VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY

INTERNATIONAL UNIVERSITY

SCHOOL OF BUSINESS

THE INFLUENCE OF CONSUMER CAUSAL BELIEFS ON ESTIMATES OF TIME TO

ONSET

THE CASE OF VIET NAM

In Partial Fulfillment of the Requirements of the Degree of

BACHELOR OF ARTS in BUSINESS ADMINISTRATION

Students name: NGUYEN LE THANH TUAN (BABAIU10127)

Advisor: Dr. BUI QUANG THONG

Ho Chi Minh City, Vietnam

2014

THE INFLUENCE OF CONSUMER CAUSAL BELIEFS ON ESTIMATES OF TIME TO

ONSET

THE CASE OF VIET NAM

APPROVED BY: Advisor APPROVED BY: Committee,

________________________ ______________________________

Dr. Bui Quang Thong Ho Thi Bich Van, Ph.D., Chair

__________________________________

Nguyen Huu Dang Khoa, MBA., Secretary

___________________________________

Le Thanh Long, Ph.D.

__________________________________

Albert Low, M.A.

THESIS COMMITTEE

ACKNOWLEDGMENTS

At this page, I would like to express my gratitude to teachers, friends, participants and my

family. For those who had supported me during the thesis period.

First and foremost, I want to give an appreciativeness to my advisor, Dr. Bui Quang

Thong. The one who gave me useful advices, encouragements

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................ vii

LIST OF FIGURES ....................................................................................................... ix

ABSTRACT .......................................................................................................................x

CHAPTER I: INTRODUCTION ....................................................................................1

I. Background .............................................................................................................1

II. Problem statement .................................................................................................3

III. Research questions and objevtives ......................................................................6

IV. Research scope and limitations ............................................................................6

V. Structure of the thesis ............................................................................................7

CHAPTER II: LITERATURE REVIEW ......................................................................8

I. Placebo effect ...........................................................................................................8

1. Theory of placebo mechanism ..........................................................................9

1.1. Expectancy theory ...................................................................................9

1.2. Classical conditioning ..............................................................................9

2. Placebo effect in marketing ...........................................................................10

II. Theory of causality...............................................................................................13

III. Cognitive load theory ........................................................................................15

IV. Effect of causality on time judgment ...............................................................16

V. Insight in the study of Faro (2010) ....................................................................18

VI. Conceptual framework ......................................................................................20

CHAPTER III: RESEARCH METHODOLOGY ........................................................20

I. Experiment method ................................................................................................21

1. Evaluation of the experiment ..........................................................................21

2. Experiment terminologies ...............................................................................23

3. Experimental research designs ..................................................................... 27

3.1. Pre-experimental designs .....................................................................28

3.2. True experiment ................................................................................... 29

3.2.1. Single factor design.................................................................... 29

3.2.2. Block design ...............................................................................30

3.2.3. Latin square design ....................................................................31

3.2.4. Factorial design .........................................................................32

3.3. Field and Laboratory Experiments .....................................................33

4. Sampling .........................................................................................................35

4.1. Definition ............................................................................................. 35

4.2. Types of sampling ................................................................................36

4.2.1. Probability sampling (representative samples) ........................ 36

4.2.2. Non-probability sampling (non-representative samples) .......... 36

4.3. Sampling in experiment ..................................................................... 36

4.3.1. Random sampling in experiment ...............................................36

4.3.2. Matching sampling in experiment ..............................................37

4.6.2. Convenience sampling in experiment .........................................37

5. Data analysis ...................................................................................................38

5.1. t-test definition .....................................................................................38

5.2. ANOVA definition ...............................................................................39

5.2. ANCOVA definition............................................................................ 40

5.4. Effect size .............................................................................................41

6. Validity in experiment .................................................................................42

6.1 Internal validity .................................................................................. 42

6.1.1. Maturation ..............................................................................42

6.1.2. Testing ......................................................................................43

6.1.3. Instrumentation .......................................................................43

6.1.4. Selection ...................................................................................43

6.1.5. Statistical regression ...............................................................44

6.1.6. Experiment Morality ................................................................44

6.1.7. Diffusion or imitation of treatment ...........................................44

6.1.8. Compensatory rivalry .............................................................45

6.2 External validity ................................................................................. 45

6.2.1. The reactivity of testing on X ....................................................45

6.2.2. Interaction of selection and X ..................................................45

6.2.3. Other reactive factors ...............................................................46

7. Generalization in experiment...................................................................46

II. Description of the two studies ..............................................................................47

1. Study 1............................................................................................................47

2. Study 2............................................................................................................49

CHAPTER IV: DATA ANALYSIS & DISCUSSION ..................................................52

I. Implementing pretest study ...................................................................................52

II. Implementing the two official experiments ........................................................53

1. The official experiment 1 ..............................................................................54

1.1. Data analysis ........................................................................................ 56

1.2. Discussion............................................................................................. 58

2. The official experiment 2 ..............................................................................59

2.1. Data analysis ........................................................................................ 61

2.2. Discussion............................................................................................. 67

CHAPTER V: GENERAL DISCUSSION & IMPLICATIONS .................................69

I. General discussion ..................................................................................................69

II. Implications for firms & consumers ...................................................................69

III. Implications for marketers .................................................................................72

IV. Limitations and recommendations for further research .................................72

V. Advices for future experiments ............................................................................73

LIST OF REFERENCES ............................................................................................... 75

APPENDIX ......................................................................................................................80

LIST OF TABLES

Table 2.1. Previous Researches Synthesis: Placebo effect in medicine and marketing

actions .............................................................................................................................. 10

Table 2.2. Summary of some studies showing an effect of causality on time judgments .17

Table 3.1. Key to design symbols ......................................................................................27

Table 3.2. An example of blocking design ........................................................................31

Table 3.3. An example of Latin Square Design ................................................................ 31

Table 3.4. An example of Factorial Design ...................................................................... 32

Table 3.5. A brief description about the difference between Field and Laboratory

experiment ....................................................................................................................... 33

Table 3.6. Single-factor design in study 1 ...................................................................... 34

Table 3.7. Single-factor design in study 2 .........................................................................34

Table 4.1. Descriptive statistics of Causal and Non-causal group .....................................56

Table 4.2. ANOVA statistics of estimates of time-to-onset between two groups .............56

Table 4.3. Descriptive statistics of line measurement between two groups ..................... 57

Table 4.4. ANOVA statistics of line measurement between two groups ......................... 57

Table 4.5. ANOVA statistics of mood measurement in causal condition during the

interval .......................................................................................................................... 57

Table 4.6. ANOVA statistics of mood measurement in causal condition during the whole

study .................................................................................................................................. 58

Table 4.7. ANOVA statistics of mood measurement in non-causal condition during the

interval ...............................................................................................................................58

Table 4.8. ANOVA statistics of mood measurement in non-causal condition during the

whole study ........................................................................................................................58

Table 4.9. ANOVA statistics of causal belief between alternative-cause-absent vs

alternative-cause-present group .........................................................................................61

Table 4.10. ANOVA statistics of causal belief between alternative-cause-absent vs strong

alternative-cause-present group vs weak alternative-cause-present ................................. 62

Table 4.11. Descriptive statistics of three conditions ....................................................... 62

Table 4.12. ANOVA statistics of estimates of time-to-onset between 3 conditions ........ 62

