Putting an artificial intelligence-generated label on it comes naturally

被引:0
|
作者
Sigurdsson, Valdimar [1 ]
Larsen, Nils Magne [2 ]
Folwarczny, Michal [3 ]
Dubois, Magalie [4 ]
Fagerstrom, Asle [5 ]
机构
[1] Reykjavik Univ, Dept Business & Econ, Menntavegur 1, IS-102 Reykjavik, Iceland
[2] UiT Arctic Univ Norway, Harstad, Norway
[3] Univ Galway, JE Cairnes Sch Business & Econ, Galway, Ireland
[4] Univ Bourgogne Franche Comte, Burgundy Sch Business, CEREN EA 7477, Dijon, France
[5] BI Norwegian Business Sch, Dept Mkt, Oslo, Norway
关键词
artificial intelligence; backgrounds; certifications; naturalness; signaling theory; sustainability labeling; sustainable wine; willingness to buy; willingness to pay; SIGNALING THEORY; CONSUMERS; FOOD; SUSTAINABILITY; BEHAVIOR; INFORMATION; CONSUMPTION; PERCEPTION; PSYTOOLKIT; KNOWLEDGE;
D O I
10.1002/mar.22137
中图分类号
F [经济];
学科分类号
02 ;
摘要
Climate change and the advent of artificial intelligence-generated content are reshaping wine marketing. The interplay between consumer focus on naturalness and sustainable farming practices and the proliferation of artificial intelligence-generated content represents a particularly salient area of research. However, the extent to which the presence of fictitious artificial intelligence-generated labels and backgrounds impacts consumers' willingness to buy and pay for wine has yet to be addressed. This research contributes to the growing body of literature on consumer susceptibility to sustainability signaling and artificial intelligence greenwashing, focusing on the impact of backgrounds and labels with different degrees of perceived naturalness. Three experiments demonstrate that wines bearing artificial intelligence-generated sustainability labels and third-party accredited sustainability labels reliably exhibit an increased willingness to buy and pay compared to those without sustainability labels. These findings indicate that fictitious, artificial intelligence-generated, and accredited labels are equally effective in influencing consumer wine choices. Customer susceptibility to food labels and wine knowledge and involvement also significantly predict willingness to buy across studies, validating the Customer Susceptibility to Front-of-Package Food Labeling scale. These findings highlight the necessity for future studies to investigate the role of responsible labeling, the susceptibility of customers to such labels, and the potential hazards associated with greenwashing practices involving artificial intelligence-generated labels.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Synthetic Realities and Artificial Intelligence-Generated Contents
    Moreira, Daniel
    Marcel, Sebastien
    Rocha, Anderson
    [J]. IEEE SECURITY & PRIVACY, 2024, 22 (03) : 7 - 10
  • [2] A Case Study in Artificial Intelligence-Generated Manuscripts
    Kammer, Michael N.
    [J]. CHEST, 2023, 164 (02) : 478 - 480
  • [3] Analyzing and Utilizing Artificial Intelligence-Generated Contents
    Mondal, Himel
    Mondal, Shaikat
    Podder, Indrasish
    [J]. INDIAN DERMATOLOGY ONLINE JOURNAL, 2024, 15 (01) : 164 - 165
  • [4] Synthetic Realities and Artificial Intelligence-Generated Contents
    Moreira, Daniel
    Marcel, Sebastien
    Rocha, Anderson
    [J]. IEEE SECURITY & PRIVACY, 2024, 22 (04) : 101 - 102
  • [5] Artificial Intelligence-Generated Research in the Literature: Is It Real or Is It Fraud?
    Stone, Jennifer A. M.
    [J]. MEDICAL ACUPUNCTURE, 2023, 35 (03) : 103 - 104
  • [6] Artificial Intelligence-Generated Facial Images for Medical Education
    Fan, Bingwen Eugene
    Chow, Minyang
    Winkler, Stefan
    [J]. MEDICAL SCIENCE EDUCATOR, 2024, 34 (01) : 5 - 7
  • [7] Artificial Intelligence-Generated Facial Images for Medical Education
    Bingwen Eugene Fan
    Minyang Chow
    Stefan Winkler
    [J]. Medical Science Educator, 2024, 34 : 5 - 7
  • [8] Artificial Intelligence-Generated Draft Replies to Patient Inbox Messages
    Garcia, Patricia
    Ma, Stephen P.
    Shah, Shreya
    Smith, Margaret
    Jeong, Yejin
    Devon-Sand, Anna
    Tai-Seale, Ming
    Takazawa, Kevin
    Clutter, Danyelle
    Vogt, Kyle
    Lugtu, Carlene
    Rojo, Matthew
    Lin, Steven
    Shanafelt, Tait
    Pfeffer, Michael A.
    Sharp, Christopher
    [J]. JAMA NETWORK OPEN, 2024, 7 (03)
  • [9] Artificial Intelligence-Generated Imagery: A New Approach to Art in Medicine
    Park, Benjamin
    Cooke, Erin
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2024, 21 (01) : 31 - 33
  • [10] Assessing the quality of artificial intelligence-generated patient counseling for rhinosinusitis
    Hill, Gregory S.
    Fischer, Jakob L.
    Watson, Nora L.
    Riley, Charles A.
    Tolisano, Anthony M.
    [J]. INTERNATIONAL FORUM OF ALLERGY & RHINOLOGY, 2024,