期刊
INTERNATIONAL JOURNAL OF GASTRONOMY AND FOOD SCIENCE
卷 33, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ijgfs.2023.100762
关键词
Sensory analysis; Seaweed; Consumer understanding; Mixed-methods; Interdisciplinary research; Sustainable food systems
The paper focuses on interdisciplinary research on the consumption of seaweed foods. Through a mixed methods approach, the authors explore the consumer's approach to new marine products, combining social science, food science, and a chemical and biotechnological perspective. They argue for flexibility and openness to disciplinary processes, while sharing empirical experiences in a pilot study. The goal is to deepen the understanding and connection between different disciplines in the research processes of seaweed consumption.
The focus of this paper is an interdisciplinary approach towards the consumption of novel seaweed foods. Combining social science, food science, and a chemical and biotechnological perspective, we ask the following questions: How do we gain knowledge about the consumer's approach to new marine products on the market? What kinds of methods are suitable and possible to use? In what way can we combine different kinds of methods and what can we learn from each other? In this paper we argue for a mixed methods approach, while ensuring flexibility and openness to disciplinary processes. Through a pilot study and by sharing empirical experiences, we explore the sensory properties of four different seaweed species, to investigate departure points for designing the larger interdisciplinary study about the consumption of novel seaweed products. Navigating the highly complex, interconnected production and consumption systems of seaweed, the question arises of how to reach a deeper understanding and more closely connect the research processes over the borders of different disciplines. We have taken our starting point in an approach that enabled a framework that encouraged engagement and learning throughout the research process.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据