Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing

Zemel, Ryan; Choudhuri, Somsubhra Gan; Gere, Attila; Upreti, Himanshu; Deite, Yehoshua; Papajorgji, Petraq; Moskowitz, Howard R.

doi:10.5772/intechopen.85532

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…Ordinary least squares (OLS) regression was used to analyze the data from each respondent. An additive constant and coefficient values for the 16 elements were determined, representing the level of interest toward the topic as a whole, and the level of interest toward each of the 16 elements, respectively [29]. A detailed description of the statistical analysis has been published previously by Bellissimo et al [30].…”

Section: Discussionmentioning

confidence: 99%

Identifying Consumer Mindsets Related to Sugars Consumption in Canadian Adults

et al. 2022

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Little is known about the attitudes and perceptions towards dietary sugars in Canadian adults. The objective of this study was to use rule-developing experimentation (RDE) to identify consumer mindsets related to dietary sugars in 18–50-year-old Canadians. Following an isomorphic permuted experimental design, participants (n = 269) each rated a unique set of 24 scenarios, each consisting of a distinct mixture of two to four messages about dietary sugars on a 5-point scale. A regression model was created for each respondent, identifying the value that each respondent attributed to each of the 16 messages. K-means clustering revealed three distinct mindset groups as follows: “Sugars Beliefs” (MS1), “Trend Analysts” (MS2), and “Health Seekers” (MS3). In conclusion, this study found that RDE is a useful methodological approach for evaluating how consumers think about dietary sugars and revealed mindset-specific messages that matter most to people who differ in their attitudes toward sugars.

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Section: Discussionmentioning

confidence: 99%

Identifying Consumer Mindsets Related to Sugars Consumption in Canadian Adults

et al. 2022

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“…(Gere et al, 2018) analyzed the minds of consumers regarding the use of milk. A study combining Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing was conducted by (Zemel et al, 2019). It is the first time that Mind Genomics was paired with Artificial Intelligence to determine how consumers perceive dairy products.…”

Section: Methodsmentioning

confidence: 99%

Measuring Client’s Feelings on Mobile Banking

Papajorgji

ILOLLARI²,

CIVICI³

2022

REASER

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Mobile banking is relatively a new service offered by banks around the world. Banks are obliged to keep investing in new technologies as otherwise, they would lose competitiveness and market share. It is interesting to know how clients react to these innovation efforts. This study aims to understand the client’s responses to bank innovation, especially to mobile banking technology in Albania. An online experiment is conceived based on Experimental Design Principles. Participants evaluate combinations of messages (elements) about mobile banking and rate each combination. The collected data are used to create individual models and later a general model to calculate the statistical relevance of each of the messages. The models use ordinary least squares regression and advanced data mining techniques (k—means clustering) to analyze the data and classify participants accordingly. At the end of the analyses, a set of two or three mindsets are depicted to show what pushes participants in the study in their decision-making process. These mindsets help banks understand client’s reactions and allow banks to address different issues to serve their clients better.

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“…Mind Genomics combines experimental psychology (psychophysics), statistics (experimental design), and consumer research (focus on the world as is, rather than put the respondent through an artificial situation, unless that artificial situation is of direct scientific interest). The past history and future opportunities Mind Genomics have been described in previous papers [13][14][15].…”

Section: Mind Genomics Offers a New Perspective On Thinkingmentioning

confidence: 99%

Motivating Mammography: Combining AI and Mind Genomics to Discover What a Patient Needs to Hear

2023

ARCH Women Health Care

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The study focused on the messages which might enhance women's participation in mammogram screening. The study followed the Mind Genomics process of presenting respondents with different combinations of relevant messages, identifying the strong performing messages for each respondent, and clustering respondents on the basis of the patterns of elements which drive positive responses. The messages themselves were developed using AI, allowing the researcher ahead of the experiment to gain a broad education-type overview of the types of messages that could be used. Two mind-sets of respondents emerged, representing different patterns of messages which are deemed motivating. Mind-Set 1, "Motivated and Informed Women," seeks improved breast health, values screenings, with the goal to minimize health risk, and maximize well-being. Mind-Set 2, "Concerns and Motivations," focuses on the mammogram experience itself, the mammogram risks, comfort, and cost, demanding accurate diagnoses. It may be possible to increase mammogram screening by assigning a person to one of these mind-sets. The Mind-Set Assigner (previously called Personal Viewpoint Identifier (PVI)) comprises a set of six questions based upon the study, the patterns of answers to which assign a person to either Mind-Set1 or Mind-Set 2. The Mind-Set assigner offers the promise of a personalized approach to understanding what people need to hear to ensure better health.

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Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing

Cited by 8 publications

References 14 publications

Identifying Consumer Mindsets Related to Sugars Consumption in Canadian Adults

Identifying Consumer Mindsets Related to Sugars Consumption in Canadian Adults

Measuring Client’s Feelings on Mobile Banking

Motivating Mammography: Combining AI and Mind Genomics to Discover What a Patient Needs to Hear

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