Past practices for determining the routes of bus rapid transit (BRT) corridors in Winnipeg, Manitoba, Canada have largely relied upon comparisons of quantitative factors. This research recommends qualitative factors to be incorporated into the process in order to present a more complete evaluation of proposed transit routes. Key Winnipeg informants were interviewed from three groups: transit officials, planners, and developers. Each group has a vested interest in the establishment of new BRT corridors and the construction of transit-oriented development (TOD) around the stations. Informants from Ottawa were interviewed to provide insights from another city having long-standing rapid transit development. The research identified eleven factors that should be taken into consideration when evaluating and selecting the routes for BRT corridors in Winnipeg. A framework of recommendations was developed, with the two foundational factors of transportation value and long-term city-building providing a basis to expand on using transit, development, and planning factors. / May 2017

Problemställning: IFRS 3 ersatte IAS 22 år 2005. Det innebär att bolag, för varje rörelseförvärv, ska beskriva faktorerna i förvärvad goodwill. Samtidigt visar studier att beskrivningar är så bristfälliga att de försvårar intressenters bedömningar av framtida värdenedgångsprövningar. Bristande beskrivningar får även följder för kapitalmarknaden. Syfte: Syftet är att undersöka hur bolag noterade på Stockholmsbörsen beskriver de faktorer som utgör förvärvad goodwill. Vidare är syftet att undersöka om det finns samband mellan beskrivningen och förvärvande bolags bransch, segmentsstorlek respektive andelen goodwill i förhållande till totala tillgångar i rörelseförvärvet. Forskningsfrågor: Finns det samband mellan beskrivningen och det förvärvande bolagets bransch respektive segmentstorlek? Finns det samband mellan beskrivningen och rörelseförvärvets goodwillkvot? Metod: Undersökningen har utgått från en kvalitativ strategi där bolagens beskrivningar av förvärvad goodwill granskats. Resultat: Av 170 rörelseförvärv med beskrivningar var andelen som innefattade lönsamhet och/eller synergier 32 %, immateriella tillgångar 5 % och lönsamhet och/eller synergier tillsammans med immateriella tillgångar 63 %. Vidare var 12 % informativa, 51 % något informativa och 37 % närmast intetsägande.Samband fanns mellan beskrivningen och hälsovårdsbolag. Samband fanns också mellan beskrivningen och Small Cap bolag. Däremot saknades samband mellan beskrivningen och goodwillkvot i rörelseförvärven. Kunskapsbidrag: Våra resultat bekräftar tidigare studier om att beskrivningar är otillräckliga som bedömningsunderlag för framtida nedgångsprövningar av goodwill. Vi har funnit samband mellan beskrivningen och hälsovårdsbolag respektive Small Cap bolag. / Presentation of the problem: In 2015, IFRS 3 replaced IAS 22. Since then, a qualitative description of the factors that make up the acquired goodwill is required for each business combination. However, studies show that the descriptions are insufficient for the stakeholders’ estimations of future impairment tests of acquired goodwill. Insufficient descriptions also have negative effects on the capital market. Purpose: The purpose is to examine how companies listed on the Stockholm Stock Exchange describe the factors that make up the acquired goodwill. Furthermore, the purpose is to examine if the description is related to the acquirer’s industry, market capitalization segment or the ratio of goodwill to assets in the business combination. Research questions: Is the description related to the acquirer's industry or market capitalization segment? Is it related to the ratio goodwill to assets in the business combination? Methodology: The study builds on a qualitative research strategy where the companies' descriptions of acquired goodwill are examined. Results: Out of 170 business combinations with a description, 32 % described future profit and/or synergies, 5 % intangible assets and 63 % future profit and/or synergies together with intangible assets. Furthermore, 12 % were informative, 51 % somewhat informative and 37% almost insignificant.Regarding relations, a connection was found between descriptions and health care companies. A connection was also found between descriptions and Small Cap companies. No connection was found between descriptions and the goodwill to assets ratio in the business combination. Contributions to knowledge: Our results confirm previous studies that descriptions are insufficient as basis for future impairments tests of recognized goodwill. We have found connections between descriptions and health care companies as well as Small Cap companies.