Table 4.13. ANOVA statistics of estimates of time-to-onset between alternative-cause-

absent vs strong alternative-cause-present condition ........................................................ 63

Table 4.14. ANOVA statistics of estimates of time-to-onset between strong alternative-

cause-present vs weak alternative-cause-present condition ...............................................63

Table 4.15. ANOVA statistics of estimates of time-to-onset between strong alternative-

cause-absent vs weak alternative-cause-present condition ................................................64

Table 4.16. ANOVA statistics of mood measurement of participants in the whole study 64

Table 4.17. ANOVA statistics of the likelihood of using an energy bar between 3

conditions .......................................................................................................................... 65

Table 4.18. ANOVA statistics of the likelihood of using an energy bar between

alternative-cause-absent vs strong alternative-cause-present condition ........................... 65

Table 4.19. ANOVA statistics of the likelihood of using an energy bar between strong

alternative-cause-present vs weak alternative-cause-present condition ........................... 66

Table 4.20. ANOVA statistics of the likelihood of using an energy bar between

alternative-cause-absent vs weak alternative-cause-present condition ............................. 66

Table 4.21. The first model regressed the ratings of causal belief on the likelihood of

using an energy bar ............................................................................................................67

Table 4.22. The second model regressed the ratings of causal belief on the estimates of

time to onset for the gum ...................................................................................................67

LIST OF FIGURES

Figure 2. Summary of the findings of Faro (2010) ........................................................... 18

Figure 3. The relationship between IV and DV in the study ............................................ 25

ABSTRACT

People sometimes feel the effect of product consumption almost instantaneously within an unrealistically short time after consumption. Such placebo-like effects are typically

attributed to conditioning, motivation, or expectations about product efficacy. The

present research shows such effects can also occur because, under some conditions,

people are more prone to underestimate the time to onset of products they have used in

the past. These recollections of too short a time to onset alter peoples experience of products and cause them to report more rapid effects. Participants who were led to

believe there was a strong causal link between having consumed a product and improved

performance on a task recalled that less time elapsed before they experienced an effect. In

subsequent consumption, they felt comfortable using the product later in time, started

working on a similar task earlier upon use, experienced the products effect sooner, and were less inclined to switch to competing products.

1

CHAPTER I

INTRODUCTION

Changing the Future by reshaping the Past: Our memory of time are shortened when we

believe products and events are related (Faro, 2010).

I. BACKGROUND

Nowadays, more and more advertisement activities are using misleading adjectives and

adverbs, intended to make people believe that a product has an effect that cannot

objectively have, however, that the company risks a lawsuit for false advertising. So, they

use terms as feelings, impressions, hair visibly healthier which only means

that you get the impression that they are, but they are not necessarily so. For example, an

advertising clip on TV shows: a wet cloth with a magic detergent that, without any

apparent effort, removes grease and fouling from a pan (Judicibus, 2011). Although

people believe that these advertisements are not totally true but they can still be

impressed and willing to buy that product. To explain for these kinds of effects, scientists

use a term called placebo effect.

The placebo effect is a fascinating yet puzzling phenomenon, which has challenged

investigators over the past 50 years (Franklin G Miller, David Wendler, Leora C

Swartzman , 2006). In terms of scientific, it is a series of positive reactions against a

therapy which does not derives so much from its active ingredients but from the patients

expectations about it (Judicibus, 2011). In practice, if a patient believes that a certain

therapy is working, he will psychologically place himself in a very positive way against

that therapy, regardless of its effectiveness. The result could be a psychosomatic effect

that leads the body itself to react to the disease, sometimes with positive results (Decher,

2013).

2

Advocates of alternative medicine have begun using the placebo effect as a way of

marketing their products by stressing that the mind can heal the body in way that medical

science cannot understand (Vitelli, 2012). In medicine, therefore, to assess the

effectiveness of a drug, the placebo effect becomes a very important benchmark. This is

true in medicine (Judicibus, 2011). But if it works in the medical field, why should not it

work in other sectors?

People often feel better simply because they believe they have been treated (Ingraham,

2013). The placebo effect shows that to be convinced of something relative to our body

leads to a specific response by the nervous system that, in some cases, produces precisely

the intended effect. If this is true for a therapy may also be true in other respects.

Realizing the power of this effect, many marketers have used the placebo effect for

several products like: toothpaste, energy drink, cosmetic to win customer in a

unfamiliar way. For instance, an advertisement for toothpaste: is intended to bring home

the sensation of cleanliness of the dentist is likely to make customer experience that

feeling after using toothpaste (Judicibus, 2011).

II. PROBLEM STATEMENT

The placebo effect makes people sometimes feel the effect of product consumption

almost immediately within an unrealistically short time after consumption (Faro, 2010).

In the past, many researches had built up to examine the impact of placebo effect on

consumer behavior, such as a study of Stewart-Williams, Steve and John Podd (2004)

suggested that classical conditioning procedures are a factor that shapes the placebo

effects on consumer belief. A study of Shiv, Baba, Ziv Carmon, and Dan Ariely (2005)

suggested that belief and expectation of consumer can be evoked by the placebo effect.

Or a study of Irmak, Caglar, Lauren G. Block, and Gavan J. Fitzsimons (2005) showed

3

the importance of motivation as a driver of marketing placebo effects. Truly, these

researches give a great support for marketers in case of changing and forming consumer

perception and behavior.

Recently, in a noticeable study with the same common concerns, has showed a different

reason for the rapid effect of products, called assessment of time to onset after

consumption. Highlighting the need for renewed research focus in this area, Faro (2010)

suggested that there is an influence of consumer casual belief on estimates of time-to-

onset, and the casual belief is a cause for the placebo effect happens quickly or slowly.

To examine for his hypothesis, Faro has implemented three experiments which were

established in his university. In the first one, participants first listened to music and later

took part in a creativity task. Half of the participants were then told the music they had

listened to earlier enhanced creativity; the rest were not given that information. "When

asked to recollect the amount of time that elapsed between listening to music and the

creativity task, the first group thought that the time was significantly shorter," Faro

writes. "Hence, even though both groups had (on average) the same experience with the

music and with the creativity task, believing that the two things were related made

participants connect them more closely in time." In a second study, participants first

chewed a stick of gum and then took part in an attention-related task. Later in the study

they were told that chewing gum increases attention. In that case, participants who

considered only the gum as a cause for increased attention gave shorter estimates of time-

to-onset than other participants who also considered another contributing cause: practice

with the task. On a later occasion, the participants said they experienced the gum's effect

earlier and they were less interested in trying a competing product. In the last study, as in

study 2, participants first chewed a cube of gum then took part in an attention/ memory

task. After that, they were told that the study was established to examine the effect of

guarana, an ingredient that contains in the gum. Half of the participants were told that

guarana can enhance attention and memory; the others were not given that information.

At the end, the result showed that participants who were given the information, judged

4

the amount of time that elapsed between starting to chew the gum and the improvement

in the task to be shorter than the others.

In overall, the first two studies show that causal beliefs about products can shape

consumers perceptions of time-to-onset for past consumption, and these perceptions can

have an independent effect on future consumption. The first study suggests that shortened

perceptions of time-to-onset may give the target product an added advantage. Participants

who held shorter perceptions of time experienced the gums effect earlier on a future

consumption occasion, and were less interested in trying other products. The second

study suggests some possibly negative consequences for the product and for consumers.

