UNIVERSIDADE FEDERAL DO RIO DE JANEIRO – UFRJ

INSTITUTO DE PÓS-GRADUAÇÃO E PESQUISA EM ADMINISTRAÇÃO -

COPPEAD

VINICIUS FARIAS RIBEIRO

DO ADVISOR GENDER AND ADVICE JUSTIFICATION IMPACT ADVICE

TAKING IN MANAGERIAL DECISION MAKING?

RIO DE JANEIRO

2017

VINICIUS FARIAS RIBEIRO

DO ADVISOR GENDER AND ADVICE JUSTIFICATION IMPACT ADVICE

TAKING IN MANAGERIAL DECISION MAKING?

Doctoral Thesis presented to the Coppead Graduate

School of Business, Federal University of Rio de Janeiro

– UFRJ, as part of the required requisites to obtain the

doctoral degree in Business Administration.

Tese de Doutorado apresentada ao Programa de Pós-

Graduação em Administração, Instituto Coppead de

Administração, Universidade Federal do Rio de Janeiro,

como parte dos requisitos necessários à obtenção do

Título de Doutor em Administração.

Advisor/Orientadora:

Adriana V. G. de Hilal, D.Sc.

Rio de Janeiro

2017

"Do Advisor Gender And Advice Justification Impact Advice Taking In Managerial

Decision Making?"

VINICIUS FARIAS RIBEIRO

Tese de Doutorado submetida à Banca Examinadora do Instituto COPPEAD de Administração,

da Universidade Federal do Rio de Janeiro – UFRJ, como parte dos requisitos necessários à

obtenção do grau de Doutor em Administração.

Aprovada por:

_____________________________________________ (Presidente da Banca)

Profª Adriana Victoria Garibaldi de Hilal, D.Sc. (COPPEAD/UFRJ)

____________________________________________

Prof. Marcos Gonçalves Avila, Ph.D.

(COPPEAD/UFRJ)

____________________________________________

Prof. Eduardo Bittencourt Andrade, Ph.D.

(EBAPE/FGV)

____________________________________________

Profª Veranise Jacubowski Correia Dubeux, D.Sc.

(ESPM)

____________________________________________

Prof. Pedro Paulo Pires dos Santos, D.Sc.

(UFF)

____________________________________________

Prof. Américo da Costa Ramos Filho, D.Sc.

(UFF)

Rio de Janeiro

2017

ACKNOWLEDGMENT

First, I want to thank my family and friends for supporting me during life until here.

Without them, probably I would have never achieved many of my goals. I want to thank

specially my mother Silvana, my grandmother Zeita and my beloved wife Deborah.

I am also grateful to all professors, researchers, colleagues and administrative

employees from COPPEAD, Universitat Pompeu Fabra (UPF), IESE and Instituto de Empresa

(IE). They contributed directly or indirectly to this work and my formation.

Several colleagues listened carefully to my doubts, concerns and gave me very good

feedback. Many of them are participants of the Management and Behavioral Research Breakfast

at the Universitat Pompeu Fabra (UPF), a select group of researchers who are often willing to

contribute to other’s work. I also appreciate the support from the participants of the Instituto de

Empresa Doctoral Consortium, who also gave me insightful contributions to the work. Thanks

also to the professors Américo Ramos from the Universidade Federal Fluminense (UFF and

UFRRJ), Eduardo Andrade from Fundação Getúlio Vargas (FGV/EBAPE), Pedro Pires (UFF)

and Veranise Dubeux (ESPM and IAG/PUC) for their critical feedback in this research. Thanks

also to Marcos Avila and Robin Hogarth for their counselling during this Doctoral Research;

and, obviously, I am very grateful to Adriana Hilal. She was not only my thesis advisor, but

also a friend who strongly supported me during these years.

I must acknowledge that without Petrobras and my coworkers support I could not

produce this research work. The company gave me a part-time license and financial support to

carry out the doctoral studies and research. I am also very grateful for the experience as Doctoral

Visiting Student at UPF the company granted me. I want thank specially my former (Sergio

Porto, Mariana Cavassin) and current (Mario Tavares) managers. They were very

comprehensive and supportive during this tough period as a doctoral student and professional

negotiator.

All in all, I have been in a surprisingly supportive environment with special people who

directly contributed to my evolution and growth. I was lucky for having so many good and

gentle people supporting me. They are undoubtedly co-responsible for any success I may

achieve or have ever achieved in my life.

Thanks very much!

ABSTRACT

RIBEIRO, Vinicius Farias. Do advisor gender and advice justification impact advice taking in

managerial decision making? Rio de Janeiro, 2017. Doctoral Thesis (DSc in Management) –

Federal University of Rio de Janeiro, Coppead Graduate School of Business. Rio de Janeiro,

2017.

In managerial decisions, do people value advice from male and female advisors equally?

When advisor gender is added to the equation, does advice justification (intuitive versus

analytic) impact the decision-making process? Previous studies indicate that people tend to

place more value on analytically justified advice. Nevertheless, prescriptive approaches to

decision making state that analysis should be used in analytic-inducing tasks and intuition in

intuitive-inducing ones. Two experiments were designed, each with two independent samples,

taken from Amazon Mechanical Turk (MTurk) workers and company professionals. Both

experiments were quasirational managerial decisions: the first one with both analytic and

intuitive inducing cues; and, the second one, a more intuitive-induced scenario where intuitive

cues prevailed. The results suggest that, in general, analytic justification is more valued than

intuitive justification. Thus, analytic justification may act as a safeguard for the accuracy of the

offered advice. Conversely, despite increasing recognition of the importance of intuition in

decision-making literature, intuitive justification does not seem to be as valued as analytic

justification. Findings also allow to infer that, depending on the advisees’ profile (MTurk

workers or professionals) and providing that advice justification is analytic, quasirational

scenarios seem to favor male advisors, whereas more intuitive-inducing settings seem to favor

female advisors. Moreover, in each scenario, gender stereotypes were active in one sample,

though not in both. Hence, results might signal an ongoing, but slow, process leading to the

mitigation of gender stereotypes in the long run. Finally, the two experiments demonstrated the

importance of the interplay among advisor gender, advice justification, and task characteristics

in advice taking.

Keywords: analysis and intuition, advisor gender, advice justification, advice taking,

judge advisor system, decision making.

RESUMO

RIBEIRO, Vinicius Farias. Do advisor gender and advice justification impact advice taking in

managerial decision making? Rio de Janeiro, 2017. Doctoral Thesis (DSc in Management) –

Federal University of Rio de Janeiro, Coppead Graduate School of Business. Rio de Janeiro,

2017.

Em decisões gerenciais, as pessoas valorizam os conselhos de homens e mulheres

igualmente? Quando o gênero do conselheiro é incluído na equação, a justificativa do conselho

(intuitiva versus analítica) impacta o processo de tomada de decisão? Estudos prévios indicam

que as pessoas tendem a dar mais valor a conselhos com justificativa analítica. No entanto,

abordagens prescritivas em tomada de decisão indicam que análise deve ser usada em tarefas

analíticas e intuição em tarefas intuitivas. Foram elaborados dois experimentos, cada um com

duas amostras independentes, obtidas com trabalhadores do Amazon Mechanical Turk (MTurk)

e por profissionais de empresas. Ambos os experimentos foram decisões gerenciais

quasirational: o primeiro tinha informações que induziam tanto análise quanto intuição; e o

segundo, foi um cenário que induzia mais a intuição, onde as informações que induziam a

intuição prevaleciam. Os resultados sugerem que, em geral, a justificativa analítica é mais

valorizada que a justificativa intuitiva. Portanto, a justificativa analítica age como uma garantia

para a acurácia do conselho oferecido. Por outro lado, apesar do crescente reconhecimento da

importância da intuição na literatura de tomada de decisão, a justificativa intuitiva parece não

ser tão valorizada. Os achados permitem inferir que, dependendo do perfil do respondente

(MTurk ou profissionais) e considerando justificativa analítica, cenários quasirational tendem

a favorecer conselheiros homens, enquanto que em tarefas intuitivas, conselheiras mulheres.

