Chambers Amber Vonona, Baker Mathew T, Leggette Holli R, Osburn Wesley N, Lu Peng
Department of Agricultural Leadership, Education and Communications, Texas A&M University, College Station, TX 77843, USA.
Department of Animal Science, Texas A&M University, College Station, TX 77843, USA.
Foods. 2023 Apr 5;12(7):1535. doi: 10.3390/foods12071535.
Recently, meat scientists have developed an innovative amino acid-based alternative meat curing system (AAACS). However, consumer skepticism toward novel foods presents challenges regarding the acceptance of food innovations like the AAACS. Effective communication about this and other food technologies is critical. Our study was a 2 × 4 randomized factorial between-groups experiment that investigated how two peripheral cues-message frame and information source-impact attitudes toward the AAACS. We used Qualtrics to randomly assign participants to one of eight treatment groups. Each group viewed a different video about the AAACS. Then, all participants were asked about their attitudes toward the alternative meat curing system. Data were analyzed using a two-way multivariate analysis of variance (MANOVA). The two-way MANOVA determined concurrently the experimental effects of message frame and information source on information recall, trust, source expertise, source credibility, and anticipated consumption behavior. A significant MANOVA was followed up using Discriminant Function Analysis (DFA). A significant main effect was found for information source. The DFA revealed only one significant underlying function and that source expertise was the most powerful discriminating variable for information source.
最近,肉类科学家开发了一种创新的基于氨基酸的替代肉类腌制系统(AAACS)。然而,消费者对新型食品的怀疑态度给像AAACS这样的食品创新的接受度带来了挑战。关于这一技术和其他食品技术的有效沟通至关重要。我们的研究是一项2×4随机析因组间实验,研究了两种外围线索——信息框架和信息来源——如何影响对AAACS的态度。我们使用Qualtrics将参与者随机分配到八个处理组之一。每个组观看了一个关于AAACS的不同视频。然后,所有参与者都被问及他们对替代肉类腌制系统的态度。使用双向多变量方差分析(MANOVA)对数据进行分析。双向MANOVA同时确定了信息框架和信息来源对信息回忆、信任、来源专业性、来源可信度和预期消费行为的实验效果。在显著的MANOVA之后,使用判别函数分析(DFA)进行跟进。发现信息来源有显著的主效应。DFA仅揭示了一个显著的潜在函数,并且来源专业性是信息来源最强大的判别变量。