Participants who held shorter perceptions of time were more likely to use the product too

late, and more likely to begin an activity which depends on the effect of the product too

early. (Gal Zauberman 2010). The last study provides some evidence for the process

through which causal beliefs affect estimates of time to onset by identifying cognitive

load as a moderator of the effect. The paper concludes with a discussion of the

implications of the findings for consumers and marketers.

This study has published on the Journal of Consumer Research which is one of the well-

known journals about consumer behavior in the world, it has ranked 6th position based on

Center of Journal Ranking in 2013. The study has been widely cited, and received a

honorable mention prize in the Robert Ferber Award competition which is held annually

to honor the best interdisciplinary dissertation article, in the year 2011.

The present research proposes a replication of the original Faro (2010) research. The

reason why the author wants to replicate Faro (2010) research can be explained in two

purposes.

First, reviewing past researches about how people estimates about past consumption such

as: in a study of Chandon, Pierre; Brian Wansink (2007) in food industry, they metioned

that consumer will tends to underestimate the caloric content of main food and to choose

5

another higher-calorie food when restaurants claim to be healthy compared to when they

do not. Or in a study of Menon (1993) about exaiming a process by which behavior

frequency judgments are generated in consumer survey. In addition, in researches about

time judgment, such as a study of LeBoeuf (2010) showed that future time intervals that

end with losses seem shorter than equivalent intervals that end with gains, or a study

demonstrated that people discount delayed task outcomes due to perceived changes over

time in supplies of slack (Zauberman, Gal, John G. Lynch Jr., 2005, see also Yeung;

Catherine W. M; Dilip Soman, 2007). These researches above provide various useful

information for markerters about frequency and recency of use, or time jugdment. But,

the point is there is no research works on estimates of time to onset. At the year 2010,

Faro study was considered as the first paper that works on estimates of time to onset.

Any time a result is surprising, researchers will try to replicate it, to see if the

phenomenon is dependable or just a fluke (a one-time occurrence) (Dewey, 2007).

Therefore, reexamine a new result is quite necessary, it can provides more evidence or

suggests some changes for the initial result.

Second, the author desires to replicate the study of Faro (2010) but in a new context

which is conducted for Vietnamese consumers. When replicating the obtained results,

one can use different manipulations, materials, or ways of measuring the dependent

variable. This may also lead to new interesting results, which could provide more insight

into the content under investigation (Stel and Vonk 2005). Actually, because of

investigating in the new sample, the research can generates some suggestions about

whether it have any difference in the consumer perception between Eastern and Western.

Replication, a re-study repeats the findings of an initial study (Seale, 2004), is divided in

various forms: external, internal, operational (Drotar, 2010). The present research will

applied the form of external replication, which refers to data gathered from new samples

collected in different settings and/or at different times than an original sample (Drotar,

2010), to examine the initial findings in the sample of Vietnamese consumer.

6

III. RESEARCH QUESTIONS AND OBJECTIVES

The research aims to answering these following questions:

How can the causal beliefs of Vietnamese people impact on estimates of time-to-

onset in a product?

Is there any difference between Western and Asia people in the assessment of

time-to-onset for past consumption?

Therefore, the objectives of study are follows:

To investigate whether the casual beliefs of people can impact on estimates of

time-to-onset, in the case of Vietnamese people.

To analyze any difference between Western and Asian consumers in the

assessment of time to onset for past consumption.

To recommend several suggestions for consumers and marketers.

IV. RESEARCH SCOPE AND LIMITATIONS

The present research is conducted from Feb 2014 to May 2014, in Ho Chi Minh City.

Due to time constraint and level of an undergraduate student, the author only

implemented two first hypotheses which were also two first experiments of Faro (2010)

research. Therefore, the findings cannot generalize for the whole results of the authentic

paper.

Participants who take part in two studies are students studying the International

University Vietnam National University. They will receive bonus mark in their course

after participating the study.

7

V. STRUCTURE OF THE THESIS

The thesis is divided into five chapters which focus on different contexts

Chapter I: Introduction

Giving a brief background about the thesis topic, followed by the research problem

statement, research questions and objectives, research scope and limitations.

Chapter II: Literature Review

Synthesizing all theories relating to the topic and conducting a theoretical framework that

will be applied in the research.

Chapter III: Research Methodology

Explaining all terminologies of experiment then giving a deep description about two

upcoming studies.

Chapter IV: Data Analysis & Discussion

Describing two studies and a discusssion about the findings from the collected data.

Chapter V: General Discussion & Implications

Giving general discussions and implications for firms and consumers.

8

CHAPTER 2

LITERATURE REVIEW

I. PLACEBO EFFECT

In terms of medical literature, the placebo effect is defined as a genuine psychological or

physiological effect produced by a substance or a procedure that has no inherent power to

create that effect (Stewart-Williams and Podd, 2004). In more colloquial terms, the

placebo is essentially a sugar pill. The study of Stewart-Williams and Podd (2004)

show a general example in a medical setting as follows: a doctor gives a his patient a pill

which, unbeknownst to the patient, is merely a sugar pill. They called this pill is the

placebo. Lately, the patients health improves, apparently because of the belief that the

pill was a real medicine, effective for the condition. The effect after consumption of

patient is called placebo effect.

The definition is even simpler than understanding process of placebo effect. Indeed,

scientists had spent a long time to examine on the nature of placebo effect and applied it

in a large number of medical settings, from relatively benign maladies, such as warts and

the common cold, to more serious diseases, such as diabetes, angina, and cancer (Kirsch,

1997). Applications about placebo effect have strongly spread in pharmaceutical science,

like pain reduction (Montgomery, Guy and Irving Kirsch, 1996), cardiovascular disease

(Bienenfeld, Frishman and Glasser, 1996) and depression (Kirsch, Guy Sapirstein, 1999).

1. Theories of placebo mechanism

According to a numerous past researches, there are two main concepts that belong to the

basis placebo effect are: expectancy theory and classical conditioning (Shiv, Carmon and

Ariely, 2005).

9

1.1 Expectancy theory

Expectancy theory is a cognitive process theory of motivation that is based on the idea

that people believe there are relationships between the effort they put forth at work, the

performance they achieve from that effort, and the rewards they receive from their effort

and performance (Lunenburg, 2011). In other words, people will be motivated if they

believe that strong effort will lead to good performance and good performance will lead

to desired rewards.

Based on the expectancy theory, placebo effects arise because beliefs about a substance,

procedure serving as a placebo activate expectations that a particular effect will occur,

which then affect the subsequent effectiveness of the substance procedure (Shiv, Carmon

and Ariely, 2005). In a simple way, the substance of placebo will produces an effect if

people expect it to have the effect.

1.2 Classical conditioning

Classical conditioning is defined as a particular form of learning when an organism is

being exposed to conditioned and unconditioned stimuli which results in relevant change

in organism state or behavior (Stewart-Williams and Podd, 2004).

For some past researches, these two theories have been contrasted and debated. For

instance, Kirsch (1991) argues that expectancy theory can explain the process of placebo

response whereas classical conditioning is unable to fully account for placebo effect. On

the other hand, Voudouris et al. (1990) try to examine that classical conditioning

processes are superior when explaining the framework of placebo effect. However,

classical conditioning and expectancy theory can support each other when creating

placebo effect. Stewart-Williams and Podd (2004), they explains that the increase

acceptance of expectations as the basic mechanism for placebo effects has led to an

increased interest in how beliefs lead to placebo effects and the role of expectancies in

10

mediating this effect. In other words, classical conditioning processes shape placebo

effect and expectancies help to mediate it.