Adicionalmente, em cada cenário, os estereótipos de gênero estavam ativos em uma amostra,

mas não em ambas. Portanto, os resultados podem apontar um processo em andamento, apesar

de lento, da mitigação do estereótipo de gênero no longo prazo. Por fim, os dois experimentos

mostraram a importância da interação entre gênero do conselheiro, justificativa de conselho e

características da tarefa na aceitação de conselhos.

Palavras-chave: análise e intuição, gênero do conselheiro, justificativa do conselho,

aceitação de conselho, sistema de decisor e conselheiro, processo decisório.

FIGURE LIST

Figure 1 - Systems 1 and 2 (Dhami & Thomson, 2012; Doherty & Kurz, 1996;

Kahneman, 2003) ..................................................................................................................... 21

Figure 2 - Modes of cognition along the Cognitive Continuum according to CCT (Dhami

& Thomson, 2012) .................................................................................................................... 24

Figure 3 - Cognitive Task Index (CTI) ......................................................................... 24

Figure 4 - Cognitive Continuum Task Properties (Dhami & Thomson, 2012; Doherty &

Kurz, 1996; Dunwoody et al., 2000; Hammond et al., 1987) .................................................. 25

Figure 5 – Judge-Advisor System: decision maker, advisor and advice. ..................... 30

Figure 6 – Questions: Cognitive Reflection Test (Frederick, 2005) ............................ 37

Figure 7 - MTurk Pilot – Participants’ Gender ............................................................. 45

Figure 8 - MTurk Pilot - Participants' Education.......................................................... 46

Figure 9 - MTurk Pilot - Participants' Age ................................................................... 46

Figure 10 - MTurk Pilot - Participants' Expertise in Product Launch - Experiment 1 . 46

Figure 11 - MTurk Pilot - Participants' Expertise - Painting Market - Experiment 2 .. 47

Figure 12 - Quasirational Scenario - MTurk Sample - Descriptive Statistics .............. 52

Figure 13 - Mean Advice Taking, Quasirational Scenario (QS): MTurk Sample ........ 53

Figure 14 - Quasirational Professionals Sample - Descriptive Statistics ..................... 53

Figure 15 - Mean Advice Taking, Quasirational Scenario (QS): Professional Sample 54

Figure 16 - More Intuitive Scenario MTurk Sample - Descriptive Statistics ............... 59

Figure 17 - Mean Advice Taking, More Intuitive-Inducing Scenario (IS): MTurk

Sample ...................................................................................................................................... 60

Figure 18 - More Intuitive Scenario - Professional Sample - Descriptive Statistics .... 60

Figure 19 - Mean Advice Taking, More Intuitive-Inducing Scenario (IS): Professional

Sample ...................................................................................................................................... 61

Figure 20 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking ...... 101

Figure 21 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking - Female

Advisor ................................................................................................................................... 101

Figure 22 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking - Male

Advisor ................................................................................................................................... 102

Figure 23 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking -

Intuitive Justification .............................................................................................................. 102

Figure 24 - Statistics Quasirational Scenario - MTurk Sample - Advice Taking -

Analytic Justification .............................................................................................................. 103

Figure 25 - Statistics Quasirational Scenario - Professional Sample - Advice Taking

................................................................................................................................................ 104

Figure 26 - Statistics Quasirational Scenario - Professional Sample - Advice Taking -

Female Advisor ...................................................................................................................... 104

Figure 27 - Statistics Quasirational Scenario - Professional Sample - Advice Taking -

Male Advisor .......................................................................................................................... 105

Figure 28 - Statistics Quasirational Scenario - Professional Sample - Advice Taking -

Analytic Justification .............................................................................................................. 105

Figure 29 - Statistics Quasirational Scenario - Professional Sample - Advice Taking -

Intuitive Justification .............................................................................................................. 106

Figure 30 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking ..... 107

Figure 31 - Statistics More Intuitive Scenario – MTurk Sample - Advice Taking -

Female Advisor ...................................................................................................................... 107

Figure 32 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking - Male

Advisor ................................................................................................................................... 108

Figure 33 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking -

Intuitive Justification .............................................................................................................. 108

Figure 34 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking -

Analytic Justification .............................................................................................................. 109

Figure 35 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking

................................................................................................................................................ 110

Figure 36 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking

– Female Advisor ................................................................................................................... 110

Figure 37 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking -

Male Advisor .......................................................................................................................... 111

Figure 38 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking

Analytic Justification .............................................................................................................. 111

Figure 39 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking -

Intuitive Justification .............................................................................................................. 112

EQUATION LIST

Equation 1 - Advice Taking .......................................................................................... 32

Equation 2 - Weight of Advice (WOA) ........................................................................ 32

ABBREVIATION AND INITIALS LIST

ANOVA – Analysis of Variance

CCT - Cognitive Continuum Theory

COPPEAD – Instituto COPPEAD de Administração

CRT – Cognitive Reflection Test

CSI – Cognitive Style Index

EBAPE – Escola Brasileira de Administração Pública e de Empresas

ESPM – Escola Superior de Propaganda e Marketing

FGV – Fundação Getúlio Vargas

HB – Heuristics and Biases

HDT - Hierarchical Decision-Making Teams

HIT – Human Intelligence Task

IE – Instituto de Empresa

IAG – Escola de Negócios da PUC-Rio

IS – Intuitive Scenario

JAS – Judge Advisor System

M – Mean

MIT – Massachusetts Institute of Technology

MRI - Magnetic Resonance Imaging

MTurk – Amazon Mechanical Turk

NFC – Need For Cognition

NFL – National Football League

PUC – Pontifícia Universidade Católica

QS – Quasirational Scenario

REI - Rational-Experiential Inventory

SAT – Suite of Assessments

SD – Standard Deviation

TCI – Task Continuum Index

UFF – Universidade Federal Fluminense

UFRJ – Universidade Federal do Rio de Janeiro

UFRRJ – Universidade Federal Rural do Rio de Janeiro

UPF – Universitat Pompeu Fabra

USA – United States of America

WOA – Weight of Advice

WPT – Wonderlic Personnel Test

SUMMARY

1. INTRODUCTION .............................................................................................. 14

1.1 CONTEXT, OBJECTIVE AND RELEVANCE ............................................ 14

1.2 DELIMITATION ............................................................................................ 15

1.3 ORGANIZATION .......................................................................................... 16

2. THEORETICAL REVIEW ................................................................................ 17

2.1 DECISION MAKING PROCESS AND OUTCOME .................................... 17

2.2 THINKING, FAST AND SLOW ................................................................... 19

2.3 COGNITIVE CONTINUUM AND TASK CHARACTERISTICS ............... 23

2.4 ADVICE TAKING ......................................................................................... 29

2.4.1 Content of Advice ..................................................................................... 32

2.4.2 Advisee ...................................................................................................... 32

2.4.3 Advisor ...................................................................................................... 33

2.4.4 Advice Justification ................................................................................... 34

2.5 GENDER AND DECISION MAKING ......................................................... 35

2.5.1 Cognitive Reflection Test ......................................................................... 36

3. METHOD ........................................................................................................... 40

3.1 SUBJECTS ..................................................................................................... 41

3.2 PILOTS ........................................................................................................... 44