Esta tese apresenta uma sistemática de seleção dos especialistas mais consistentes e dos fatores de ajuste mais relevantes com vistas ao aprimoramento da acurácia da previsão de demanda gerada por métodos quantitativos. Para tanto, são testados sete modelos quantitativos: Médias Móveis (MM-3, MM-6 e MM-9), Suavização Exponencial Simples e Dupla e o modelo de Holt-Winters multiplicativo e aditivo. O modelo utilizado na previsão quantitativa foi aquele que gerou a melhor aderência aos dados e acurácia preditiva com base nos indicadores R2 e Erro Percentual Médio Absoluto (MAPE), respectivamente, extraídos mediante a quebra da série histórica na proporção 80% (banco de treino) e 20% (banco de teste) para cada produto. Com base nesse critério, tanto o leite UHT quanto o queijo mussarela foram modelados através da Suavização Exponencial Dupla (SED). Na sequência, especialistas e fatores utilizados para ajuste qualitativo da demanda foram selecionados de forma a reter somente os especialistas mais consistentes e os fatores mais influentes para tal fim. O método reteve os 5 especialistas mais consistentes dos 15 inicialmente entrevistados. Dos 23 fatores iniciais, apenas os 13 mais representativos foram retidos. Através da previsão corrigida para o leite UHT, o MAPE foi reduzido de 14,29% para 6,44%. Já previsão ajustada do queijo mussarela possibilitou reduzir o MAPE de 15,25% para 8,72%. / This thesis presents a systematic selection of the most consistent experts and most relevant adjustment factors aimed at improving the accuracy of forecasting demand generated by quantitative methods. For this, seven quantitative models are tested: Moving Averages (MM-3, MM-6 and MM-9), Single and Double Exponential Smoothing and Holt-Winters multiplicative and additive model. The model used in quantitative forecasting was one that generated the best adherence to data and predictive accuracy based on the indicators R2 and Mean Absolute Percentage Error (MAPE), respectively, extracted by breaking the time series in the ratio 80 % (workout bench) and 20% (test bank) for each product . Based on this criterion , both UHT milk and mozzarella cheese were modeled by Double Exponential Smoothing (SED). Further, experts and qualitative factors used to adjust demand were selected so to retain only the most consistent experts and the most influential factors for this purpose. The method retained the 5 most consistent experts of the 15 interviewed initially. Of the 23 initial factors, only the 13 most significant were retained. Through prediction corrected for UHT milk the MAPE was reduced from 14.29 % to 6.44 %. It had forecast adjusted mozzarella cheese possible to reduce the MAPE of 15.25% to 8,72.

Esta tese apresenta uma sistemática de seleção dos especialistas mais consistentes e dos fatores de ajuste mais relevantes com vistas ao aprimoramento da acurácia da previsão de demanda gerada por métodos quantitativos. Para tanto, são testados sete modelos quantitativos: Médias Móveis (MM-3, MM-6 e MM-9), Suavização Exponencial Simples e Dupla e o modelo de Holt-Winters multiplicativo e aditivo. O modelo utilizado na previsão quantitativa foi aquele que gerou a melhor aderência aos dados e acurácia preditiva com base nos indicadores R2 e Erro Percentual Médio Absoluto (MAPE), respectivamente, extraídos mediante a quebra da série histórica na proporção 80% (banco de treino) e 20% (banco de teste) para cada produto. Com base nesse critério, tanto o leite UHT quanto o queijo mussarela foram modelados através da Suavização Exponencial Dupla (SED). Na sequência, especialistas e fatores utilizados para ajuste qualitativo da demanda foram selecionados de forma a reter somente os especialistas mais consistentes e os fatores mais influentes para tal fim. O método reteve os 5 especialistas mais consistentes dos 15 inicialmente entrevistados. Dos 23 fatores iniciais, apenas os 13 mais representativos foram retidos. Através da previsão corrigida para o leite UHT, o MAPE foi reduzido de 14,29% para 6,44%. Já previsão ajustada do queijo mussarela possibilitou reduzir o MAPE de 15,25% para 8,72%. / This thesis presents a systematic selection of the most consistent experts and most relevant adjustment factors aimed at improving the accuracy of forecasting demand generated by quantitative methods. For this, seven quantitative models are tested: Moving Averages (MM-3, MM-6 and MM-9), Single and Double Exponential Smoothing and Holt-Winters multiplicative and additive model. The model used in quantitative forecasting was one that generated the best adherence to data and predictive accuracy based on the indicators R2 and Mean Absolute Percentage Error (MAPE), respectively, extracted by breaking the time series in the ratio 80 % (workout bench) and 20% (test bank) for each product . Based on this criterion , both UHT milk and mozzarella cheese were modeled by Double Exponential Smoothing (SED). Further, experts and qualitative factors used to adjust demand were selected so to retain only the most consistent experts and the most influential factors for this purpose. The method retained the 5 most consistent experts of the 15 interviewed initially. Of the 23 initial factors, only the 13 most significant were retained. Through prediction corrected for UHT milk the MAPE was reduced from 14.29 % to 6.44 %. It had forecast adjusted mozzarella cheese possible to reduce the MAPE of 15.25% to 8,72.

