Abstracts in English, Afrikaans and Southern Sotho / The prevalence of hate victimisation in South Africa remains unknown, as does its full impact. Anecdotal evidence, borne out by recent research findings, suggests hate-based attacks on non-nationals have increased in recent years, distinctly reflecting a picture of heightened vulnerability. For several reasons, the severity of such victimisation, and their physical and psychological impact, go mostly unseen. Hate-motivated incidents, such as hate speech and intentional unfair discrimination, are possible precursors to additional criminal victimisation. Records of such incidents can be helpful to demonstrate both a context of harassment and evidence of escalating patterns of violence. Worldwide, under-reporting of hate victimisation is a longstanding concern and requires an urgent solution. In South Africa, under-reporting has contributed to the nonrecognition of hate crime as a separate crime category. Towards aiding in finding a solution, this study explored the psychological barriers to reporting xenophobic victimisation to relevant authorities. The study, furthermore, explored with a group of victims who experienced xenophobia whether they reported victimisation, the reasons for reporting and under-reporting, and their thoughts and opinions on the workability of a third-party reporting mechanism. Non-probability sampling, specifically applying convenience and purposive sampling was used to obtain 19 participants for the four semi-structured focus groups. While all participants reported being victimised because of their nationality, the study found that multiple psychological barriers prevent such victims of xenophobia from reporting victimisation to authorities. Many of the participants do not believe in the workability of third-party reporting mechanisms. / Dit is onbekend hoe algemeen viktimisering op grond van haat in Suid-Afrika voorkom, en daarom ook wat die volle impak daarvan is. Onlangse navorsingsresultate dui egter daarop dat aanvalle op nielandsburgers wat uit haat voortspruit, toegeneem het die afgelope paar jaar, wat hulle groter kwesbaarheid duidelik weerspieël. Die intensiteit van hierdie viktimisering, asook die fisieke en sielkundige impak daarvan word in die meeste gevalle om verskeie redes ook nie bekendgemaak nie. Voorvalle wat uit haat voortspruit, soos haatspraak en doelbewuste onregverdige diskriminasie, is moontlik voorlopers van verdere kriminele viktimisering. Die optekening van sulke gevalle kan help om bewys te lewer van die teisteringskonteks, sowel as van patrone van toenemende misdaad. Die gebrekkige aanmelding van viktimisering op grond van haat is wêreldwyd lank reeds ’n probleem, en een waarvoor daar dringend ’n oplossing gevind moet word. In Suid-Afrika het gebrekkige aanmelding daartoe bygedra dat haatmisdaad nie as ’n aparte misdaadkategorie erken word nie. Ten einde ’n oplossing te help vind, het die navorser vir die doeleindes van hierdie studie die sielkundige faktore ondersoek wat verhoed dat xenofobiese viktimisering by die betrokke owerhede aangemeld word. Die studie bevat ook die terugvoer van ’n groep slagoffers van xenofobie oor hulle aanmelding van die viktimisering al dan nie, die redes waarom hulle dit aangemeld het of nie aangemeld het nie, en hulle gedagtes en menings oor hoe lewensvatbaar ’n stelsel vir derdeparty-aanmelding is. Niewaarskynlikheid-steekproefneming, en spesifiek doelbewuste en gemaksteekproefneming is gebruik om 19 deelnemers vir die vier semigestruktureerde fokusgroepe te vind. Alhoewel al die deelnemers bevestig het dat hulle geviktimiseer is op grond van hulle nasionaliteit, het die navorser met hierdie studie bevind dat verskeie sielkundige faktore die slagoffers van xenofobie verhoed om die viktimisering by die owerhede aan te meld. Talle van die deelnemers glo nie dat stelsels vir derdeparty-aanmelding ’n werkbare oplossing is nie. / Hore na tshwaro e mpe ka lebaka la lehloyo e atile hakae Afrika Borwa ho ntse ho sa tsejwe, le ditlamorao tsa yona ha di tsejwe. Bopaki bo sa netefatswang, bo hlaheletseng dipatlisisong tsa morao tjena, bo bontsha hore ditlhaselo tse etswang ho batho ba tswang dinaheng tse ding di eketsehile morao tjena, e leng se bontshang hore ba kotsing le ho feta. Ho na le mabaka a mmalwa a etsang hore ho pharalla ha tshwaro e mpe jwalo, ho hlokofatswa mmeleng le maikutlong ho se ke ha bonahala. Diketso tse hlohleletswang ke lehloyo, tse kang dipuo tse nang le lehloyo le kgethollo e etswang