4. EXPERIMENTAL MATERIAL & RESULTS ................................................. 48

4.1 EXPERIMENT 1 – QUASIRATIONAL SCENARIO WITH BOTH

ANALYTIC AND INTUITIVE CUES (QS) ....................................................................... 48

4.1.1 Design ....................................................................................................... 50

4.1.2 Procedure .................................................................................................. 50

4.1.3 Dependent variable ................................................................................... 51

4.1.4 Results ....................................................................................................... 51

4.1.5 Discussion ................................................................................................. 54

4.2 EXPERIMENT 2 – A QUASIRATIONAL MORE INTUITIVE-INDUCED

SCENARIO (IS) ................................................................................................................... 55

4.2.1 Design ....................................................................................................... 57

4.2.2 Procedure .................................................................................................. 57

4.2.3 Results ....................................................................................................... 58

4.2.4 Discussion ................................................................................................. 61

5. FINAL CONSIDERATIONS ............................................................................. 64

5.1 LIMITATIONS ............................................................................................... 66

5.2 CONTRIBUTIONS, IMPLICATIONS, AND FUTURE RESEARCH ......... 68

REFERENCES ............................................................................................................. 70

APPENDIX A – EXPERIMENTAL MATERIAL – PORTUGUESE ........................ 84

APPENDIX B – EXPERIMENTAL MATERIAL - ENGLISH ................................ 101

APPENDIX C – EXPERIMENT 1 – STATISTICAL RESULTS – MTURK SAMPLE

101

APPENDIX D – EXPERIMENT 1 – STATISTICAL RESULTS – PROFESSIONAL

SAMPLE 104

APPENDIX E – EXPERIMENT 2 – STATISTICAL RESULTS – MTURK SAMPLE

107

APPENDIX F – EXPERIMENT 2 – STATISTICAL RESULTS – PROFESSIONAL

SAMPLE 110

14

1. INTRODUCTION

1.1 CONTEXT, OBJECTIVE AND RELEVANCE

In decision-making literature, there is a widely-accepted distinction between two

different modes of thinking (Kahneman, 2003, 2011; Stanovich & West, 2000). One form,

known as System 1, is described as intuitive, fast, automatic, associative, and effortless; while

the other, System 2, is analytic, slow, deliberate, conscious, and effortful. Historically, in

decision-making literature, intuitive judgment has not been greatly valued by researchers,

despite the increasing recognition of its importance (Hogarth, 2010). Accordingly, academics

often defend the idea that intuition has a lower status compared to the use of analysis in problem

solving (Courtney, Lovallo, & Clarke, 2013; Dawes, Faust, & Meehl, 1989; Kahneman, 2003;

Russo & Schoemaker, 2002). Moreover, Tversky and Kahneman (1974) presented a vast

amount of evidence showing that intuitive thinking is subject to heuristics and biases. Hence,

intuition is still frequently perceived as a sloppy way of thinking (Hogarth, 2001).

Nevertheless, in some circumstances, intuitive thinking can outperform analysis (Dane

& Pratt, 2007; Gigerenzer & Gaissmaier, 2011; Hogarth, 2010; Kahneman & Klein, 2009),

while the combined use of intuition and analysis can perform better than the analytic mode

alone (Blattberg & Hoch, 1990). Tzioti, Wierenga, and Osselaer (2014) looked at advice giving

explicitly based on intuition. Their results show that the utilization of intuitive (versus analytic)

advice varies depending on the advisor’s seniority and type of task for which the advice is given.

They suggest that future research could explore other cues which affect the perceived value of

analytical versus intuitive justification in advice taking. Hence, this study took up the challenge

by adding the advisor’s gender to the advice-taking equation.

In terms of gender, Nemecek (1997) cites Keller, a feminist historian and philosopher:

“western tradition has a history of viewing rational thinking as masculine and intuition as

feminine.” Furthermore, Hogarth (2008) refers to Graham and Ickes (1997), who distinguish

between what they call the different empathic abilities of men and women. They show that

women possess greater intuitive ability than men in vicarious emotional responding and

nonverbal decoding ability, though not in emphatic accuracy. Complementarily, Frederick

(2005) suggests that men are more likely to reflect on their answers and are less inclined to go

with their intuitive responses.

So, what happens when the advisor’s gender (male or female) is considered in advice

taking? Does the interplay between advisor gender and advice justification (analytic versus

15

intuitive) influence the acceptance of advice? The objective of this study was thus to find if—

and in what circumstances—advisor gender and advice justification influence advice taking, by

asking the following research question: What is the effect of advisor gender and advice

justification on advice taking in different managerial decision-making contexts? In general,

people are skeptical about intuitive thinking or hunches. To answer this question, two different

quasirational managerial-decision experiments were designed: the first one with both analytic

and intuitive inducing cues, and the second one, a more intuitive-induced scenario where

intuitive cues prevailed. It was expected that the nature of the decision problem, the advisor

gender and advice justification would likely affect advice utilization.

This topic is of concern to scholars and businesspeople, as shown in recent studies

published in both business magazines and scientific journals (Bednarik & Schultze, 2015;

Locke, 2015; Soyer & Hogarth, 2015; Tzioti et al., 2014). It is also relevant to both managerial

and general decision making, as people are usually advice seeking (Bandura & Jourden, 1991;

Sims Jr & Manz, 1982) and, in the real world, decisions are often made interactively (Heath &

Gonzalez, 1995).

1.2 DELIMITATION

There are mainly three activities involved in the advising process: advice seeking,

advice giving and advice taking. In scientific literature, there are articles that focus in one or

more of these; and, scholars may study them jointly or separately. In this work, the focus is the

advice taking activity.

Additionally, in this field, there are discussions regarding the presence or absence of a

pre-advice decision, number of advisors, amount of interactions between the decision-maker

and advisor(s) and among advisors themselves, whether the advisee can choose if and when to

receive advice (Bonaccio & Dalal, 2006). However, this study has not addressed these

variables. Furthermore, although people often decide under time pressure, this study does not

manipulate this variable either.

This research is focused on the decision making and advice taking processes and not in

the accuracy or effectiveness of their outcomes. Since some tasks do not have a right or a better

answer, sometimes it is not even possible to evaluate the quality of the decision-making

outcome.

16

1.3 ORGANIZATION

Advice taking studies are part of a broader research field named judgment and decision-

making. In the literature review chapter, before entering in advice taking itself, this work will

present an introductory perspective of the decision-making field. It covers the differences of

decision making process and outcome, the two different ways of thinking and how task

characteristics impact the thinking process. In the following section, the advice taking literature

is presented. It contemplates how the content of advice, advisee, advisor and advice justification

impact the advice-taking process. The last section in the theoretical review is about gender and

decision making.

The following chapter is about Method, covering methodological details including

subjects’ sampling and pilots. The next chapter presents the experimental material, hypotheses,

results and discussions. Finally, in the last chapter - Final Considerations, limitations,

contributions, implications and future research are presented.

17

2. THEORETICAL REVIEW

2.1 DECISION MAKING PROCESS AND OUTCOME

Judgment and decision making is a multidisciplinary field which has been studied for a

long time (Buchanan & O Connell, 2006; Gilovich et al., 2002). It has been object of

discussions since ancient Greek philosophers such as Socrates, Plato and Aristotle and has been

considered a relevant topic until nowadays (Buchanan & O Connell, 2006; Hackforth, 1972;

Locke, 2015; MIT, 2013; Overholser, 1993).

Before any further discussion about the topic, it deems necessary to highlight the

difference between a decision-making process and its outcome. Frequently common sense may

be misguiding, making people assume that a good decision-making process will always lead to

the desired result. This is not accurate and does not happen necessarily (Soyer & Hogarth,

2015). Besides the quality of the judgment and decision-making process, there are variables,

such as context, that may influence the result of the decision (Sommers, 2011). Therefore a

good process will not necessarily result in a good outcome and a good outcome not necessarily

is a result of a good process (Ariely, 2010; Klein, 1998).