Esta tese apresenta uma sistemática de seleção dos especialistas mais consistentes e dos fatores de ajuste mais relevantes com vistas ao aprimoramento da acurácia da previsão de demanda gerada por métodos quantitativos. Para tanto, são testados sete modelos quantitativos: Médias Móveis (MM-3, MM-6 e MM-9), Suavização Exponencial Simples e Dupla e o modelo de Holt-Winters multiplicativo e aditivo. O modelo utilizado na previsão quantitativa foi aquele que gerou a melhor aderência aos dados e acurácia preditiva com base nos indicadores R2 e Erro Percentual Médio Absoluto (MAPE), respectivamente, extraídos mediante a quebra da série histórica na proporção 80% (banco de treino) e 20% (banco de teste) para cada produto. Com base nesse critério, tanto o leite UHT quanto o queijo mussarela foram modelados através da Suavização Exponencial Dupla (SED). Na sequência, especialistas e fatores utilizados para ajuste qualitativo da demanda foram selecionados de forma a reter somente os especialistas mais consistentes e os fatores mais influentes para tal fim. O método reteve os 5 especialistas mais consistentes dos 15 inicialmente entrevistados. Dos 23 fatores iniciais, apenas os 13 mais representativos foram retidos. Através da previsão corrigida para o leite UHT, o MAPE foi reduzido de 14,29% para 6,44%. Já previsão ajustada do queijo mussarela possibilitou reduzir o MAPE de 15,25% para 8,72%. / This thesis presents a systematic selection of the most consistent experts and most relevant adjustment factors aimed at improving the accuracy of forecasting demand generated by quantitative methods. For this, seven quantitative models are tested: Moving Averages (MM-3, MM-6 and MM-9), Single and Double Exponential Smoothing and Holt-Winters multiplicative and additive model. The model used in quantitative forecasting was one that generated the best adherence to data and predictive accuracy based on the indicators R2 and Mean Absolute Percentage Error (MAPE), respectively, extracted by breaking the time series in the ratio 80 % (workout bench) and 20% (test bank) for each product . Based on this criterion , both UHT milk and mozzarella cheese were modeled by Double Exponential Smoothing (SED). Further, experts and qualitative factors used to adjust demand were selected so to retain only the most consistent experts and the most influential factors for this purpose. The method retained the 5 most consistent experts of the 15 interviewed initially. Of the 23 initial factors, only the 13 most significant were retained. Through prediction corrected for UHT milk the MAPE was reduced from 14.29 % to 6.44 %. It had forecast adjusted mozzarella cheese possible to reduce the MAPE of 15.25% to 8,72.

ABSTRACT PARAMETER OPTIMIZATION OF CHEMICALLY ACTIVATED MORTARS CONTAINING HIGH VOLUMES OF POZZOLAN BY STATISTICAL DESIGN AND ANALYSIS OF EXPERIMENTS Aldemir, BaSak M.S., Department of Industrial Engineering Supervisor: Prof. Dr. &Ouml / mer Saat&ccedil / ioglu Co-Supervisor: Assoc. Prof. Dr. Lutfullah Turanli January 2006, 167 pages This thesis illustrates parameter optimization of early and late compressive strengths of chemically activated mortars containing high volumes of pozzolan by statistical design and analysis of experiments. Four dominant parameters in chemical activation of natural pozzolans are chosen for the research, which are natural pozzolan replacement, amount of pozzolan passing 45 &amp / #956 / m sieve, activator dosage and activator type. Response surface methodology has been employed in statistical design and analysis of experiments. Based on various second-order response surface designs / experimental data has been collected, best regression models have been chosen and optimized. In addition to the optimization of early and late strength responses separately, simultaneous optimization of compressive strength with several other responses such as cost, and standard deviation estimate has also been performed. Research highlight is the uniqueness of the statistical optimization approach to chemical activation of natural pozzolans.

