Ranking journals: the Best-Worst scaling approach applied to the field of wine economics

Outreville, J. François; Agnoli, Lara; Fur, Éric Le

doi:10.1080/00036846.2021.1980493

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…We then selected the 20 most productive (number of publications) for the study (see Table 1 ). The selection was consistent with the ranking, based on academic researchers' opinions and citation-based analyses, reported by Outreville et al [ 21 ] for the field. Therefore, our bibliometric analysis takes data only from the core journals that published scientific works on wine marketing: drawing only on leading publications is a common bibliometric strategy for evaluating the intellectual structure and scientific knowledge in a research field [ 14 , 22 ].…”

Section: Methodssupporting

confidence: 77%

Three decades of research on wine marketing

Martínez-Navarro,

Sellers-Rubio

2024

Heliyon

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Section: Methodssupporting

confidence: 77%

Three decades of research on wine marketing

Martínez-Navarro,

Sellers-Rubio

2024

Heliyon

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“…For example, art, classic cars, coins, rare whiskeys, stamps and fine wines are the major components of collectible assets held by individual investors (Le Fur, 2018, 2020). Therefore, since the 1990s, scholars have taken a growing interest in these investments (Burton & Jacobsen, 1999; Le Fur, 2021a, 2021b; Outreville et al, 2021). In this context, fine wines are a popular investment asset (Fogarty, 2010; Storchmann, 2012; Bouri, 2015), a store of value (Bouri, 2014), and have little correlation with traditional investments (Kourtis et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

What can we learn from the analysis of the fine wines market efficiency?

2022

Self Cite

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This study analyzes the efficiency hypothesis for three major wine indices using different parametric and non‐parametric tests for the short and long‐term horizons. Based on monthly data over the period 2003–2019, our analysis identifies two main findings. First, in the short term, there is market inefficiency allowing for investment and diversification opportunities and a need for greater market transparency. Second, the spread between wine markets and financial markets can disappear in the long term, that is, there is a long‐run relationship that is relatively compatible with efficiency, except for the Bordeaux Legends 50 index. This implies greater efficiency and therefore better information and fewer investment and diversification opportunities.

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A machine learning and linear programming aided approach to wine ranking and selection

Tranzola Santos,

de Araújo Costa,

Moreira

et al. 2024

IJWBR

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Purpose This paper aims to mitigate the subjective nature of wine rating by introducing statistical and optimization tools for analysis, providing a unique approach not found in existing literature. Design/methodology/approach The research uses an unsupervised machine learning algorithm, k-means, to cluster wines based on their chemical characteristics, followed by the application of the PROMETHEE II multicriteria decision-making model to rank the wines based on their sensorial characteristics and selling price. Lastly, a linear programming model is used to optimize the selection of wines under different scenarios and constraints. Findings The study presents a method to rank wines based on both chemical and sensorial characteristics, providing a more comprehensive assessment than traditional subjective ratings. Clustering wines based on their characteristics and ranking them according to sensorial characteristics provides the user/consumer with meaningful information to be used in an optimization model for wine selection. Practical implications The proposed framework has practical implications for wine enthusiasts, makers, tasters and retailers, offering a systematic approach to ranking and selecting/recommending wines based on both objective and subjective criteria. This approach can influence pricing, consumption and marketing strategies within the wine industry, leading to more informed and precise decision-making. Originality/value The research introduces a novel framework that combines machine learning, decision-making models and linear programming for wine ranking and selection, addressing the limitations of subjective ratings and providing a more objective approach.

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Ranking journals: the Best-Worst scaling approach applied to the field of wine economics

Cited by 3 publications

References 54 publications

Three decades of research on wine marketing

Three decades of research on wine marketing

What can we learn from the analysis of the fine wines market efficiency?

A machine learning and linear programming aided approach to wine ranking and selection

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