Usually there is at least a brief analysis before making a decision. This analysis is known

as judgment, in other words: ‘the process of forming an opinion or evaluation by discerning and

comparing’ (Merriam-Webster, 2014). Furthermore, after this evaluation, there is the decision

itself that can be defined as ‘the resolution to do something’, or better: ‘the resolution to behave

in a certain way’, which also includes deciding to do nothing. In this sense, behavior sometimes

is interpreted as observable states of the individual, which, in the case of deciding to do nothing,

makes the observable state of the individual harder to pinpoint. Notwithstanding, decision

implies a choice; people decide whether to act someway or not (Szaniawski, 1980), considering,

of course, their will and freedom of choice (Drubach, Rabinstein, & Molano, 2011; Zalta, 2013).

Alternatively, instead of choosing between acting or not, such as in a yes or no

alternative, people can also make decisions considering different options; for example: if

confronted with a simple decision situation such as choosing what to drink, one may have

alternatives such as soda, water or beer; and pick one or more than one from the available

alternatives. In addition, people should take into account that sometimes the subject or issue

which is the focus of a decision is not well defined or clearly defined. As a consequence,

alternatives get much more complicated to be listed and evaluated. The decision maker also

needs to check the feasibility of the alternatives. However, at the time of alternative evaluation,

18

it is not possible to know with certainty if the alternatives are feasible or not. The only way to

know it for sure is by implementing them (Szaniawski, 1980).

Additionally, the decision maker’s knowledge and experience will support alternative

generation, evaluation (goal-relevance, feasibility) and; finally, implementation. Once people

make a choice, they have to feel committed to their decision, which, in turn, leads to

implementation. Without the required commitment, people could choose an alternative and do

not even try to implement it, or instants after their decision, change their opinion. The corpus

of individual’s beliefs and values is essential to decision-making (Ford & Richardson, 1994),

as it influences the whole process: from problem diagnosis to the implementation and control

of the selected solution, not excluding, alternative generation, criteria definition and alternative

evaluation (Szaniawski, 1980).

Although some people may still think the best decisions arise from a perfect rational

process, there is abundant evidence that this is not so. Due to contextual, psychological,

neurological and others constraints; most decisions cannot be made in a purely rational manner.

Complex circumstances, time and mental computational power restraints limit decision makers

to bounded rationality (Damasio, 2005; Etzioni, 2001; Kahneman, 2011; Klein, 1998).

In judgment and decision-making, researchers have two distinct theoretical approaches;

the first one, called prescriptive or normative which focuses on how decisions should be

made; while the second one, which is descriptive, focuses on how decision-making actually

happens (Bazerman & Moore, 2012). Herbert Simon, the 1978 Economics Nobel Prize,

suggests descriptive perspective as preferred in order to understand the decision-making

process. That is due to people’s behavior that does not comply with axiomatic rules. Thus,

describing and explaining people’s motivation and the decision making process, instead of

focusing only on the prescriptive decisional analysis, should contribute more to the

development of theory on decision-making (March & Simon, 1958; Simon, 1957). However,

the field has seen the development of a better dialogue between normative and descriptive

approaches (Tsay & Bazerman, 2009).

Considering that sometimes there is no correct normative answer, a different yardstick

should be used. A good decision may be one in which the decision maker is happy or less

unhappy with the decision and will stay committed to it (Shiv, 2011). Alternatively, a poor

decision can be defined as when the decision maker regrets the decision one has made if the

knowledge gained would lead to a different decision if a similar situation arises in the future

(Klein, 1998).

19

2.2 THINKING, FAST AND SLOW

This section title is named after the Daniel Kahneman’s book (Kahneman, 2011). He is

the 2002 Economics Nobel Prize winner. In his book, he summarizes decades of his research

in judgment and decision making, often in collaboration with late Amos Tversky.

Several years before Kahneman’s book, Adam Smith also published a distinguished

work - The Wealth of Nations (Smith, 1863), where he defended that if an individual pursues

his self-interest, he would, indirectly, promote good for society as a whole. He expected people

to act rationally and in an egoistic manner and this idea forged the foundations of classical

economics, leading to the concept of the rational man. Influenced by this rational approach,

normative proposals were developed considering the probability of outcomes and the search of

utility maximization (Simon, 1955).

The rational economic model has dominated the decision making research field for

many years, until a turnaround in 1950’s that changed the scenario dramatically (Gilovich &

Griffin, 2002). Researchers began to indicate limitations of rationality in decision-making

process. At that time, Herbert Simon published his doctoral thesis and coined the expression

‘bounded rationality’.

In his work, Simon rejects the classical approach that decisions are perfectly rational.

He defends that due to costs of information acquisition, executives make good-enough

decisions, based on their bounded rationality (Simon, 1948, 1955, 1957, 1959). Simon also

coined the term satisfice, a portmanteau of satisfy and suffice, used to explain when the decision

maker picks an alternative and stops the search for better solutions, renouncing to optimize it

(Simon, 1956).

Yet about bounded rationality, some studies demonstrated that sometimes people decide

first and then rationalize their decision; and even when there is no argumentation or evidences

supporting their opinion, they seldom change their minds, mainly when discussing moral issues

(Haidt, 2001; Haidt & Hersh, 2001). Emotions and gut feelings often drive automatically and

unconsciously decisions. Furthermore, justifications, many times plausible, come because of

the decision, but they are not cause.

Following Simon’s work, Tversky and Kahneman revolutionized academic research on

human judgment and decision-making (Tversky & Kahneman, 1974). They pondered that

instead of an extensive algorithmic processing, people would use simplifying heuristics, as

shortcuts, to evaluate alternatives and decide. Questions would be replaced by simpler ones

(Kahneman & Frederick, 2002). Many times heuristics may be useful, but they can also lead to

20

biased judgments and errors, violating axioms, normative rules and leading to non-optimal

decisions (Tversky & Kahneman, 1986). This approach was called Heuristics and Biases (HB)

or error and biases (Gilovich & Griffin, 2002).

Although, heuristics can also lead to good decisions, the HB research has focused on

people’s biases and decision making errors (Gilovich, Vallone, & Tversky, 1985; Kahneman,

2003; Kahneman & Tversky, 1972; Thaler, Tversky, Kahneman, & Schwartz, 1997; Tversky

& Kahneman, 1971, 1973, 1974, 1983, 1986, 1991; Tversky, Kahneman, & Choice, 1981).

Despite the findings seem quite simple nowadays; as Kahneman says in his book (2011), it was

a great discovery at that time in descriptive judgment and decision making.

When people make a decision, a thought, even briefly, usually precedes the decision

itself (Libet, 2002; Libet, Gleason, Wright, & Pearl, 1983). This judgment may be intuitive or

rational and in order to describe these two different ways of thinking the terms System 1 and

System 2 were coined (Stanovich & West, 2000). This theoretical approach to describe decision

making process is called dual-process theory. Many scholars have published in accordance to

this dual-process way of thinking (Epstein, 1994; Evans & Over, 1996; Evans & Stanovich,

2013; Klein, 1998; Posner & Snyder, 2004; Sloman, 1996). Stanovich & West (2000) presents

a detailed comparison for the several dual-process theories. Find below more details and

definitions about these two ways of thinking.

“System 1 operates automatically and quickly, with little or no effort and no sense of

voluntary control.” (Kahneman, 2011, p. 22)

“System 2 allocates attention to the effortful mental activities that demand it, including

complex computations. The operations of System 2 are often associated with the

subjective experience of agency, choice, and concentration.” (Kahneman, 2011, p. 22)

Figure 1 briefly summarizes them, considering the process and content involved in

judgment and decision-making.

21

Figure 1 - Systems 1 and 2 (Dhami & Thomson, 2012; Doherty & Kurz, 1996; Kahneman, 2003)

At a first glance, Figure 1 may lead people to evaluate superficially the quality of the

decisions resulting from System 1 and System 2, given their characteristics. In general, people

tend to believe decisions based on System 2 ought to be better than the ones from System 1.

