Loading…

Analysis of the wine consumer’s behavior: an inferential statistics approach

PurposeThe purpose of this paper is to attempt to outline the standard profile of the typical wine consumer, by identifying some relevant features that can influence his/her purchasing choices. Therefore, the purpose of the research is to identify the pre-eminent attributes for wine consumers and th...

Full description

Saved in:
Bibliographic Details
Published in:British food journal (1966) 2020-02, Vol.122 (3), p.884-895
Main Authors: Lanfranchi, Maurizio, Alibrandi, Angela, Zirilli, Agata, Sakka, Georgia, Giannetto, Carlo
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c311t-f1cc39b4cef71bec018e9950f41b0561b40ba26c1387ef68aa8c3254f16ccc2e3
cites cdi_FETCH-LOGICAL-c311t-f1cc39b4cef71bec018e9950f41b0561b40ba26c1387ef68aa8c3254f16ccc2e3
container_end_page 895
container_issue 3
container_start_page 884
container_title British food journal (1966)
container_volume 122
creator Lanfranchi, Maurizio
Alibrandi, Angela
Zirilli, Agata
Sakka, Georgia
Giannetto, Carlo
description PurposeThe purpose of this paper is to attempt to outline the standard profile of the typical wine consumer, by identifying some relevant features that can influence his/her purchasing choices. Therefore, the purpose of the research is to identify the pre-eminent attributes for wine consumers and the different levels of importance that consumers ascribe to the attributes identified at the time of purchase.Design/methodology/approachIn order to collect the necessary data, an ad hoc questionnaire was utilized. The questionnaire, which was anonymous, was directly distributed with the face-to-face method. In total, 1,500 copies of the questionnaire had been prepared. The data collected were processed through the use of the binary logistic regression model and the ordinal logistic regression model. The first binary logistic regression model allows to evaluate the dependence of the dichotomous variable on some potential predictors. The ordinal logistic regression model, known in literature as a cumulative model of proportional quotas, is generally appropriate for situations in which the ordinal response variable has discrete categories.FindingsThe results returned by the elaboration of the binary logistic regression model refer to the influence of the variables sex, age, educational status and income on the “wine consumption” result, which is a dichotomous variable. The only variables found to be statistically significant are gender and educational status. The most significant variables that emerged from the implementation of the ordinary logistic regression model are gender, brand, choice based on price, place of production, harvest and certification. The analysis carried out has shown that with reference to wine as a product, it is essential to focus on several attributes, among which there are of course quality and brand.Research limitations/implicationsAlthough field experiments are extremely useful for testing behavioral hypotheses, they are often limited by a small sample. Future research in this area might focus on the knowledge level of sustainable wine of the consumer. In relation to the knowledge of the characteristics of the wine, it is possible to estimate the willingness to pay a surplus for a wine produced with sustainable methods by the consumer and the possible level of price premium.Originality/valueThe originality of the research lies mainly in a deeper knowledge of wine consumption trends. This information is useful to better define the wine m
doi_str_mv 10.1108/BFJ-08-2019-0581
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1108_BFJ_08_2019_0581</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2534588191</sourcerecordid><originalsourceid>FETCH-LOGICAL-c311t-f1cc39b4cef71bec018e9950f41b0561b40ba26c1387ef68aa8c3254f16ccc2e3</originalsourceid><addsrcrecordid>eNptkE9LAzEQxYMoWKt3jwHPsTP7N-utFusfRC8K3kI2TmjKdrcmW6U3v4Zfz09ilnoRPD2GeW-G92PsFOEcEeTkcn4nQIoEsBKQS9xjIyxzKbK43GcjACgFlPByyI5CWA5jUpYj9jBtdbMNLvDO8n5B_MO1xE3Xhs2K_PfnV-A1LfS76_wF1y13rSVPbe90w0Ovexd6ZwLX67XvtFkcswOrm0Anvzpmz_Orp9mNuH-8vp1N74VJEXth0Zi0qjNDtsSaDKCkqsrBZlhDXmCdQa2TwmAqS7KF1FqaNMkzi4UxJqF0zM52d-Pbtw2FXi27jY9VgkryNMulxAqjC3Yu47sQPFm19m6l_VYhqIGaitRUlIGaGqjFyGQXoVhfN6__Jf5wTn8AmI5vOg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2534588191</pqid></control><display><type>article</type><title>Analysis of the wine consumer’s behavior: an inferential statistics approach</title><source>ABI/INFORM Global (ProQuest)</source><source>Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list)</source><creator>Lanfranchi, Maurizio ; Alibrandi, Angela ; Zirilli, Agata ; Sakka, Georgia ; Giannetto, Carlo</creator><creatorcontrib>Lanfranchi, Maurizio ; Alibrandi, Angela ; Zirilli, Agata ; Sakka, Georgia ; Giannetto, Carlo</creatorcontrib><description>PurposeThe purpose of this paper is to attempt to outline the standard profile of the typical wine consumer, by identifying some relevant features that can influence his/her purchasing choices. Therefore, the purpose of the research is to identify the pre-eminent attributes for wine consumers and the different levels of importance that consumers ascribe to the attributes identified at the time of purchase.Design/methodology/approachIn order to collect the necessary data, an ad hoc questionnaire was utilized. The questionnaire, which