2. Placebo effect in marketing

In economics, it is universally acknowledged that the more quality of product, the more

expensive to produce. In case of consumers who have inadequate information about the

quality of alternative products, they may evaluate the quality of product based on the

price of this product. Then, a belief and expectation about higher priced goods are of

higher quality or more effective will come to their mind. If the beliefs and expectations

derived from the price of a product are strong enough, it will be conceivable that the price

of the product could trigger a placebo effect and actually influence how effective the

product is.

In a series of experiments, Shiv, Carmon and Ariely (2005) suggest that the price of the

product affects performance through the commonly known placebo effect. Shiv and his

coauthors express that consumer beliefs and expectations, shaped by experiences in their

daily lives, often influence their judgments of products and services and affect their

subjective experiences. Besides price, they also point out other researches about placebo

effect applied in marketing such as: a drink may taste better if it has a favorite brands

label than if it is unlabeled (McClure et al. 2004), meat that is labeled 75% fat free tastes

better than the same meat that is labeled as containing 25% fat (Levin and Gaeth, 1988).

Table 2.1. Previous Researches Synthesis: Placebo effect in medicine and marketing

actions

Topic Tittle Author Journal Content

Theoretical

background

of placebo

effect

The placebo effect:

dissolving the

expectancy versus

conditioning debate

Stewart-

Williams and

Podd

Psychological

bulletin, (2004),

130(2), 324340

The authors provide an

extensive overview of the

literature regarding the

placebo effect. They define

and separate such notions

as placebo, placebo effect,

11

nocebo. The main focus of

Stewart-Williams and Podd

(2005) is to understand

which of two main theories

that are believed to

contribute to the placebo

effect classical conditioning and

expectancy theory is the basis of placebo processes.

The main conclusion is that

one shouldnt separate these two theories when

finding the basis of placebo

effect. On the contrary, the

relationship of two theories

gives rise to placebo effect

Placebo

effect in

medicine

The importance of

placebo effects in

pain treatment and

research.

Turner et al

J.A.M.A.,

(1994), 271(20),

16091614

The article provides an

overview of the relevant

literature with an objective

to identify the importance

of placebo effects in the

treatment of pain. The

authors examine placebo

effects of medical

treatments and sham

surgeries. The factors

influencing the placebo

responses both form the

patient and provider sides

are examined and assessed.

Placebo

effect in

Placebo Effects of

Marketing Actions:

Consumers May Get

Shiv, Carmon

and Ariely

Journal of

Marketing

Research, 2005,

The authors conducted a

series of experiments to

prove the influence of

marketing actions (in

particular, price

promotions) on the actual

efficacy of products.

Moreover the research

focuses on the role of

expectations in mediating

placebo effect (higher

12

marketing

actions

What They Pay For

XLII

(November),

383393.

expectancy level lead to

greater placebo effect). One

of the main findings is that

non-conscious expectations

about the price-quality

relation can influence

consumers and encourage

placebo effect.

The authors also revealed a

number of important

statements regarding the

placebo effect in

marketing:

- buying product with a

discount produces greater

placebo effect than paying

more for a product

-favorable ads can

reinforce negative price-

quality perceptions

-drawing attention to

positive marketing claims

(encouraging expectations)

stimulates the amplitude of

the placebo effect.

Placebo

effect in

marketing

actions

The Placebo Effect

in Marketing:

Sometimes You Just

Have to Want It to

Work

Irmak, Block

and Fitzsimons

Journal of

Marketing

Research,

(2005), XLII

(November),

406409

Authors conducted an

experiment with energy

drink revealing the

importance of motivation

as an important factor

contributing to placebo

effect in marketing

Influence of

Marketing

Actions

The Effect of Price,

Brand Name, and

Store Name on

Buyers Perception of Product Quality:

an Integrative

Review

Rao and

Monroe

Journal of

Marketing,

(1989), 26(3),

351357

Authors investigate the

influence on highlighting

the effectiveness of a

product using various

marketing tools. On the one

hand, it stimulates initial

purchase but might have a

negative post-purchase

experience that would hurt

pong-term sales. However

Rao and Monroe (1989)

13

Source: (Chigirinova, 2012)

II. THEORY OF CAUSALITY

David Hume (1711 1776), one of the British empiricists of the early modern period, is

famous for the theory of casual relation which has become the starting point for most

modern treatments of causation. According to Hume, causality is a regular succession of

event-types: A and B, we say that A causes B when the two always occurs together, that

is, are constantly conjoined. Whenever we find A, we also find B, and we have a

certainty that this conjunction will continue to happen (Lorkowski, 2010). In other words,

argue that this happens

only if the effectiveness

cues are salient in the

consumption context and

are removed in the

consumption context

Influence of

Marketing

Actions

Influence of Beer

Brand Identification

on Taste Perception

Allison, R. I., &

Uhl, K. P.

Journal of

Marketing

Research,

(1964), 1(3), 36-

39.

The authors conducted an

experiment aimed at

identifying the influence of

brand name on taste

perception. The market of

beer has been chosen as an

example. The results

suggest that taste

perception arise primarily

with the help of marketing

actions rather than from

physical product

differences. From a

managerial point of view it

means that physical

product differences had

little to do with the various

brands' relative success or

failure in the market

14

if we believe a causal relationship exists between two events, then one of these events,

the cause, will readily recall the other, the effect (Shanks, 1985). For example, one ball

colliding and causing the movement with a second ball.

The concepts of "cause" and "effect" are used both for defining simultaneous events,

events that are contiguous in time, and events whose effect is born with the cause

(Spirkin, 1983). Borrowing from his example, a solar flare causes magnetic storms on the

earth and a consequent temporary interruption of radio communication. Spirkin (1983)

proposes a formula for the mediate connection between cause and effect: if A is the cause

of B and B is the cause of C, then A may also be regarded as the cause of C. Though it

may change, the cause of a phenomenon survives in its result. An effect may have several

causes, some of which are necessary and others accidental.

More specifically, Woo-kyoung and Charles (2002) explain the definition of causality by

stating two simple questions: how people think about causal relations and how people

indentify causes. Based on two questions, Woo-kyong and his coauthor propose the

explanation of causality in two views: mechanism view and regularity view .

In mechanism view, they suggest that people beliefs about causal relations include 3

factors: a notion of force or necessity, a belief in a causal process that takes place

between a cause and an effect, and a set of more or less elaborated beliefs about the

nature of that mechanism, described in theoretical terms. In regularity view, they also

suggest that an associationist approach characterizes the belief that A caused B to be

primarily an expectation about a general pattern of co-variation between A and B. By

showing through some examples, they propose that people may have strong intuitions

about causes even in the absence of good evidence or expectations about patterns of co-

variation about general cases. Therefore, Woo-kyoung and Charles (2002) conclude that

to believe that A caused B is to believe that there is a consistent relation between events

of type A and events of type B.

