UNIVERSITY OF ECONOMICS AND MANAGEMENT

Nárožní 2600/9a, 158 00 Praha 5

University of Economics and Management

This email address is being protected from spambots. You need JavaScript enabled to view it. / www.vsem.cz

DIPLOMA THESIS

MASTER OF BUSINESS ADMINISTRATION

UNIVERSITY OF ECONOMICS AND MANAGEMENT

Nárožní 2600/9a, 158 00 Praha 5

University of Economics and Management

This email address is being protected from spambots. You need JavaScript enabled to view it. / www.vsem.cz

TITLE OF DIPLOMA THESIS

Segmentation for health improvement by simulations

DATE OF GRADUATION AND DIPLOMA THESIS DEFENCE (MONTH/YEAR)

June/2018

NAME AND SURNAME OF THE STUDENT/ STUDY GROUP

Štěpán Vančuřík

NAME OF THE SUPERVISOR

Doc. Ing. Zdeněk Linhart, CSc.

STUDENT’S DECLARATION

I declare that this Diploma thesis is my own work, and the bibliography contains all the literature that I have referred to in

writing of the thesis.

I am aware of the fact that this work will be published in accordance with the §47b of the Higher Education Act, and I agree

with that publication, regardless of the result of the defended thesis.

I declare that the information I used in the thesis come from legitimate sources, ie. in particular that it is not subject to state,

professional or business secrets or other confidential sources, which I wouldn’t have the rights to use or publish.

Date and Place: April 30 2018, Prague, Czech Republic

ACKNOWLEDGEMENT

I would first like to thank my thesis supervisor Doc. Ing. Zdeněk Linhart, CSc. from VŠEM. 

UNIVERSITY OF ECONOMICS AND MANAGEMENT

Nárožní 2600/9a, 158 00 Praha 5

University of Economics and Management

This email address is being protected from spambots. You need JavaScript enabled to view it. / www.vsem.cz

SUMMARY

1. Main objective:

Validation of proposed hypothesis that public trusts and believes in positive impacts of the utilization of

computer simulations in health care and medical environment in order to make betterments in treatment

techniques and health care quality for people suffering with serious diseases.

2. Research methods:

 

Development of a topic specific questionnaire in order to investigate validation of the proposed hypothesis.

Formation of three different real world stories (advertisements) dealing with blood glucose levels. Providing

these stories to respondents prior to the completion of the questionnaire and analysing the differences in

responses of the respondents according to which story they read.

3. Result of research:

Responses and statistical analysis calculations tend to show that all the respondents would be interested in the

inclusion of computer simulations in health care can make positive impacts on research development in

medicine and betterment of patient treatment. The results demonstrate that people under the age of 60 years are

more pro the inclusion of computer simulations than people over the age of 60.

4. Conclusions and recommendation:

The work uses a topic specific questionnaire in order to validate the proposed hypothesis. The validation was

met to an extent by statistical analysis computations and observations of the results of the questionnaire.

However, the total number of respondents is not high enough to consider the results to be a clear description of

general public and market. Future proposals are to expand the number of respondents and then provide more

and different statistical calculations in order to fully validate the hypothesis and the research.

KEYWORDS

Computer Simulations, Blood Glucose, Diabetes, Medical environment, Marketing and Advertising

JEL CLASSIFICATION

C63, I12, M31, M37

UNIVERSITY OF ECONOMICS AND MANAGEMENT

Nárožní 2600/9a, 158 00 Praha 5, Czech Republic

DIPLOMA THESIS ASSIGNMENT

Name and surname: Štěpán Vančuřík

Study program: Master of Business Administration Eng (MBA)

Study group: MBA EN03

Title of the thesis: Segmentation for health improvement by simulations

Content of the thesis: 1. Introduction

2. Theoretical-methodological part – simulations from mass

data – image, trust and loyalty towards hard—sell and

soft-sell ads - methodology.

3. Analytical part – characteristics of seriousness of illness,

analysis and results of comparative advertising,

transferability of changed attitudes to other domains,

commercialisation.

4. Conclusions

References:

(at least 4 sources)

 DIANOUX, C., LINHART, Z. The effectiveness of female

nudity in advertising in three European countries.

International Marketing Review, 2010, 27, 5, pp. 562-578.

 GREWAL, D. L. L. Marketing with Practice Marketing Access

Card. McGraw-Hill Education, 2012. ISBN

9780077713294.

 PORTER, M. Competitive Strategy (revised ed.). The Free

Press, 1998. ISBN 0-684-84148-7.

 PRIDE, W. F. Marketing. Cengage Learning, 2013. 832 p.

ISBN 9781133939252.

Schedule:  Aim and methods till: 01.12.2017

 Theoretical part till: do 01.02.2018

 Results till: 01.04.2018

 Final version till: 01.05.2018

Supervisor: doc. Ing. Zdeněk Linhart, CSc.

 Prof. Ing. Milan Žák, CSc.

rector

In Prague 

Contents

1 INTRODUCTION........................................................................................................................1

2 THEORETICAL-METHODOLOGICAL PART ........................................................................2

 2.1 SIMULATIONS FROM MASS DATA ...............................................................................3

 2.1.1 COMPUTER SIMULATION ......................................................................................3

 2.1.2 BIG AND MASS DATA .............................................................................................4

 2.2 IMAGE, TRUST, AND LOYALTY TOWARDS HARD-SELL AND SOFT-SELL ADS 5

 2.2.1 MARKETING AND ADVERTISING ........................................................................5

 2.2.2 MARKET SEGMENTATION.....................................................................................6

 2.2.3 COMPARATIVE ADVERTISING .............................................................................7

 2.2.4 COMPETITIVE STRATEGY .....................................................................................8

 2.2.5 IMAGE, TRUST, AND LOYALTY............................................................................8

 2.2.6 HARD-SELL AND SOFT-SELL ADS .......................................................................9

 2.2.7 COMMERCIALIZATION.........................................................................................10

 2.3 METHODOLOGY..............................................................................................................10

3 ANALYTICAL PART...............................................................................................................12

 3.1 CHARACTERISTICS OF SERIOUSNESS OF ILLNESS................................................19

 3.2 ANALYSIS AND RESULTS OF COMPARATIVE ADVERTISING .............................28

 3.2.1 FIRST STORY RESULTS ANALYSIS....................................................................29

 3.2.2 SECOND STORY RESULTS ANALYSIS...............................................................39

 3.2.3 THIRD STORY RESULTS ANALYSIS ..................................................................48

 3.3 TRANSFERABILITY OF CHANGED ATTITUDES TO OTHER DOMAINS ..............58

 3.4 COMMERCIALIZATION..................................................................................................67

4 CONCLUSIONS........................................................................................................................72

1

1 Introduction

Nowadays, there is a boom in the development, advancement, and growth of new

technologies, resources, and research works all over the world. Public seems to be interested in

new discoveries and novel innovations in the world of science and technology. People, generally,

tend to show an interest about their health conditions and they seem to be attracted by new feasible

ways of the use and utilization of modern methods, procedures, and techniques in the medical

environment. On account of the fact that there is an occurrence of technological expansion and

modernization in the field of medicine, research works are under the way in many medicine related

areas.

At a noticeable number of colleges and universities all over the world, there are innovative

studies and experiments to further the technology happening every day. This way the educational

and academic institutions make their efforts to contribute to the modernization of science and

technology fields by creations and developments of new approaches and novel designs. It is

definitely no different for the area of medicine and health care environments. Many students,

especially graduate students, across the world are involved in various research works to propose

such new designs. Students usually take this opportunity of being involved in any research work to

make their attempts of contributing to real world work, to write their final theses and papers, and

to possibly make themselves more valuable to the market.

A technique that is often used for new developments in the technological side of medical

and health care is utilizations of various computer programs and simulations. Computer simulations

in medical environment are usually used to mimic illnesses, to simulate and predict a probable

course of illnesses, to estimate the best treatment methods available, and to mathematically

calculate certain body functions. Needless to say, computer simulations are often used in the

medical environment in order to make an attempt for the creation of contributions to the

development and advancement of new healing and treatment techniques and procedures.

One of the main reasons for the construction of this paper is author’s own experience with

certain research works in biomedical environment, specifically with the utilization of computer

simulations for analysis and testing purposes of blood glucose levels for the possible betterment of

diabetics’ treatment. The author was acquainted with research works in this area of study and also

attempted to make contributions to the research by the development of a novel design for blood

glucose characteristics computations, calculations, and predictions. It is believed to be helpful for

this paper to mention author’s own involvement with certain research as it explains the reasons for

the creation of this paper and also the selection of the main aims and goals of the thesis.

Hypothesis that is attempted to be validated in this work is the idea that public trusts and

believes in positive impacts of the utilization and possible inclusion of computer simulations in the

medical environment in order make betterments in treatment techniques and health care quality for

people suffering with serious diseases. In order to investigate the levels of trust of people towards

the utilization of computer simulations into health care, a topic specific questionnaire was

developed and created. Even though the main goal of the questionnaire is to collect pieces of

information for the validation or negation of the hypothesis, the results of the questionnaire can

provide other valuable pieces of information. Therefore, other smaller goals for this work has also

been proposed. The other aims are to determine the relationship that the public holds towards

science and technology, to understand initial reactions people possess when seeing a general

advertisement, to understand initial reactions people hold when they are introduced to an

advertisement concentrated on a medicine or a healing method, to discover how many people have 

2

experience with serious diseases, and lastly to see the techniques people prefer to use in order to

get healthy, stay healthy, and get rid of diseases. The final objectives of this paper are to make

comparisons and evaluations of the responses of the respondents after reading different health care

related stories and to evaluate and analyze the attitudes of the respondents towards certain healing

methods and approaches according to their health conditions and information provided to them.

Even though the responses to the questions and statements used in the questionnaire will probably

provide more information than to answer the set goals, the main aim is to validate the hypothesis

and analyze and evaluate the set sub goals. The additional pieces of information that the responses

of the questionnaire can also provide is for instance the position of the respondents on the issue of

problem solving techniques.

The paper is divided into four major parts, Introduction, Theoretical-Methodological Part,

Analytical Part, and Conclusions. The Introduction provides a problem statement, introduces the

overall issue, sets objectives of the work, and introduces the structure of the paper. The TheoreticalMethodological Part is concentrated on the theoretical part of concepts related to the research such

as marketing, advertising, computer simulations, and big data processing. It also discusses some

academic works and researches completed in the mentioned fields of study. This section ends with

a methodology section, where the development of the topic specific questionnaire is described and

explained, and the choice of respondents is debated. The Analytical part of this paper provides all

the sections and parts of the finished questionnaire, it provides three different stories provided to

the respondents during the process of completions of the questionnaire, it offers responses and

results of the questionnaire, interpretation of the results, and meaning of the results, and it delivers

certain statistical analysis of the results. Final discussions on the results of the paper and proposals

for future work and possible future extension and advancement of this study are provided in the

Conclusions section at the end of this paper. Bibliography section, detailing all the used resources

during the paper formation, is provided after the Conclusions section at the end of this paper. At

the very end of the thesis, abstract, key words, and JEL classifications are delivered.

2 Theoretical-Methodological Part

This section provides a brief theory background information and overview of marketing,

advertising, computer simulations, and big data processing. Specifically, it discusses the concepts,

simulation, computer simulation, big and mass data, marketing, advertising, image, trust, and

loyalty in marketing, hard-sell and soft-sell advertising approaches, comparative advertising,

competitive strategy, and commercialization. The overview and discussions in this section are

provided and summarized from books, online sources, and certain research works and papers. Not

only this section provides theoretical background of the concepts, it also provides some real world

applications of the concepts and examples of particular research works. Lastly, it provides a

description of methodology used in order to tackle the aims of the paper and possibly prove the

hypothesis of the work. The methodology section provides principles and methods used in the

process of development and assembly of the topic specific questionnaire. All the resources used

along the way of creation of this section of the paper are listed in the Bibliography section at the

end of the thesis. 

3

2.1 Simulations from mass data

This section of the Theoretical-Methodological part of the paper consists of two sub

sections named Computer simulation and Big and mass data. This section focuses on the theoretical

background description of the following terms, simulation, computer simulation, and big data. It

points out the small difference between the terms simulation and computer simulation, it highlights

the meaning of the two terms, and it provides real life utilizations of both. The section also

addresses the importance and possible use of big data in modern technology and technological

approaches.

2.1.1 Computer simulation

When a term simulation is mentioned in a conversation today, many automatically think of

computer simulation. Even though computer simulation is always a simulation, the term simulation

refers to a broader general term than computer simulation does. Nonetheless, for the purpose of

this research study, the difference between computer simulation and simulation is not essentially

fundamental. The terms are further discussed in the following paragraphs.

Simulation is an imitation of an operation achieved by a real-world system or a real-world

process, where the first step in the process of simulation development is a formation of a model.

This model represents key characteristics, behaviors, and functions of the real-world system or the

real-world process that is desired to be examined. Thus, the model is a representation of the system

or the process that is desired to be analyzed, and the simulation represents the operation

characteristics of the model over time (Ledesma and Garcia, 2013).

Computer simulation term refers to an imitation of a behavior of a system or a process using

a mathematical model. As it applies for simulation, computer simulation also needs a model. This

model is algorithms, code, and equations used to capture a behavior of the desired modeled system

or a process and the computer simulation of a system represents the actual running of the model.

Computer simulations are commonly small-scale or large-scale computer programs, where smallscale ones often run instantly on small devices and large-scale ones often run for a longer period

of time on greater devices or even a network of devices (Kumar, Rajshree, and Shamim, 2015).

Computer experiments are typically used to study and analyze simulation models and to

execute performance testing on various systems where the environment conditions are too complex

for classic analytical solutions (Kumar, Rajshree, and Shamim, 2015). In other words, simulations

are often used when a real-world system or a real-world process cannot be normally engaged for

various reasons and issues such as safety (Ledesma and Garcia, 2013).

Both simulations and computer simulations have become important tools in many academic

and professional fields such as technology, safety engineering, optimization processes, education,

training programs, and testing (Ledesma and Garcia, 2013). Simulations can even go as far as to

create a virtual reality for various reasons such as entertainment, educational purposes, and video

games (Erra, Malandrino, and Pepe, 2018). Furthermore, simulations are often used for

optimization purposes of product development processes and reduction of production costs in

businesses. Simulations are utilized by companies on account of the fact that they typically provide

certain benefits to the stages of product development. The utilization of simulations to drive

product innovations has basically become a norm in the modern manufacturing as simulation tools

help with extractions of needed insights for the development and optimization of new high quality

product offerings (Cavanaugh, 2016). 

4

Simulations were also proven to be effective educational tools in health care and medical

technical setting. Simulations in these fields are used as a part of strategical performance

improvements for physicians so as to replace or amplify the pieces of experience of a patient with

guided experiments (Burden and Pukenas, 2018). Innovations in various simulations are also often

used in nursing to engage and provide and effective learning experience for students and scholars

(Andersen, Baron, Bassett, and others, 2018). Therefore, simulations can provide an essential

educational experimental opportunity for both scholars and professionals. With the use of

simulations, professional can have the opportunity to use simulations to recreate rare scenarios

involving challenging situations, where the professionals have the opportunity to replay and

examine their actions (Burden and Pukenas, 2018).

Computer simulations can also be used as tools for betterment of treatment in health care

for people suffering with various serious diseases such as diabetes. These simulations help with

and analysis and testing of the variability of blood glucose measurements and the levels of blood

glucose alterations over time. One approach for blood glucose analysis and testing by simulation

is meal detection. Meal detection algorithms have been developed by researchers to provide a first

step in the process of automation and creation of artificial pancreas for diabetics (Khatri, 2015) or

to be used as a part of an artificial beta-cell for patients suffering with different types of diabetes

(Dassau, Bequette, Buckingham, and Doyle, 2008).

2.1.2 Big and mass data

The term big data is continually reconsidered and it continually evolves as it remains the

driving force behind the waves of digital transformation. Various kinds and types of data are

generated very often, almost every time anyone goes online as almost every online action or

interaction leaves a certain digital trail (Marr, 2018).

