resistance that can be applied to a variety of digital products/services and contexts. To bridge this gap in the extant literature, we have formulated a framework to guide future research. As a referent, this proposed framework uses the RAIC (Resistanceadoptioninertia continuance) frameworkproposed by Seth et al.(2020),which modelled pre-adoption barriers aswell astheadoption/investment behaviourofretailinvestors.Thepro- posedframework,the resistance communicationadoption framework (RCA model), brings together the ﬁndings of the reviewed studiesandourinsightsdevelopedthroughanextensivereviewof therelatedliterature(Fig.8).TheRCAmodelcanbetestedbyfu- tureresearchersinvarieddigitalproductsandservices.

TheRCA modelisconceptualisedto capturethe differencesin theresistanceofthethreeresistantgroups,namelyrejectors,post- ponersand opponents,and examine themechanics of the trans- lation of resistance into adoption/non-adoption by including the influenceof communication. Itdraws upon the theoretical lenses ofIRT (Ramand Sheth, 1989) andthe dual-factor concept (DFC) (Cenfetelli, 2004) to provide grounding forthe propositions.The modelcomprises fourdistinct blocks.Blockone isbased onDFC, anditincludesdigital innovationandconsumercharacteristicsas twobroaddualfactorsthatactasantecedentsofresistance.Resis- tanceis modelledin block two, utilising theIRT barriers divided into two broad groups: functional and psychological. The third block comprises the three degrees of resistance, and the fourth block represents how consumers finally make the adoption/non- adoption decision under the influence of communication by the firm/serviceprovider/marketer.Furthermore,alltheseassociations arehypothesisedtobe moderatedbyahostofculturalandother socio-demographicfactors.

The dual factors, digital innovation characteristics and con- sumercharacteristics,havebeendiscussedindetailasamongthe emerging themesinthe precedingpartofthe SLR. Bothofthese contain inhibiting andexacerbating factors, asdiscussed by DFC.

Thedigitalinnovationcharacteristicsarerepresentedbyperceived complexity, intrusiveness, relative advantage, perceived risk, perceived novelty, price, trust, economic benefit, perceived control, perceivedusefulness,compatibility,trialability,systemqualityand product quality. Similarly, consumer characteristics are perceived enjoyment, self-image, emotion, innovativeness, social influence, self-efficacy,lifestyle,self-congruityanddependence.Wepositthat these two factors act together to stimulate or inhibit the barri- ersthatconsumersmayhavetowardsthedigitalinnovationunder consideration.

ThefunctionalandpsychologicalbarriersrepresenttheIRTbar- riers: usage, risk, value, image andtradition barriers. In addition tothesegeneric barriers,othermiscellaneousbarrierscanalsobe modelledinRCAdependingonthedigitalproductorservicebeing investigated.Weconsiderthesebarriers keybecausewe feelthat digitalinnovations havepercolatedenoughgloballyforconsumers to be attracted towards them, yet adopting them might still be challenging.Thishasalsobeenemphasisedbythereviewedstud- ies.The framework hypothesisesthese barriersas the dependant variablesimpactedbydigitalinnovationcharacteristicsontheone handandtheconsumercharacteristicsontheother.

TheIRTbarriersarefurtherhypothesisedtoimpact thedegree ofresistance,measuredintermsofrejection,oppositionandpost- ponement. This impliesthat the barriers may lead to a different level of resistant response of consumers towards digital innovations. Prior scholars havediscussed the varying degrees ofresis- tant behaviour,particularly thefact that oppositionor postpone- mentcanculminate aseitherrejectionoradoption.Toreﬂectthe possibilityofsuchatransformation,inthelastblockoftheframe- work,wehavepresentedthepossibilityoftheroleofcommunica- tionthatmanagersmayutilisetoinﬂuencethethreenon-adopter groups, namely, information andguidance provided through per-

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Fig. 8. Resistance communication adoption framework (RCA model).

sonalinteraction and mass media. Informationand guidancecan lowerresistancebyaddressing theapprehensionsthatprospective users mayhave in their mind. In addition to communication, as suggestedbypreviousstudies,otheractionstomotivateconsumers toadopt,dependingonthespeciﬁc digitalproductorserviceun- derconsideration, can alsobe included in themodel. Finally,we hypothesise that under the inﬂuence of communication, opposition,rejectionorpostponementmaytransformintoadoption.Ad- ditionally,wehavealsomodelledthepossibilityofcommunication remaining ineffective, thereby causing consumers to take a non- adoptiondecision.

To accommodate the role of personal differences among con- sumersandtheirimpactonresistancetowardsdigitalinnovations, wehavesuggestedthecontextsthatmaybeconsideredasmoder- atingvariables.The suggestedmoderators includeculturaldimen- sionsandsocio-demographicfactors,suchasgovernment,country, age,gender,incomeandeducation.Theculturaldimensionthatthe studyproposestouseisoneofHofstede’sdimensions,uncertainty avoidance.