Väsentlighet utgör en viktig hörnsten i revisorns bedömning och styr hur omfattande en granskning ska vara och i vilken riktning granskningen ska gå. Syftet med uppsatsen är att undersöka vilka kvalitativa faktorer anses ha en påverkan på revisorns väsentlighetsbedömning. Kvalitativa faktorer som valts att fokusera på är revisorns erfarenhet, byråtillhörighet, revisionsklientens företagsspecifika karaktär samt klientförhållandet. En kvantitativ metod med en deduktiv ansats har använts för att undersöka studiens syfte. En modell konstruerats för att illustrera hur väsentlighetsbedömningen påverkas av de kvalitativa faktorerna. Modellen har utformats utifrån tidigare vetenskapliga artiklar som behandlar ämnet väsentlighet. Vidare har empiri insamlats genom en enkätundersökning som skickades ut till samtliga godkända och auktoriserade revisorer i Sverige varav 154 respondenter gav fullständiga svar. Resultatet visar att signifikanta skillnader i hur väsentlighetsnivån fastställs beroende på hur länge revisorn hade arbetat och även på om revisorn arbetade på en av de större byråerna. Det framkom även att skillnader fanns när hänsyn togs till positiva och negativa faktorer hänförliga till om klienten var börsnoterat eller inte, verksam i en riskfyllt eller stabil bransch samt hur så tog revisorerna även hänsyn till huruvida de arbetade med en ny klient eller en klient de haft erfarenhet av. Vår undersökning indikerar även att revisorer tenderat att reagera starkare på negativa faktorer. Med en godare förståelse för vilka faktorer som påverkar revisorns bedömning föreslår vi framtida studier som undersöker klientförhållandet och dess inverkan på väsentlighetsbedömningen då detta området gav en signifikant skillnad men är i tidigare forskning outforskat. / Materiality is an important cornerstone of the auditor's assessment that governs how extensive an audit should be and which direction the audit should go. The purpose of this paper is to investigate which qualitative factors are considered to have an impact on the auditor's materiality assessment. Qualitative factors such as focus the auditor's experience, audit firm, the audit client's company-specific character and the auditor-client relationship. A quantitative method with a deductive approach has been used where a model was designed to illustrate how materiality assessment is influenced by the qualitative factors. The model has been designed based on previous scientific articles dealing with materiality. In addition, empirical evidence has been collected through a survey sent to all authorized auditors in Sweden, of which 154 respondents gave complete answers. The results shows differences in the level of materiality are determined depending on the auditor’s experience and whether the auditor worked at one of the larger agencies. There were also differences when considering positive and negative factors related to whether the client was listed or not, active in a risky or stable industry, and the auditor’s consideration to  whether they were working with a new client. Auditors tend to react more strongly to negative factors. With a better understanding of which factors affect the auditor's assessment, we propose future studies on the client relationship and its impact on materiality judgement, as this area gave a significant difference, but has been not explored that much in earlier research.

El objetivo de la tesis es profundizar en el estudio de la materialidad en auditoría, y, más concretamente, de los factores cualitativos asociados al concepto. En el primer capítulo, se analizan los fundamentos teóricos y normativos. En el segundo capítulo, se revisa la literatura previa. En el tercer capítulo, se desarrolla un estudio empírico dirigido auditores de cuentas y directores financieros de empresas españolas. Como conclusión principal se establece que la utilización efectiva de los factores cualitativos de la materialidad en auditoría constituye un instrumento válido que puede contribuir al esfuerzo de los auditores para mejorar la calidad de sus trabajos y ofrecer un mejor servicio a los usuarios, que redunde en una información financiera más fiable y transparente, para dar respuesta así a las críticas recibidas, recuperar la credibilidad de sus actuaciones y superar la actual situación de crisis que atraviesa la función. / The aim of the thesis is to study in depth the materiality in auditing, in general, and the qualitative factors associated to the concept, more specifically. In Chapter I we analyse the theoretical and normative foundations. In Chapter II we review the previous literature. In Chapter III we develop an empirical research over financial auditors and directors in Spanish companies. The main conclusion is that the effective use of qualitative materiality factors appears to be a useful tool in improving the quality of audits and in service provided to the financial statements' users. Through this improvement, the reliability and transparency of financial information could be increased, the numerous critiques received could be replied, the confidence in audit practice could be restored, and the current crisis of audit function could be overcame.