Big data describes a large volume of data (Davenport, 2018). The term usually refers to

data that is too large for a memory of a system to be loaded all at once (MathWorks, 2018). The

large volume of data can be structured, semi-structured, and unstructured, it can come from various

sources such as social media or business transactions, it can come in all types of formats such as

numeric data, text documents, audio, video, emails, and financial transactions, and streams in at an

unprecedented speed. It is proposed by the professionals that the data must be dealt with in a timely

manner. The definition of big data usually consists of five dimensions, volume, velocity, variety,

variability, and complexity (Davenport, 2018).

Big data usually consists of datasets that are so voluminous and complex that traditional

application pieces of software cannot efficiently and effectively deal with them. In other words, the

datasets are so huge that they are very often beyond the ability of common software tools to capture,

manage, and process the datasets precisely and within a tolerable amount of time (Byeong, Bong,

Seon, and Tag, 2017). The amount of data that is being created and stored on a global level keeps

growing and is almost inconceivable. However, key business information insights can be gathered

from the collection of all the digital datasets available online (Davenport, 2018). The available

digital datasets thus have a potential to be mined in order to collect valuable pieces of information

that can be used to make business predictions and to gain new business insights in order to

eventually make smarter business decisions (Marr, 2018).

It is usually the case that when big data is combined with high-powered analytics, many

business related tasks can be accomplished in a reasonable amount of time. These business related

tasks include determination of the roots of failures of business or processes and precise quick 

5

portfolio recalculations (Davenport, 2018). In order to use and mine big data, a process of building

models based on the collected data and running simulations to test the models is usually

implemented. By the use of this approach, companies today already make incredibly accurate

prediction of customers’ wants and needs (Marr, 2018).

Big data has also received an attention from other fields than business. These fields are

medicine, science, engineering, politics, disaster responses, and others (Byeong, Bong, Seon, and

Tag, 2017). In these fields, big data are used to predict and respond to natural and man-made

disasters, to help police forces to prevent crimes, and to further technological innovations (Marr,

2018). For the technological innovations in mass spectrometry data research, the analysis of big

data proved that ordinary linear model approaches are not sufficient with large chunks and amounts

of data (Murie, Sandri, Sandberg, and others, 2018). In research works concentrated on

chromatographic data, different challenges were demonstrated with big data analyses such as issues

of high complexity including low signal to noise ratios (Johnsen, Skou, Khakimov, and Bro, 2017).

Needless to say, there are severe challenges in computer science today and one of them is

undoubtedly the management of large amounts of data (Wang, Xiong, Li, and others, 2018).

However, it is essential to point out that there is many challenges with big data that

consumers are worried about. These challenges include information privacy, data analysis, data

capturing, data storage, data security, and data sources. It is clear that big data contains pieces of

information about personal lives that many people desire to keep private. Therefore, the ability to

leverage big data is going to become increasingly critical in the coming years for businesses and

other fields (Marr, 2018).

It has been shown by various research works that the best option to deal with big data at

this point in time is big data analytics and simulations, where the simulations and big data analytics

are most valuable when used together. Complex simulation models allow designers to experiment

with big data at fast processing speeds and big data analytics rapidly processes simulation data.

This enables designers to extract valuable information and convert it into better decision making.

Simulation based design pieces of software typically enable designers to improve product quality

while reducing cycle times and unnecessary costs (Cavanaugh, 2016).

 

2.2 Image, trust, and loyalty towards hard-sell and soft-sell ads

This section of the Theoretical-Methodological part of the paper consists of seven

subsections and deals with the basic theoretical explanations of marketing, advertising, market

segmentation, comparative advertising, competitive strategy, image, trust, and loyalty, hard-sell

and soft-sell advertising approaches, and commercialization. It also mentions some possible

utilizations of the mentioned concepts in real world practice and highlights certain research works

and paper concentrated on the aforementioned issues.

2.2.1 Marketing and advertising

Marketing, one of the main components of business management, is a study in which the

center of attention is a customer and customer satisfaction. It is a study and management of

exchange relationships (Hunt, 1976). Some research works considered marketing to be an isolated

study in academic research (Sombultawee, Boon, 2018). Marketing is a study which takes demands

of dynamic business environment into account and addresses ways of adaptions to these demands

(Jobber and Ellis-Chadwick, 2013). Nowadays a major marketing transformation is described by 

6

the use and utilization of digital, social media, and mobile marketing (Muller, Pommeranz,

Weisser, and others, 2018).

Marketing concept attacks the notion that an organization should anticipate needs and wants

of customers in order to sufficiently satisfy organization objectives of consumer satisfaction. Since

customer needs and wants are a core concept in marketing, an understanding of the terms needs

and wants in marketing environment is crucial. Needs are things that people find necessary to

possess in order to live a safe, stable, and healthy lives. Wants are things that people desire and

things that are not essential for human survival (Hunt, 1976).

Backbone of marketing is fundamentally created by its core subject areas such as market

segmentation, organizational behavior, customer behavior, innovation, and pricing. Marketing is

considered to be a very strong discipline in business environment, and thus there is a growing

community of academics and researchers in the field, and there are many marketing conferences

around Europe presenting latest research works (Jobber and Ellis-Chadwick, 2013). Teachers and

researchers generally agree with the idea that experimental learning, understanding the insights of

social media as a new influence of marketing, and the power of internet are vital concepts for

students to learn in order to fully grasp the study of marketing. Teachers and researchers agree that

marketing professionals should focus on the concepts and tools that help marketers to create values

for customers through branding, packaging, pricing, retailing, service, and advertising in order to

prepared for dynamic marketing environment (Grewal, 2013).

Advertising is a form of marketing communication in a visual or audio form. Advertising

is a study that tackled the issue of promotion of a product, service, or an idea to the market.

Advertising is typically communicated through various mass media such as newspapers,

magazines, television, radio, outdoor advertising, direct mail, search results, blogs, social media,

websites, and text messages. An advertisement or an ad in short is the actual presentation of the

message in a particular medium (Stanton, 1984). Advertisements demonstrate the way of how

marketers present their products in promotion campaigns (Jobber and Ellis-Chadwick, 2013).

Commercial advertisements often seek to generate increased consumption of certain products or

services by branding (Stanton, 1984).

A number of marketing and advertising researches point out to the fact that the attitudes

towards advertising are multi-dimensional, meaning that they consist of attitudes towards the

institution as well as towards the instruments used by the advertiser (Muehling, 1987). The interest

of advertisers to use nudity in advertising campaigns in several European countries to influence the

attention of the public has also been a target to certain research works. Research underlines that

nationality typically does not appear to have an influence on the preference of selecting

advertisements with or without nudity. The research demonstrates that women tend to adapt more

negative attitudes towards advertisements that use sexy female models than men do (Dianoux and

Linhart, 2010).

2.2.2 Market segmentation

Market segmentation is the process of defining and dividing of a large homogeneous market

into a clearly identifiable and better reachable smaller market segments. It is commonly an aim of

market segmentation to divide the market into segments that have similar needs, wants, and demand

characteristics and to use particular marketing mix that matches the expectations of the customers

in the targeted market segment in order to be profitable or have profit growth potential (Pride,

2013). In other words, market segmentation is a dividing process of a broad market into smaller 

7

groups of consumers based on certain shared characteristics such as common interests, similar

lifestyles, and shared needs (Jobber and Ellis-Chadwick, 2013). Three criteria that can be used to

identify different market segments, homogeneity, distinction, and reaction, where homogeneity

means common needs within a segment, distinction means being unique from other groups, and

reaction means similar response to the market are typically adapted by people performing market

segmentation (Pride, 2013). Therefore, market segmentation can be described as the process of

splitting a large market into smaller groups of clusters, where the similarities with each segment

indicate a similar purchasing behavior (Liu and Ong, 2008). There are four basic market

segmentation strategies that are commonly used. The strategies are behavioral, demographic,

geographical, and psychographic differences (Pride, 2013).

Market segmentation assumes that different market segments require different offers,

prices, and promotions. Even though market segmentation is mainly concentrated on identifying

the most profitable segments, it also develops key concepts to understand the needs and purchase

motivations of the customers in each of the smaller market segment (Jobber and Ellis-Chadwick,

2013). Market segmentation is typically used by companies to enable them targeting of different

categories of consumers who perceive and recognize the value of certain products and services

(Pride, 2013).

Several research works investigate the necessity of customer segmentation regarding

digital, social media, and mobile marketing. The works identified three major changes of

information during the process of customer segmentation, increasing requirements for information,

increasing number of sources, and increasing information demands regarding data security (Muller,

Pommeranz, Weisser, and others, 2018). Research works tend to prove the fact that market

segmentation is a technique widely used by businesses to target smaller market segments for the

purpose of having decision makers performing research and understand customer needs and wants

effectively. Research works typically state that the difference of the demands of customers is a

central concept in both marketing theory and marketing practice (Liu and Ong, 2008).

2.2.3 Comparative advertising

Comparative advertising is any form of advertising that identifies goods or services offered

by competitors in either explicit or implicit manner (Emons and Fluet, 2012). Comparative

advertising is a marketing strategy where a certain service or a product of a competitor is

specifically mentioned for the purpose of comparison and presentation of a similar product or

service of a company clarifying the company’s product or service as better and superior (Chen,

Joshi, Rajju, and Zhang, 2009). Comparative advertising is an important tool in marketing and

advertising as it increases the knowledge of information about alternative products to customers. It

typically allows potential consumers and customers to evaluate and compare performances of

certain goods and products of competitors (Emons and Fluet, 2012). Comparative advertising

strategies can be directly or indirectly comparative and can have positive or negative tones. It is;

however, a commonly used practice to utilize negative tones more often than the positive ones.

Therefore, comparative advertising attempts to associate and differentiate competing brands for

consumers (Chen, Joshi, Rajju, and Zhang, 2009).

Campaigns of comparative advertising may involve different comparison techniques such

as printing images displaying two similar products, where one product is the creation of the

company and the other one is the creation of the competitor. The two products are usually printed

next to each other to make the comparisons of the products vivid and easy to recognize by the 

8

consumers. Comparisons of the products or services can be made for instance based their price or

value. Characteristically, it is the main aim of a comparative advertising campaigns to show that

their products, both old and new, are better than products available on the market (Chen, Joshi,

Rajju, and Zhang, 2009).

Research works dealing with comparative advertising tend to demonstrate that comparative

advertising tends to perform better than non-comparative advertising in making customers

recognize the company products as the better ones in comparison to the products of competitors.

Research papers ale point out that companies generally only use comparative advertising

techniques and strategies when there is a significant difference between the products or services of

the companies and their competitors (Emons and Fluet, 2012). Nonetheless, comparative

advertising might also cause negative and reluctance feelings among consumers. Positive feelings

towards comparative advertising are usually caused by the valuable information provided about the

tackled products or services. Negative feelings are usually caused as a result of consumer thinking

that marketers are making attempts to mislead them and misinform the consumers (BambauerSasche and Heinzle, 2018).

2.2.4 Competitive strategy

Competitive strategy is a marketing and advertising technique created in order to help a

company to achieve a competitive advantage over the competitors by a creations of an action plan.

Competitive advertising is often used as a tool to discredit and weaken good reputations of products

and services produced by competitors in advertising campaigns. Competitive strategy is an

important strategy when a market segment or industry segment is very competitive and heavily

flooded with alternative products and services. Competitive strategy has filled the void in

management thinking and attempts to bring a disciplined structure to the approaches that

companies should address and take in order to achieve superior profitability over their competitors.

The keys to achieve this are usually low costs, strategic positioning, and differentiation. However,

it is very crucial to point out that companies need to be careful when performing competitive

strategies as there are law restrictions to them. The laws and restrictions usually differ according

to different countries and states (Porter, 1998).

2.2.5 Image, trust, and loyalty

When terms image, trust, and loyalty are discussed in marketing and advertising focused

conversations, the terms typically refer to brand or product image, brand trust, and customer brand

loyalty. Brand image is an image, impression, or a current view that customers form in their minds

about a particular brand. Brand image classically signifies unique associations in the minds of

consumers on what the brand presently stands for. In other words, it is an image or an impression

that eventually forms in the minds of customers when reading about, hearing about, or seeing an

ad on a particular brand. Product image is a very similar concept to brand image. The difference is

that the mental image formed in the minds of consumers is associated with a particular product

rather than a brand. Thus, the mental image signifies what the product currently stands for in the

minds of customers (Jobber and Ellis-Chadwick, 2013).

Brand trust is a feeling of security that consumers hold when interacting with a certain

brand. It is based on perceptions of customers that the brand is reliable and responsible for the 

9

interests and welfare of the customers. It is typically related to an individual belief of a customer

whether or not the brand accomplishes its value promise and if the brand satisfies the needs of the

customer. Consumer brand loyalty refers to a possibility of a costumer shifting to another brand

due to various reasons and changes such as price, quality, and features of a certain brand product.

Typically, when brand loyalty increases, customers respond less competitive in moving and

shifting to competitive brands. Customer loyalty is widely accepted as an important aspect and

issue for all business organizations (Jobber and Ellis-Chadwick, 2013).

There are many research areas dealing with the aforementioned concepts. Some of them

underline the fact that it is very important to use images strategically in order to get an attention

from potential customers. Research states that web pages and blogs with images receive 94% more

views than web pages and blogs without images (DeMers, 2014). Another research performed a

study of relationships between brand trust, brand image, and customer loyalty. The research

showed that trust is an essential component for the development of customer loyalty and it is

considered one of the key variables in it. The research also indicated that product or brand image

has significant impact on customer loyalty (Upamannyu, Gulati, and Mathur, 2014). Other research

studies for instance examine the relationships between the cognitive and affective trust, perceived

value, and loyalty behavior intentions (Chai, Malhotra, and Alpert, 2015).

Researchers are also focus on the identification of the impacts of brand image, brand trust,

and brand affection on brand extension attitude. Results reveal that brand image, brand trust, and

brand affection are positively associated with brand extension attitude (Anwar, Gulzar, Sohail, and

Akram, 2011). Another study examines the roles of love and trust components of customers on

loyalty concept. It suggests that customer experience of love and trust are both significant

predictors to customer loyalty, where love is stronger driver than trust (Chen and Quester, 2015).

Purpose of another research paper was to examine the influences of brand communication, brand

image, and brand trust as potential antecedents of brand loyalty. The results point out to the fact

that brand communication has strong effects on brand image, brand image strongly influences

brand trust, and brand trust has strong robust relationship with brand loyalty. Therefore, brand

communication can have strong influences on brand trust and brand loyalty through brand image

(Chinomona, 2016).

Research works also tend to show that store brands play an important role in differentiation

strategies based on assortment and positioning in terms of distributor’s prices. The results highlight

that positive and well-though brand names and store brand names positively affect customer loyalty

to the store brands (Rubio, Villasenor, and Yague, 2017). Another research demonstrates that

Facebook pages and Facebook community of colleges and universities attract students to them by

the creation of university brand. The university brand is then turned and related to trust and loyalty

of the students to the university brand (Nevzat, Amca, Tanova, and Amca, 2016). Research works

also deal with tourism firms concentrated on rural area tourism. Researches created a model to

explore relationship between image, quality, satisfaction, and trust of tourists. Results tend to show

a confirmation that image is a direct antecedent of perceived quality, satisfaction, trust, and tourists’

loyalty (Loureiro and Gonzalez, 2008).

2.2.6 Hard-sell and Soft-sell ads

In advertising, hard-sell and soft-sell ad approaches are considered to be the opposites of

on another. Hard-sell approach is typically direct, forceful, and underlines obvious sales message.

Soft-sell approach typically uses delicate, casual, and friendly sales message. Hard-sell approach 

10

usually describes aggressive sales techniques used by company representatives, which includes

doorstep selling. Soft-sell approach is typically less irritating to customers. Hard-sell advertising

normally uses a direct reason why, attempting to cause rational decisions of the customers to

purchase certain good or a certain product. Soft-sell advertising normally uses an indirect approach

attempting to emphasize rational beliefs and evoke positive emotional response associations to a

brand or a service. It is not an exception to use humor and friendly elements in soft-sell advertising

(Adage, 2003).