15

III. COGNITIVE LOAD THEORY

The idea of cognitive load is first published by Sweller et al. in 1998, a theory relating

working memory characteristics and the design of instructional systems. In fact, theories

of structure of human memory give a distinction between long term memory and short

term memory (Jong, 2010). Long term memory is defined as a part of memory that store

large amount of information, whereas short term memory is the memory system where

small amount of information are stored (Cowan, 2001). In recent papers, the term short

term memory has been replaced by working memory, because this part of memory has

responsible for the processing of information.

According to Van Gerven et al., (2003), cognitive load theory is an instructional theory

that comes from the idea that people working memory is overloaded with respesct to the

amount of information it can hold, and also the number of operations it can perform on

that information. Van Gerven suggests that learners should be encouraged to use his or

her limited working memory efficiently when learning a difficult task. Learning is

hampered when working memory capacity is exceeded in a learning task (Jong, 2010).

Moreover, the main trait of this theory is the relation between long term memory and

working memory, and how instructional materials interact with this cognitive system

(Ayres, 2006).

Categorization of cognitive load theory

Based on cognitive load theory, there are three different types of cognitive load can be

distinguished:

Intrinsic cognitive load: refers to the number of elements that must be processed

simultaneously in working memory for schema construction (i.e., element

interactivity) (Artino, 2008).

16

Extraneous cognitive load: (or ineffective cognitive load) is the result of

instructional techniques that require learners to engage in working memory

activities that are not directly related to schema construction or automation

(Artino, 2008).

Germane cognitive load: (or effective cognitive load) is the result of beneficial

cognitive processes such as abstractions and elaborations that are promoted by the

instructional presentation (Artino, 2008)

IV. EFFECT OF CAUSALITY ON TIME JUDGMENT

The perception and estimation of elapsed time, and temporal information more

generally, is a complex process subject to various conscious and unconscious

influences (Faro, 2010). Faro proposes that consumers assessments of the time-to-

onset of an effect refer to the amount of time they believe it takes for a product to

show its effect after consumption. For instance, in several products such as

pharmaceuticals sold by prescription and over-the-counter medications, consumers

care not only about whether the product has an effect, but also how quickly it begins.

Moreover, consumers must often assess the time-to-onset to decide future

consumption because they may need to experience the effect of a product before

starting another activity. Borrowing his examples, caffeine before an exam, digestion

aids before food consumption, relaxation drugs before a flight, and erectile

dysfunction drugs before a sexual encounter. In such cases, assessments of time-to-

onset can affect the timing of consumption, the timing of subsequent activity, and,

consequently, both the perceived and real effectiveness of the product.

The relationship between time and causality is bidirectional (Faro et al., 2013), the

perception of causality can affect peoples judgments of time in such a way that they

perceive events that they know to be causally related to have occurred closer in time

17

to each other. For example, a series of researches focused on intentionality of action

and showed that perceived time between a voluntary movement (e.g., pressing a key)

and its effect (e.g., an auditory tone) was shorter compared to a baseline condition in

which the action and its effect occurred within the same amount of time but without

any causal link (the intentional binding effect; Haggard et al., 2002; see also Moore et

al., 2009; Ebert and Wegner, 2010). Faro et al. (2013) also point out several

researches about estimates of time elapsed between pairs of historical events that

were causally related with estimates of time between historical events that were not

causally related (see more in Faro et al., 2013) to conclude that perceived causality

shortened estimates of elapsed time.

Table 2.2. Summary of some studies showing an effect of causality on time

judgments (David Faro, Ann L. McGill, Reid Hastie, 2013)

References Experimental task

(operationalization

of cause and effect)

Method of

interval

assessment

Range of

studied time

intervals Proposed process Implications

Buehner and

Humphrey,

2009;

Press keyhear auditory tone

Numeric

estimates, event

synchronization 150 ms4s

Priming of general

causality-time

relationship

Anticipated action

timing

Ebert and

Wegner, 2010

Pull/push joysticksee object move on

Numeric

estimates 100700 ms

Retrospective

inference

Binding associated

with explicit sense of

authorship

Engbert and

Wohlschlger,

2007

Press keyhear auditory tone

Libet clock

method 250 ms

Predictive motor

process based on

expectations and

perceptual

associative process

Privileged

representation of

intentional actions

Faro, 2010

Take energy

productfeel enhanced alertness

Numeric

estimates,

reproduction

38 s6.5 min

Retrospective

inference based on

general causality-

time relationship

Delayed

consumption, early

experience of effect,

reluctance to switch

to alternative

actions

Faro et al.,

2005, 2010

Sputnik launch Apollo 11 landing

(historical events)

Numeric

estimates 3184 years

Retrospective

inference based on

physical-

mechanical

Evaluation of actions

undertaken by others

18

causality

Haggard et al.,

2002

Press keyhear auditory tone

Libet clock

method 250600 ms

Predictive motor

control process

linking intentional

actions and their

outcomes

Coherent experience

of agency, early

experience of effect

Moore and

Haggard,

2008; Moore

et al., 2009

Press keyhear auditory tone

Libet clock

method, numeric

estimates

100700 ms

Predictive motor

control process and

retrospective

inference

Coherent experience

of agency, early

experience of effect

V. INSIGHT IN THE STUDY OF FARO (2010)

Faro (2010) claims that altered perceptions of customer about time to onset may affect to

their future consumption decisions and experience. Experience in the past consumptions

plays an important role in generating the future expectations (Bettman, 1979). According

to Faro (2010), in case of strong causal belief, consumers hold perceptions that the time

to onset is shorter in the past consumptions, then, they might feel the effect of the product

earlier in the future. This influence is more noticeable when the effect of product is quite

ambiguous (Hoch and Ha, 1986). The result of the study of Faro (2010) sumarized as

follows:

Figure 2. Summary of the findings of Faro (2010)

Target Products

Consumers believe that its effect

have a short time to start because

of stronger causal belief

for the subsequent consumption

The effect of alternatives

might starts slower

are products have used in the past

(especially products have ambiguous

effect)

compare to alternative products

They might tend to believe that

the effect of product starts sooner

19

In Faro (2010) study, he manipulated the causality after the consumption of a product;

however, Faro mentions that the same result might be occurred if the causality was

manipulated before the consumption of the product. Based on the results of Haggard et al.

(2002), the stronger causal beliefs result in estimates of shorter time if causality were

manupulated before consumption might happens. Nevertheless, causal intervals were

judged to be shorter, proposing that the effect on estimates of time to onset can extend to

situations in which causal beliefs differ before product consumption. Furthermore, the

judged causal intervals in study of Haggard et al. (2002) were of milliseconds, whereas

Faro claims that product consumptions normally requires longer durations. In several

cases, a previous changes to causal beliefs may trigger factors unrelated to causality (e.g.,

mood, arousal, attention to time) that can alter the subjective experience of the interval

and affect time estimates as a result (Faro, 2010).

The effects of causal belief on future consumptions and experience are assumed to be

motivated by recollections of time to onset. However, if we ignore the appearance of

time-to-onset, does the causal belief about product will directly affect to future

consumptions and experience? For instance, similar to expectations of performance of

efficacy, strong causal belief about a product may leads to a quicker experience of its

effect (Faro, 2010; see also Baba Shiv, Ziv Carmon, Dan Ariely, 2005). Faro proposes

that recollection of time to onset can have an effect above and beyond any such direct

effects of causal beliefs. That is. Recollections of time to onset have a life of their own;

they are an independent piece of information on which future decisions and experiences

are based (Faro, 2010)

20

VI. CONCEPTUAL FRAMEWORK

As mentioned, the present research only replicates the two first hypotheses of Faro (2001)

as follows:

Previous researches showed a stronger perceived causal relationship between two events

results in estimates of shorter time (Haggard et al., 2002; see also David Faro, Ann L.

