Dieticians' intentions to recommend functional foods: The mediating role of consumption frequency of functional foods

This study explored the conceptual framework of dieticians' intentions to recommend functional food and the mediating role of consumption frequency. A web-based survey was designed using a self-administered questionnaire. A sample of Korean dieticians (N=233) responded to the questionnaire that included response efficacy, risk perception, consumption frequency, and recommendation intention for functional foods. A structural equation model was constructed to analyze the data. We found that response efficacy was positively related to frequency of consumption of functional foods and to recommendation intention. Consumption frequency also positively influenced recommendation intention. Risk perception had no direct influence on recommendation intention; however, the relationship was mediated completely by consumption frequency. Dieticians' consumption frequency and response efficacy were the crucial factors in recommending functional foods. Dieticians may perceive risks arising from the use of functional foods in general, but the perceived risks do not affect ratings describing dieticians' intentions to recommend them. The results also indicated that when dieticians more frequently consume functional foods, the expression of an intention to recommend functional foods may be controlled by the salience of past behaviors rather than by attitudes

Creating an Instrument to Measure Student Response to Instructional Practices

BackgroundCalls for the reform of education in science, technology, engineering, and mathematics (STEM) have inspired many instructional innovations, some research based. Yet adoption of such instruction has been slow. Research has suggested that students’ response may significantly affect an instructor’s willingness to adopt different types of instruction.PurposeWe created the Student Response to Instructional Practices (StRIP) instrument to measure the effects of several variables on student response to instructional practices. We discuss the step‐by‐step process for creating this instrument.Design/MethodThe development process had six steps: item generation and construct development, validity testing, implementation, exploratory factor analysis, confirmatory factor analysis, and instrument modification and replication. We discuss pilot testing of the initial instrument, construct development, and validation using exploratory and confirmatory factor analyses.ResultsThis process produced 47 items measuring three parts of our framework. Types of instruction separated into four factors (interactive, constructive, active, and passive); strategies for using in‐class activities into two factors (explanation and facilitation); and student responses to instruction into five factors (value, positivity, participation, distraction, and evaluation).ConclusionsWe describe the design process and final results for our instrument, a useful tool for understanding the relationship between type of instruction and students’ response.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136692/1/jee20162_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136692/2/jee20162.pd