Supporting these beliefs, there are experiments indicating better decisions are made when

people decide in a structured way (Payne, Samper, Bettman, & Luce, 2008).

However, both Systems 1 and 2 are responsible when someone make a wrong intuitive

judgment. That is because when System 1 suggests the incorrect answer and System 2 accepts

and expresses it as correct both are responsible for the error. Both systems are subject to make

mistakes. System 2 errors causes are usually ignorance or laziness (Kahneman, 2011). The

systems do not work totally separated. They may interact and work together. There is a

continuum between System 1 and System 2 way of thinking. However, when closer to System

1, people get further from System 2 and vice-versa (Hammond, 1986; Hammond, Hamm,

Grassia, & Pearson, 1987; Kahneman, 2011). Analytical thinking demands attention and effort

(scarce resources) and it is usually activated when an intuitive answer is not suggested by

System 1. Another characteristic of System 2 is to control System 1 impulses. When System 2

is cognitively busy or depleted, System 1 has more influence in decision and behaviors. For

instance, if somebody is working heavily in an activity that demands a lot of intellectual

attention and effort, one becomes more prone to choose a sweet instead of a fruit (Kahneman,

2011). In a normative perspective, analytical decisions tend to maximize outcomes, using

expected utility for example. Rationality in decision making shall also comply with axioms

(Tversky & Kahneman, 1986).

Property Intuition (System 1) Analysis (System 2)

Consistency/reliability of judgments

or cognitive control Low High

Awareness of cognitive activity Low High

Memory Little encoding Complex encoding

Metaphors used Pictorial, qualitative Verbal, quantitative

Information used Flexible Consistent

Confidence in outcome High Low

Confidence in method Low High

Errors in judgment Many but small and

normally distributed

Few but large and non-

normally distributed

Cognition Speed Fast Slow

22

As presented, System 2 decisions will not necessarily always be superior to System 1

decisions. It is possible to have high quality decisions at a low cost with System 1, mainly when

considering the pursuit of information, the cognitive effort and time. Decisions in System 1 can

bring good results specially when dealing with problems considered too complex or with too

much information (Dijksterhuis & Nordgren, 2006). Since humans have a limited

computational capacity, in case of information overload, people generally experience

restrictions in understanding and, consequently, making decision. Usually it happens when

there is too much information or when it is presented in a fuzzy way (Chervany & Dickson,

1974; Eppler & Mengis, 2004; Hwang & Lin, 1999). Experts tend to perform better in these

situations (Dane, Rockmann, & Pratt, 2012; Kahneman & Klein, 2009).

Prevalent in System 1, the intuition has many different definitions. Robin Hogarth in his

book Educating Intuition stated that: “The essence of intuition or intuitive responses is that they

are reached with little apparent effort, and typically without conscious awareness. They involve

little or no conscious deliberation” (Hogarth, 2001, p.14). He also argues that “intuition is the

result of learning” (Hogarth, 2010, p. 2) or “learning shaped by experience” (Hogarth, 2001,

p.19).

Furthermore, intuition has also been defined as “capacity for attaining direct knowledge

or understanding without the apparent intrusion of rational thought or logical inference”

(Sadler-Smith & Shefy, 2004, p. 77). Another one: ability of evaluating situations holistically

and identifying patterns (Klein, 1993; Klein, 2002). Moreover, intuition may have emotional

origins, such as fear (Damasio, 2005).

Many authors made efforts to define intuition (Dane & Pratt, 2007; Epstein, 2010;

Hogarth, 2001, 2010; Sadler-Smith & Burke, 2009). Only in Dane & Pratt’s (2007) work there

are seventeen different definitions for intuition. As literature evidences, there is not yet a

consensus among scholars about intuition definition, suggesting the existence of subjectivity in

the tacit assumptions that may influence scholars when using it. However, there is a common

ground among some authors that intuition includes characteristics as holistic, rapid and

nonconscious (Dane & Pratt, 2007, 2009; Hogarth, 2010).

Intuition is also a close concept of tacit knowledge, non-articulated knowledge that

arises without explicit attempt to link environmental stimulation to phenomenological

experience. Additionally, tacit knowledge helps to deal with practical issues (Cianciolo,

Matthew, Sternberg, & Wagner, 2006). Wagner (1987) developed a measuring tool to check

tacit knowledge. It compared what regular people would do in comparison to acknowledged

experts. Wagner (1987) found that businessmen with more years of experience have more tacit

23

knowledge than businessmen with fewer years of experience. Per Dane & Pratt (2007), two

broad sets of factors influence intuition effectiveness: domain knowledge and task

characteristics.

Additionally, culture also shapes intuition. It depends on what people have lived, their

beliefs and values, not excluding religion, as well. They form what is called ‘cultural capital’

(Hogarth, 2001, 2010). It is virtually impossible for two people to have identical experiences;

thus, cultural capital is always going to be different between them. Therefore, no people have

the same level and type of intuition. Studies indicate that, in specific contexts, some individuals

are more intuitive than others are and, therefore, they can comprehend situations better. This

conclusion was possible due to different measuring tools to assess intuition (Allinson & Hayes,

1996; W. Taggart & Valenzi, 1990) and in different tasks; such as lie detection (Ekman,

O'Sullivan, & Frank, 1999) and empathic accuracy (W. Ickes, 1993; Marangoni, Garcia, Ickes,

& Teng, 1995).

In management, some exploratory studies collected evidences using the Cognitive Style

Index (CSI) questionnaire which indicated that entrepreneurs may be more intuitive than the

general population of managers, middle and junior managers. Furthermore, senior managers

and executives were identified as being as much intuitive as entrepreneurs (Allinson, Chell, &

Hayes, 2000).

In some cases, the use of intuition may not be desired (Kahneman, 2011; Russo &

Schoemaker, 2002), for example: calculating the total amount to be paid for a cart in the

supermarket, evaluating a client risk profile to issue a credit card or weather forecasting.

Nevertheless, scholars defend that the use of intuition can lead to good results in management;

especially when used together with analytical thinking (Blattberg & Hoch, 1990; Simon, 1987).

Individual differences such as personality traits (Franken & Muris, 2005) may lead to

different decision making. When evaluating decision-making processes between entrepreneurs

and large company managers, researchers also found different decision making processes

(Busenitz & Barney, 1997).

2.3 COGNITIVE CONTINUUM AND TASK CHARACTERISTICS

As mentioned, there are intuitive and analytical ways of thinking - Systems 1 and 2,

respectively (Kahneman, 2011; Stanovich & West, 2000). Hammond et al (1987) went further;

and, instead of a dichotomy, proposed a compromise through the Cognitive Continuum Theory

(CCT). There would be multiple ways of thinking. This would bring a truce between analysis

and intuition, reducing a potential rivalry between them (Hammond, 1996). These multiple

24

ways would lie on a continuum between pure analysis and pure intuition (Dhami & Thomson,

2012; Dunwoody et al., 2000; Hamm, 1988a; Hammond, 1981, 1986, 1996; Hammond et al.,

1987). Any point not in the polar extremes of the continuum between analysis and intuition is

called quasirationality, which includes both analytical and intuitive thought components.

Most judgments involve some mix of both intuition and analysis (Dhami & Thomson,

2012). When performing a task, people may oscillate their way of thinking in decision-making

processes. Success inhibits movements in the scale, while failures, stimulates them (Dhami &

Thomson, 2012; Hammond, 1996). The amount of intuitive or analytical thoughts could vary

in a Cognitive Continuum Index (CCI) as illustrated in Figure 2.