McGill, Reid Hastie, 2013). Stated another way, in case of strong causal belief,

consumers hold perceptions that the time to onset is shorter in the past consumptions,

then they might feel the effect of the product earlier in the future (Faro, 2010). Based on

the following previous researches as well as Faro (2010) hypothesis, I propose the first

hypothesis as follows:

H1: Altered by stronger causal beliefs, estimates of shorter time to onset for past

consumption will lead to earlier experience of the effect in future consumption.

Furthermore, Faro claims that if these consumers compare the product to another

alternative products in the aspect of time to onset, the alternatives may appear slower

than if stronger causal beliefs had not previously shortened the consumers estimates of

time to onset for the target. This tendency may bring an advantage for the product

because if consumers want to change to alternative products for any motivation, they

might less likely to do so. The second hypothesis is established as follows:

H2: Altered by stronger causal beliefs, estimates of shorter time to onset for past

consumption will reduce the inclination to switch to competing products when fast action

is desired

21

CHAPTER III

RESEARCH METHODOLOGY

I. EXPERIMENT METHOD

Experiments are studies involving intervention by the researcher beyond that required for

measurement. The usual intervention is to manipulate some variable in a setting and

observe how it affects the subjects being studied (e.g. people or physical entities). The

researcher manipulates the independent or explanatory variable and then observes

whether the hypothesized dependent variable is affected by the intervention (Copper and

Schindler, 2011).

1. Evaluations of the experiment

Copper and Schindler (2011) proposes that there are four reasons that bring advantage to

the experiment as follows:

Due to the main ability of researchers is to manipulate the independent variable

then the probability that changes in the dependent variable are a function of that

manipulation increases.

Contamination from extraneous variables can be controlled more effectively than

in order designs. By this way, researchers may find it easy to isolate experiment

variables and evaluate their impact over time.

Because of the convenience and cost of experimentation are higher than other

methods, it allows the experimenter opportunistic scheduling of data collection

and the flexibility to adjust variables and conditions that evoke extremes not

observed under routine circumstances. In addition, the researcher can collect

combinations of variables for testing rather than having to search for their random

appearance in the study environment.

22

One of popular methods in experiment called replication which is replicating an

experiment with different subject groups and conditions, leading to the discovery

of an average effect of the independent variable across people, situations and

times. If a treatment is truly effective, the long-term averaging effect of

replication will reflect its experimental worth. If it is not effective, then the few

members of the experimental population who may have reacted to the treatment

will be negated by the large numbers of subjects who were unaffected by it.

Replication reduces variability in experimental results, increasing their

significance and the confidence level with which a researcher can draw

conclusions about an experimental factor.

Experimenters can use naturally occurring events and, to some extent, field

experiments (a study of the dependent variable in actual environment conditions)

to reduce subject perceptions of the researcher as a source of intervention or

deviation in their daily lives.

By contrast, there are several limitations that experiment cannot deter:

The artificiality of the laboratory is accidental the major disadvantage of the

experimental method.

Almost nonprobability samples can pose problems despite random assignment.

The extent to which a study can be generalized from college students to managers

or executives is open to question. And when an experiment is unsuccessful

disguise, volunteer subjects are often those with the most interest in the topic.

Despite the low costs of experimentation, many applications of experimentation

far outrun the budgets for other primary data collection methods.

Experimental studies of the past are not feasible, and studies about intentions or

predictions are difficult.

Management research is often concerned with the study of people. There are

limits to the types of manipulation and controls that are ethical.

23

2. Experiment terminologies

Independent variable (IV): variable that the experimenter manipulates (i.e. changes)

assumed to have a direct effect on the dependent variable (McLeod, 2012)

Dependent variable (DV): variable that the experimenter measures (McLeod, 2012)

In a causal relationship must have at least two variables are: independent variable (IV)

and dependent variable (DV), in some way the IV causes the DV to occur. Copper and

his coauthor mention that there are three types of evidence form the basis that

experimenter must complete before going to conclude the result:

Firstly, there must be an agreement between independent and dependent variables. The

presence or absence of one is associated with the presence or absence of the other.

Secondly, beyond the correlation of independent and dependent variables, the time order

of the occurrence of the variables must be considered. The dependent variable should not

precede the independent variable. Both of them may occur almost immediately, or the

independent variable should appear before the dependent variable.

Thirdly, to ensure that experimenters are confident that other extraneous variables did not

influence the dependent variable, they control their ability to confound the planned

comparison. Under laboratory conditions, standardized conditions for control can be

arranged.

Extraneous variables (EV): all variables which are not the independent variable, but

could affect the results (DV) of the experiment (McLeod, 2012)

Treatment: are the different procedures we want to compare. These could be different

kinds or amounts of fertilizer in agronomy, different long distance rate structures in

24

marketing, or different temperatures in a reactor vessel in chemical engineering (Oehlert,

2010).

Control group: A control group in a scientific experiment is a group separated from the

rest of the experiment where the independent variable being tested cannot influence the

results. This isolates the independent variable's effects on the experiment and can help

rule out alternate explanations of the experimental results (Helmenstine, 2014).

Blinding: occurs when the evaluators of a response do not know which treatment was

given to which unit. Blinding helps prevent bias in the evaluation, even unconscious bias

from well-intentioned evaluators. Double blinding occurs when both the evaluators of the

response and the (human subject) experimental units do not know the assignment of

treatments to units. Blinding the subjects can also prevent bias, because subject responses

can change when subjects have expectations for certain treatments (Oehlert, 2010).

Informed Consent: being designed to ensure that participants who take part in the

experiment are completely voluntary, also, this form must states clearly about the nature

of the experiment and the rights of the participant. Before the start of the experiment,

participants should be asked to read this form, and sign it to indicate that they have read

and understood their rights. The experimenter must promise that there is no personal data

is collected, or if it collected, that it will not be published, and will be destroyed. If a

participant appears to be experiencing any stress (for example due to task difficulty, or

perhaps through factors unrelated to the experiment), it is important to remind them that

they are free to withdraw at any time. If a participant is experiencing physical pain (e.g.

because of extensive use of the mouse for the task) then abort the experiment

immediately and consult a senior colleague or the appropriate university ethics committee

for advice on whether to proceed with the experimental procedure (Kristensson, 2012)

25

Participant briefing: for the purposes of experimental control, every participant should

be given the same instructions before they commence the experimental task. Briefing

instructions are normally written out in full, in order to ensure that this is done. The

instructions can either be read from a script by the experimenter, or given to the

participant to read, after which they are asked if they have understood everything, and are

ready to start. If an experimenter script is used, it is a good idea for this to include all

instructions and actions that the experimenter must carry out throughout the experimental

session (Kristensson, 2012)

Debriefing: at the end of an experimental session, participants should normally be

debriefed. Debriefing involves a short interview, often semi-structured, with some

prepared questions that you ask every participant and follow-up questions in the event

that interesting points are raised. This provides a valuable data collection opportunity,

especially as participants' subjective experience of the experiment could be of value in

interpreting either their individual performance, or behavior observed more broadly

across the sample group (Kristensson, 2012).