There is a long debate among advertising professionals over which strategy is better and if

there is a middle ground to the two approaches (Adage, 2003). Research works go even as far as to

state that hard information is a standard assumption for strategic information sharing and

advertising (Schmitz, 2007). Even though soft-sell approach is usually better perceived than hardsell approach, and is also more similarly perceived across market than hard-sell approach,

researches tend to show that hard-sell approaches also do not cause significant negative responses

across the markets as they demonstrate certain homogeneous acceptance (Mueller and Taylor,

2010).

2.2.7 Commercialization

Commercialization is a very important concept of business and marketing. It is a process

of introduction of a novel or new product or a service into a market. It is the process of making a

new product or a service available to the market. The commercialization process and product

development process have many stages and both end with the process of product launch into the

market. Typically, advertising, sales promotions, and other marketing efforts encourage companies

to take commercialization adaptions to be more successful in the dynamic marketing environment

(Jobber and Ellis-Chadwick, 2013). Commercialization, for certain research works, is also referred

to as a process of patenting and licensing of inventions. Research works highlight that

commercialization circles around knowledge that is generated from academically driven research

(Kalantaridis, Kuttim, Govind, and Sousa, 2017).

2.3 Methodology

In order to validate the hypothesis and analyze the aims and goals specified in the in the

Introduction section at the beginning of this paper, a topic specific questionnaire was developed by

the author. Questionnaire as a tool of validating set goals was chosen as questionnaires are the most

commonly used market research techniques and tools available (Litschmannová, 2009). The

methodology of this study, the development of the questionnaire, and the validation of the

responses consisted of seven major steps. The first step was to form a hypothesis and characterize

main goals and smaller objectives of the study. The second step was to develop questions and

statements to assemble the questionnaire in a way so that the information to evaluate the set goals

can be extracted. The third step was to select respondents and chose different age groups of

respondents for the study. The fourth step was to come up with brief instructions for the respondents

to ensure a desired way of completions of the questionnaire and to briefly write three different

stories dealing with blood glucose to provide them to the respondents in order to compare their

responses according to what information they were provided. The fifth step was to collect all the

completed questionnaires and input the responses into tables so that the results can be clearly visible

for analysis and evaluation purposes. The sixth step was to create evaluations of the responses and 

11

perform statistical analysis of the results. The seventh and the last step of the methodology was to

formulate conclusion from the responses and the statistical analysis results, and to discuss the

meanings and significance of the results. Some of the aforementioned steps of the methodology are

discussed in the following paragraphs.

The questionnaire was created using closed multiple choice questions with the option of

selecting multiple answers for certain questions. Finalized questionnaire was divided into three

smaller sections and consisted of total 44 questions. The first section was named Section A –

General Questions, the second section was named Section B – Topic Specific Questions, and the

third section was named Section C – Questions on Logic and Feelings. Section A consisted of 10

questions, Section B consisted of 23 questions, and Section C consisted of 11 questions. After the

completion of the first section of the questionnaire, the respondents were provided with a short

article discussing a story that deals with blood glucose treatment and analysis. These stories were

provided to the respondents in order to see the differences in the responses for the Section B of the

questionnaire according to which story the respondents read. Every respondent was given one of

the three stories. The selection for a story to a respondent was random; however, the stories were

distributed so that each story would eventually have similar number of responses to it. The first

story was about the use of computer simulation for blood glucose levels analysis of pregnant

women. The second story was about the utilization of computer simulations for blood glucose

values analysis of laboratory rats. The third story was about the application of a special diet using

the mushroom Rei-Shi in order to reduce high values of sugar in blood of a patient. For the

evaluation purposes, the story was used and thought of as a possible advertisement.

Even though it is generally not a good practice to start a questionnaire with a question

asking the age of the respondents according to a particular study (Litschmannová, 2009), the second

question of the questionnaire asks the respondents to select their age groups. There were five

different age groups from which the respondents could choose. The age groups were 0-15 years,

15-25 years, 25-45 years, 45-60 years, and 60-80 years. For the children in the age group of 0-15

years, the questionnaire was completed by one of the parents of the children. The age groups were

not selected randomly as such, there were several reasons for the age group selections. In the age

group of 0-15 years, children usually do not suffer with chronic diseases and the attitude of the

parents towards the health of the children can be measured. In the age group of 15-25 years, people

are, most of the times, less likely to get sick, are usually in very good health conditions, and

typically have low to no interest in health preventions, treatment methods, and advertisements

focused on health care. In the age group of 25-45 years, people usually tend to have a high interest

in maintaining good health conditions as they are in their productive years. In this age group people

might also experience the start of the occurrence of certain chronic diseases. In the age group of

45-60 years, people usually have an interest in maintaining good health conditions as they are still

productive but more tired than people in the younger age group and might hold certain interest in

advertisements concentrated on health care. In the last age group of 60-80 years, chronic diseases

are likely to occur and issues challenging movement systems might also appear or at least start

occurring.

The questionnaire was prepared in both electronic and paper format so that the respondent

can select the option of completing the questionnaire they prefer. The questionnaire was also

prepared in both Czech and English languages, so that people can again select their preference of

language and possibly better understand the questions. The respondents were family members,

classmates, friends, and patients asked in several medical offices of practitioners. Many people

asked in the age groups of 15-25 years and 25-45 years were classmates, friends, and family

members. Most of them preferred to complete an electronic copy of the questionnaire online. 

12

Respondents in the age group of 0-15 years were mainly parents asked in practitioners medical

offices. Respondents in the age group of 46-50 years were mainly patients asked in rehabilitation

clinics and practitioners medical offices. Respondents in the age group of 60-80 years were mainly

family members and friends of the family members.

Before each respondent began the completion of the questionnaire, he or she was provided

brief clear instructions about the way of proper completion of the questionnaire such as the option

of selecting multiple answers to certain questions and to read the story prior to completion of the

Section B of the questionnaire. Additionally, there was a 1-7 point scale used in the questionnaire,

so the scale was also briefly explained to the respondents prior to completion. The blood glucose

stories and the 1-7 point scale are explained in the Analytical Part below.

Lastly, Excel software was used in order to collect and highlight all the responses in tables

and to compute and calculate the results from the responses. Excel software was also used for

statistical analysis calculations. The completed final questionnaire, finalized tables with responses

of the respondents, and computed results with statistical analysis are provided in the Analytical

Part below.

3 Analytical Part

After a detailed research, it can be concluded that there are many people studying,

researching, and exploring options of computer simulations, computer simulations of mass data,

computer simulation tools for handling big data, computer simulations utilization in medical

environment, and comparative advertising. In order to make a contribution to the aforementioned

areas, a topic specific questionnaire was created. The questionnaire, in its final version, including

all three blood glucose stories is provided below. Once again, the respondents were not provided

all the three stories, each respondent read only one of the three stories prior to the completion of

the Section B of the questionnaire.

All three blood glucose stories are provided prior to Section B, after the question 10

representing the end of the Section A of the questionnaire. Questions 1-10 are considered to be the

general questions, questions 11-33 are considered to be topic specific questions, and question 34-

44 are recognized as logical and feelings questions of the questionnaire. There was a 1-7 point scale

used in certain parts of the questionnaire in order to measure and evaluate the level of trust of the

respondents towards certain questions and statements in the questionnaire. On this scale, 1 means

completely agree, 2 means somewhat agree, 3 means more agree than disagree, 4 means I do not

know, 5 means more disagree than agree, 6 means somewhat disagree, and 7 means completely

disagree. This scale was explained to each of the respondents prior to each section where the scale

was used. The 1-7 point scale was used in Section B and Section C of the questionnaire.

This Analytical part of the paper contains four subsections, where each subsection holds

certain results obtained from the questionnaire responses. The first subsection, named

Characteristic of seriousness of illness, provides responses and results of the Section A of the

questionnaire as well as comments on the results. The responses are presented in tables. Except for

responses of the questions 1, 2, and 8, where the results are presented in one table a question, all

the other responses for questions 3-7 and 9-10 are divided into two tables a question according to

gender. The first table of the two always shows results from the responses of the male respondents.

These tables provide results separated for the specified age groups. The second subsection, named

Analysis and results of comparative advertising, presents responses and results of the Section B of

the questionnaire as well as comments on the results. This subsection is divided into three smaller 

13

parts. Each of these parts present results of the respondents after reading different blood glucose

story. Hence, three blood glucose stories and three smaller parts of this subsection. The results are

again presented in tables; however, the results are only provided for the different age groups and

are not separated by gender. The third subsection, named Transferability of changed attitudes to

other domains, shows results and responses to the Section C of the questionnaire as well as

comments on the results. The responses are again presented in tables and all the responses are

provided in two tables per question as they are again divided according to gender. Again, the first

table from the two present data collected from male respondents and all these tables provide results

separated for the specified age groups. The fourth and last subsection of this part of the paper,

named Commercialization, presents statistical analysis computations for validation of the

hypothesis and goals set in the introduction. It also mentions several thoughts on possible

commercialization process of the inclusion and utilization of computer simulation in the health care

for the purpose of treatment of people suffering with serious diseases such as diabetes and possible

use of simulations for the betterment of life quality for people.

Final Questionnaire

Section A – General Questions

1. What is your gender?

a) Male

b) Female

2. What is your age group (in years)?

a) 0-15 (Parent completing the questionnaire on behalf of a child)

b) 15-25

c) 25-45

d) 45-60

e) 60-80

3. How many times a day do you see an advertisement in any form?

a) Never

b) Once or twice

c) Three to five times

d) Five to ten times

e) More than ten times

4. My attitude towards an advertisement generally is (please select an answer to all the options

a-c):

a) Good/Bad

b) Positive/Negative

c) Favorable/Unfavorable 

14

5. When I see an advertisement highlighting a medicine or a healing method, it does catch my

attention and makes me read it:

a) Every time

b) Most of the times

c) Sometimes (every now and then)

d) Rarely

e) Never

6. Me or my family member(s) suffer with the following diseases (Select all that applies):

a) Diabetes

b) Cancer

c) Stroke and/or heart attack

d) High blood pressure

e) Issues with movement apparatus

f) Allergies

g) None of the above

7. I see a doctor or a physician:

a) Once a month, regularly

b) Four times a year, regularly

c) Twice a year, regularly

d) Once a year, regularly

e) Irregularly

f) Only when acute illness occurs

8. I suffer with diabetes

a) Yes

b) No

9. Last time I read about new research techniques and modern methods in medicine was:

a) A week ago

b) A month ago

c) A year ago

d) I do not care for it

10. I have read or heard about new medicine research in the following medical environment

(Select all that applies):

a) Diabetes

b) Cancer

c) Stroke and/or heart attack

d) High blood pressure

e) Issues with movement apparatus

f) Allergies

g) None of the above

15

Section B – Topic Specific Questions

Here, the respondents are divided into three groups. Each group is provided with a different

blood glucose story (advertisement). There are three different advertisements (stories). First story

deals with blood glucose simulations performed on blood samples of pregnant women. Second

story deals with blood glucose simulations performed on blood samples of laboratory rats. Third

story deals with a special diet using mushroom rei-Shi in order to reduce high blood sugar levels

of a middle age patient recommended by a physician.

Instructions: Please read the following article containing a story (an advertisement) dealing

with blood glucose (blood sugar).

First story - The application of a computer simulation in the medical environment for testing

purposes in order to analyze blood glucose levels of pregnant women.

Simulations (computer based mathematical algorithms) were used and applied for testing

purposes in order to analyze blood glucose (blood sugar) levels of pregnant women. The women

were divided into three distinct categories according to their weight due to BMI standards. The

three categories were normal weight, overweight, and obese. The blood glucose levels of the

pregnant women were measured every five minutes over the time frame of four days. The

simulations were designed to detect return meal intake times of the women. In other words, the

simulations were developed in order to return time frames in which a certain woman ate only using

the measured values of blood glucose without any additional information from the user. The

simulations were able to achieve a time frame detections with 86% accuracy. Therefore, these

simulations could have, after further improvements, considerable impact on the quality of medical

care, life betterment, and possible life extension for people and patients suffering with diabetes.

Moreover, the simulations could have considerable impact on the quality of medical care for

pregnant women, who have higher probability to develop diabetes during the pregnancy or

eventually after giving birth.

Second story - The application of a computer simulation in the medical environment in

order to analyze blood glucose levels of laboratory rats.

Simulations (computer based mathematical algorithms) were utilized and applied for

testing purposes in order to analyze blood glucose (blood sugar) levels of 4 laboratory rats as test

subjects. The blood glucose levels of these laboratory rats were measured every ten seconds over

the time frame of three months. These measurements were used in the computer simulations in

order to make blood glucose levels predictions of the test subjects after meal intake. The

simulations were designed so that the blood glucose measurements were divided into two

fragments. The first fragment was used as a history database for the simulation in a way that it was

compiled of the measurements taken over the time frame of the first month and a half of the

measurement process. The second fragment was compiled of the measurements taken over the rest

of the measurement process, hence the second month and a half. The simulation used the first

fragment of the data in order to make predictions of the characteristics and behavior of the blood

glucose values and levels of the four laboratory rats. Therefore, the simulations worked in a way

that it used a month and a half of real blood glucose measurements and using the measurement

data, the simulation returned prediction values of the blood glucose levels of the rats after meal 

16

intake for the other month and a half. The prediction values returned by the simulations were then

compared to the actual measurements. There were multiple simulations developed, some of them

were more than 90% accurate when compared to the real measured values. Therefore, these

simulations could have, after further improvements, considerable impact on the quality of medical

care, life betterment, and possible life extension for people and patients suffering with diabetes.

Third story - The utilization of a special diet using the mushroom Rei-Shi in order to reduce

high values of sugar in blood of a patient.

A middle aged man suffered with injury during a motorcycle accident. Six months after the

accidents, his wounds were still not healing and closing as it would be desired since he has also

suffered with obesity, high blood pressure, and diabetes for roughly 5 years. His overall health

conditions were very limiting for overall movement and life in general. One doctor recommended

a special diet for him with regular intake of mushroom Rei-Shi. After two months positive changes

started occurring, he lost some weight, his high blood pressure was stabilized, his diabetes was

significantly improved, and his wounds were completely healed. The patient continued in the

recommended treatment method for a couple more weeks very satisfied with his improved health

conditions.

Instructions for the questions 11-29. Please select your answer on a scale 1-7 where:

1 means completely agree

2 means somewhat agree

3 means more agree than disagree

4 means I do not know

5 means more disagree than agree

6 means somewhat disagree

7 means completely disagree

11. Simulations (computer based mathematical models) can make a positive impact in the

medical environment on the health conditions of animals.

1.....2.....3.....4.....5.....6.....7

12. Widespread healing and recovery actions and treatments can make a positive impact on the

health conditions of animals.

1.....2.....3.....4.....5.....6.....7

13. Self-control and self-treatment (guided by animal caregivers) can make a positive impact

on the health conditions of animals.

1.....2.....3.....4.....5.....6.....7

14. Simulations (computer based mathematical models) can make a positive impact in the

medical environment on the health conditions of people.

1.....2.....3.....4.....5.....6.....7

15. Widespread healing and recovery actions and treatments can make a positive impact on the

health conditions of people.

1.....2.....3.....4.....5.....6.....7

17

16. Self-control and self-treatment can make a positive impact on the health conditions of

people.

1.....2.....3.....4.....5.....6.....7

17. Simulations can make a positive impact on the development and research in medicine and

overall medical environment.

1.....2.....3.....4.....5.....6.....7

18. Widespread healing and recovery actions and treatments can make a positive impact on the

development and research in medicine and overall medical environment.

1.....2.....3.....4.....5.....6.....7

19. Self-control and self-treatment can make a positive impact on the development and research

in medicine and overall medical environment.

1.....2.....3.....4.....5.....6.....7

20. Simulations can indirectly influence blood glucose (blood sugar) levels of people who ARE

NOT suffering with diabetes.

1.....2.....3.....4.....5.....6.....7

21. Widespread healing and recovery actions and treatments can indirectly influence blood

glucose (blood sugar) levels of people who ARE NOT suffering with diabetes.

1.....2.....3.....4.....5.....6.....7

22. Self-control and self-treatment can indirectly influence blood glucose (blood sugar) levels

of people who ARE NOT suffering with diabetes.

1.....2.....3.....4.....5.....6.....7

23. Simulations can indirectly influence blood glucose (blood sugar) levels of people who ARE

suffering with diabetes.