Abstracts in English, Southern Sotho and Swahili / The broad aim of this explanatory sequential mixed-methods study was to extend the extant literature by developing a weighted polychotomous accountability index (PAI) that, in turn, was used to measure and evaluate the extent and quality of integrated annual reports (IARs) prepared by the Johannesburg Stock Exchange (JSE) listed companies for the period 2013 to 2016. The study was motivated by a paucity of research on whether corporate accountability, through corporate reporting, has improved (extent and quality) under integrated reporting (<IR>) through improved integrated reporting quality (IRQ) scores. The study was conducted in two phases. The first phase was for developing the PAI through the Delphi Inquiry method. In the same phase, through qualitative and quantitative content analysis, the PAI was used to measure and evaluate the extent and quality of IARs for the JSE Top 100 companies over the four-year period (2013–2016). The second phase, in the form of semi-structured interviews, aimed at investigating the factors that contributed to the change in IRQ scores over that period. Eight respondents (preparers of IARs), representing five companies, were interviewed. Through the Delphi Inquiry method, the PAI was developed (major contribution of the study), which has eight categories, 44 constructs, a total possible score of 152 and a total weight of 100%. Furthermore, the PAI has a six-point ordinal scoring system from 0 to 5. For the IRQ scores, mean annual IRQ scores were computed as 52.45% for 2013, 58.48% for 2014, 64.72% for 2015 and 68.29% for 2016. As for the JSE sectors, the highest IRQ scores were 66.45%, 71.05%, 75% and 81.25% for 2013, 2014, 2015 and 2016 respectively. From an industry perspective, the results showed highest IRQ scores of 66.45%, 72.37%, 70.72% and 62.42% for 2013, 2014, 2015 and 2016 respectively. The steady increase in the mean IRQ scores for 2013, 2014, 2015 and 2016 shows that there is significant improvement in the extent and quality of IARs produced by the JSE listed companies. This improvement in the IRQs is due to different reasons, which include: preparers taking <IR> seriously, teamwork, benchmarking, training, experience, addressing stakeholder needs and understanding the principles before implementing <IR>. Moreover, some companies fail to produce quality IARs due to a number of factors that include: an inadequate understanding of <IR> by some preparers of IARs; some entities not seeing value in preparing quality IARs hence they present poor quality IARs; partial buy-in, especially by the executive management; a paucity of skills and resources; outsourcing that was identified as bringing with it poor quality work and some entities preferring to chase prestigious awards at the expense of the company’s actual <IR> philosophy, hampering the quality of IARs in the process. Different conclusions were reached. It was noted that some <IR> concepts and principles should be more synchronised so that they are not in conflict with each other. Rules should be introduced so that <IR> may be a blend of principles and rules as this could minimise preparer judgement. The International Integrated Reporting Council (IIRC) must align its terminology with that of other guideline bodies, such as rating agencies, to give more meaning to <IR>. The IIRC needs to improve <IR> in order to suit companies in the service industry. Integrated reporting has to be more compatible with the