Figure 2 - Modes of cognition along the Cognitive Continuum according to CCT (Dhami & Thomson, 2012)

There is evidence that depending on task properties, people are influenced in their way of

thinking. Although hard to precisely define, the location of cognitive activity on the cognitive

continuum will depend on: (i) number of task properties present; (ii) which task properties are

presented; and, (iii) the amount of a property presented (Hammond et al., 1987). Furthermore,

parallel to CCI, tasks may also be ordered on a continuum – Task Continuum Index (TCI), as

per Figure 3. It considers their capacity to evoke analysis, quasirationality or intuitive thinking

(Hammond, 1996).

Figure 3 - Cognitive Task Index (CTI)

As expected, intuitive-induced tasks induce intuitive thinking and analytically induced

tasks induce analytical thinking (Dunwoody et al., 2000; Hamm, 1988b; Hammond et al.,

Similar Intuition

& Analysis

Mostly Intuition &

Some Analysis

Mostly Analysis

& Some Intuition

Pure Intuition Pure Analysis

Cognitive Continuum Index

Quasirationality

Similar Intuition

& Analysis

Mostly Intuition &

Some Analysis

Mostly Analysis

& Some Intuition

Intuitive-Induced Analytic-Induced

Task Continuum Index

Quasirationality

25

1987). Intuitive-induced activities may include a strong visual component and aesthetical

appreciation, such as paintings evaluation, for example. Moreover, if cues are presented

simultaneously, are numerous, measured perceptually or have no organizing principles for

judgement, then people will more likely use their intuitive system to make their decisions. On

the other hand, pushing task characteristics to the analytical side, they will probably contain

numbers and formulas and have fewer cues (Hammond, 1988, 1996; Hammond et al., 1987;

Hogarth, 2001, 2005).

Task properties consider (i) task complexity (number of information cues, their

redundancy and principles information combination), (ii) content ambiguity level (existence of

an organizing principle, content familiarity and potential judgement accuracy) and (iii) its

presentation (decomposability, visual or quantitative cues and time pressure). The two task

properties identified dimensions are: well-structured and ill-structured (Cader, Campbell, &

Watson, 2005). A non-exhaustive list of task characteristics is presented in Figure 4.

Figure 4 - Cognitive Continuum Task Properties (Dhami & Thomson, 2012; Doherty & Kurz, 1996; Dunwoody

et al., 2000; Hammond et al., 1987)

However, between the intuitive and analytical-induced extremes in the scale there are

tasks that evoke both cognition styles and include both intuitive and analytical properties. These

tasks could be considered a compromise between analysis and intuition in the Cognitive

Continuum - quasirationality (Dhami & Thomson, 2012; Hammond et al., 1987).

Task Properties Intuition Analysis

Number of cues Large Small

Measurement of cues Perceptual Objective and reliable

Cues Presentation Simultaneously Successively

Decomposition of task Low High

Degree of certainty in task Low Certainty High Certainty

Relation between cues and criterion Linear Nonlinear

Availability of organizing principle Unavailable Available

Time period Brief Long

Familiarity with task Familiar Unfamiliar

Prior training/knowledge with task None Some

Information format Pictorial Quantitative

Interpretation of information Subjective Objective

26

Although not recommended by prescriptive theory due to possible deterioration of

decision performance (Shanteau, 1988, 1992), analytical thinking can be applied to intuitive-

induced tasks and intuition can be applied to analytical-induced tasks. Time is a variable that

may influence the decision maker cognitive strategy. If there is much time, they may consider

carrying out analytical thinking to solve an intuitive-induced task. On the other hand, if there is

time restriction, decision makers are likely to think intuitively in analytical-induced tasks

(Hammond et al., 1987).

Besides all this theoretical background, some empirical research was conducted using

CCT and TCI. The first empirical research in CCT was conducted by its developer, Hammond,

with his colleagues (Hammond et al., 1987). They invited 21 male highway expert engineers

(30-70 years of age) to run the decision-making experiment. The variable manipulation

consisted of surface and depth task characteristics. They formed a 3x3 experimental setting,

considering for each independent variable: (i) intuitive, (ii) quasirational and (iii) analytical

inducing characteristics. In the surface variable, the manipulation was done in film strips, bar

graphs and formulas for intuitive, quasirationality and analytical inducing, respectively. For the

depth manipulation, it was aesthetics, safety and capacity, respectively. All the subjects were

presented with the surface condition in the same order: first, the film strips, second, the bar

graphs and third the material for formula construction. The researchers decided deliberately not

to counterbalancing because analytical reasoning influence subsequent intuitive judgment,

whereas, the inverse is not true (Jones & Harris, 1982).

Hammond et al (1987) findings indicate that the surface and depth properties of tasks

can induce the expected cognition mode (intuitive, analytic or quasirational). Furthermore, the

efficacy of the three cognition modes indicates that intuition and quasirationality can

outperform analysis by the same person. Another finding was that analytic reasoning can

produce extreme errors. Moreover, when the correspondence between task and cognitive

properties is high, the subject’s accuracy is also higher. In line with prescriptive approaches to

decision making, their study confirms that the best cognition mode for a specific task is the one

that has a better fit with the task characteristics.

Different from Hammond et al (1987) that employed different decisions (highway

capacity, safety and aesthetics), Dunwoody et al (2000) have not varied the decision task. All

108 college student participants answered about the same topic: military aircraft threat

assessment. It was a between-subjects design. Researchers manipulated both surface (task

representation) and depth (task structure). The task-surface manipulation considered an iconic

display (perceptual measurement of cues, intuitive-induced) and a numeric display (objective

27

measurement of cues, analytical-induced). The depth manipulation was using cues and

redundancy to induce analysis, intuition and quasirationality.

Participants were trained receiving feedback and they had to prove that they learned to

proceed to experiment. From the original 108 participants, 4 were not allowed to proceed. They

have not demonstrated that they have learned about aircraft threats after 50 trial rounds of

decision and feedback. The study reported that CCT provided support for task-surface effects,

but different from Hammond, researchers could find support for task-depth effects on cognitive

mode. Researchers suggest that satisficing and task complexity could be responsible for this

non supported result (Dunwoody et al., 2000).

While Hammond et al (1987) used expert engineers in their research; Dunwoody et al

(2000) used undergraduate students. This highlights an important issue regarding participants

selection (Cooksey, 2000). While the experts are very familiar with the task, the undergraduate

participants were probably unfamiliar and inexperienced with the task. Despite the novice

training prior to experiment measurement, it hardly would make the undergraduate to perform

task in a similar way that experts do. Thus, intuitive decision making within these two different

subjects profile are likely to differ.

Another difference between Hammond and Dunwoody works is that the first used

within-subjects while the second used between-subjects experimental design. To evaluate how

people oscillate in their way of thinking considering task characteristics, a within-subjects

design can provide more reliable results. Notwithstanding, behavioral researchers are aware

about the trade-offs between within- and between-subject designs. Moreover, in Hammond

experiment, although subjects were highway engineer experts, they were asked to perform a

task that they were not used to: evaluate highway aesthetics. In this case, they were familiar but

inexperienced with the task (Cooksey, 2000).

In managerial studies, scholars evaluated how managers decide in circumstances where

the amount of information increases during time, a situation frequently found in professionals’

routines. Researchers measured how the decision makers performed comparing statistical

model, managerial intuition (expertise) and quasirational approaches (mixed) to problem

solving. The experienced managers’ tasks were to forecast catalog sales of fashion merchandise

or coupon redemption rate. The findings pointed that quasirationality: managerial intuition

combined to statistical model analysis lead for better results than pure analysis or pure intuition.

Managers were able to intuitively pick up to 25% of the unexplained variance in statistical

models (Blattberg & Hoch, 1990). This results is aligned to other research, where

28

quasirationality financial predictions brought better results than the ones pure based in statistical

models (Conroy & Harris, 1987).

