Evaluating Consumer Intentions to Purchase Seafood Cultured with New Liquid Fertilizer: A Comparison of Logistic and Lasso Regression Models
-
- Zhen WU
- School of International Business, Zhejiang International Studies University
-
- Weixiang WANG
- School of International Business, Zhejiang International Studies University
-
- 髙橋 義文
- 九州大学大学院農学研究院農業資源経済学部門
-
- 矢部 光保
- 九州大学大学院農学研究院農業資源経済学部門
この論文をさがす
説明
The deterioration of marine ecosystems due to intensified human activities has led to a significant decline in the availability of marine food sources. In response, some regions have implemented measures such as adding fertilizers to marine environments to promote the growth of seafood like clams. This study explores consumers’ willingness to purchase seafood produced using Bio–Concentrated Liquid Fertilizer (Bio–CLF), a new eco-friendly fertilizer. An internet survey was conducted in Japan with 1,546 respondents, and the data was analyzed using logistic regression and LASSO regression to examine the influence of environmental awareness, knowledge of environmental solutions, and resistance to environmental innovations on purchase intention. The results indicate that consumers with higher environmental awareness and better understanding of environmental solutions are more inclined to purchase Bio–CLF cultured seafood. These findings suggest that effective communication and education about the benefits and scientific basis of such innovations are crucial. Recommendations for seafood marketers and government agencies are provided to promote sustainable seafood production and enhance organic waste recycling.
収録刊行物
-
- Journal of the Faculty of Agriculture, Kyushu University
-
Journal of the Faculty of Agriculture, Kyushu University 70 (1), 27-36, 2025
Faculty of Agriculture, Kyushu University
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390584940356780032
-
- NII書誌ID
- AA00247166
-
- DOI
- 10.5109/7340479
-
- HANDLE
- 2324/7340479
-
- ISSN
- 00236152
-
- 本文言語コード
- en
-
- 資料種別
- departmental bulletin paper
-
- データソース種別
-
- JaLC
- IRDB
- Crossref