Identifying factors of millennial publics risk information seeking and processing strategies of genetically modified food

Date

2017-08-01

Journal Title

Journal ISSN

Volume Title

Publisher

Kansas State University

Abstract

Genetically modified crops have been beneficial to farmers in terms of saved time, money, and energy while increasing yields and often times reducing pesticide dependency. These benefits outweigh the increased costs, allowing genetically modified crops to become one of the fastest adopted farm technologies in history. Despite overwhelming approval of genetically modified crops among farmers, consumers have been hesitant to consume genetically modified food. Consumers see genetically modified food as a risk without immediate reward. Millennial consumers are a powerful population segment that rivals or overtakes other population segments in terms of size, influence, and purchasing abilities. Yet, they are often lumped into one homogenous group by marketers when they are a diverse group comprised of unique segments. The purpose of this study was to better understand how millennial consumers find and process risk information about genetically modified food so that agricultural communicators can better strategize communication efforts. Applying the Situational Theory of Publics and the Risk Information Seeking and Processing Model, this study went one step further by differentiating between Supportive and Non-supportive publics. The research objectives of this study are as follows: 1) Identify the individual characteristics of both Supportive and Non-supportive millennial publics of genetically modified food; 2) Examine relevant channel beliefs of Supportive and Non-supportive millennial publics of genetically modified food; 3) Identify and describe the information gap of Supportive and Non-supportive millennial publics of genetically modified food; 4) Define the perceived information gathering capacity of Supportive and Non-supportive millennial publics of genetically modified food; and 5) Characterize the information seeking and processing behavior of Supportive and Non-supportive millennial publics of genetically modified food. An Internet survey was distributed to individuals between the ages of 18 and 36 within the United States. The majority of Non-supportive publics had a high level of issue involvement and the majority of Supportive publics had a low level of issue involvement. Meaning, the majority of Non-supportive publics are more active about the issue than Supportive publics. Age was found to be correlated with systematic processing and information avoidance with older millennials more likely to systematically process information and less likely to avoid information. Additionally, this study found that regardless of knowledge level, wealthier individuals who do not support genetically modified food are more likely to be actively involved in the issue and wealthier individuals who support the technology are more likely to be passive about the issue. The majority of millennial publics in all eight groups reported a knowledge deficit to some degree. The research also found that heuristic processing was negatively correlated to systematic processing and higher levels of information avoidance were negatively correlated with lower levels of active information seeking. Non-supportive Active publics (high issue involvement/high knowledge) were found to have the highest mean active information seeking and systematic processing scores and lowest mean heuristic processing and information avoidance scores; supporting past findings that knowledge does not always equate to support and that communication practitioners may have trouble changing the opinion of a large portion of Non-supportive publics.

Description

Keywords

Genetically modified food, Millennials, Risk Information Seeking and Processing, Situational Theory of Publics

Graduation Month

August

Degree

Master of Science - Agricultural Education and Communication

Department

Department of Communications and Agricultural Education

Major Professor

Jason D. Ellis

Date

2017

Type

Thesis

Citation