Marketing researchers also used CCT to evaluate consumer behavior (Mathwick,

Malhotra, & Rigdon, 2002), specially its perception on consumer experience. They predicted

some effects depending on task characteristics and consumer goals. The research was conducted

in a major direct retailer that commercialized its products both by internet (analytic) and by

catalog (intuitive) channels. The subjects were consumers randomly sampled. They collected

data using surveys and offered no incentive for subjects. Participants were separated as

exclusively catalog buyers (n = 299) and exclusively internet buyers (n = 213). Men represented

one out of five in internet channel, while one out of twenty in catalog channel. Catalog

consumers are usually more experiential (intuitive) shopper, while internet consumers are more

goal-oriented (analytic) shoppers. At last, the client reported higher return on investment on

their time, money and effort when buying goal-oriented in internet. On the other hand,

experiential shoppers reported higher enjoyment in their experience in catalog buying.

Therefore, based in these findings, retailers should focus in delivering an efficient shopping

experience to the right consumer in the right channel. Possibly, retailers should not try to

replicate the consumer experience in different channel. Each channel may have its properties

and specific consumer profile.

A CCT’s principle, correlation between cognitive modes and task properties, was also

verified in nursing decision-making (Cader et al., 2005). For example, in a well-structured task

like analyzing the electrocardiogram tracing, the analytical way of thinking is induced and

generally practiced due to task properties. Notwithstanding, when a nurse is attempting to avoid

a patient fall, an ill-structured task, it elicits the intuitive decision-making. The time restriction

for this decision and its low outcome certainty are characteristics of intuitive-induced tasks. In

this field’s empirical research, data collection consisted of asking the nurses to ‘think aloud’,

where participants verbalized their thoughts, use of questionnaires, interviews, case scenarios,

among others (Cader et al., 2005; Offredy, Kendall, & Goodman, 2008).

Medicine researcher also use CCT in order to describe clinical decision-making process.

Per Custers (2013), most judgments and decisions are quasirational, between full analysis and

full intuition, containing both analytical and intuitive thought components. Furthermore,

Custers (2013) defends that novices in clinical diagnosing should not rely heavily in their

intuition because of their lack of expertise. Their pattern recognition should not be as accurate

and efficient as expert physicians. Therefore, medical education should preferentially

emphasize analytical processes for novices to evaluate patients. Medical students and

29

professionals will have their intuition and expertise developed through intense and reiterated

exposition to several different cases along their formation and career. Afterwards, they will in

better conditions to rely in their intuitive reasoning.

All in all, CCT and TCI have been theoretically and empirically used and tested in

several different fields. There is robustness in their principles. Evidences support Hammond’s

theoretical contributions for decision-making research area.

Despite the empirical findings suggesting that analytic reasoning often does not deliver

the best results, managers usually prefer this way of thinking. Traditional approaches to

management and management education focus on analysis and planning and ignore the intuitive

approach required in some situations (Hayes, Allinson, & Armstrong, 2004). Frequently

executives do not have access to all information necessary to make decisions and should rely,

to some extent, on their hunches (Mintzberg, 1976). Thus, top managers tend to make decisions

based on a mix of intuition and analysis (Isenberg, 1991).

2.4 ADVICE TAKING

Research on advice seek, giving and taking, also known as Judge-Advisor System

(JAS), is about three decades old (Brehmer & Hagafors, 1986). Correlated studies have been

conducted under the signature of Hierarchical Decision-Making Teams (HDT) (Hollenbeck et

al., 1995; Humphrey, Hollenbeck, Meyer, & Ilgen, 2002). Although sometimes the advice

taking papers do not mention them, HDT studies have also contributed to the field (Bonaccio

& Dalal, 2006).

Society lives in an informational overload environment nowadays (Chervany &

Dickson, 1974; Edmunds & Morris, 2000; Eppler & Mengis, 2004; Hwang & Lin, 1999).

People are not able to collect and analyze much information before making decisions. It is

especially true for executives. They have to trust their team and colleagues. Among the five

different methods of managerial decision-making classified and presented by Vroom & Jago

(1988), in three of them the judge-advisor system is present. Indeed, advice seeking is

preponderant in strategic managerial decision-making (Alexiev, Jansen, Van den Bosch, &

Volberda, 2010; Arendt, Priem, & Ndofor, 2005; McDonald & Westphal, 2003). In some

circumstances, managers do not feel comfortable in asking for advice. They feel afraid of

appearing weak or incompetent. Nevertheless, this is not necessarily true. Individuals perceive

advice seekers as more competent when the task is difficult (Brooks, Gino, & Schweitzer,

2015).

30

As presented in Figure 5, the advising process comprises advice seeking, advice giving

and advice taking. Nevertheless, the first one, advice seeking, is not always present. Decision

makers may receive advice without having previously looked for it. Even if decision maker has

not sought for advice, one may receive it and consider it for judgment and decision-making.

However, when decision makers solicit advice they are more likely to follow the

recommendation than if they have received it without soliciting it (Bonaccio & Dalal, 2006).

The advising process is a blend of individual decision-making with group decision-making

process (Tzioti et al., 2014).

Figure 5 – Judge-Advisor System: decision maker, advisor and advice.

Advising is to give a recommendation of action to someone. It includes the most

common that is a recommendation of which alternative someone should choose, but may also

comprise what someone should not do, information about alternatives and recommendation

about how to make a decision (Dalal & Bonaccio, 2010).

In addition to advice definition, there are other important concepts to define as well. The

judge, advisee or decision maker is the person who receives the advice and shall decide what

to do with it. He is responsible for making the final decision (Harvey & Fischer, 1997; Yaniv,

2004a). The advisor or adviser also has an important role. As the name suggests, advisor is the

source of advice or suggestion.

One of the most relevant findings about advice taking is egocentric discount effect

(Yaniv & Kleinberger, 2000). According to the authors, decision makers have privileged access

to internal reason for holding their own opinion (Yaniv, 2004a; Yaniv & Kleinberger, 2000);

31

however, the same does not happen to advisors’ internal reason. Therefore, advisees do not

follow advice, as they should to benefit truly from them. Usually they overweigh their own

opinions (Harvey & Fischer, 1997; Krueger, 2003; Tzioti et al., 2014). Other attributed causes

for the egocentric discount effect are anchoring (Tversky & Kahneman, 1974) and egocentric

bias (Krueger, 2003). The discounting effect has been verified several times in different

circumstances (Bonaccio & Dalal, 2006).

On a routine basis, to share accountability for decision outcome and increase chances of

making good decisions, many decision makers seek out and take advice (Harvey & Fischer,

1997; Yaniv, 2004a, 2004b). When decision makers interact with others and talk about problem,

it may lead them to think about and consider it from other perspectives (Schotter, 2003). It may

provide them with new alternatives and information for judging (Heath & Gonzalez, 1995). In

addition, if advisor is credible, framing effect may be reduced (Druckman, 2001). Also,

depending on perceived advisor’s ability, advice taking is also affected (Bednarik & Schultze,

2015). Besides possible benefits from advice taking, there are also social reasons for it. Once

advice is refused, it may not be offered again (Sniezek & Buckley, 1995).

In general, advisees weigh advice more heavily when task is difficult than when it is an

easy one (Gino & Moore, 2007; Gino, Shang, & Croson, 2009). Similar effect is found when

advice is costly to obtain rather than when it is free (Gino, 2008; Patt, Bowles, & Cash, 2006).

Additionally, aspects of advisee’s internal state influence how receptive they will be to

advice. For instance, if decision maker is much confident about his judgment, he will be less

receptive to advice (Bonaccio & Dalal, 2006). His emotional state will also influence advice

taking process. If decision maker is feeling angry, he tends to be more confident in his judgment

(Gino & Schweitzer, 2008). Furthermore, culture will also impact advice (Bonaccio & Dalal,

2006): what kind of advice is going to be given, how much of the advice is going to be used,

who is going to advise, who is going to be advised, when people will seek advice, what is the

advice content, and so on.