ka boomo, e ba selelekela sa diketso tsa bonokwane tsa tshwaro e mpe. Ho tlalehwa ho diketso tseo ho ka thusa ho bontsha maemo a lebisang tshwarong e mpe mme ha fana ka bopaki ba hore diketso tse mabifi di ntse di eketseha. Lefatsheng ka bophara, taba ya ho se tlalehwe ha tshwaro e mpe e hlohleletswang ke lehloyo haesale e le qaka mme ho hlokahala tharollo ka potlako. Afrika Borwa, ho se tlalehwe hona ho entse hore diketso tsa bonokwane tse hlohleletswang ke lehloyo di se ke tsa nkwa e le diketso tse ikemetseng tsa bonokwane. Ho thusa ho fumana tharollo, phuputso ena e lekola mathata a maikutlo a sitisang matswantle ho tlaleha tshwaro e mpe ho ba boholong ba ikarabellang. Ho feta moo, phuputso ena e lekola matswantle ao e leng mahlatsipa a tshwaro e mpe hore na a ile a e tlaleha, mabaka a entseng hore a e tlalehe, a se ke a tlaleha le hore na a nahanang ka ho sebediswa ha mokena-dipakeng. Ho kgethilwe bankakarolo ba 19 ka hloko e le sampole, ba kgethwa ka sepheo le morero o tobileng hore ba be dihlopheng tse nne tse sa hlophiswang ka ho feletseng. Le hoja bankakarolo bohle ba tlalehile hore ba tshwerwe hampe ka lebaka la botjhaba ba bona, phuputso e fumana hore ho na le mathata a mmalwa a maikutlo a thibelang mahlatsipa a tshwaro e mpe ya matswantle ho tlalehela ba boholong. Bankakarolo ba bangata ha ba dumele hore ho tlalehela mokena-dipakeng ho tla thusa. / Psychology / M.A. (Psychology)

Abstracts in English, Zulu and Southern Sotho / The advent of Basel II Capital Accord has revolutionised credit risk measurement (CRM) to the extent that the once “perceived riskier bank assets” are now accommodated for lending. The Small and Medium Enterprise (SME) sector has been traditionally perceived as a riskier and unprofitable asset for lending activity by Commercial Banks, in particular. But empirical studies on the implementation of the Basel II internal-ratings-based (IRB) framework have demonstrated that SME credit risk is measurable. Banks are still finding it difficult to forecast SME loan default and to provide credit to the sector that meet Basel’s capital requirements. The thesis proposes to construct an empirical credit risk measurement (CRM) model, specifically for SMEs, to ameliorate the adverse effects of SME credit inaccessibility due to high information asymmetry between financial institutions (FI) and SMEs in Zimbabwe. A well-performing and accurate CRM helps FIs to control their risk exposure through selective granting of credit based on a thorough statistical analysis of historical customer data. This thesis develops a CRM model, built on a statistically random sample, known-good-bad (KGB) sample, which is a better representation of the through-the-door (TTD) population of SME loan applicants. The KGB sample incorporates both accepted and rejected applications, through reject inference (RI). A model-based bound and collapse (BC) reject inference methodology was empirically used to correct selectivity bias inherent in CRM domain. The results have shown great improvement in the classification power and aggregate supply of credit supply to the SME portfolio of the case-studied bank, as evidenced by substantial decrease of bad rates across models developed; from the preliminary model to final model designed for the case-studied bank. The final model was validated using both bad rate, confusion matrix metrics and Area under Receiver Operating Characteristic (AUROC) curve to assess the classification power of the model within-sample and out-of-sample. The AUROC for the final model (weak model) was found to be 0.9782 whilst bad rate was found to be 14.69%. There was 28.76% improvement in the bad rate in the final model in comparison with the current CRM model being used by the case-studied bank. / Isivumelwano seBasel II Capital Accord sesishintshe indlela yokulinganisa ubungozi bokunikezana ngesikweletu credit risk measurement (CRM) kwaze kwafika ezingeni lapho izimpahla ezazithathwa njengamagugu anobungozi “riskier bank assets” sezimukelwa njengesibambiso sokuboleka imali. Umkhakha wezamaBhizinisi Amancane naSafufusayo, phecelezi, Small and Medium Enterprise (SME) kudala uqondakala njengomkhakha onobungozi obukhulu futhi njengomkhakha ongangenisi inzuzo, ikakhulu njengesibambiso sokubolekwa imali ngamabhange ahwebayo. Kodwa izifundo zocwaningo ezimayelana nokusetshenziswa nokusetshenziswa kwesakhiwo iBasel II internal-ratings-based (IRB) sezikhombisile ukuthi ubungozi bokunikeza isikweletu kumabhizinisi amancane nasafufusayo (SME) sebuyalinganiseka. Yize kunjalo, amabhange asathola ukuthi kusenzima ukubona ngaphambili inkinga yokungabhadeleki kahle kwezikweletu kanye nokunikeza isikweletu imikhakha enemigomo edingekayo yezimali kaBasel. Lolu cwaningo beluphakamisa ukwakha uhlelo imodeli ephathekayo yokulinganisa izinga lobungozi bokubolekisa ngemali (CRM) kwihlelo lokuxhasa ngezimali ama-SME, okuyihlelo elilawulwa yiziko lezimali ezweni laseZimbabwe. Imodeli ye-CRM esebenza kahle futhi eshaya khona inceda amaziko ezimali ukugwema ubungozi bokunikezana ngezikweletu ngokusebenzisa uhlelo lokunikeza isikweletu ababoleki abakhethekile, lokhu kususelwa ohlelweni oluhlaziya amanani edatha engumlando wekhasimende. Imodeli ye-CRM ephakanyisiwe yaqala yakhiwa ngohlelo lwamanani, phecelezi istatistically random sample, okuluphawu olungcono olumele uhlelo lwe through-the-door (TTD) population lokukhetha abafakizicelo zokubolekwa imali bama SME, kanti lokhu kuxuba zona zombili izicelo eziphumelele kanye nezingaphumelelanga. Indlela yokukhetha abafakizicelo, phecelezi model-based bound-and-collapse (BC) reject-inference methodology isetshenzisiwe ukulungisa indlela yokukhetha ngokukhetha ngendlela yokucwasa kwisizinda seCRM. Imiphumela iye yakhombisa intuthuko enkulu mayelana namandla okwehlukanisa kanye nokunikezwa kwezikweletu kuma SME okungamamabhange enziwe ucwaningo lotho., njengoba lokhu kufakazelwa ukuncipha okukhulu kwe-bad rate kuwo wonke amamodeli athuthukisiwe. Imodeli yokuqala kanye neyokugcina zazidizayinelwe ibhange. Imodeli yokugcina yaqinisekiswa ngokusebenzisa zombili indlela isikweletu esingagculisi kanye negrafu ye-Area under Receiver Operating Characteristic (AUROC) ukulinganisa ukwehlukaniswa kwamandla emodeli engaphakathi kwesampuli nangaphandle kwesampuli. Uhlelo lwe-AUROC lwemodeli yokugcina (weak model) lwatholakala ukuthi luyi 0.9782, kanti ibad rate yatholakala ukuthi yenza i-14.69%. Kwaba khona ukuthuthuka nge-28.76% kwi-bad rate kwimodeli yokugcina uma iqhathaniswa nemodeli yamanje iCRM model ukuba isetshenziswe yibhange elithile. / Basel II Capital Accord e fetotse tekanyo ya kotsi ya mokitlane (credit risk measurement (CRM)) hoo “thepa e kotsi ya dibanka” ka moo e neng e bonwa ka teng, e seng e fuwa sebaka dikadimong. Lekala la Dikgwebo tse Nyane le tse Mahareng (SME) le bonwa ka tlwaelo jwalo ka lekala le kotsi e hodimo le senang ditswala bakeng sa ditshebetso tsa dikadimo haholo ke dibanka tsa kgwebo. Empa dipatlisiso tse thehilweng hodima se bonweng kapa se etsahetseng tsa tshebetso ya moralo wa Basel II internal-ratings-based (IRB) di supile hore kotsi ya mokitlane ya SME e kgona ho lekanngwa. Leha ho le jwalo, dibanka di ntse di thatafallwa ke ho bonelapele palo ya ditlholeho tsa ho lefa tsa diSME le ho fana ka mokitla lekaleng leo le kgotsofatsang ditlhoko tsa Basel tsa ditjhelete. Phuputso ena e ne sisinya ho etsa tekanyo ya se bonwang ho mmotlolo wa kotsi ya mokitlane (CRM) tshebetsong ya phano ya tjhelete ya diSME e etswang ke setsi sa ditjhelete (FI) ho la Zimbabwe. Mmotlolo o sebetsang hantle hape o fanang ka dipalo tse nepahetseng o dusa diFI hore di laole pepeso ya tsona ho kotsi ka phano e kgethang ya mokitlane, e thehilweng hodima manollo ya dipalopalo ya dintlha tsa histori ya bareki. Mmotlolo o sisingwang wa CRM o hlahisitswe ho tswa ho sampole e sa hlophiswang, e leng pontsho e betere ya setjhaba se ikenelang le monyako (TTD) ya batho bao e kang bakadimi ba tjhelete ho diSME, hobane e kenyelletsa bakopi ba amohetsweng le ba hannweng. Mokgwatshebetso wa bound-and-collapse (BC) reject-inference o kentswe tshebetsong ho nepahatsa tshekamelo ya kgetho e leng teng ho lekala la CRM. Diphetho tsena di bontshitse ntlafalo e kgolo ho matla a tlhophiso le palohare ya phano ya mokitlane ho diSME tsa banka eo ho ithutilweng ka yona, jwalo ka ha ho pakilwe ke ho phokotseho ya direite tse mpe ho pharalla le dimmotlolo tse hlahisitsweng. Mmotlolo wa ho qala le wa ho qetela e ile ya ralwa bakeng sa banka. Mmotlolo wa ho qetela o ile wa netefatswa ka tshebediso ya bobedi reite e mpe le mothinya wa Area under Receiver Operating Characteristic (AUROC) ho lekanya matla a kenyo mekgahlelong a mmotlolo kahare ho sampole le kantle ho yona. AUROC bakeng sa mmotlo wa ho qetela (mmotlolo o fokotseng) e fumanwe e le 0.9782, ha reite e mpe e fumanwe e le 14.69%. Ho bile le ntlafalo ya 28.76% ho reite e mpe bakeng sa mmotlolo wa ho qetela ha ho bapiswa le mmotlolo wa CRM ha o sebediswa bankeng yona eo. / Graduate School of Business Leadership / D.B.L.