Executing the framework: Researchers can use this framework in part or fully to empirically measure the hypothesised associ- ations. The barriers and degrees of resistance representing non- adoptergroupscanbe measuredthrough thepre-validatedscales availableinthepriorliterature.Tomakethefindingsmorerobust, thescalescanbeadaptedtothecontextofstudythroughaquali- tativeapproachin theformof open-endedessays orfocusgroup discussions. Such an exploratory qualitative study can also help inthe identificationof additionalbarriers specific to theproduct orservice underconsideration. In addition, while collecting data forempirical analysis, future researcherscan measure innovation andconsumer characteristics as second-order constructs, in con- sonance with the approach utilised by Talwar et al. (2020c) to

measure the initial trust in mobile payments. Lastly, to examine thetranslationofvaryingdegreesofresistanceintoadoption/non- adoption, we suggest a longitudinal studymay be conducted by forming two groups of non-adopters, wherein the first group is thecontrolgroupthatissubjectedtocommunication,andthesec- ond group is tested againthrough cross-sectional data collection withoutanysuchexternalinfluence.Withregardtothemoderat- ingvariables,theframeworkofferstheflexibilityofemployingone ormore ofthesemoderators whilecontrolling forothers.Forin- stance,the studycan be conductedina particularcountry while usingage,genderanduncertaintyavoidanceascontrolvariables.

7. Implicationsofthestudy 7.1. Theoreticalimplications

OurSLRuncoveredseveralresearch-relatedpatternsintheex- tant literature on consumer resistance to digital innovations and indicated several areas where academic researchers can under- takeimpactfulresearchtoinfluencepractice.Specifically,ourstudy makes theoretical contributions: First, it provides a deep insight into the theories andmethods utilised by prior scholars. Forin- stance, it revealsthat IRT (Ram, 1987; Ram and Sheth, 1989) is the mostpopularly applied theory ofconsumer resistancein the existingliterature ondigitalinnovations,followedby thetechnol- ogyacceptancemodel(Davis,1989) andinnovationdiffusionthe- oryordiffusionofinnovationtheory(Rogers,2003).Furthermore, itbrings forth thecontinueduseofan adoptionlens intheeval- uation ofresistance andthenarrowness ofthe research methods employedbythestudiesinthearea.Thesefindingscanhelpinthe advancement and enrichment of theory-based research by help- ing academicians identifythe theoriesand frameworksthat have

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proven validity and are valuable enough to be takenforward for investigatingtheresistancetovarieddigitalinnovations.

Second,thestudyprovidesacloselookatthemediating,moderating andcontrol variables utilisedby prior studies andunder- scores the key variables utilised, thereby spotlighting the signif- icance of examining such variables to better elucidate consumer resistance.

Third,itidentiﬁes keygeographies,academicarticlesandpub- licationsourcesintheareaforthereferenceoffutureresearchers.

Such proﬁle-based research inputs can help scholars identify re- latedgeographytoinvestigateandpublicationsoutletsthatwould bemorereceptivetowardsstudiesfocusedonconsumerresistance todigitalinnovations.

Fourth, it critically analyses the literature to present gaps in ﬁndings and potential research areasbased on thesegaps to en- courage academic research in the area. The proposed research pathsandavenues,basedontheappreciationofthefactthatcon- sumerresistancecan doomthebestofdigitalinnovations tofail- ure,willelevatethequalityanddepthofdiscussioninthearea.

Fifth, itisthefirst SLRtodefine theconceptual boundariesin the area of consumer resistance by grouping past studies under thebroadheadsofresistancetodigitalinnovations,organisational resistanceto technologicalinnovations,resistanceto technological healthcare innovationsandconsumerresistance toinnovations in offlinespace.Suchconceptualisationisexpectedtohelpfuturere- searchers endeavouring to systematically review the literature in theareaaswellasadvanceresearchingroupsthatremainunder- representedsofar.TheSLRhasalsoproposedadefinitionofcon- sumerresistanceinthespecificcontextofconsumerresistance.No otherstudyhasdefinedconsumerresistanceinthiscontextbefore.

Lastly, motivatedbythe awarenessthat theaccumulatedfind- ingsaredeficientinthecontextofcomprehensiveframeworksand models to examine consumer resistance, the study has built on thefindingsoftheselectedstudiestopresentamulti-dimensional frameworkforassessingconsumerresistancetodigitalinnovations.

This framework is quite versatile as it accommodates key con- structsandbarriersidentiﬁedbypaststudiesandincorporatesthe possibility of building in individual differences among the con- sumersbeingexamined.Futureresearchcapturingalltheseaspects can be expected to yield robust results that can aid managerial decision-making.

