Modelling consumer preferences heterogeneity in emerging wine markets: a latent class analysis

ABSTRACT

The purpose of this study is to explore the Russian and Chinese emerging markets for imported wines, by assessing Beijing and Moscow consumer demand for different attributes. This study employs the Discrete Choice Experiments technique to evaluate consumer preferences and willingness-to-pay for the selected wine attributes (medals, alcohol level, landscape certification, country of origin, grape variety and price). Results from Latent Class models provide evidence of preference heterogeneity and suggest the existence of two distinct consumer segments in China, and three segments in Russia. In both samples, the wine medals and country of origin are the top selected characteristics. Conspicuous consumption tendencies were found in some segments, with price having a positive impact on utility, probably signalling quality.

KEYWORDS:

• Latent class models
• discrete choice
• consumer preferences
• emerging markets

JEL CLASSIFICATION:

• C25
• D12

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ To facilitate the reading, we added a reference category expressed in euros, to income and price variables. Exchange rates: 1 RUB = 0.012 EUR; 1 Yuan = 0.13 EUR.

² Included for the none option and it represents the respondent n’s preference towards the opt-out choice compared to the three alternatives included in our experiment.

³ WTP of an attribute k is given by the ratio of the attribute coefficient, ${\beta }_k$, and the price coefficient, ${\beta }_{price}$, for each class.

Additional information

Funding

This work is financed by the project NORTE-01-0145-FEDER-000038 (INNOVINE & WINE – Innovation Platform of Vine & Wine, Portugal) and by European and Structural and Investment Funds in the FEDER, and supported through the Operational Competitiveness and Internationalization Program, Portugal (COMPETE 2020) [Project No. 006971 UIC/SOC/04011)]; and by national funds, through the FCT – Portuguese Foundation for Science and Technology under the project UIDB/04011/2020 and UID/ECO/03182/2019.