digital world and not necessarily paper based. More research must be done about what users need to see in IARs to enhance the relevance of the IAR to different stakeholders. Furthermore, the IIRC must proactively educate decision-makers for an improved buy-in of <IR>. Pertaining to transformation, de facto and de jure transformation remain merely theoretical without substantial changes on the ground. Government and the JSE should consider the nature of current disincentives since these seem not to sufficiently challenge the current status quo. Finally, more training on capitals and business models should be conducted in order to improve the quality of reporting since these two constructs are perceived to be complex and hence difficult to implement, especially through quantification. / Maikaelelo a a anameng a thutopatlisiso eno e e tlhalosang ya mekgwa e e tswakantsweng ya tatelano e ne e le go atolosa dikwalo tse di gona ka go dira tshupane ya maikarabelo ya polychotomous (PAI) e morago e neng ya dirisediwa go lekanyetsa le go sekaseka bogolo le boleng jwa dipegelo tsa ngwaga le ngwaga tse di golaganeng (diIAR) tse di rulaganngwang ke ditlamo tse di kwadisitsweng kwa Johannesburg Stock Exchange (JSE) mo pakeng ya 2013 go fitlha 2016. Thutopatlisiso e rotloeditswe ke tlhaelo ya dipatlisiso tse di malebana le gore a maikarabelo a ditlamo, ka dipegelo tsa ditlamo, a tokafetse (bogolo le boleng) ka fa tlase ga dipegelo tse di golaganeng (<IR>) ka maduo a a tokafatseng a boleng jwa dipegelo tse di golaganeng (IRQ). Thutopatlisiso e dirilwe ka magato a le mabedi. Legato la ntlha e ne e le la go dira PAI ka mokgwa wa Delphi Inquiry. Mo legatong leo, ka tshekatsheko ya diteng go dirisiwa mokgwa o o lebelelang dipalopalo le o o lebelelang mabaka, go dirisitswe PAI go lekanyetsa le go sekaseka bogolo le boleng wa diIAR tsa ditlamo tse di kwa Godimo tse 100 tsa JSE mo pakeng ya dingwaga tse nne (2013–2016). Legato la bobedi, le le neng le le mo sebopegong sa dipotsolotso tse di batlileng di rulagana, le ne le ikaeletse go batlisisa dintlha tse di tshwaetseng mo diphetogong tsa maduo a IRQ mo pakeng eo. Go botsoloditswe batsibogi ba le robedi (barulaganyi ba diIAR), ba ba emetseng ditlamo di le tlhano. Ka mokgwa wa Delphi Inquiry, go tlhamilwe PAI (tshwaelo e kgolo ya thutopatlisiso), e e nang le dikarolo tse robedi, ka megopolo e le 44, maduo otlhe a a kgonagalang a 152 le boima jotlhe jwa 100%. Mo godimo ga moo, PAI e na le thulaganyo ya maduo ya dintlha tse thataro go tswa go 0 go ya go 5. Malebana le maduo a IRQ, palogare ya maduo a ngwaga le ngwaga a IRQ, e tlhakanyeditswe go nna 52.45% ka 2013, 58.48% ka 2014, 64.72% ka 2015 le 68.29% ka 2016. Malebana le maphata a JSE gona, maduo a a kwa godimodimo a IRQ e ne e le 66.45%, 71.05%, 75% le 81.25% ka 2013, 2014, 2015 le 2016 ka tatelano eo. Go ya ka indaseteri, dipoelo di bontshitse maduo a a kwa godimodimo a IRQ a 66.45%, 72.37%, 70.72% le 62.42% ka 2013, 2014, 2015 le 2016 ka tatelano eo. Koketsego ka iketlo ya palogare ya maduo a IRQ a 2013, 2014, 2015 le 2016 e bontsha gore go na le tokafalo e e bonalang mo bogolong le boleng jwa diIAR tse di tlhagisiwang ke ditlamo tse di kwadisitsweng mo JSE. Tokafalo eno ya diIRQ ke ka ntlha ya mabaka a a farologaneng, a a akaretsang: barulaganyi ba tsotelela <IR> thata, tirisanommogo ya setlhopha, go itshwantsha le ba bangwe, katiso, maitemogelo, go samagana le ditlhokego tsa baamegi le go tlhaloganya dintlhatheo pele ga go diragatsa <IR>. Mo godimo ga moo, ditlamo