1.....2.....3.....4.....5.....6.....7

24. Widespread healing and recovery actions and treatments can indirectly influence blood

glucose (blood sugar) levels of people who ARE suffering with diabetes.

1.....2.....3.....4.....5.....6.....7

25. Self-control and self-treatment can indirectly influence blood glucose (blood sugar) levels

of people who ARE suffering with diabetes.

1.....2.....3.....4.....5.....6.....7

26. Simulations can positively influence serious diseases.

1.....2.....3.....4.....5.....6.....7

27. Widespread healing and recovery actions and treatments can positively influence serious

diseases.

1.....2.....3.....4.....5.....6.....7

18

28. Self-control and self-treatment can positively influence serious diseases.

1.....2.....3.....4.....5.....6.....7

29. If simulations return good results on animal subjects, it is good enough reason for the

purpose of starting testing these simulations on human patients.

1.....2.....3.....4.....5.....6.....7

30. Simulations are believable when they lead to a life extension of an individual for at least

the time frame of:

a) A day

b) A month

c) A year

d) 10 years

e) 30 years

31. Widespread healing and recovery actions and treatments are believable when they lead to

a life extension of an individual for at least the time frame of:

a) A day

b) A month

c) A year

d) 10 years

e) 30 years

32. Self-control and self-treatment are believable when they lead to a life extension of an

individual for at least the time frame of:

a) A day

b) A month

c) A year

d) 10 years

e) 30 years

33. My attitude toward the advertisement (story) provided in this questionnaire is (please select

an answer to all the options a-c):

a) Good/Bad

b) Positive/Negative

c) Favorable/Unfavorable

Section C – Questions on Logic and Feelings

Instructions for the questions 34-44. Please select your answer on a scale 1-7 where:

1 means completely agree

2 means somewhat agree

3 means more agree than disagree

4 means I do not know

5 means more disagree than agree

6 means somewhat disagree

7 means completely disagree

19

34. I prefer self-therapy, self-control, and self-treatment in order to take responsibility for my

health conditions.

1.....2.....3.....4.....5.....6.....7

35. Generally, I prefer simple problem solutions rather than complicated ones.

1.....2.....3.....4.....5.....6.....7

36. Generally, I prefer problem solutions which require short periods of time rather than long

ones.

1.....2.....3.....4.....5.....6.....7

37. I prefer learning and mastering new solutions technique in order to use it in a routine fashion

to solve problems.

1.....2.....3.....4.....5.....6.....7

38. I like solving and dealing with assignments which are challenging and require deep

thinking.

1.....2.....3.....4.....5.....6.....7

39. Generally, I like learning new problem solving techniques.

1.....2.....3.....4.....5.....6.....7

40. I prefer challenging situations which require abstract way of thinking.

1.....2.....3.....4.....5.....6.....7

41. After a challenging task is solved, I feel relief rather than satisfaction.

1.....2.....3.....4.....5.....6.....7

42. I am generally satisfied with achieving a goal without really caring about the way of getting

there.

1.....2.....3.....4.....5.....6.....7

43. I do not prefer tasks and assignments which require emotions or which are emotional

themselves.

1.....2.....3.....4.....5.....6.....7

44. If a mistake occurs during a thought process of a problem solution, the mistake is usually

caused by emotions involved during the process.

1.....2.....3.....4.....5.....6.....7

3.1 Characteristics of seriousness of illness

This section provides the results of the first section, Section A, of the questionnaire, which

was focused on general statements and questions. This section of the questionnaire divided the

respondents into specific age groups and by gender. The questions and statement in this section of

the questionnaire attempt to discover the information of attitudes of the respondents in the different 

20

age groups towards general advertising, advertising concentrated on health and medicine,

experience with particular illnesses, regularity of visiting a doctor or a physician, and recent

knowledge of medicine related research works and studies. This part of the paper provides

responses and results to the first ten questions of the questionnaire. All the responses are provided

in tables and comments on the results are provided after the last table of this section. A detailed

description of information is in each table is provided in the paragraph below. Each table is

numbered and titled.

There is 17 tables in this section. Results to the first question are provided in Table 1 and

results to the second question are provided in Table 2. For questions 3, 4, 5, 6, and 7, the responses

are always provided in two tables a question, thus Tables 3-12. The first table always provides the

responses of male respondents in the different age groups and the second table always provides the

responses of female respondents in the different age groups. Results of the eight question are in

Table 13. For the questions 9 and 10, the same approach as for questions 3-7 applies. Therefore,

responses to the questions 9 and 10 are in Tables 14, 15, 16, and 17.

Table 1 provides in the information of a number of respondents separated by gender. Table

2 provides the number of respondents in each age group, also divided by gender. There are five

different age groups, 0-15 years, 15-25 years, 25-45 years, 45-60 years, and 60-80 years. Tables 3

and 4 provide the pieces of information of how often in a day the respondents see an advertisement

in any form. Tables 5 and 6 highlight the data of the attitudes of the respondents towards

advertisements in general. Tables 7 and 8 present the information of the attitudes of the respondents

towards advertisements concentrated on a medicine or a healing treatment method. Tables 9 and

10 highlight the experience of the respondents or respondents’ family members with certain

chronical diseases. Tables 11 and 12 provide the information of regularity of physician or doctor

visits of the respondents. Table 13 gives the information of how many respondents suffer with

diabetes. Tables 14 and 15 provide the information of how long ago the respondents read about

new researches in medicine field. Lastly, Tables 16 and 17 point out the information on the area of

medicine research about which the respondents read or heard about.

Table 1 Respondents divided by gender

 Heading – Gender of the respondents

 Legend – Way of representation the number of respondents

Gender Number of Respondents % representation [%]

Male 112 48.7

Female 118 51.3

Source: Own processing

21

Table 2 Respondents divided into age groups

 Heading – Age groups of the respondents

 Legend – Gender information

Age Group [Years] Male Female

0-15 22 16

15-25 28 26

25-45 23 27

45-60 26 31

60-80 13 18

Source: Own Processing

Table 3 Frequency in which male respondents see any form of advertisement a day

 Heading – Frequency of advertisement noticing

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Never 0 0 0 0 0

Once or twice 2 0 0 0 0

Three to five

times

6 4 4 4 5

Five to ten times 10 7 10 12 7

More than ten

times

4 17 9 10 1

Source: Own Processing

Table 4 Frequency in which female respondents see any form of advertisement a day

 Heading – Frequency of advertisement noticing

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Never 0 0 0 0 0

Once or twice 3 2 0 1 2

Three to five

times

8 3 3 4 3

Five to ten times 5 7 17 5 8

More than ten

times

0 14 7 21 5

Source: Own Processing

22

Table 5 Attitudes of male respondents towards general advertisements

 Heading – Attitudes towards advertisements

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Good 7 3 6 8 7

Bad 15 25 17 18 6

Positive 6 3 8 11 8

Negative 16 25 15 15 5

Favorable 6 4 7 8 8

Unfavorable 16 24 16 18 5

Source: Own Processing

Table 6 Attitudes of female respondents towards general advertisements

 Heading – Attitudes towards advertisements

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Good 4 3 5 9 10

Bad 12 23 22 22 8

Positive 5 3 4 10 11

Negative 11 23 23 21 7

Favorable 4 3 5 8 10

Unfavorable 12 23 22 23 8

Source: Own Processing

Table 7 Regularity in which male respondents notice advertisement on medicine or treatment

method

 Heading – Regularity of noticing health focused advertisement

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Every time 2 0 0 1 2

Most of the

times

6 0 2 2 6

Sometimes 6 4 2 6 3

Rarely 5 7 4 10 1

Never 3 17 15 7 1

Source: Own Processing

23

Table 8 Regularity in which female respondents notice advertisement on medicine or treatment

method

 Heading – Regularity of noticing health focused advertisement

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Every time 1 0 0 0 1

Most of the

times

4 0 2 3 5

Sometimes 4 4 4 7 7

Rarely 4 8 6 10 3

Never 3 14 15 11 2

Source: Own Processing

Table 9 Male respondents with family experience with chronical diseases

 Heading – Illness

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Diabetes 4 2 5 8 6

Cancer 3 2 3 4 3

Stroke/heart

attack

5 3 3 5 4

High blood

pressure

9 3 13 22 12

Issues with

movement

12 5 11 19 10

Allergies 14 18 16 11 4

None of the

above

0 2 0 0 0

Source: Own Processing

24

Table 10 Female respondents with family experience with chronical diseases

 Heading – Illness

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Diabetes 5 4 6 12 11

Cancer 2 2 4 10 8

Stroke/heart

attack

2 3 4 9 6

High blood

pressure

7 6 15 21 16

Issues with

movement

7 6 11 20 17

Allergies 11 11 16 14 5

None of the

above

0 1 0 0 0

Source: Own Processing

Table 11 Regularity in which male respondents see a physician or a doctor

 Heading – Regularity of visit

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Once a month 1 1 2 6 5

Four times a

year

4 1 4 5 3

Twice a year 10 5 8 11 5

Once a year 5 7 7 4 0

Irregularly 0 0 0 0 0

Only when

acute illness

2 14 2 0 0

Source: Own Processing

Table 12 Regularity in which female respondents see a physician or a doctor

 Heading – Regularity of visit

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Once a month 2 0 2 3 3

Four times a

year

4 1 5 16 9

Twice a year 7 18 16 8 6

Once a year 3 4 3 4 0

Irregularly 0 0 0 0 0

Only when

acute illness

0 3 1 0 0

Source: Own Processing

25

Table 13 Respondents who suffer with diabetes disease

 Heading – Gender

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Male 1 1 2 6 4

Female 2 0 2 3 3

Source: Own Processing

Table 14 Last time male respondents read about new research in medicine

 Heading – Time specification

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Week ago 2 2 4 3 7

Month ago 9 6 3 5 2

Year ago 3 5 6 7 1

Do not care 8 15 10 11 3

Source: Own Processing

Table 15 Last time female respondents read about new research in medicine

 Heading – Time specification

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Week ago 4 4 6 6 7

Month ago 6 5 3 4 2

Year ago 2 4 6 7 4

Do not care 4 13 12 14 5

Source: Own Processing

26

Table 16 Area of medicine that male respondents read or heard about

 Heading – Area of medicine

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Diabetes 2 0 3 7 3

Cancer 2 0 1 3 4

Stroke/heart

attack

1 1 2 4 4

High blood

pressure

9 3 8 14 6

Issues with

movement

7 2 9 13 7

Allergies 13 17 7 5 2

None of the

above

1 7 2 0 1

Source: Own Processing

Table 17 Area of medicine that female respondents read or heard about

 Heading – Area of medicine

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Diabetes 2 1 2 4 3

Cancer 0 0 1 3 5

Stroke/heart

attack

1 0 1 2 4

High blood

pressure

2 2 11 14 7

Issues with

movement

3 4 6 15 8

Allergies 7 13 9 8 2

None of the

above

3 9 2 1 1

Source: Own Processing

The total number of respondents was 230. Out of the 230 respondents, 112 respondents

were male and 118 were female. In the age group of 0-15 years, for which the questionnaires were

completed by the parents of the children, there was 22 male respondents, meaning 22 boys, and 16

female respondents, meaning 16 girls. In the age group of 15-25 years, there was 28 male and 26

female respondents. In the age group of 25-45 years, there was 23 male and 27 female respondents.

In the age group of 45-60 years, there was 26 male and 31 female respondents. In the age group of

60-80 years, there was 13 male and 18 female respondents. Therefore, there was 38 respondents in

the youngest groups, there was 54 respondents in the second youngest group, there was 50

respondents in the middle group, there was 57 respondents in the second oldest group, and there

was 31 respondents in the oldest group.

None of the male and female respondent see no advertisement in a day. All of the

respondents see an advertisement at least once a day. Majority of the male respondents see 

27

advertisements more than five times a day. Majority of the male respondents in the age group 15-

25 years see advertisements more than ten times a day. Most of the female respondents see

advertisements more than five times a day. Female respondents in the age groups of 15-25 and 45-

60 years see ads more than ten times a day. All the respondents in age groups different than 60-80

years do not like advertisements, their attitudes towards them are bad rather than good, negative

rather than positive, and unfavorable rather than favorable. Respondents in the age group 15-25

years like advertisements the least out of all the respondents. More than 50% of respondents in the

age group 60-80 years like general advertisements. When it comes to noticing and reading

advertisements on a medicine or a treatment method, male respondents in the age groups 0-15 and

60-80 are likely to notice and read the advertisements most of the times. Male respondents in age

groups 15-25 and 25-45 years close to never notice and read the advertisements. Male respondents

in the age group 45-60 years sometimes or barely notice and read them. Female respondents in the

age groups 15-25, 25-45, and 45-60 years rarely to never notice health related ads. The position of

female respondents in the age group 0-15 years differ drastically and it is all over the answer

options. Female respondents in the age group 60-80 years mostly or sometimes notice the

advertisements and want to read them.

This paragraph highlights the information of the respondents’ own experience or experience

among family members with several diseases. Male respondents in the age group 0-15 have

experience mostly with allergies and issues with movement apparatus, some with high blood

pressure, and small to none with cancer. Male respondents in the age group 15-25 have experience

mostly with allergies and small to none with diabetes and cancer. Male respondents in the age

group 25-45 have experience mostly with high blood pressure, issues with movement apparatus,

and allergies and small to none with cancer, strokes, and heart attacks. Male respondents in the age

group 45-60 have experience mostly with high blood pressure and issues with movement apparatus,

some with allergies, and small to none with cancer. Male respondents in the age group 60-80 have

experience mostly with high blood pressure and issues with movement apparatus, and small to

none with cancer. Female respondents in the age group 0-15 have experience mostly with allergies,

some with high blood pressure and issues with movement apparatus, and small to none with cancer,

stroke, and heart attack. Female respondents in the age group 15-25 have experience mostly with

allergies, some with high blood pressure and issues with movement apparatus, and small to none

with cancer. Female respondents in the age group 25-45 have experience mostly with allergies and

high blood pressure, some with issues with movement apparatus, and small to none with cancer,

stroke and heart attack. Female respondents in the age group 45-60 have experience mostly with

high blood pressure and issues with movement apparatus and small to none with stroke and heart

attack. Female respondents in the age group 60-80 have experience mostly with high blood pressure

and issues with movement apparatus and small to none with allergies.

This paragraph highlights the information of the regularity in which the respondents visit a

doctor or a physician. None of the respondents selected the answer option irregularly. Male

respondents in the age group 0-15 years typically see a doctor twice a year and rarely once a month.

Male respondents in the age group 15-25 years typically visit a doctor only when an acute illness

occurs and rarely once a month or four times a year. Male respondents in the age group 25-45 years

typically see a doctor once or twice a year and hardly when an acute illness occurs. Male

respondents in the age group 45-60 years typically see a doctor twice a year and none of them see

a physician only when an acute illness occurs. Male respondents in the age group 60-80 years

classically visit a doctor once a month and two or four times a year, none of the respondents in this

age group see a physician only when an acute illness occurs. Female respondents in the age group

0-15 years mostly see a doctor twice a year and none of them only when acute illness occurs. 

28

Almost all the female respondents in the age group 15-25 years visit a doctor twice a year and none

of them once a month. Almost all the female respondents in the age group 25-45 years see a doctor

twice a year and hardly anyone only when acute illness occurs. Majority of the female respondents

in the age group 45-60 years see a doctor four times a year, roughly middle value in the responses

twice a year, and none of the respondents only when acute illness occurs. Female respondents in

the age group 0-15 years mostly a see doctor two or four times a year and none of them only when

acute illness occurs.

Overall, there were 24 out of 230 respondents suffering with diabetes disease. The diabetics

were one boy and two girls from the youngest age group, one boy from the second youngest age

group, two men and two women from the middle age group, six men and three women from the

second oldest age group, and four men and three women from the oldest age group. Thus, most

diabetics among the respondents were males in the second oldest group.

This last paragraph deals with the time frame of how long ago the respondents heard about

any kind of research in medical environment and then information of specificity of the medicine

field they read or heard about. The parents of the youngest group of male respondents usually heard

about allergies or high blood pressure researches around a month ago. They almost never hear of

stroke and heart attack researches. The parents of the youngest group of female respondents mostly

heard about allergies researches around a month ago. None of them heard about cancer research.

