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Enregistrement W7042750902

Price Volatility, Competitiveness, and Innovation in the South African Wine Industry

2023· dissertation· en· W7042750902 sur OpenAlexaboutno aff

Notice bibliographique

RevueOpen University of Cape Town (University of Cape Town) · 2023
Typedissertation
Langueen
DomaineBusiness, Management and Accounting
ThématiqueWine Industry and Tourism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWineCompetition (biology)VineyardProduct (mathematics)AgribusinessWine grapeJob creationQuarter (Canadian coin)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The South African wine industry is the eighth largest producer of wine in the world. Despite its progress since the 1990s, the industry has come under increased competition from other wine producers. The wine industry grew rapidly from 1994 until 2005 when it started to face major challenges that led to only a quarter of wine farmers producing at financially sustainable levels, reductions in the number of grape farmers and the area of vineyard under cultivation, and job cuts, low pay, and low prices. The most immediate constraints to the competitiveness of the wine industry are global competition, inadequate innovation, and volatile input costs of production. Therefore, this thesis contributes to the debate on these issues by (i) examining South Africa's wine competitiveness against other Southern Hemisphere New World (SHNW) producers; and (ii) investigating wine consumers' perceptions of and willingness to pay for product innovation by the South African wine industry; and (iii) investigating the impact of energy price volatility on South African wine prices. The main research questions/contributions are addressed in Chapters 3, 4, 5 and 6 as follows: Chapter 3 investigates the competitiveness of South African wines against rivals in the SHNW, and the determinants of their performance in the global wine market. The aim is to identify priority areas to focus on to enhance South Africa's competitiveness and sustain the industry. Global competition, especially among the SHNW producers, has been cited as one of the causes of the poor performance by the South African wine industry (Esterhuizen and Van Rooyen, 2006; Van Rooyen et al., 2011; Van Rooyen and Boonzaaier, 2018). The export market is critical for South African wines considering that half of its annual production is sold abroad (SAWIS, 2020). Also that exports attract higher earnings for the industry. Therefore, comparing the degree of South African wine competitiveness with other SHNW producers and investigating factors influencing specialisation will help identify priority areas to focus on to enhance South Africa's competitiveness and sustain the industry. The 1990 to 2018 panel data from Global Wine Markets is used for the analysis. Trade specialisation and their determinants are determined through (i) the computation of the Revealed Comparative Advantage, (ii) estimation of the Galtonian regression model and (iii) estimation of the panel regression model. The findings reveal that all countries migrated to higher price points with variable degrees of success. As a result, South African wines were relatively less competitive than their rivals. The study reveals the relative position of the South African wine industry and draws inferences on priority areas for enhancing competitiveness. Chapter 4 investigates wine consumers' perceptions of and willingness to pay for product innovation by the South African wine industry. The aim is to elicit information on whether consumers are ready to pay for wines made with natural preservatives (Rooibos & Honeybush), organic production, and a higher quality score. Determining whether consumers choose such wines is crucial as it would reveal whether wine players can exploit this source of avenue to save the struggling industry. It contributes to knowledge by being the first study to estimate the premium price for not having added SO2 in wine by substituting the conventional wine preservative with Rooibos and honeybush — a natural preservatives. The Discrete Choice Experiment (DCE) approach elicited willingness to pay for the innovative alternative. Consumers' preferences for organic wine, wine quality, and cost were also examined. Results from the mixed logit model indicate that consumers are willing to pay additionally R60.40 per bottle of wine with natural preservatives, while they are ready to pay R19.45 more for organic wine and R1.55 for each point on quality score. Chapter 5 investigates the robustness of the empirical estimates obtained in chapter 4. Identified as a potential methodological issue in the DCE literature, the chapter contributes to knowledge by examining the influence of ‘price framing' on empirical estimates in environmental valuations. The tests are performed using data from a choice experiment on preferences for natural preservatives in wine. The same respondents completed a nearly identical DCE survey, one with a real price and another with a percentage price change. 611 respondents completed the survey, and a panel mixed logit model was used for the analysis. Results demonstrate that ‘price framing' significantly influenced respondents Willingness To Pay for wine attributes. The study sheds light on the establishment of guidelines for developing valid cost attributes in DCE. Chapter 6 investigates whether energy price volatility influences South African wine prices. The aim is to establish the extent to which energy price volatility affects wine prices. The implication of the findings is that a formula of dependence and risk between energy and wine markets could be established and used to protect the wine industry from spillovers and foster wine market stability. The study contributes to knowledge by thoroughly examining the volatility behaviours of energy (fuel and electricity) and wine prices. Using a Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model, the chapter estimates own- and cross-volatility spillovers and persistence between the South African energy and wine markets using monthly Consumer Price Index data from 2002 to 2018. Results from the analysis indicate positive and significant volatility effects, with a substantial spillover from the energy market to the wine market. The results also indicate that only fuel and wine prices move together in the long run.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0040,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,224
Écart entre enseignants0,197 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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Même revueOpen University of Cape Town (University of Cape Town)Même sujetWine Industry and TourismTravaux en français237 207