Pointing out experiment terminologies in the present research

In this research, independent variables are considered as causal belief of participants

when they take part in two experiments. The first one is try to make participants believe

that listening to music will enhance creativity and the second one is about chewing gum

will increases attention. Dependent variable is considered as shortening estimates of

time to onset of participant when they attend attention tasks.

Figure 3.1. The relationship between IV and DV in the study

Based on the evaluation of experiments, the author realized several benefits when

choosing the experiment as the main method in the present research. Firstly, the abilities

Stronger causal belief

(IV)

Shortened estimates

of time to onset (DV)

26

when manipulate the independent variables which are the causal belief of participants. In

fact, the author finds this easy to control and detect the causal belief of a group of people

in a small room instead of observing its fortuitous appearance in the outside environment.

For example, I give them a piece of music and then saying that music increases creativity

of people, by this way, it may somewhat affects to the belief of participants because of

environment, limited knowledge will increase persuasiveness of participants. Secondly,

the author can controls extraneous variables in an effective way. In a first study of the

research, when participants arrive to the setting room, they will receive a consent form

informing them that the study would involve various consumer experiences, such as

listening to music. However, interestingly, to prevent participants from looking at their

watches when estimating time to onset, they will be asked to put away mobile phones,

watches, and any other devices that could cause distraction or make noise (the procedure

of removing watches was similar in all subsequent studies). Last but not least, the recent

research is the replication of the authentic study of Faro (2010), therefore, it may

discovers more average effect of the causal belief across region (Western and Asia),

people (different universities) and time.

Stating about experiment terminologies, in the present research, participants after

listening to the music and taking to the creativity task, haft of them will be told that the

music they had listened to earlier enhance creativity, and the rest wont be given that

information. In this case, the two groups are considered as the treatment group. In the

second experiment, participants first chew a stick of gum and then take part in an

attention-related task. Later in the study they are told that chewing gum increases

attention. Likewise, we also have 2 treatment groups are participants who considered

only the gum as a cause for increased attention and other participants who also

considered another contributing cause. This experiment also gives the participants the

blinding condition, because they cannot determine which group they belong to (strong

causal belief or weak causal belief).

27

Furthermore, in the first study, participants will receive a consent form (read more in the

appendix) informing them that the study would involve various consumer experiences,

such as listening to music. In the second study, another consent form will be design to

inform that participants will would take part in consumer experiences, including trying

chewing gum and completing various other evaluation tasks. Those who have food

allergies can withdraw from the study and take part in other available studies. At the end

of two studies, participants will be debriefed about the actual purpose of the experiment

and thanks for their contribution.

3. Experimental research designs

Researchers design the experiment to control infection of the relationship between

independent and dependent variables. According to Cooper and Schindler (2011), there

are three popular accepted designs including pre-experiments, true experiments and filed

experiments.

Table 3.1. Key to design symbols (Cooper and Schindler, 2011)

The Xs and Os in the diagram are read from left to right in temporal order.

When multiple Xs and Os appear vertical to each other, this indicates that the stimuli

and/or the observations take place stimultaneously

X An X represents the introduction of an experimental stimulus to a group. The effects

of this independent variable(s) are of major interest.

O An O indentifies a measurement or observation activity

R An R indicates that the group members have been randomly assigned to a group

E An E represents the effect of the experiment and is presented as an equation

O X O O

Time

28

Parallel rows that are not separated by dashed lines indicate that comparison groups have

been equalized by the randomization process

Those separated with a dashed line have not been so equalized

3.1 Pre-experimental designs

All three pre-experimental designs are weak in their scientific measurement power that

is, they fail to control adequately the various threats to internal validity. This is especially

true of the after-only study.

After-only study

This may be diagram as follows:

X O O

Time q

X O O

O X O

O

X

Treatment or manipulation

of independent variable

O Observation or measurement

of dependent variable

29

One-Group Pretest-Posttest Design

This design meets the various threats to internal validity better than the after-only study,

but it is still a weak design

Static Group Comparison

This design provides for two groups, one of which receives the experimental stimulus

while the order serves as a control.

3.2 True experiment

3.2.1 Single factor design

According to Hoshmand (2006), in this design, a single factor varies while order factors

are held constant. For example, when the experimenter is interest in finding whether one

variable is superior to another, he or she will use a single-factor experiment in which the

single variable factor is the variety, and the treatments or factor levels are different

variables.

Single-factor experiment can be grouped under two distinct experimental designs. The

first design involves a small number of treatments and is called complete block design.

As the name implies, complete block designs are characterized by blocks, each of which

contains at least one complete set of treatments. The second group of design is

incomplete block designs. These designs contain a large number of treatments and are

also characterized by blocks. However, each block contains only a fraction of the number

of treatments.

O Pretest

X Manipulation

O Posttest

X

O2

O1

30

Figure 3.2. An example of single factor design (Creswell, 2009)

Baseline A Treatment B Baseline A

O-O-O-O-O-O-X-X-X-X-X-X-X-O-O-O-O-O

This design involves multiple observations of a single individual. The target behavior of a

single individual is established over time and is referred to as a baseline behavior. The

baseline behavior is assessed, the treatment provided, and then the treatment is

withdrawn.

3.2.2 Block Design

If there is a single major extraneous variable, the randomized block design is used.

Random assignment is still the basic way to produce equivalence among treatment

groups, but the researcher may need additional assurances. First, if the sample being

studied is very small, it is risky to depend on random assignment along to graduate

equivalence. Second, block design can help experimenter learns whether treatment bring

different results among various groups of participants.

In this design, one can measure both main effects and interaction effects. The main effect

is the average direct influence that a particular treatment of the independent variable has

on the dependent variable, independent of other factors. The interaction effect is the

influence of one factor or variable on the effect of another. Whether the randomized

block design improves the precision of the experimental measurement depends on how

successfully the design minimizes the variation within blocks and maximizes the

variation between blocks. If the response patterns are about the same in each block, there

is little value to the more complex design. Blocking may be counterproductive.

31

Table 3.2. An example of blocking design (Cooper and Schindler, 2011)

Active Factor

Price Difference

Blocking Factor

Customer Income

High Medium Low

7 cents R X1 X1 X1 12 cents R X2 X2 X2 17 cents R X3 X3 X3

3.2.3 Latin Square Design

The Latin square design may be used when there are two major extraneous factors.

Treatments are then randomly assigned to these cells so that a given treatment appears

only once in each row and column (Cooper and Schindler, 2011). Because of this

restriction, a Latin Square must have the same number of rows, columns, and treatments.

Table 3.3. An example of Latin Square Design (Cooper and Schindler, 2011)

Store Size Customer Income

High Medium Low

Large X3 X1 X2 Medium X2 X3 X1 Small X1 X2 X3

Treatments can be assigned by using a table of random numbers to set the order of

treatment in the first row. For example, the pattern may be 3,2,1 as shown above.

Following this, the other two cells of the first column are filled similarly, and the

remaining treatments are assigned to meet the restriction that there can be no more than

one treatment type in each row and column. The experiment takes place, sales results are

gathered, and the average treatment effect is calculated. From this, we can determine the

main effect of various price spreads on the sales of company and national brands. The

cost information allows us to discover which price differential produces the greatest

margin.