The male respondents in the second youngest age group often do not care about researches.

Nonetheless, when they heard of a research, it was research on allergies. The female respondents

in the second youngest age group often do not care about researches. If they did hear of a research,

it was research on allergies. Male respondents in the middle group usually do not show an interest

in new researches, when they do however, it is in movement apparatus issues, high blood pressure,

and allergies. Female respondents in the middle group usually do not show an interest in new

researches, when they do hear about a research though, it is in high blood pressure or allergies.

Male respondents in the second oldest group usually do not care for new researches, when they do

however, it is in movement apparatus issues or high blood pressure areas. Female respondents in

the second oldest group typically do not care for new researches, when they do hear about a

research though, it is in high blood pressure or movement apparatus. Respondents in the oldest

group usually heard or read about new researches a week ago. Men mostly about research in

movement apparatus issues areas and women typically in high blood pressure and movement

apparatus issues.

 

3.2 Analysis and results of comparative advertising

This section provides the results of the second section, Section B, of the questionnaire,

which was focused on asking topic specific questions and statement opinions. This section is

divided into three subsections according to the story or advertisement that was provided to the

respondents prior to completion of the Section B of the questionnaire. There were three different

stories about blood glucose. The first story explained the situation where computer simulations

were created in order to analyze and test blood glucose levels in blood samples of pregnant women

patients for possible impact on treatment of people suffering with diabetes. The second story

explained the situation where computer simulations were created and used in order to analyze and

test blood glucose levels in blood samples of four different laboratory rats for possible impact on

treatment of people suffering with diabetes. The third story explained the situation where a doctor

recommended a special diet using mushroom Rei-Shi to help a middle aged man with reducing 

29

high values of sugar in his blood. Each respondent was provided with only one of the three stories.

This was done in order to see and analyze the differences in the responses of the respondents after

being acquainted to different stories.

Each of the subsections provides responses and results for one blood glucose story. The

first subsection deals with the responses of the respondents after reading the first story, the second

subsection deals with the responses of the respondents after reading the second story, and the third

subsection deals with the responses of the respondents after reading the third story. All the

responses and results in this section are provided in tables. Detailed descriptions of the pieces of

information demonstrated in each table in each subsection are provided in the first paragraph of the

given subsection. Results of each subsection are also commented on below all the tables of each

subsection. This section does not provide results and responses of the respondents divided by

gender, it only provides the responses of the respondents divided into the different age groups.

3.2.1 First story results analysis

This subsection provides responses results of the Section B of the questionnaire, where the

respondents were provided the advertisement of the utilization of computer simulations for blood

glucose analysis purposes on blood glucose measurements of pregnant women. There is 23 tables

in this subsection. These tables provide responses and results to the questions 11-33 of the

questionnaire. For the questions that relate to tables 18-36, answers were given on the scale 1-7,

where 1 means completely agree, 2 means somewhat agree, 3 means more agree than disagree, 4

means I do not know, 5 means more disagree than agree, 6 means somewhat disagree, and 7 means

completely disagree.

Results to the question 11 are provided in Table 18, results to the question 12 are presented

in Table 19, results to the question 13 are shown in Table 20, results to the question 14 are

demonstrated in Table 21, results to the question 15 are delivered in Table 22, results to the question

16 are delivered in Table 23, results to the question 17 are shown in Table 24, results to the question

18 are provided in Table 25, results to the question 19 are presented in Table 26, results to the

question 20 are demonstrated in Table 27, results to the question 21 are provided in Table 28,

results to the question 22 are shown in Table 29, results to the question 23 are presented in Table

30, results to the question 24 are provided in Table 31, results to the question 25 are demonstrated

in Table 32, results to the question 26 are highlighted in Table 33, results to the question 27 are

given in Table 34, results to the question 28 are presented in Table 35, results to the question 29

are shown in Table 36, results to the question 30 are delivered in Table 37, results to the question

31 are provided in Table 38, results to the question 32 are provided in Table 39, and results to the

question 33 are provided in Table 40.

Table 18 provides the information of the stance of the respondents on the issue of simulation

making a positive impact on health conditions of animals. Table 19 delivers the information of the

position of the respondents on the issue of widespread healing and recovery actions making a

positive impact on health conditions of animals. Table 20 provides the information of the stance of

the respondents on the issue of self-treatment making a positive impact on health conditions of

animals. Table 21 presents the information of the standpoint of the respondents on the issue of

simulation making a positive impact on health conditions of people. Table 22 delivers the

information of the position of the respondents on the issue of widespread healing and recovery

actions making a positive impact on health conditions of people. Table 23 provides the information

of information of the stand of the respondents on the issue of self-treatment making a positive

impact on health conditions of people. Table 24 gives the information if the respondents think that 

30

simulations can make a positive impact on research in medical environment. Table 25 demonstrates

the information if the respondents think that widespread healing and recovery actions can make a

positive impact on research in medical environment. Table 26 provides the information if the

respondents think that self-treatment can make a positive impact on research in medical

environment. Table 27 shows results of the responses of the respondents whether or not simulations

can indirectly influence blood glucose levels of people not suffering with diabetes. Table 28

provides results of the responses of the respondents whether or not widespread healing and

recovery actions can indirectly influence blood glucose levels of people not suffering with diabetes.

Table 29 provides results of the responses of the respondents whether or not self-treatment can

indirectly influence blood glucose levels of people not suffering with diabetes. Table 30 provides

results of the responses of the respondents whether or not simulations can indirectly influence blood

glucose levels of people suffering with diabetes. Table 31 demonstrates results of the responses of

the respondents whether or not widespread healing and recovery actions can indirectly influence

blood glucose levels of people suffering with diabetes. Table 32 gives results of the responses of

the respondents whether or not self-treatment can indirectly influence blood glucose levels of

people suffering with diabetes. Table 33 provides the information of simulations as positive

influence for serious diseases. Table 34 presents the information of widespread healing and

recovery actions as positive influence for serious diseases. Table 35 presents the information of

self-treatment as positive influence for serious diseases. Table 36 provides the information of

whether or not simulations with sufficient results on animal subject should be tested on human

patients. Table 37 provides the information of how long simulation needs to prolong life for in

order to be believable. Table 38 demonstrates the information of how long widespread healing and

recovery actions needs to prolong life for in order to be believable. Table 39 provides the

information of how long self-treatment needs to prolong life for in order to be believable. Table 40

presents the information of the attitudes of the respondents towards the provided story.

Table 18 Responses on the positive impacts of simulations on health conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 4 5 4 0

2 3 8 7 6 4

3 5 5 6 5 1

4 1 0 0 2 4

5 1 2 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

31

Table 19 Responses on the positive impacts of widespread healing and recovery actions on health

conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 9 6 5 2

2 5 4 5 5 4

3 3 4 7 6 1

4 1 0 0 1 2

5 1 2 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 20 Responses on the positive impacts of self-treatment on health conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 4 8 7 5 2

2 3 4 5 7 4

3 3 5 6 4 1

4 1 0 0 1 2

5 1 2 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 21 Responses on the positive impacts of simulations on health conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 7 7 4 1

2 5 8 8 6 4

3 3 2 3 5 1

4 1 0 0 2 3

5 1 2 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

32

Table 22 Responses on the positive impacts of widespread healing and recovery actions on health

conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 7 8 9 7 5

2 4 4 7 6 3

3 0 5 2 4 1

4 1 1 0 0 0

5 0 1 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 23 Responses on the positive impacts of self-treatment on health conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 10 10 12 6

2 9 5 5 4 2

3 1 4 3 2 1

4 0 0 0 0 1

5 0 0 0 0 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 24 Responses on the positive impact of simulation on development and research in medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 7 12 9 8 2

2 2 6 7 5 4

3 2 1 2 4 1

4 0 0 0 0 2

5 1 0 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

33

Table 25 Responses on the positive impact of widespread healing and recovery actions on

development and research in medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 2 4 4 3

2 2 6 7 8 4

3 8 9 7 5 2

4 1 2 0 0 1

5 0 0 1 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 26 Responses on the positive impact of self-treatment on development and research in

medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 0 0

2 0 0 0 0 0

3 1 0 0 0 0

4 1 1 0 1 1

5 1 4 5 4 1

6 5 8 7 6 2

7 4 6 8 7 6

Source: Own Processing

Table 27 Responses on simulations as indirect influence of blood glucose for people without

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 4 3 2 0

2 2 5 6 3 4

3 5 6 6 7 3

4 4 4 3 5 3

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

34

Table 28 Responses on widespread healing and recovery actions as indirect influence of blood

glucose for people without diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 6 7 7 3

2 5 8 5 5 4

3 3 5 6 3 1

4 1 0 1 2 1

5 0 0 0 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 29 Responses on self-treatment as indirect influence of blood glucose for people without

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 6 9 8 8 5

2 3 6 6 7 3

3 2 4 4 2 1

4 1 0 1 0 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 30 Responses on simulations as indirect influence of blood glucose for people with diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 5 8 4 5 2

2 4 5 6 3 4

3 2 4 6 7 3

4 1 2 2 2 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

35

Table 31 Responses on widespread healing and recovery actions as indirect influence of blood

glucose for people with diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 6 11 10 9 5

2 3 5 5 5 4

3 2 3 3 2 0

4 1 0 1 1 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 32 Responses on self-treatment as indirect influence of blood glucose for people with

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 6 11 9 8 5

2 3 4 6 8 3

3 2 4 3 1 1

4 1 0 1 0 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 33 Responses on simulations as a positive influence of serious diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 3 2 2 0

2 1 6 5 3 4

3 6 8 6 9 3

4 1 2 5 2 1

5 2 0 1 1 0

6 0 0 0 0 1

7 0 0 0 1 1

Source: Own Processing

36

Table 34 Responses on widespread healing and recovery actions as a positive influence of serious

diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 3 4 2 0

2 3 5 5 5 4

3 5 10 8 9 3

4 1 0 1 1 1

5 1 0 1 1 0

6 0 1 0 0 2

7 0 0 0 0 0

Source: Own Processing

Table 35 Responses on self-treatment as a positive influence of serious diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 0 0

2 0 0 0 0 0

3 1 2 1 2 1

4 3 1 3 1 1

5 4 8 6 2 2

6 1 4 5 5 0

7 0 4 3 8 6

Source: Own Processing

Table 36 Responses on testing simulations on human patients when results are sufficient on animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 4 7 8 7 0

2 3 5 4 5 1

3 3 6 5 4 3

4 1 0 1 1 4

5 1 0 1 1 0

6 0 1 0 0 2

7 0 0 0 0 0

Source: Own Processing

37

Table 37 Responses on the time frame by which simulations need to prolong life in order to be

believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 0 0 0 1 1

Year 3 8 11 9 7

10 years 7 7 6 8 2

30 years 2 3 1 0 0

Source: Own Processing

Table 38 Responses on the time frame by which widespread healing and recovery actions need to

prolong life in order to be believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 0 1 2 2 6

Year 7 7 9 11 2

10 years 4 9 7 5 2

30 years 1 1 0 0 0

Source: Own Processing

Table 39 Responses on the time frame by which self-treatment need to prolong life in order to be

believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 2 1 0 1 5

Year 7 12 11 16 3

10 years 3 5 6 1 2

30 years 0 0 1 0 0

Source: Own Processing

38

Table 40 The attitude of the respondents towards the first story

 Heading – Attitude towards the provided story

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Good 10 14 15 14 9

Bad 2 4 3 4 1

Positive 10 14 15 14 9

Negative 2 4 3 4 1

Favorable 10 14 13 13 9

Unfavorable 2 4 5 5 1

Source: Own Processing

The respondents in this section were provided the story computer simulation utilization for

analysis and testing purposes of blood glucose levels from blood samples of pregnant women.

There was 76 respondents who read the first story and then responded to the questions and

statements in the Section B of the questionnaire. Nearly all the respondents younger than 60 years

were pro the notion that computer simulation could make positive impacts on health conditions of

animals. Most of the people older than 60 years do not have an opinion on this issue. The same

applied for responses of the respondents when asked about the possible betterment of health

conditions of animals using healing and recovery actions and self-control. In these statements, older

people were more convinced that there treatment techniques could help. When asked the similar

question but on the possible betterment of health conditions of people, all the respondents were

strongly pro that all simulations, recovery and healing actions, and self-control can make positive

contributions. For simulations, roughly 40% of the oldest respondents were unsure about the

answer. With some exceptions in the oldest age group for simulations, all the respondents stated

that they hold the view that simulations and recovery actions can make positive contributions to

the research in medicine. However, when it came to self-control, nearly all the respondents think

that it cannot make positive contributions to the new research works in medical environment. All

the respondents think that simulations, healing actions, and self-control could eventually indirectly

influence blood glucose levels for both diabetics and people without suffering diabetes. When it

comes to possible treatments of serious diseases, respondents are strongly pro that healing and

recovery action can have positive influence, most of the people think that simulations could make

positive impacts, but most of the people think that self-control cannot really have a big positive

influence on serious diseases treatment.

Respondents younger than 60 years think that if simulations return good and sufficient

results on animal subjects, it is a good enough reason to start testing these simulations on humans.

Respondents older than 60 years mostly do not know about this issue. Respondents older than 25

years hold the view that if simulation would prove it could extent life of an individual by a year,

then the simulation would be believable. People younger than 25 years mostly thought that it should

prove life extension of 10 years. Respondents younger than 60 years think that healing and

treatment actions are believable if they would prove possible life extension of a year or 10 years.

Respondents over the age of 60 think that they would be believable if only point out to a life

extension of a month. Nearly all the respondents think that self-control is believable if it could

make life extension of a year, respondents older than 60 years think it is believable if it proved an

extension of a month. Attitudes towards the story provided for this group of respondent were very

affirmative as the respondents stated the story was good, positive, and favorable. 

39

3.2.2 Second story results analysis

This subsection provides responses results of the Section B of the questionnaire, where the

respondents were provided the advertisement of the utilization of computer simulations for blood

glucose analysis purposes on blood measurements of laboratory rats. There is 23 tables in this

subsection. These tables provide the results to the questions 11-33 of the questionnaire. For the

questions that relate to tables 41-59, answers were given on the scale 1-7, where 1 means

completely agree, 2 means somewhat agree, 3 means more agree than disagree, 4 means I do not

know, 5 means more disagree than agree, 6 means somewhat disagree, and 7 means completely

disagree.

Results to the question 11 are provided in Table 41, results to the question 12 are provided

in Table 42, results to the question 13 are presented in Table 43, results to the question 14 are

shown in Table 44, results to the question 15 are provided in Table 45, results to the question 16

are presented in Table 46, results to the question 17 are given in Table 47, results to the question

18 are highlighted in Table 48, results to the question 19 are stated in Table 49, results to the

question 20 are provided in Table 50, results to the question 21 are provided in Table 51, results to

the question 22 are presented in Table 52, results to the question 23 are demonstrated in Table 53,

results to the question 24 are shown in Table 54, results to the question 25 are given in Table 55,

results to the question 26 are demonstrated in Table 56, results to the question 27 are highlighted

in Table 57, results to the question 28 are given in Table 58, results to the question 29 are provided

in Table 59, results to the question 30 are presented in Table 60, results to the question 31 are

shown in Table 61, results to the question 32 are provided in Table 62, and results to the question

33 are demonstrated in Table 63.

Table 41 provides the information of the stance of the respondents on the issue of simulation

making a positive impact on health conditions of animals. Table 42 gives the information of the

position of the respondents on the issue of widespread healing and recovery actions making a

positive impact on health conditions of animals. Table 43 highlights the information of the stance

of the respondents on the issue of self-treatment making a positive impact on health conditions of

animals. Table 44 provides the information of the standpoint of the respondents on the issue of

simulation making a positive impact on health conditions of people. Table 45 demonstrates the

information of the position of the respondents on the issue of widespread healing and recovery

actions making a positive impact on health conditions of people. Table 46 tackles the information

of information of the stand of the respondents on the issue of self-treatment making a positive

impact on health conditions of people. Table 47 demonstrates the information if the respondents

think that simulations can make a positive impact on research in medical environment. Table 48

provides the information if the respondents think that widespread healing and recovery actions can

make a positive impact on research in medical environment. Table 49 presents the information if

the respondents think that self-treatment can make a positive impact on research in medical

environment. Table 50 shows results of the responses of the respondents whether or not simulations

can indirectly influence blood glucose levels of people not suffering with diabetes. Table 51

provides results of the responses of the respondents whether or not widespread healing and

recovery actions can indirectly influence blood glucose levels of people not suffering with diabetes.