7.2. Managerialimplications

The findings of our study and the RCA model offer five in- ferences for practice: First,the findings suggestedthat forsmart productsandservices,perceivedsecuritycouldbeamajorbarrier, soemphasisshouldbe placedonenhanced securitytomakecon- sumersadopttheseofferings.Simpleset-upprocedures shouldbe formulatedtoovercomeperceivedcomplexity.Healthriskconcerns intheuseofthesedevicescanbeovercomethroughsafetylabels certifiedbyindependentbodies.Advertisementstoaddresstheis- sue of perceived compatibility may be used to reduce this barrier by showingcompatibility betweenthe product andthe self- image ofconsumers in terms oftheir habitsand behaviour.Fur- thermore, technology anxiety can be reduced by communication strategiesthat enhance consumers’perceptionofpower andcontrol, andperceived usefulnesscanbe enhancedby providingade- quatesupportservice,personalisingservices(e.g.nutritionaladvice witha smartwatch)andinvolvingconsumersatan early stageof productconception.Improvementinperceivedusefulnesscanalso decreasetheperceivedpricebarrier,whichmaybehigh,especially in the case of younger consumers. Any strategy to reduce resis- tancetosmartproductsandservicesshouldaddresstheperception of intrusion andprivacy concerns by providing quick delete features andrunning awarenesscampaigns aboutdata collectedand disclosed(Abbasetal.,2017;ManiandChouk,2017,2018).

Second, in the case of online shopping for physical goods, a value barrier may be overcome through discounts and tradition barriers through free samples andreal-time online support.Fur- thermore, web-page design and multi-media tools can be used to promote products online. An image barrier in online shopping can be overcome by stimulating positive e-WOM (word of mouth)throughasocialcommunity.Inaddition,assigniﬁcantdif- ferenceswere observed within the non-adopter groups, postpon- erscan be transformed into adoptersby loweringusage barriers throughfriendlyuser-trainingmaterialandonlinedemos(Lianand Yen,2013;NelandBoshoff,2019).

Third, since the majority ofthe studies were focused on mobile andInternet banking, some important implications forman- agerialstrategiestocounterresistanceinthissectorhaveemerged.

Thekeyimplicationsformanagersinthissectorarerelatedtothe useofinformationandguidancetolowerresistance.Meaningfully, such information and guidance may be one-to-one or through massmedia,dependingonthetypeofbarrier.Forinstance,asthe value barrier is an intense one to banking, most studies empha- sisetherole of marketing campaigns (massmedia) that commu- nicate the beneﬁts of mobile banking as compared to its tradi- tional form, whereas the usage barrier may be handled through one-to-onepersonal educationfrombanking personnel.Similarly, non-adoptersandpostponers,whogenerallyhaverisk-relatedbar- riers,maybe handledthroughtargetedmarketactionslike assur- ancesrelatedtobreakinginconnectionanderrorwarningstoad- dresssafetyissues.In thecaseofopponents, wherethe tradition barrierintermsofhabitishigh,inadditiontothestrategiesused forpostponers,managerscanenhancetheimageoftheserviceus- ingbothmassmediaandpersonalinteraction.Forrejectors,banks shoulduse one-to-onecommunicationmoreto convincethem of theusefulness,safetyandbeneﬁtsofmobileandInternetbanking.

Massmediacanplayasupportiverole.

Fourth, trust, another important consideration in the case of mobileandInternetbanking,needstobeaddressedthroughmain- taining system quality. Further, since some studies found differences in the resistance offered by consumers in mature versus younger age groups, an innovation modiﬁcation strategy is sug- gestedforloweringtheriskbarrierofmatureconsumers.Itisalso suggested that information and guidance in both forms may be used along with trials to lower the barriers related to the neg- ativeimage of thistype ofbanking (e.g. Cheminguiand BenIal- louna,2013;LaukkanenandKiviniemi,2010).

Lastly, from the managerial perspective, our proposed framework provides a 360-degree view of the aspects to be managed whenlaunching anydigitalinnovation.It suggeststhat thebarri- ers, whichare the outcomeof thecharacteristics ofdigital products or services as well as those of consumers, can be lowered through information and guidance,which can be in the form of massaswellaspersonalcommunication.Furthermore,theframe- workunderscorestheimportanceofindividualdifferencescoming fromthe country ofresidence, age,gender and culture.This impliesthat, attheplanningstage, the managersshoulddesign the product/service afterdeﬁning the target geography and segment, as the innovation may need to be adapted to the speciﬁc context. The promotional material should also be designed keeping theseaspectsinmind.Forinstance,ifthetargetsegmentisyoung consumers,theperformance-to-pricevalue shouldbe emphasised morethanease-of-usewhilepromotingthedigitalproduct/service.

8. Conclusion

Firmsoftenincurhighresearch anddevelopmentexpenditures tocatalyseinnovations,which,inturn,createpressureforspeedy return on investment through the successful diffusion of innovation across markets. However, the failure rate of user innovations has been quite high in the past (Barczaket al., 2009) and