32

A limitation of the Latin square is that we must assume there is no interaction between

treatments and blocking factors. Therefore, we cannot determine the interrelationships

among store size, customer income and price spreads. This limitation exists because there

is not an exposure of all combinations of treatments, store sizes and customer income

groups. Such an exposure would require a table of 27 cells, while this one has only 9. If

one is not especially interested in interaction, the Latin square is much more economical.

3.2.4 Factorial Design

One commonly held misconception about experiment is that the researcher can

manipulate only one variable at a time. This is not true, with factorial designs, you can

deal with more than one treatment simultaneously (Cooper and Schindler, 2011).

Table 3.4. An example of Factorial Design

Unit price

Information?

- Price spread - 7 cents 12 cents 17 cents

Yes X1Y1 X1Y2 X1Y3 No X2Y1 X2Y2 X2Y3

The illustration is known as a 2x3 factorial design in which we use two factors: one with

two levels and one with three level of intensity. The version shown here is completely

randomized, with the stores being randomly assigned to one of six treatment

combinations. With such a design, it is possible to estimate the main effects of each of the

two independent variables and the interactions between them. The result can help to

answer the following questions:

1. What are the sales effects of the different price spreads between company and

national brands?

2. What are the sales effects of using unit-price marking on the shelves?

3. What are the sales effect interrelations between price spread and the presence of

unit-price information?

33

3.3 Field and Laboratory Experiments

Table 3.5. A brief description about the difference between Field and

Laboratory experiment (McLeod, 2012)

Laboratory experiment Field experiment

Definition A study is conducted in a well-

controlled environment and accurate

measurements are possible. The

researcher decides where the

experiment will take place, at what

time, with which participants, in

what circumstances and using a

standardized procedure. Participants

are randomly allocated to each

independent variable group.

Although the name is laboratory but

experiment can set up in another

place which has well-designed and

well-controlled

A study conducted outside the

laboratory, in a natural setting. Field experiments are conducted

in open, natural settings. The

experimenter still manipulates

the independent variable, but in a

real-life setting (so cannot really

control extraneous variables)

Advantages It is easier to replicate a laboratory

experiment. Because of a

standardized procedure is used.

Also, researcher can easily control

of extraneous and independent

variables precisely.

Behavior in a field experiment is

more likely to reflect life real

because of its natural setting (i.e.

higher ecological validity than a

lab experiment). In addition,

there is less likelihood of

demand characteristics affecting

the results, as participants may

not know they are being studied.

Disadvantages The artificiality of the setting may

produce unnatural behavior that

does not reflect real life (i.e. low

ecological validity). This means it

would not be possible to generalize

the findings to a real life setting.

Another one is demand

characteristics or experimenter

effects may bias the results and

become confounding variables.

There is less control over

extraneous variables that might

bias the results. This makes it

difficult for another researcher to

replicate the study in exactly the

same way.

34

Applied experimental design in the present research

Because of the current research is replicating from the origin study of Faro (2010), so the

author desires to use the same experimental designs of Faro study in this research. The

research is laboratory experiment which is set up in a well-designed room in the

university. There are a total of two primary experiments in the study and both of them are

applied the single-factor experimental design. In the first study, the single factor is

considered as causality as well as the only one factor that varies while others are kept

constant. The 2-level designs of causality are casual condition and non-causal condition.

More specific about the unchanged factors like: time all participants are received the

same amount of time when taking part in the study; creativity task all participants also

receive the same positive feedback from the experimenter after finishing the task.

Therefore these factors are seemed constant in the study.

Table 3.6. Single-factor design in study 1

2-level Causality

causal non-causal

In the study 2, the single factor is also the changing of causality between alternative cause

absent and alternative cause present. All unchanged factors are the same as study 1.

Table 3.7. Single-factor design in study 2

2-level

Causality

Alternative cause

absent

Alternative cause

present

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4. Sampling

4.1 Definition

Cooper and Schindler (2011) proposes the basic idea of sampling is that collecting some

of the elements in a population, we may draw conclusion about the whole population. In

other words, sampling is a process of taking any portion of the population or universe as

representative of that population or universe. The main objective of drawing a sample is

to make inferences about the larger population from the smaller sample.

General terminologies relating to sampling:

Population element: is the individual participant or object on which the measurement is

taken. The element which is considered as unit of study may be a person or easily

something else (ibid).

Population: is the total collection of elements about which we wish to make some

inferences (ibid).

Sampling frame: is simply a list of the study population, or the actual list of sampling

units from which the sample, or some stage of the sample, is selected (ibid).

Sampling error: is the degree of error to be expected for a given sample design or the

difference between the sample mean and the population mean (ibid).

Sampling bias: refers to the notion that those selected are not "typical" or

"representative" of the larger populations that have been chosen from (ibid).

Sample size: the number of elements in the obtained sample (ibid).

Margin of error: refers to the precision needed by the researcher. A margin of error of 5

percent means that the actual findings could vary by as much as 5 points either positively

or negatively (ibid).

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4.2 Types of sampling

4.2.1 Probability sampling (representative samples)

Probability samples are selected in such a way as to be representative of the population.

They provide the most credible results because they reflect the characteristics of the

population from which they are selected (e.g., residents of a particular community,

students at an elementary school, etc.).

4.2.2 Non-probability sampling (non-representative samples)

Non-probability samples are chosen subjectively. Each member of the population does

not have a known chance of being included. Although these kinds of sample are less

desirable than probability samples, however, a researcher may not care about

generalizing to a larger population. The validity of non-probability samples can be

increased by trying to approximate random selection, and by eliminating as many sources

of bias as possible. The limitation of these samples is generalization; they do not truly

represent a population, so the experimenter cannot make valid inferences about the larger

group from which they are drawn.

4.3 Sampling in experiment

4.3.1 Random sampling in experiment

In this technique, each population element is given an equal chance of being selected as

subject. The entire process of sampling is done in a single step with each subject selected

independently of the other members of the population. The key to random selection is

that there is no bias involved in the selection of the sample. Any variation between the

sample characteristics and the population characteristics is only a matter of chance

(Sommer, 2011).

There are many methods to proceed with simple random sampling. The most primitive

and mechanical would be the lottery method. Each member of the population is assigned

a unique number. Each number is placed in a bowl or a hat and mixed thoroughly. The

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blind-folded researcher then picks numbered tags from the hat. All the individuals

bearing the numbers picked by the researcher are the subjects for the study. Another way

would be to let a computer do a random selection from your population. For populations

with a small number of members, it is advisable to use the first method but if the

population has many members, a computer-aided random selection is preferred

(Explorable.com, 2009)

4.3.2 Matching sampling in experiment

Matching is useful when randomization cannot assign participants to groups due to

recruit a nonprobability quota sampling approach (Cooper and Schindler, 2011).

Matching sampling tends to allocate each experimental and control subject accorded on

every feature applied in the study. As a result, the experimenter might face complex

because of the raise of groups and variables amount. However, if the experiments

features only relate to treatment or the dependent variable, investigator can define, match

and manipulate (ibid.).

4.3.3 Convenience sampling in experiment

Convenience sample is nonprobability samples that are unrestricted area. They are the

least reliable design but normally the cheapest and easiest to conduct. Saying another

way, a convenience sample is simply one in which the researcher use