Table 52 gives results of the responses of the respondents whether or not self-treatment can

indirectly influence blood glucose levels of people not suffering with diabetes. Table 53 provides

results of the responses of the respondents whether or not simulations can indirectly influence blood

glucose levels of people suffering with diabetes. Table 54 provides results of the responses of the 

40

respondents whether or not widespread healing and recovery actions can indirectly influence blood

glucose levels of people suffering with diabetes. Table 55 presents results of the responses of the

respondents whether or not self-treatment can indirectly influence blood glucose levels of people

suffering with diabetes. Table 56 provides the information of simulations as positive influence for

serious diseases. Table 57 provides the information of widespread healing and recovery actions as

positive influence for serious diseases. Table 58 shows the information of self-treatment as positive

influence for serious diseases. Table 59 demonstrates the information of whether or not simulations

with sufficient results on animal subject should be tested on human patients. Table 60 provides the

information of how long simulation needs to prolong life for in order to be believable. Table 61

provides the information of how long widespread healing and recovery actions needs to prolong

life for in order to be believable. Table 62 highlights the information of how long self-treatment

needs to prolong life for in order to be believable. Table 63 demonstrates the information of the

attitudes of the respondents towards the provided story.

Table 41 Responses on the positive impacts of simulations on health conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 5 9 8 7 2

2 4 5 6 5 4

3 2 4 2 4 2

4 1 2 0 2 2

5 1 0 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 42 Responses on the positive impacts of widespread healing and recovery actions on health

conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 7 4 3 1

2 6 6 5 6 3

3 3 5 7 8 4

4 2 0 0 1 2

5 0 2 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

41

Table 43 Responses on the positive impacts of self-treatment on health conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 5 4 3 2

2 4 4 8 6 4

3 6 9 4 8 2

4 0 1 0 1 2

5 1 1 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 44 Responses on the positive impacts of simulations on health conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 3 2 1 1

2 3 7 6 4 2

3 6 8 6 11 2

4 2 1 2 2 3

5 1 1 1 1 3

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 45 Responses on the positive impacts of widespread healing and recovery actions on health

conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 5 2 5 1

2 7 5 5 6 4

3 2 8 9 7 5

4 1 1 0 0 0

5 0 1 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

42

Table 46 Responses on the positive impacts of self-treatment on health conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 5 3 8 2

2 9 8 8 4 2

3 2 7 5 7 6

4 0 0 0 0 1

5 0 0 0 0 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 47 Responses on the positive impact of simulation on development and research in medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 7 12 9 8 2

2 2 6 5 6 5

3 3 2 2 4 1

4 0 0 0 0 2

5 1 0 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 48 Responses on the positive impact of widespread healing and recovery actions on

development and research in medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 2 4 3 2

2 2 7 6 6 3

3 7 8 5 8 4

4 1 3 1 1 2

5 0 0 1 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

43

Table 49 Responses on the positive impact of self-treatment on development and research in

medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 0 0

2 0 0 0 0 0

3 1 0 0 0 0

4 1 1 0 1 1

5 2 5 3 3 1

6 5 6 5 6 3

7 4 8 10 7 6

Source: Own Processing

Table 50 Responses on simulations as indirect influence of blood glucose for people without

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 2 1 0 0

2 2 4 5 3 2

3 5 9 7 9 4

4 5 5 3 6 5

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 51 Responses on widespread healing and recovery actions as indirect influence of blood

glucose for people without diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 6 7 7 3

2 5 8 5 5 4

3 4 6 4 5 2

4 1 0 1 2 1

5 0 0 0 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

44

Table 52 Responses on self-treatment as indirect influence of blood glucose for people without

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 6 9 6 3 3

2 3 6 8 7 5

3 3 5 2 8 2

4 1 0 1 0 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 53 Responses on simulations as indirect influence of blood glucose for people with diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 6 2 3 2

2 6 5 5 4 3

3 4 7 7 9 5

4 1 2 2 2 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 54 Responses on widespread healing and recovery actions as indirect influence of blood

glucose for people with diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 8 10 9 3

2 4 5 5 3 4

3 5 7 0 5 2

4 1 0 2 1 2

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

45

Table 55 Responses on self-treatment as indirect influence of blood glucose for people with

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 4 8 7 6 2

2 3 4 6 7 3

3 5 8 3 5 5

4 1 0 1 0 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 56 Responses on simulations as a positive influence of serious diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 3 3 2 0

2 3 6 3 4 2

3 6 9 5 9 4

4 1 2 5 2 3

5 2 0 1 1 0

6 0 0 0 0 1

7 0 0 0 1 1

Source: Own Processing

Table 57 Responses on widespread healing and recovery actions as a positive influence of serious

diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 3 2 2 0

2 3 7 6 4 5

3 5 9 7 11 4

4 2 0 1 1 1

5 1 0 1 1 0

6 0 1 0 0 1

7 0 0 0 0 0

Source: Own Processing

46

Table 58 Responses on self-treatment as a positive influence of serious diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 0 0

2 0 0 0 0 0

3 1 2 1 2 1

4 3 1 3 1 1

5 4 9 5 3 2

6 2 6 4 5 1

7 0 2 3 8 6

Source: Own Processing

Table 59 Responses on testing simulations on human patients when results are sufficient on animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 7 6 5 0

2 5 5 4 5 1

3 5 7 5 7 5

4 1 0 1 1 3

5 1 0 1 1 0

6 0 1 0 0 2

7 0 0 0 0 0

Source: Own Processing

Table 60 Responses on the time frame by which simulations need to prolong life in order to be

believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 0 0 0 1 1

Year 3 8 10 11 7

10 years 8 8 5 7 3

30 years 2 3 1 0 0

Source: Own Processing

47

Table 61 Responses on the time frame by which widespread healing and recovery actions need to

prolong life in order to be believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 0 1 2 2 6

Year 8 8 7 12 3

10 years 4 9 7 5 2

30 years 1 1 0 0 0

Source: Own Processing

Table 62 Responses on the time frame by which self-treatment need to prolong life in order to be

believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 2 1 0 1 5

Year 8 15 7 17 4

10 years 3 3 8 1 2

30 years 0 0 1 0 0

Source: Own Processing

Table 63 The attitude of the respondents towards the second story

 Heading – Attitude towards the provided story

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Good 10 15 13 16 10

Bad 3 4 3 3 1

Positive 10 15 13 16 10

Negative 3 4 3 3 1

Favorable 10 15 13 16 10

Unfavorable 3 4 3 3 1

Source: Own Processing

The respondents in this section were provided the story computer simulation utilization for

analysis and testing purposes of blood glucose levels from blood samples of laboratory rats. There

was 78 respondents who read the second story and then responded to the questions and statements

in the Section B of the questionnaire. Nearly all the respondents younger than 45 years were pro

the notion that computer simulation could make positive impacts on health conditions of animals.

Most of the people older than 45 years do not have an opinion on this issue. The same applied for

responses of the respondents when asked about the possible betterment of health conditions of

animals using healing and recovery actions and self-control. In these statements, older people were 

48

more convinced that there treatment techniques could help. When asked the similar question but

on the possible betterment of health conditions of people, all the respondents younger than 60 years

were little more pro than against that simulations can make positive impact contributions.

Respondents older than 60 years mostly did not know or were against. Almost all the respondents

expressed the opinion that both recovery and healing actions and self-control can make positive

contributions to the issue. With some exceptions in the oldest age group, where people were unsure

for simulations, all the respondents stated that they hold the view that simulations and recovery

actions can make positive contributions to the research in medicine. When it came down to selfcontrol, nearly all the respondents think that it cannot make positive contributions to the new

research works in medical environment. All the respondents think that healing actions and selfcontrol could eventually indirectly influence blood glucose levels for both diabetics and people

without suffering diabetes. When asked about simulations, people more agreed than disagreed, but

there was a noticeable amount of people who did not know. When it came down to possible

treatments of serious diseases, respondents are strongly pro that healing and recovery action can

have positive influence, most of the younger people think that simulations could make positive

impacts, noticeable amount of older people was unsure about the simulations. Most of the people

think that self-control cannot really have a big positive influence on serious diseases treatment.

Respondents younger than 60 years think that if simulations return good and sufficient

results on animal subjects, it is a good enough reason to start testing these simulations on humans.

Evident number of respondents older than 60 years disagree with the statement. Respondents

younger than 60 years hold the view that if simulation would prove it could extent life of an

individual by 10 years, then the simulation would be believable. People older than 60 years would

be satisfied with a year extension. Respondents younger than 60 years think that healing and

treatment actions are believable if they would prove possible life extension of a year or 10 years.

Respondents over the age of 60 think that they would be believable if only point out to a life

extension of a month. Nearly all the respondents think that self-control is believable if it could

make life extension of a year, respondents older than 60 years think it is believable if it proved an

extension of a month. Attitudes towards the story provided for this group of respondent were very

affirmative as the respondents stated the story was good, positive, and favorable.

3.2.3 Third story results analysis

This subsection presents responses results of the Section B of the questionnaire, where the

respondents were provided the advertisement of the special diet using mushroom Rei-Shi for a

middle aged man in order to reduce high levels of sugar in his blood. There is 23 tables in this

subsection. These tables provide the results to the questions 11-33 of the questionnaire. For the

questions that relate to tables 64-86, answers were given on the scale 1-7, where 1 means

completely agree, 2 means somewhat agree, 3 means more agree than disagree, 4 means I do not

know, 5 means more disagree than agree, 6 means somewhat disagree, and 7 means completely

disagree.

Results to the question 11 are provided in Table 64, results to the question 12 are highlighted

in Table 65, results to the question 13 are provided in Table 66, results to the question 14 are given

in Table 67, results to the question 15 are presented in Table 68, results to the question 16 are

provided in Table 69, results to the question 17 are demonstrated in Table 70, results to the question

18 are shown in Table 71, results to the question 19 are demonstrated in Table 72, results to the

question 20 are presented in Table 73, results to the question 21 are provided in Table 74, results

49

to the question 22 are given in Table 75, results to the question 23 are presented in Table 76, results

to the question 24 are shown in Table 77, results to the question 25 are delivered in Table 78, results

to the question 26 are provided in Table 79, results to the question 27 are presented in Table 80,

results to the question 28 are given in Table 81, results to the question 29 are provided in Table 82,

results to the question 30 are provided in Table 83, results to the question 31 are provided in Table

84, results to the question 32 are delivered in Table 85, and results to the question 33 are provided

in Table 86.

Table 64 presents the information of the stance of the respondents on the issue of simulation

making a positive impact on health conditions of animals. Table 65 provides the information of the

position of the respondents on the issue of widespread healing and recovery actions making a

positive impact on health conditions of animals. Table 66 gives the information of the stance of the

respondents on the issue of self-treatment making a positive impact on health conditions of animals.

Table 67 tackles the information of the standpoint of the respondents on the issue of simulation

making a positive impact on health conditions of people. Table 68 points out the information of the

position of the respondents on the issue of widespread healing and recovery actions making a

positive impact on health conditions of people. Table 69 deals with the information of information

of the stand of the respondents on the issue of self-treatment making a positive impact on health

conditions of people. Table 70 demonstrates the information if the respondents think that

simulations can make a positive impact on research in medical environment. Table 71 provides the

information if the respondents think that widespread healing and recovery actions can make a

positive impact on research in medical environment. Table 72 delivers the information if the

respondents think that self-treatment can make a positive impact on research in medical

environment. Table 73 gives results of the responses of the respondents whether or not simulations

can indirectly influence blood glucose levels of people not suffering with diabetes. Table 74

provides results of the responses of the respondents whether or not widespread healing and

recovery actions can indirectly influence blood glucose levels of people not suffering with diabetes.

Table 75 highlights results of the responses of the respondents whether or not self-treatment can

indirectly influence blood glucose levels of people not suffering with diabetes. Table 76 provides

results of the responses of the respondents whether or not simulations can indirectly influence blood

glucose levels of people suffering with diabetes. Table 77 demonstrates results of the responses of

the respondents whether or not widespread healing and recovery actions can indirectly influence

blood glucose levels of people suffering with diabetes. Table 78 shows results of the responses of

the respondents whether or not self-treatment can indirectly influence blood glucose levels of

people suffering with diabetes. Table 79 delivers the information of simulations as positive

influence for serious diseases. Table 80 provides the information of widespread healing and

recovery actions as positive influence for serious diseases. Table 81 demonstrates the information

of self-treatment as positive influence for serious diseases. Table 82 provides the information of

whether or not simulations with sufficient results on animal subject should be tested on human

patients. Table 83 provides the information of how long simulation needs to prolong life for in

order to be believable. Table 84 shows the information of how long widespread healing and

recovery actions needs to prolong life for in order to be believable. Table 85 presents the

information of how long self-treatment needs to prolong life for in order to be believable. Table 86

presents the information of the attitudes of the respondents towards the provided story. 

50

Table 64 Responses on the positive impacts of simulations on health conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 1 2 2 0

2 3 2 4 4 1

3 3 10 6 7 1

4 4 3 4 5 5

5 3 2 1 2 3

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 65 Responses on the positive impacts of widespread healing and recovery actions on health

conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 5 5 2 2

2 5 4 3 8 1

3 5 7 8 8 4

4 1 0 0 1 2

5 1 2 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 66 Responses on the positive impacts of self-treatment on health conditions of animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 5 8 5 5 1

2 3 5 5 7 3

3 2 3 6 6 3

4 1 0 0 1 2

5 1 2 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

51

Table 67 Responses on the positive impacts of simulations on health conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 1 0 1 0

2 3 3 4 3 1

3 6 7 9 8 1

4 3 5 3 5 7

5 1 2 1 3 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 68 Responses on the positive impacts of widespread healing and recovery actions on health

conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 11 11 12 14 6

2 1 4 3 4 2

3 0 1 1 1 1

4 1 1 0 0 0

5 0 1 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 69 Responses on the positive impacts of self-treatment on health conditions of people

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 10 10 12 15 6

2 2 7 3 5 2

3 1 1 1 0 1

4 0 0 0 0 1

5 0 0 0 0 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

52

Table 70 Responses on the positive impact of simulation on development and research in medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 1 2 2 0

2 3 5 5 6 1

3 7 11 8 9 4

4 1 1 1 2 4

5 1 0 1 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 71 Responses on the positive impact of widespread healing and recovery actions on

development and research in medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 4 4 4 3

2 2 6 5 8 4

3 7 6 7 7 1

4 1 2 0 0 2

5 0 0 1 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 72 Responses on the positive impact of self-treatment on development and research in

medicine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 0 0

2 0 0 0 0 0

3 6 7 8 9 2

4 1 3 2 1 1

5 3 2 5 7 3

6 3 5 3 3 2

7 0 1 0 0 1

Source: Own Processing

53

Table 73 Responses on simulations as indirect influence of blood glucose for people without

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 1 2 0 0

2 2 2 4 3 1

3 6 8 5 5 3

4 3 4 3 7 4

5 2 2 2 4 2

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 74 Responses on widespread healing and recovery actions as indirect influence of blood

glucose for people without diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 4 8 7 5 4

2 5 6 6 9 3

3 3 4 3 3 1

4 1 0 1 2 1

5 0 0 0 1 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 75 Responses on self-treatment as indirect influence of blood glucose for people without

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 6 11 8 9 4

2 4 4 4 4 4

3 2 3 4 2 1

4 1 0 1 0 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

54

Table 76 Responses on simulations as indirect influence of blood glucose for people with diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 1 2 1 0

2 2 2 4 3 1

3 5 8 6 4 3

4 3 4 2 7 4

5 2 2 2 4 2

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 77 Responses on widespread healing and recovery actions as indirect influence of blood

glucose for people with diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 8 12 12 10 6