was anonymous, was directly distributed with the face-to-face method. In total, 1,500 copies of the questionnaire had been prepared. The data collected were processed through the use of the binary logistic regression model and the ordinal logistic regression model. The first binary logistic regression model allows to evaluate the dependence of the dichotomous variable on some potential predictors. The ordinal logistic regression model, known in literature as a cumulative model of proportional quotas, is generally appropriate for situations in which the ordinal response variable has discrete categories.FindingsThe results returned by the elaboration of the binary logistic regression model refer to the influence of the variables sex, age, educational status and income on the “wine consumption” result, which is a dichotomous variable. The only variables found to be statistically significant are gender and educational status. The most significant variables that emerged from the implementation of the ordinary logistic regression model are gender, brand, choice based on price, place of production, harvest and certification. The analysis carried out has shown that with reference to wine as a product, it is essential to focus on several attributes, among which there are of course quality and brand.Research limitations/implicationsAlthough field experiments are extremely useful for testing behavioral hypotheses, they are often limited by a small sample. Future research in this area might focus on the knowledge level of sustainable wine of the consumer. In relation to the knowledge of the characteristics of the wine, it is possible to estimate the willingness to pay a surplus for a wine produced with sustainable methods by the consumer and the possible level of price premium.Originality/valueThe originality of the research lies mainly in a deeper knowledge of wine consumption trends. This information is useful to better define the wine market and to allow, especially to small businesses, to establish effective marketing strategies in relation to the real preferences of consumers and the decision-making process of choice put in place by them. In order to achieve this, the influence of all the variables on the “satisfaction of wine consumption” result was evaluated. The strength of this paper is the use of an adequate statistical approach based on the use of models, typical of inferential statistics, to reach conclusions that can be extended to the entire population of wine growers.</description><identifier>ISSN: 0007-070X</identifier><identifier>EISSN: 1758-4108</identifier><identifier>DOI: 10.1108/BFJ-08-2019-0581</identifier><language>eng</language><publisher>Bradford: Emerald Publishing Limited</publisher><subject>Consumers ; Consumption ; Decision making ; Economic indicators ; Economic statistics ; Education ; Field tests ; Gender ; Market strategy ; Purchasing ; Questionnaires ; Quotas ; Regression models ; Small business ; Social networks ; Statistical analysis ; Wine ; Wineries &amp; vineyards ; Wines</subject><ispartof>British food journal (1966), 2020-02, Vol.122 (3), p.884-895</ispartof><rights>Maurizio Lanfranchi, Angela Alibrandi, Agata Zirilli, Georgia Sakka and Carlo Giannetto</rights><rights>Maurizio Lanfranchi, Angela Alibrandi, Agata Zirilli, Georgia Sakka and Carlo Giannetto. This work is published under https://creativecommons.org/licenses/by-nc/3.0/legalcode (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c311t-f1cc39b4cef71bec018e9950f41b0561b40ba26c1387ef68aa8c3254f16ccc2e3</citedby><cites>FETCH-LOGICAL-c311t-f1cc39b4cef71bec018e9950f41b0561b40ba26c1387ef68aa8c3254f16ccc2e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2534588191?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,44363</link.rule.ids></links><search><creatorcontrib>Lanfranchi, Maurizio</creatorcontrib><creatorcontrib>Alibrandi, Angela</creatorcontrib><creatorcontrib>Zirilli, Agata</creatorcontrib><creatorcontrib>Sakka, Georgia</creatorcontrib><creatorcontrib>Giannetto, Carlo</creatorcontrib><title>Analysis of the wine consumer’s behavior: an inferential statistics approach</title><title>British food journal (1966)</title><description>PurposeThe purpose of this paper is to attempt to outline the standard profile of the typical wine consumer, by identifying some relevant features that can influence his/her purchasing choices. Therefore, the purpose of the research is to identify the pre-eminent attributes for wine consumers and the different levels of importance that consumers ascribe to the attributes identified at the time of purchase.Design/methodology/approachIn order to collect the necessary data, an ad hoc questionnaire was utilized. The questionnaire, which was anonymous, was directly distributed with the face-to-face method. In total, 1,500 copies of the questionnaire had been prepared. The data collected were processed through the use of the binary logistic regression model and the ordinal logistic regression model. The first binary logistic regression model allows to evaluate the dependence of the dichotomous variable on some potential predictors. The ordinal logistic regression model, known in literature as a cumulative model of proportional quotas, is generally appropriate for situations in which the ordinal response variable has discrete categories.FindingsThe results returned by the elaboration of the