dingwe di palelwa ke go tlhagisa diIAR tsa boleng ka ntlha ya dintlha di le mmalwa tse di akaretsang: go tlhaloganya go go sa lekanang ga <IR> ke barulaganyi bangwe ba diIAR; ditheo dingwe di sa bone boleng jwa go baakanya diIAR tsa boleng mme seo se dira gore di tlhagise diIAR tsa boleng jo bo kwa tlase; tshegetso e e sa lekanang, bogolo segolo ya botsamaisikhuduthamaga; tlhaelo ya bokgoni le ditlamelo; theko ya ditirelo kwa ntle, e leng se se supilweng se tla ka boleng jo bo kwa tlase jwa tiro le ditheo dingwe di tlhopha go lelekisa dikgele tsa mabono mme di ikgatholosa filosofi ya nnete ya <IR> ya setlamo, mme ka go rialo di ama boleng jwa diIAR. Go fitlheletswe diphitlhelelo tse di farologaneng. Go lemogilwe gore megopolo mengwe le dintlhatheo tsa <IR> di tshwanetse go rulaganngwa ka tsamaisano gore di se ke tsa ganetsana. Go tshwanetse ga itsisewe melanwana gore <IR> e nne motswako wa dintlhatheo le melawana gonne seno se ka fokotsa go atlhola ga barulaganyi. Lekgotla la Boditšhabatšha la Dipegelo tse di Golaganeng (IIRC) le tshwanetse go lepalepanya mareo a lona le a ditheo tse dingwe tse di kaelang, go tshwana le ditheo tse di lekanyetsang, gore <IR> e nne le bokao jo bo oketsegileng. Lekgotla la IIRC le tshwanetse go tokafatsa <IR> gore e siamele ditlamo tse di mo indasetering ya ditirelo. Dipegelo tse di golaganeng di tshwanetse go tsamaelana le lefatshe la dijitale mme e seng fela gore e nne tse di mo dipampiring. Go tshwanetse ga dirwa dipatlisiso tse dingwe malebana le gore badirisi ba tlhoka go bona eng mo diIAR go tokafatsa bomaleba jwa IAR mo baameging ba ba farologaneng. Go feta foo, lekgotla la IIRC le tshwanetse go ruta batsayaditshwetso gore go nne le tshegetso e e tokafetseng ya <IR>. Malebana le diphetogo, diphetogo tse di gona le tsa tshwanelo e sala go nna tiori fela mme go se na diphetogo tse di bonalang. Puso le JSE ba tshwanetse go lebelela dintlha tsa ga jaana tse di kgobang marapo ka ntlha ya fa go sa bonale fa di gwetlha seemo sa ga jaana mo go lekaneng. Kwa bokhutlong, go tshwanetse ga dirwa katiso e nngwe ya letlotlo le dikao tsa kgwebo go tokafatsa boleng jwa go dira dipegelo ka ntlha ya fa megopolo eno e mebedi e lebega e le marara mme ka jalo go se bonolo go e diragatsa, bogolo segolo ka dipalo. / Ndivho khulwane ya ṱhalutshedzo iyi ya ngona yo ṱanganelanaho ya thevhekano ho vha u engedza maṅwalwa a zwino nga u bveledza indekisi ya vhuḓifhinduleli yo khethekanywaho (PAI) ine ya dovha ya, shumiswa u kala na u ela vhuphara na ndeme ya mivhigo ya ṅwaha nga ṅwaha yo ṱanganelanaho (dzi IAR) yo lugiswaho nga vha khamphani dzo ṅwaliswaho kha Johannesburg Stock Exchange (JSE) lwa tshifhinga tsha vhukati ha 2013 u swika 2016. Ngudo dzo ṱuṱuwedzwa nga u shaea ha ṱhoḓisiso dza nga ha uri vhuḓifhinduleli, u mona na u vhiga ha tshiofisi ho no khwiṋisea na (vhuphara na ndeme) nga fhasi ha u vhiga ho ṱanganelanaho (<IR>) nga kha zwikoro zwa ndeme ya u vhiga ho ṱanganelanaho (IRQ). Ngudo dzo itwa fhethu huvhili nga maga mavhili. Ḽiga ḽa u thoma ḽo vha ḽi ḽa u bveledza PAI nga kha ngona dza Ṱhoḓisiso dza Delphi. Kha ḽiga ḽeneḽo, nga kha musaukanyo wa vhungomu wo sedzaho ndeme na tshivhalo, PAI yo shumiswa u kala na u ela vhuphara na ndeme ya dzi IAR kha khamphani dza 100 dza nṱha dza JSE kha tshifhinga tsha miṅwaha miṋa (2013–2016). Ḽiga ḽa vhuvhili nga tshivhumbeo tsha inthaviwu dzo dzudzanywaho zwiṱuku dzi sengulusaho zwivhumbi zwi dzhenelelaho kha tshanduko ya zwikoro zwa IRQ lwa tshifhinga. Vhafhinduli vha malo (vhadzudzanyi vha dzi