2 4 5 3 7 4

3 0 1 1 1 1

4 1 0 1 1 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 78 Responses on self-treatment as indirect influence of blood glucose for people with

diabetes

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 8 14 13 16 7

2 4 3 2 2 1

3 0 1 1 1 1

4 1 0 1 0 1

5 0 0 0 1 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

55

Table 79 Responses on simulations as a positive influence of serious diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 1 0 0

2 1 6 5 3 2

3 7 9 5 13 3

4 3 3 5 2 3

5 2 0 1 1 0

6 0 0 0 0 1

7 0 0 0 1 1

Source: Own Processing

Table 80 Responses on widespread healing and recovery actions as a positive influence of serious

diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 3 4 2 0

2 4 3 5 2 4

3 5 11 6 10 3

4 1 0 1 1 1

5 1 0 1 1 0

6 0 1 0 0 2

7 0 0 0 0 0

Source: Own Processing

Table 81 Responses on self-treatment as a positive influence of serious diseases treatment

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 0 0

2 0 0 0 0 0

3 1 2 1 2 1

4 3 1 3 1 1

5 4 7 4 6 2

6 1 4 5 6 2

7 1 4 3 5 4

Source: Own Processing

56

Table 82 Responses on testing simulations on human patients when results are sufficient on animals

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 1 1 3 3 0

2 1 6 4 6 1

3 8 9 8 9 3

4 2 1 1 1 4

5 1 0 1 1 0

6 0 1 0 0 2

7 0 0 0 0 0

Source: Own Processing

Table 83 Responses on the time frame by which simulations need to prolong life in order to be

believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 0 0 0 1 1

Year 3 8 11 9 7

10 years 8 6 4 10 2

30 years 2 3 1 0 0

Source: Own Processing

Table 84 Responses on the time frame by which widespread healing and recovery actions need to

prolong life in order to be believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 0 1 2 2 6

Year 8 6 7 13 2

10 years 4 9 7 5 2

30 years 1 1 0 0 0

Source: Own Processing

57

Table 85 Responses on the time frame by which self-treatment need to prolong life in order to be

believable

 Heading – Time Frame of life extension as a purpose for believability

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Day 0 0 0 0 0

Month 2 1 0 1 5

Year 8 11 9 18 3

10 years 3 5 6 1 2

30 years 0 0 1 0 0

Source: Own Processing

Table 86 The attitude of the respondents towards the third story

 Heading – Attitude towards the provided story

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

Good 13 16 14 19 10

Bad 0 1 2 1 0

Positive 13 16 14 19 10

Negative 0 1 2 1 0

Favorable 13 16 14 19 10

Unfavorable 0 1 2 1 0

Source: Own Processing

The respondents in this section were provided the story of special diet using a mushroom

Rei-Shi of a middle age man in order to reduce sugar levels in his blood. There was 76 respondents

who read the third story and then responded to the questions and statements in the Section B of the

questionnaire. Respondents aged between 15 and 60 years were mostly pro the notion that

computer simulation could make positive impacts on health conditions of animals. Parents of

children and respondents in the oldest age group were more divided on this issue, many did not

know some were pro and some against. Most of the respondents hold the view that self-control and

treatment and recovery action could make a positive impact on health conditions of animals. When

asked the similar question but on the possible betterment of health conditions of people, all the

respondents younger than 60 years were little more pro than against that simulations can make

positive impact contributions. Respondents older than 60 years mostly did not know. Almost all

the respondents expressed the opinion that both recovery and healing actions and self-control can

make positive contributions to the issue. With some exceptions in the oldest age group, where

people were unsure for simulations, all the respondents stated that they hold the view that

simulations and recovery actions can make positive contributions to the research in medicine.

When it came down to self-control, nearly all the respondents were half and half whether or not it

can make positive contributions to the new research works in medical environment. All the

respondents think that healing actions and self-control could eventually indirectly influence blood

glucose levels for both diabetics and people without suffering diabetes. When asked about

simulations, younger people more agreed than disagreed, respondents older than 45 years were

either against or did not know. When it came down to possible treatments of serious diseases, 

58

respondents are pro that healing and recovery actions can make a positive influence, most of the

respondents believed that simulations could make positive impacts, but there was noticeable

amount of do not know answers in all age groups. Most of the people think that self-control cannot

really have a big positive influence on serious diseases treatment.

Respondents younger than 60 years think that if simulations return good and sufficient

results on animal subjects, it is a good enough reason to start testing these simulations on humans.

Respondents older than 60 years are all over the place about the statement. Respondents younger

than 60 years hold the view that if simulation would prove it could extent life of an individual by

10 years, then the simulation would be believable. People older than 60 years would be satisfied

with a year extension. Respondents younger than 60 years think that healing and treatment actions

are believable if they would prove possible life extension of a year. Respondents over the age of

60 think that they would be believable if only point out to a life extension of a month. Nearly all

the respondents think that self-control is believable if it could make life extension of a year,

respondents older than 60 years think it is believable if it proved an extension of a month. Attitudes

towards the story provided for this group of respondent were very affirmative as the respondents

stated the story was good, positive, and favorable.

3.3 Transferability of changed attitudes to other domains

This section provides the results of the third and last section, Section C, of the questionnaire,

which was focused on statements and question on feelings and logic. The questions in this section

of the questionnaire attempt to gather the information of self-healing responsibility preferences,

inclinations towards easy, routine, and fast problem solutions, leanings towards problem solutions

requiring abstract and deep thinking, feelings after task completions, and the views on emotional

involvement in problem solution processes of the respondents in the different age groups. For all

these questions, answers were given on the scale 1-7, where 1 means completely agree, 2 means

somewhat agree, 3 means more agree than disagree, 4 means I do not know, 5 means more disagree

than agree, 6 means somewhat disagree, and 7 means completely disagree. This part of the paper

provides the results to the question 34-44 of the questionnaire in tables. A detailed description of

what information is in each table is provided in the paragraph below. The results are then discussed

below all the tables at the end of this section of the paper.

There is 22 tables in this section. For all the questions in this section, responses are always

provided in two tables a question. The first table for each question always provides the responses

of male respondents in the different age groups and the second table always provides the responses

of female respondents in the different age groups. Tables 87 and 88 provide the results to question

34. Tables 89 and 90 present the results to question 35. Tables 91 and 92 give the results to question

36. Tables 93 and 94 deliver the results to question 37. Tables 95 and 96 provide the results to

question 38. Tables 97 and 98 provide the results to question 39. Tables 99 and 100 present the

results to question 40. Tables 101 and 102 deliver the results to question 41. Tables 103 and 104

provide the results to question 42. Tables 105 and 106 provide the results to question 43. Tables

107 and 108 give the results to question 44.

Tables 87 and 88 provide the pieces of information of the preference of self-healing practice

of the respondents. Tables 89 and 90 highlight the data of the preference on simple versus

complicated problem solutions. Tables 91 and 92 point out the data of the preference on short time

versus long time problem solutions. Tables 93 and 94 give the information whether or not the

respondents prefer mastering new solving techniques and then using them in routine fashion. 

59

Tables 95 and 96 deliver the information of liking assignments requiring deep thinking. Tables 97

and 98 provide the information on the liking of learning new problem solving techniques. Tables

99 and 100 provide the pieces of information liking situations requiring abstract thinking Tables

101 and 102 highlight the data of task completion feelings. Tables 103 and 104 point out the data

of the lust of knowing approaches of completed tasks. Tables 105 and 106 highlight the inclinations

towards the involvement of emotion in assignments. Lastly, Tables 107 and 108 point out the

information on the view of the respondents on emotional involvement in problem solution

processes.

Table 87 Self-Healing preference of male respondents towards their own health

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 2 3 0

2 5 0 5 2 0

3 12 8 10 10 3

4 1 11 0 0 0

5 4 9 6 8 3

6 0 0 0 4 7

7 0 0 0 0 0

Source: Own Processing

Table 88 Self-Healing preference of female respondents towards their own health

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 1 3 1

2 0 0 3 2 0

3 4 3 7 9 2

4 3 17 4 4 0

5 9 6 7 8 9

6 0 0 5 3 6

7 0 0 0 0 0

Source: Own Processing

60

Table 89 Preference of male respondents on simple versus complicated problem solutions

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 13 19 15 11 6

2 4 5 3 7 6

3 2 1 4 3 1

4 0 3 0 1 0

5 3 0 1 3 0

6 0 0 0 1 0

7 0 0 0 0 0

Source: Own Processing

Table 90 Preference of female respondents on simple versus complicated problem solutions

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 8 12 15 17 11

2 3 8 7 7 6

3 4 3 3 5 1

4 0 2 2 0 0

5 1 1 0 2 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 91 Preference of male respondents on short-time versus long-time problem solutions

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 20 27 18 16 2

2 2 1 3 6 2

3 0 0 0 1 6

4 0 0 0 0 3

5 0 0 2 3 0

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

61

Table 92 Preference of female respondents on short-time versus long-time problem solutions

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 14 24 17 21 6

2 0 1 3 5 3

3 2 1 5 4 7

4 0 0 0 0 0

5 0 0 2 1 2

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 93 Preference of male respondents on mastering new solving techniques and its usage as a

routine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 17 20 16 16 0

2 3 4 3 5 2

3 2 0 3 2 5

4 0 4 0 2 4

5 0 0 1 1 2

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 94 Preference of female respondents on mastering new solving techniques and its usage as a

routine

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 9 19 13 20 0

2 2 2 6 3 3

3 5 2 4 5 2

4 0 1 1 1 3

5 0 2 3 2 6

6 0 0 0 0 4

7 0 0 0 0 0

Source: Own Processing

62

Table 95 Liking of the male respondents towards assignments requiring deep thinking

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 0 0 0 0

2 4 0 3 0 0

3 3 3 3 2 1

4 4 2 1 1 2

5 7 4 9 15 7

6 2 14 4 4 3

7 0 5 3 4 0

Source: Own Processing

Table 96 Liking of the female respondents towards assignments requiring deep thinking

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 1 0 0

2 1 0 0 0 0

3 2 3 3 2 0

4 1 2 0 0 0

5 9 4 6 17 3

6 2 7 8 5 6

7 1 9 9 7 9

Source: Own Processing

Table 97 Liking of the male respondents towards learning new problem solving techniques

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 1 0 2 0

2 0 0 8 9 0

3 4 2 3 5 2

4 0 0 0 0 0

5 11 7 7 7 1

6 7 5 5 2 1

7 0 13 0 1 11

Source: Own Processing

63

Table 98 Liking of the female respondents towards learning new problem solving techniques

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 1 2 0

2 0 0 2 1 0

3 3 2 4 4 0

4 0 0 0 0 0

5 5 3 14 3 2

6 8 7 2 8 0

7 0 14 4 13 16

Source: Own Processing

Table 99 Preference of male respondents on situations requiring abstract way of thinking

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 1 0

2 0 0 2 1 0

3 0 2 1 3 0

4 1 0 0 0 0

5 2 0 7 4 0

6 5 2 9 8 2

7 14 24 4 9 11

Source: Own Processing

Table 100 Preference of female respondents on situations requiring abstract way of thinking

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 0 0 0 0

2 0 0 0 0 0

3 1 0 1 2 0

4 0 0 0 0 0

5 3 0 2 0 1

6 6 3 6 3 1

7 6 23 18 26 16

Source: Own Processing

64

Table 101 Feelings of male respondents after solving a challenging task, relief versus satisfaction

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 14 19 15 3 0

2 0 4 1 6 0

3 3 4 4 4 6

4 0 0 0 4 3

5 5 1 3 7 4

6 0 0 0 2 0

7 0 0 0 0 0

Source: Own Processing

Table 102 Feelings of female respondents after solving a challenging task, relief versus satisfaction

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 9 19 15 3 3

2 2 3 2 14 9

3 2 4 5 6 2

4 0 0 0 1 0

5 3 0 3 5 3

6 0 0 2 0 1

7 0 0 0 2 0

Source: Own Processing

Table 103 Desire of male respondents on knowing the approach of completed tasks

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 2 23 15 3 2

2 4 4 1 4 2

3 9 0 5 9 7

4 0 0 0 0 0

5 3 0 2 5 2

6 4 1 0 3 0

7 0 0 0 2 0

Source: Own Processing

65

Table 104 Desire of female respondents on knowing the approach of completed tasks

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 16 15 7 0

2 4 5 7 6 3

3 5 4 3 9 7

4 0 0 0 2 2

5 3 1 2 5 4

6 0 0 0 2 2

7 1 0 0 0 0

Source: Own Processing

Table 105 Preferences of male respondents on tasks requiring emotion involvement

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 9 17 5 8 7

2 3 5 10 6 5

3 7 6 7 10 0

4 0 0 1 2 1

5 3 0 0 0 1

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 106 Preferences of female respondents on tasks requiring emotion involvement

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 3 6 5 6 3

2 4 7 9 8 5

3 6 13 12 15 6

4 1 0 0 2 2

5 0 2 0 0 2

6 0 1 0 1 0

7 2 0 1 0 0

Source: Own Processing

66

Table 107 Thoughts of male respondents on emotions as a cause of failure during problem solving

process

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 6 2 3 4 2

2 3 8 9 5 2

3 11 17 6 14 6

4 0 0 1 2 1

5 2 1 4 5 2

6 0 0 0 0 0

7 0 0 0 0 0

Source: Own Processing

Table 108 Thoughts of female respondents on emotions as a cause of failure during problem solving

process

 Heading – 1-7 Agreement scale

 Legend – Different age groups

0-15 15-25 25-45 45-60 60-80

1 0 1 3 3 2

2 5 5 8 6 5

3 7 13 10 13 7

4 1 2 1 3 2

5 1 4 2 2 1

6 2 1 1 1 0

7 0 0 2 3 1

Source: Own Processing

The respondents for the youngest age group reached a disagreement. Parents of boys are

more for self-treatment and self-help as an approach of taking responsibility of their children

health. However, parents of boys are of the opposite view. Respondents in the second youngest age

group mostly do not know or do not care. Male respondents in the middle group mostly prefer

taking responsibility about their health by self-control and female respondents are roughly half and

half on this issue. Respondents in the second oldest group are much divided on this issue. Overall,

men in this group have a little preference for self-treatment, women are the opposite. Almost all

the respondents in the oldest age group prefer not to take a responsibility for their health by selfcontrol.

All the respondents incline very much towards preferring simple problem solutions over

complicated ones. The strongest preferences of this are in the age groups 15-25 years and 60-80

years for male respondents and 25-45, 45-60, and 60-80 for female respondents. The least pro this

idea are men in the second oldest age group. All the respondents also prefer problem solutions that

require short amount of time to do rather than long time frames. There are couple of exceptions for

respondents in the oldest age group of women and age groups 25-45 and 45-60 years for both

categories of respondents. Majority of respondents prefer learning a new solution technique for

problem solving purposes and then keep using it in a routine solving fashion. Exceptions to this are 

67

female respondents in the oldest age group. All the respondents to not like assignments requiring

deep thinking. Respondents in the youngest age group are little all over the place with the

responses, but they still prefer situation that do not require deep thinking. Female respondents do

like assignments requiring deep thinking even less than male respondents. Respondents in the age

groups 15-25 and 60-80 years are the most out of all the group of respondents who do not prefer

learning new problem solving techniques. Majority of the female respondents do not prefer learning

new problem solution techniques. Male respondents in the age groups 25-45 and 45-60 are pretty

much half and half on this issue. Enormous majority of all the respondents do not prefer situations

that require abstract way of thinking. All female respondents are strongly against such situations.

There are couple of rare exceptions to this in the male categories of 25-45 and 45-60 years.

The male respondents in the age groups of 0-15, 15-25, and 25-45 years typically feel relief

rather than satisfaction after a challenging task is completed. Male respondents in the age groups

45-60 and 60-80 are mostly half and half on this issue. Female respondents are in most cases feeling

relief rather than satisfaction after solving challenging assignments. Couple of exceptions to this

for female respondents occur in the age group 45-60 years. All the male respondents are satisfied

with achieving a goal without really understand the way of achieving the aim. The respondents

mostly pro this idea are in the age groups 15-25 and 25-45. Male respondents in the age group 45-

60 are almost half and half on this issue. Female respondents are also satisfied with achieving a

goal without really understand the way of getting there. Female respondents in age groups 45-60

and 60-80 years are also pro this idea, but not as strongly as the rest.