binary logistic regression model refer to the influence of the variables sex, age, educational status and income on the “wine consumption” result, which is a dichotomous variable. The only variables found to be statistically significant are gender and educational status. The most significant variables that emerged from the implementation of the ordinary logistic regression model are gender, brand, choice based on price, place of production, harvest and certification. The analysis carried out has shown that with reference to wine as a product, it is essential to focus on several attributes, among which there are of course quality and brand.Research limitations/implicationsAlthough field experiments are extremely useful for testing behavioral hypotheses, they are often limited by a small sample. Future research in this area might focus on the knowledge level of sustainable wine of the consumer. In relation to the knowledge of the characteristics of the wine, it is possible to estimate the willingness to pay a surplus for a wine produced with sustainable methods by the consumer and the possible level of price premium.Originality/valueThe originality of the research lies mainly in a deeper knowledge of wine consumption trends. This information is useful to better define the wine market and to allow, especially to small businesses, to establish effective marketing strategies in relation to the real preferences of consumers and the decision-making process of choice put in place by them. In order to achieve this, the influence of all the variables on the “satisfaction of wine consumption” result was evaluated. The strength of this paper is the use of an adequate statistical approach based on the use of models, typical of inferential statistics, to reach conclusions that can be extended to the entire population of wine growers.</description><subject>Consumers</subject><subject>Consumption</subject><subject>Decision making</subject><subject>Economic indicators</subject><subject>Economic statistics</subject><subject>Education</subject><subject>Field tests</subject><subject>Gender</subject><subject>Market strategy</subject><subject>Purchasing</subject><subject>Questionnaires</subject><subject>Quotas</subject><subject>Regression models</subject><subject>Small business</subject><subject>Social networks</subject><subject>Statistical analysis</subject><subject>Wine</subject><subject>Wineries &amp; vineyards</subject><subject>Wines</subject><issn>0007-070X</issn><issn>1758-4108</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>XDTOA</sourceid><sourceid>M0C</sourceid><recordid>eNptkE9LAzEQxYMoWKt3jwHPsTP7N-utFusfRC8K3kI2TmjKdrcmW6U3v4Zfz09ilnoRPD2GeW-G92PsFOEcEeTkcn4nQIoEsBKQS9xjIyxzKbK43GcjACgFlPByyI5CWA5jUpYj9jBtdbMNLvDO8n5B_MO1xE3Xhs2K_PfnV-A1LfS76_wF1y13rSVPbe90w0Ovexd6ZwLX67XvtFkcswOrm0Anvzpmz_Orp9mNuH-8vp1N74VJEXth0Zi0qjNDtsSaDKCkqsrBZlhDXmCdQa2TwmAqS7KF1FqaNMkzi4UxJqF0zM52d-Pbtw2FXi27jY9VgkryNMulxAqjC3Yu47sQPFm19m6l_VYhqIGaitRUlIGaGqjFyGQXoVhfN6__Jf5wTn8AmI5vOg</recordid><startdate>20200228</startdate><enddate>20200228</enddate><creator>Lanfranchi, Maurizio</creator><creator>Alibrandi, Angela</creator><creator>Zirilli, Agata</creator><creator>Sakka, Georgia</creator><creator>Giannetto, Carlo</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>XDTOA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7QF</scope><scope>7QQ</scope><scope>7QR</scope><scope>7RQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X2</scope><scope>7XB</scope><scope>8BQ</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AXJJW</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>K6~</scope><scope>KR7</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0K</scope><scope>M0Q</scope><scope>M7S</scope><scope>P64</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20200228</creationdate><title>Analysis of the wine consumer’s behavior: an inferential statistics approach</title><author>Lanfranchi, Maurizio ; Alibrandi, Angela ; Zirilli, Agata ; Sakka, Georgia ; Giannetto, Carlo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c311t-f1cc39b4cef71bec018e9950f41b0561b40ba26c1387ef68aa8c3254f16ccc2e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Consumers</topic><topic>Consumption</topic><topic>Decision making</topic><topic>Economic indicators</topic><topic>Economic statistics</topic><topic>Education</topic><topic>Field tests</topic><topic>Gender</topic><topic>Market strategy</topic><topic>Purchasing</topic><topic>Questionnaires</topic><topic>Quotas</topic><topic>Regression models</topic><topic>Small business</topic><topic>Social networks</topic><topic>Statistical analysis</topic><topic>Wine</topic><topic>Wineries &amp; vineyards</topic><topic>Wines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lanfranchi, Maurizio</creatorcontrib><creatorcontrib>Alibrandi, Angela</creatorcontrib><creatorcontrib>Zirilli, Agata</creatorcontrib><creatorcontrib>Sakka, Georgia</creatorcontrib><creatorcontrib>Giannetto, Carlo</creatorcontrib><collection>Emerald Open Access</collection><collection>CrossRef</collection><collection>Global News &amp; ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>Aluminium Industry Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Career &amp; Technical Education Database</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>METADEX</collection><collection>ProQuest Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>Asian &amp; European Business Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global (ProQuest)</collection><collection>Agriculture Science Database</collection><collection>European Business Database</collection><collection>Engineering Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>British food journal (1966)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lanfranchi, Maurizio</au><au>Alibrandi, Angela</au><au>Zirilli, Agata</au><au>Sakka, Georgia</au><au>Giannetto, Carlo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of the wine consumer’s behavior: an inferential statistics approach</atitle><jtitle>British food journal (1966)</jtitle><date>2020-02-28</date><risdate>2020</risdate><volume>122</volume><issue>3</issue><spage>884</spage><epage>895</epage><pages>884-895</pages><issn>0007-070X</issn><eissn>1758-4108</eissn><abstract>PurposeThe purpose of this paper is to attempt to outline the standard profile of the typical wine consumer, by identifying some relevant features that can influence his/her purchasing choices. Therefore, the purpose of the research is to identify the pre-eminent attributes for wine consumers and the different levels of importance that consumers ascribe to the attributes identified at the time of purchase.Design/methodology/approachIn order to collect the necessary data, an ad hoc questionnaire was utilized. The questionnaire, which was anonymous, was directly distributed with the face-to-face method. In total, 1,500 copies of the questionnaire had been prepared. The data collected were processed through the use of the binary logistic regression model and the ordinal logistic regression model. The first binary logistic regression model allows to evaluate the dependence of the dichotomous variable on some potential predictors. The ordinal logistic regression model, known in literature as a cumulative model of proportional quotas, is generally appropriate for situations in which the ordinal response variable has discrete categories.FindingsThe results returned by the elaboration of the binary logistic regression model refer to the influence of the variables sex, age, educational status and income on the “wine consumption” result, which is a dichotomous variable. The only variables found to be statistically significant are gender and educational status. The most significant variables that emerged from the implementation of the ordinary logistic regression model are gender, brand, choice based on price, place of production, harvest and certification. The analysis carried out has shown that with reference to wine as a product, it is essential to focus on several attributes, among which there are of course quality and brand.Research limitations/implicationsAlthough field experiments are extremely useful for testing behavioral hypotheses, they are often limited by a small sample. Future research in this area might focus on the knowledge level of sustainable wine of the consumer. In relation to the knowledge of the characteristics of the wine, it is possible to estimate the willingness to pay a surplus for a wine produced with sustainable methods by the consumer and the possible level of price premium.Originality/valueThe originality of the research lies mainly in a deeper knowledge of wine consumption trends. This information is useful to better define the wine market and to allow, especially to small businesses, to establish effective marketing strategies in relation to the real preferences of consumers and the decision-making process of choice put in place by them. In order to achieve this, the influence of all the variables on the “satisfaction of wine consumption” result was evaluated. The strength of this paper is the use of an adequate statistical approach based on the use of models, typical of inferential statistics, to reach conclusions that can be extended to the entire population of wine growers.</abstract><cop>Bradford</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/BFJ-08-2019-0581</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0007-070X
ispartof British food journal (1966), 2020-02, Vol.122 (3), p.884-895
issn 0007-070X
1758-4108
language eng
recordid cdi_crossref_primary_10_1108_BFJ_08_2019_0581
source ABI/INFORM Global (ProQuest); Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list)
subjects Consumers
Consumption
Decision making
Economic indicators
Economic statistics
Education
Field tests
Gender
Market strategy
Purchasing
Questionnaires
Quotas
Regression models
Small business
Social networks
Statistical analysis
Wine
Wineries & vineyards
Wines
title Analysis of the wine consumer’s behavior: an inferential statistics approach
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T13%3A54%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analysis%20of%20the%20wine%20consumer%E2%80%99s%20behavior:%20an%20inferential%20statistics%20approach&rft.jtitle=British%20food%20journal%20(1966)&rft.au=Lanfranchi,%20Maurizio&rft.date=2020-02-28&rft.volume=122&rft.issue=3&rft.spage=884&rft.epage=895&rft.pages=884-895&rft.issn=0007-070X&rft.eissn=1758-4108&rft_id=info:doi/10.1108/BFJ-08-2019-0581&rft_dat=%3Cproquest_cross%3E2534588191%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c311t-f1cc39b4cef71bec018e9950f41b0561b40ba26c1387ef68aa8c3254f16ccc2e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2534588191&rft_id=info:pmid/&rfr_iscdi=true