IAR), vho imelaho khamphani ṱhanu vho vhudziswa. Nga kha Ngona ya Ṱhoḓisiso dza Delphi, ho bveledzwa PAI (zwidzheneleli zwihulwane kha ngudo), dzi re na khethekanyo dza malo, miṱalukanyo ya 44, ṱhanganyelo dza zwikoro zwine zwa nga vha hone zwa 152 na ṱhanganyelo ya tshileme ya 100%. Zwiṅwe hafhu, PAI dzi na sisiṱeme ya zwikoro ya odinaḽa zwa phoindi dza rathi u bva kha 0 u swika kha 5. U itela zwikoro zwa IRO, zwikoro zwa vhukati zwa ṅwaha nga ṅwaha zwo rekanywa zwa vha 52.45% nga 2013, 58.48% nga 2014, 64.72% nga 2015 na 68.29% for 2016. Kha sekithara dza JSE, zwikoro zwa nṱhesa zwa IRQ zwo vha zwi 66.45%, 71.05%, 75% na 81.25% nga 2013, 2014, 2015 na 2016 nga u tevhekana. U ya nga kuvhonele kwa nḓowetshumo, mvelelo dzo sumbedza zwikoro zwa nṱhesa zwa IRQ zwa 66.45%, 72.37%, 70.72% na 62.42% nga 2013, 2014, 2015 na 2016 nga u tevhekana. U gonya zwiṱuku kha zwikoro zwa vhukati zwa IRQ zwa 2013, 2014, 2015 na 2016 zwi sumbedza uri hu na u khwiṋisea hu hulwane kha vhuphara na ndeme ya dzi IAR dzo bveledzwaho vha khamphani dzi re kha JSE. U khwiṋisea uhu ha dzi IRQ ndi nga ṅwambo wa zwiitisi, zwine zwa katela vhadzudzanyi vha dzhielaho <IR> nṱha, u shuma sa thimu, u vhambedza, vhugudisi, tshenzhelo, u livhana na ṱhoḓea dza vhadzheneleli na u pfesesa milayo phanḓa ha musi i tshi shumiswa <IR>. Nṱhani ha izwo, dziṅwe khamphani dzi a kundelwa u bveledzwa dzi IAR nga ṅwambo zwa zwiitisi zwo vhalaho , zwi katelaho u sa pfesea lwo lingaho ha <IR> nga vhaṅwe vhadzudzanyi vha dzi IAR, zwiṅwe zwiimiswa zwi sa vhoni ndeme ya u ita dzi IAR dza ndeme zwa sia vha tshi bvledza dzi IAR dza ndeme i sa takadzi, u zwi ṱanganedza hu si nga mbilu dzoṱhe nga maanḓa vha vhalanguli vhahulwane; u shaea ha zwikili na zwiko; u ṱunḓa tshumelo nnḓa zwine zwo topolwa sa zwi ḓisaho mushumo wa ndeme i sa takadzi na zwiṅwe zwiimiswa zwi tshi funa u gidimisana na pfufho dza maimo hu sa dzhielwi nṱha fiḽosofi ya vhukuma ya <IR> dza khamphani, zwine zwa thivhela ndeme ya dzi IAR kha kuitele kwa zwithu. Ho swikelelwa khunyeledzo dzo fhambanaho. Ho vhonala uri miṅwe miṱalukanyo ya <IR> na milayo i tea u dzudzanywa u itela uri i sa vhe na khuḓano. Milayo i tea u ḓivhadzwa u itela uri <IR> dzi vha ṱhanganyelo ya milayo na maitele saizwi zwi tshi nga fhungudza khaṱhulo dza vhadzudzanyi. Khoro ya Dzitshakatshaka yo Ṱanganelanaho ya u Vhiga (IIRC) i tea u dzudzanya mathemo ayo na ayo a zwiimiswa nyendedzi, zwi nga ho sa mazhendedzi a u fhima, u ṋea ṱhalutshedzo ya khwiṋe kha <IR>. Vha IIRC vha tea u khwiṋisa <IR> u itela uri dzi elane na nḓowetshumo dza tshumelo. U vhiga ho ṱanganelanaho hu tea u elana vhukuma na ḽifhasi ḽa didzhithala nahone hu sa ḓisendeke nga bammbiri. Hu tea u itwa ṱhoḓisiso nga ha zwine vhashumisi vha vhona kha dzi IAR u khwaṱhisedza u tea ha IAR dza vhashumisani vho fhambanaho. Dziṅwe hafhu, IIRC i tea u funza vhadzhii vha tsheo lwo khwaṱhaho u itela u khwiṋisa u ḓidzhenisa kha <IR>. Zwi tshi elana na tshanduko, tshanduko ya de facto na ya de jure i sokou dzula i ya thyori hu si na tshanduko dzi vhonalaho ngeno fhasi. Muvhuso na JSE vha tea dzhiela nṱha lushaka lwa sa vha hone ha zwiṱuṱuwedzi saizwi izwi zwi tshi tou nga zwi ṋekedza khaedu lwo linganaho tshiimo tsha zwithu tsha zwino. Tsha u fhedzisela, vhugudisi kha zwiedza zwa pfuma na bindu vhu tea u itwa u itela u khwiṋisa ndeme ya u vhiga saizwi izwo zwifhaṱo zwivhili zwi tshi vhonala sa zwi konḓaho nahone zwi konḓaho u shumisa, nga maanḓa nga kha u vhekanya ndeme / Financial Accounting / D. Phil. (Accounting Sciences)