All the male respondents are against being involved in assignments and tasks that require

emotions. Little exception to this are few responses of parents of the children in the youngest age

group. Female respondents are also mostly against being involved in assignments requiring

emotions. Exceptions to this are in age groups 0-15, 15-25, and 60-80 years, where the exceptions

in the age group 0-15 years were radically against. Many male respondents hold the view that when

a mistake occurs in a thought process of a solution, it is cause by emotions. In the age groups 25-

45 and 45-60 years, there are noticeable exceptions to this fact. Female respondents also mostly

agree to this statement.

3.4 Commercialization

This section provides statistical analysis calculations of the results presented in the above

section of the Analytical part of the paper. This section provides the computation results as well as

comments on these results. In order to ensure that the statistical results are well-described, some

pieces of information from the paper are repeated here. There were three stories dealing with blood

glucose provided to the respondents prior to the completion of the Section B of the questionnaire.

Each respondent was presented only with one of the three stories. A 1-7 point scale was used for a

majority of questions and statements in the Section B of the questionnaire. On this scale, 1 means

completely agree, 2 means somewhat agree, 3 means more agree than disagree, 4 means I do not

know, 5 means more disagree than agree, 6 means somewhat disagree, and 7 means completely

disagree. Only questions and statements that were focused on simulations and had the answer

choice of the aforementioned scale were used for the statistical analysis calculations. There were

three different statistical calculations performed. Two of them calculated means of certain

responses and one was computations of paired T-Test.

The first computations of mean calculations were used for comparisons between the

responses of respondents younger than 60 years and older than 60 years. For this evaluation, three 

68

statements were taken into account. The three statements were that simulations can make positive

impacts on health conditions of people, simulations can make a positive impact on the development

and research in medicine, and simulations can positively influence serious diseases. From the

means, it is very clear that people under 60 years of age are way more pro simulation utilization in

medicine than people older than 60 years. The calculated means for the three questions are

presented in Table 109 below. In the Table A represents the first blood glucose story, B represents

the second blood glucose story, and C represents the third blood glucose story.

Table 109 Calculated means of the responses for simulation based questions of people under and

over 60 years of age

 Heading – Simulation based statements

 Legend – Story read by respondents under and over 60 years of age

A under 60 B under 60 C under 60 A over 60 B over 60 C over 60

Betterment of

health

conditions of

people by

simulations

2.21 2.75 3.19 2.90 3.46 3.80

Simulations

as tools of

development

in medicine

research

1.74 1,77 2.72 2.60 2.55 3.50

Simulations

as tools of

positively

influence for

serious

diseases

2.85 2.38 3.12 3.40 3.73 3.80

Source: Own Processing

The second calculations computed means of the responses of seven different simulation

questions answered by respondents after reading one of the three blood glucose stories. The seven

questions were simulations can make positive impacts on health conditions of animals, simulations

can make positive impacts on health conditions of people, simulations can make positive impact

on the development and research in medicine, simulations can indirectly influence blood glucose

for people who are not suffering with diabetes, simulations can indirectly influence blood glucose

for diabetics, simulations can positively influence serious diseases, and if simulations return

sufficient results on animals, it is good enough to be started tested on human patients. The mean

calculations for the responses of the respondents to the aforementioned questions are provided in

Table 110 below. In the Table, A represents the first blood glucose story, B represents the second

blood glucose story and C represents the third blood glucose story. From the results, it is obvious

that the attitudes of people on simulations and the level of trust of the respondents towards

simulations and their utilization in medical environment rapidly changes with the fact of whether

or not they read a story about computer simulation utilization in health care or not. Overall, the

mean calculations show that when a story of computer simulation utilization on human patients is

provided to the respondents, the respondents have strong level of trust towards simulations. When 

69

a simulation utilization on animal subjects is presented to the respondents, they still hold a strong

level of trust towards simulations; however, the level of trust is weaker than for the previous story.

When a different story than simulation one is presented to the respondents, they are still bit more

positive towards simulations than negative. Nonetheless the level of trust is noticeably weakest out

of the three stories.

Table 110 Calculated means of the responses for simulation based questions of respondents after

reading different blood glucose stories

 Heading – Simulation based statements

 Legend – Story read by respondents

Story A Story B Story C

Health

conditions

betterment by

simulations,

animals

2.47 2.11 3.22

Health

conditions

betterment by

simulations,

people

2.26 2.80 3.20

Simulations

for medicine

research

1.80 1.76 2.80

Simulations

as indirect

influence of

blood

glucose, no

diabetics

2.75 3.04 3.37

Simulations

as indirect

influence of

blood

glucose,

diabetics

2.18 2.45 3.29

Simulations

positively

influence

serious

diseases

2.87 2.88 3.13

Animal

testing

sufficient,

next step

human

patients

2.4 2.38 2.83

Source: Own Processing

70

The last statistical calculations were paired T-Tests for the evaluation purposes of the same

questions and same respondents as presented in the Table 110. The responses were compared by

the statistical paired T-Tests for the different stories the respondents read. Therefore, responses for

the aforementioned questions after reading story A with responses after reading story B were

compared by the paired T-Tests and so on. The biggest differences are definitely between the

responses after reading the story A and story C. Thus, it can be easily stated that the attitudes and

level of trust towards the utilization of computer simulations in medical environment very much

depends on the information that is provided to the respondents. The results of this paired T-Tests

are provided in table 111 below. The numbers in the table represent the t-statistic, which represent

the fact that the results of the respondents occur certain number of standard deviations from the

mean. In other words, the lower the t-statistic number, the closer the responses are. 

71

Table 111 Calculated t-statistic comparison values by the paired T-Tests of the responses for

simulation based questions of respondents after reading different blood glucose stories

 Heading – Simulation based statements

 Legend – Stories read by respondents

Stories A-B Stories A-C Stories B-C

Health

conditions

betterment by

simulations,

animals

4.94 15.00 12.20

Health

conditions

betterment by

simulations,

people

8.30 16.30 5.78

Simulations

for medicine

research

0.83 14.81 13.69

Simulations

as indirect

influence of

blood

glucose, no

diabetics

4.93 11.02 4.99

Simulations

as indirect

influence of

blood

glucose,

diabetics

4.59 18.14 11.59

Simulations

positively

influence

serious

diseases

0 4.363 4.86

Animal

testing

sufficient,

next step

human

patients

0.39 5.26 7.80

Source: Own Processing

Even though statistical computations were performed only for the aforementioned data,

there are other conclusions that can be made from the responses that could eventually help in a

commercialization process. From the results of the research, it can be concluded that people in

general do not like advertisements no matter what the advertisements are concentrated on.

However, the stories that were provided to the respondents prior to the completion of the Section

72

B of the questionnaire that also measured the attitudes of the respondents towards simulations were

broadly and extremely well accepted and appreciated by the respondents.

The responses of the results tend to demonstrate and present three major segments of people

towards which the commercialization process of the simulations inclusion process could be made.

People suffering with diabetes, people who enjoy solving assignments requiring deep thinking, and

people who appreciate situations that require abstract thinking were all pro and positive towards

computer simulations. Therefore, the first wave of possible commercialization process would be to

concentrate on the aforementioned groups of people. Second wave would be to concentrate on

younger people, research tend to demonstrate people under the age of 60, since their responses

were much more pro computer simulationsin medicine in compare to the responses of older people,

people over 60 years of age.

4 Conclusions

This paper provides theory pieces of information on computer simulations, big data,

marketing, advertising, and certain theoretical concepts in marketing and advertising. The work

presents a hypothesis of the research work as well as some smaller aims of the work. The paper

delivers a topic specific questionnaire, form which the responses and results are gathered so as to

address the goals set in the introduction of the paper. The questionnaire was developed according

to the vision of the student. The questionnaire was completed by a number of respondents that the

student felt comfortable with in order to proceed with the research and perform certain statistical

analysis calculations. The total number of respondents is not high enough to accurately represent

the real world market; however, it is high enough in the eyes of the student for demonstration of

the analysis and evaluations techniques to arrive into certain conclusions. All the results and

statistical computations are provided in clear tables and certain possible proposals for

commercialization of the concept of computer simulations inclusion into medical environment for

a possible betterment of health care of patients has been made. Proposals of future work for this

paper and research have also been developed.

Overall, 230 respondents completed the questionnaire. There was over 30 respondents in

each specified age group. Most respondents was in the age category 45-60 years, 57 respondents.

Least respondents was in the oldest age category, 60-80 years, only 31 respondents. Respondents

were roughly half and half when it came down to gender, there was a little more female

respondents. It was a good decision to create the questionnaire in two different languages, English

and Czech as well as it was a good decision to make it as hard copy paper form and online electronic

form. Due to these reasons people felt more comfortable with completions of the questionnaire as

they could chose the option they preferred the most. Typically, older people preferred hard copies

of the questionnaire and younger people preferred electronic copy.

Respondents in the first four age groups generally express their attitudes towards general

advertisements they see daily as negative, bad, and unfavorable. However, more than 50% of the

respondents in the oldest age group liked advertisements they see daily and express their feeling

towards the ads as positive, good, and favorable. Advertisements concentrated on a medicine or

health treatment method received very similar responses as general advertisements. More

enthusiasm for medicine related advertisements came from parents of children in the youngest age

group and respondents in the oldest age category.

Older respondents typically visit a doctor or a physician more often than younger people.

Male respondents visit doctor less often than female respondents. Young and middle age 

73

respondents, in the range from 0 to 45 years usually have experience or family experience with

allergies and high blood pressure, the least experience on the other hand with cancer, stroke, and

heart attacks. Older people in the age range from 45 to 80 years have typically experience or family

experience with issues with movement apparatus and high blood pressure. The least experience of

the respondents are with cancer diseases. There were 24 diabetics among all the respondents. There

were no diabetics among female respondents in the age group of 15-25 years. Most diabetics were

male respondents in the age group of 45-60 years. In general, the respondents from 15 to 60 years

did not look into new research works in medical environments. Parents of the children usually read

about new researches in medicine monthly. The oldest respondents typically read about new

researches in health care weekly. When they hear or read about ongoing researches in medicine,

young respondents and parents of children usually focused on allergies. People older than 25 years

usually read about researches in issues with movement apparatus and high blood pressure. Even

though it did not leave a significant number in the overall results, it is important to mention that all

people suffering with diabetes attempt to read research works in medicine daily, mainly then in the

area of blood glucose and diabetes.

The results and paired T-Test statistical calculations tend to show that people younger than

60 years think that simulations can make a positive impact when it comes to health conditions of

animals and people, make a positive impact on new researches and development in the medical

environment, can make positive impact on treatment processes of serious disease, and that they

could indirectly influence blood glucose levels for diabetics and healthy patients. Respondents in

the oldest age group were mostly unsure about simulations. Younger respondents also thought that

if simulations provide good results on animal subjects, it is a good enough reason to start testing

the simulations on human patients. Younger people think that simulations would be believable if

they provided an evidence that they could cause a life extension of ten years, older people were

satisfied with a yearlong extension.

The results and statistical analysis also demonstrated that there is a noticeable difference

when it came down to answers to the questions and statements on simulations depending on which

story the respondent read. Typically, when the respondent was provided story talking about

computer simulations use on human data, the respondents had the greatest level of trust towards

simulations. The respondents who were provided the story of computer simulation utilization on

animal data were still pro simulations, but not as much as the group which read the story about

simulations performance on human data. Respondents who read the story about special diet

treatment using mushroom Rei-Shi were the least positive towards simulations out of the three

respondent segments.

Respondents in all age categories were positive about healing and treatment actions. They

thought that these actions can make positive impacts on animals’ health, humans’ health,

developments in new research works, treatment of serious diseases, and can be of an indirect

influence of blood glucose in human body. Respondents were positive that self-control can make

positive impacts on animals’ health and humans’ health, can indirectly influence blood glucose

levels of people. Most of the respondents think that self-control cannot make a big impact on new

development in medicine and that it cannot make a big positive impact on treatment of serious

diseases. Respondents who read the story about reduced blood sugar values of a patient because of

a special diet were more positive towards questions and statements on self-control.

Most of the respondents overall prefer to take responsibility for their help with the approach

of self-control and self-treatment. The deviation from this are respondents in the oldest age group,

they do not prefer self-control and self-treatment. Nearly all the respondents state that they prefer

simple solutions over the complicated ones and that they prefer problem solutions that require short 

74

amount of time over solutions requiring long amount of time. Majority of the respondent in all age

groups do not fancy situations and assignments which require deep and abstract thinking. Female

respondents and older male respondents do not enjoy learning new problem solving strategies and

techniques. Male respondents in the age range from 25-60 years are roughly half and half on this

issue. Nearly all the female respondents and majority of male respondents younger than 45 years

feel moods of relief rather the feelings of satisfaction when a challenging task was completed. Male

respondent older than 45 years are roughly fifty-fifty on this issue. Although it may not have left

such significance in the displayed results, it is crucial to mention that all the respondents who liked

assignment requiring deep thinking and situations that require abstract thinking were always pro

simulations utilization in the medical environment. It is also essential to mention that younger

people are way more pro the application of computer simulation in medicine than older people and

male respondents are generally more pro than female respondents. Lastly, nearly all the patients

suffering with diabetes stated that computer simulations could help medicine in helping of the

treatment of diabetics.

It can be concluded that the goals set in the introduction section of the paper were met to

an extent. Main goal, a validation of the proposed hypothesis by the use of a topic specific

questionnaire was completed. Information on the level of trust that people have towards computer

simulations and their utilization in medical environment was gathered. It can be stated that all the

respondents were more pro the inclusion of simulations into health care than against it. People

younger than 60 years of age were much more pro simulation than people over the age of 60. Also,

people who were provided a real world story with an example of computer simulation utilization

into health care had a greater level of trust towards the hypothesis than people who were not

provided such story. The relationship of the public with science and technology was somewhat

tackled, the initial reactions of people when seeing a general advertisement or medicine based

advertisement was grasped, number of people from the respondents who have experience with

several diseases was obtained, and the technique respondents use to get rid of diseases was tackled.

Comparison of the results of the respondents who were provided different advertisements and

stories was performed. Statistical analysis calculations for some simulation based questions were

completed. Overall the goals were met; however, the number of respondents was too small to

classify the result of the paper as a result representing the general public.

 Undoubtedly, there is several future proposals and plans for the possible expansion of this

work that can be made. The most obvious and important one in the eyes of the author of this paper

is to make the sample size of the respondents much greater. Although 230 respondents can do when

it comes to the purpose of demonstration the possible usage of the questionnaire and computer

simulations in the medicine, it is not even close enough to the number that would be required for

actual real world commercialization purposes. Also, with a greater number of respondents,

statistical analysis calculations could provide much greater insights on the obtained results.

Another proposal for the future direction of the study would be to provide the questionnaire

to doctors and specialists in the fields of computer science and blood glucose treatments in order

analyze responses of real world professionals. Then, of course it would be interesting to see the

comparisons of the responses of the professionals versus the responses of the respondents. Another

proposal is to include more general questions to the questionnaire, so that the respondents could be

separated by more than just age and gender. A general dividing question that comes to mind would

be a question of the education level of the respondents to see if the amount of education the

respondents have make a difference in their responses. Extra proposal for the work could be

providing more than three stories to the respondents from different technologically-medicine based 

75

fields other than computer simulation dealing with blood glucose. Finally, the results of the

questionnaire could be studied more in order to develop different meanings of the results.

 

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Abstract

An enormous advancement in new technological developments in the area of medicine is all over

the world. Computer simulations are commonly used tools in various health care related research

works. This paper attempts to make a contribution to the by a creation of questionnaire, which

responses should validate the hypothesis that computer simulations can positively influence

research and development in health care and thus help treatment of patients. The results of the

questionnaire and statistical analysis calculations tend to show that people under the age of 60 years

would be heavily interested in an inclusion of simulations into health care. The number of

respondents; however, is not great enough in order to consider the results to be a clear description

of the general public.

Key Words

Computer simulations

Blood glucose

Diabetes

Medical environment

Marketing and Advertising

JEL Classification

C 63

I 12

M 31

M 37