In order to evaluate how much the decision maker was receptive to advice, common

measures used in the JAS research are Advice Taking (Harvey & Fischer, 1997; Sniezek,

Schrah, & Dalal, 2004) and Weight of Advice (WOA) (Gino, 2008; Yaniv, 2004a). These

measures gauges to what extent individuals revise their initial estimates towards the advisor’s

suggestion. Several studies have used these tools in in this research field (Gino, 2008; Gino &

Moore, 2007; Gino & Schweitzer, 2008; Gino et al., 2009; Harvey & Fischer, 1997; Tzioti et

al., 2014; Yaniv, 2004a; Yaniv & Foster, 1997).

32

Advice Taking = 𝐹𝑖𝑛𝑎𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒−𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒

𝐴𝑑𝑣𝑖𝑠𝑒𝑑 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒−𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒

Equation 1 - Advice Taking

The unique difference between Advice Taking and WOA is that the latter considers the

absolute values.

WOA = |𝐹𝑖𝑛𝑎𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒−𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒|

|𝐴𝑑𝑣𝑖𝑠𝑒𝑑 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒−𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒|

Equation 2 - Weight of Advice (WOA)

In theory, according to prescriptive approach, if individuals believe they and their

advisors are equally informed, decision makers should weigh their own and the advisors’

estimates equally, and the WOA score would equal 0.5 (Larrick & Soll, 2006). Furthermore,

depending how far the decision maker’s first estimate is from received advice, judges may take

more or less from it. For instance, the closer advice is to initial opinions, more it serves as a

means for social validation, increasing decision-makers’ confidence in the accuracy of their

final opinions, leading to a higher advice taking (Bednarik & Schultze, 2015).

2.4.1 Content of Advice

When an advice is given, it is full of meaning. It consists of what is advised (Dalal &

Bonaccio, 2010; Yaniv & Foster, 1997; Yaniv & Kleinberger, 2000), about what it is advised,

considering task difficulty and specificity (Benjamin & Budescu, 2015; Gino & Moore, 2007;

Schrah, Dalal, & Sniezek, 2006), for example: when an advice is about something complex,

judges tend to be more receptive to advice. Content of the advice is also important. It can be

information about the alternatives or recommendation to choose one of the available options,

for example (Dalal & Bonaccio, 2010).

Usually, judges learn to discard poor advice (Yaniv & Kleinberger, 2000). If advice is

more richly described and advisor expresses higher confidence, judge tends to value it more

(Benjamin & Budescu, 2015). However, decision maker’s inference about advice quality may

not be accurate (Yates, Price, Lee, & Ramirez, 1996).

2.4.2 Advisee

The advisee is the judge or decision maker; and, obviously, its profile and conditions

influence advice-taking process (Harvey & Fischer, 1997; Sniezek et al., 2004; Yaniv, 2004a;

Yaniv & Kleinberger, 2000). So, depending who the advisee is, advice is going to be received

33

and used differently. Studies evidenced that emotional condition, such as anger and love (Gino

& Schweitzer, 2008) or happiness and sadness (Tingting, Xiufang, & Jia, 2014) impact people’s

receptivity to advice.

More experienced decision makers shall rely more in intuition than younger ones. It

may relies on the belief that intuition may be developed as a result of experienced learning

(Hogarth, 2001, 2010). Additionally, young professional are, in general, heavily trained in

analytical methods, which may bias them towards analytically justified advice (Tzioti et al.,

2014).

Another characteristic that is relevant in JAS is confidence. Less confident judges tend

to seek more for advice (Bonaccio & Dalal, 2006). Advisees may rely on a confidence

heuristics, where the judges use the advisor’s confidence in order to measure advisor’s expertise

and knowledge about the subject of advice (Price & Stone, 2004). Furthermore, people tend to

weigh significantly more other’s advice when making judgment about others behaviors than

when making judgments about their own behavior (Gino et al., 2009).

2.4.3 Advisor

It matters who the advisor is. Several different characteristics may affect the advising

process, leading advisee to weigh advice differently. Individuals usually weigh more heavily

advice when advisors are more experienced or more knowledgeable than decision makers

themselves are (Bednarik & Schultze, 2015; Feng & MacGeorge, 2006; Goldsmith & Fitch,

1997; Harvey & Fischer, 1997; Sniezek et al., 2004; Swol & Sniezek, 2005; Tzioti et al., 2014;

Yaniv, 2004a; Yaniv & Kleinberger, 2000; Yaniv & Milyavsky, 2007). Similarly, people are

more likely to value more an advice when advisor shows confidence in advice quality (Bonaccio

& Dalal, 2006; Sniezek & Buckley, 1995; Sniezek & Van Swol, 2001; Swol & Sniezek, 2005;

Yaniv, 1997). Previous relationship between advisee and advisor is also relevant for JAS (Swol

& Sniezek, 2005).

An important finding is that if the advisor is being paid for advice giving, advisees tend

to weigh more the advice than when it is free (Gino, 2008). The researcher defends that this is

consistent with the tendency to escalate commitment. People tend to consider the sunk cost in

their decisions. Another interesting, but not surprising, finding was people tend to value advice

from similar advisor more than dissimilar ones. Some dimensions were considered in this study,

such as age and education (Gino et al., 2009). On the other hand, advisee tend to value more

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advice from advisor with greater age, education, life experience and wisdom than themselves

(Feng & MacGeorge, 2006).

Similar to advisees, confidence is also relevant for advisor’s role. For instance, when

the advisor is confident, recommendations addressed to judges are more likely to be followed

(Phillips, 1999; Sniezek & Buckley, 1995; Sniezek & Van Swol, 2001; Swol & Sniezek, 2005;

Yaniv & Foster, 1997). Expressed confidence matters in JAS, both for advisees and advisors.

2.4.4 Advice Justification

Just adding an advice justification, either intuitive or analytical, can make advisees more

receptive to advice. It happens because in case of justifying the advice, decision makers would

have more information about how the advisor built the advice. That is consistent with

experiments that have found that adding a reason to a request increases compliance, even if the

reason transmits no information (Langer, Blank, & Chanowitz, 1978). Obviously, decision

maker’s perception about the advisor and its advice quality will also influence advice taking.

However, Tzioti’s experiments’ findings point out that adding an intuitive justification

for an quasirational task may destroy perceived value of advice to advisee. It may have

happened due to advisee’s perception that the advisor was not very careful in his judgment, had

a sloppy thinking, or advisor is not an expert on the subject. Furthermore, decision makers

valued more advice when advisors had not added advice justification than when they had given

an intuitive justification. As expected, analytical advice justification was the most valued

(Tzioti et al., 2014).

Advisor profile seems to affect weight on advice. In an experiment, when the advisor is

described as a senior professional, the advisor’s intuitive advice was much more valued than an

intuitive advice given by a junior professional (Tzioti et al., 2014).

Thus, the results of the experiment suggest that individuals value more advice that have

analytical justification in quasirational tasks rather than advice that have an intuitive

justification. Furthermore, decision makers value more intuitive advice when given by a senior

advisor to a junior advisee in an intuitive induced task (Tzioti et al., 2014). Notwithstanding,

explicit reasoning degrades judgment quality when the task is mostly intuitive (Hammond et

al., 1987). On the other hand, in experiments it was found that explicit reasoning enhanced

judgment quality in analytical tasks (McMackin & Slovic, 2000).

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2.5 GENDER AND DECISION MAKING

Stereotypes are usually simple, overgeneralized, and widely accepted by the so-called

“common wisdom.” They can be quite inaccurate. It is simply not true that all men are analytic,

rational, and objective; it is also not true that all women are intuitive, sensitive, and emotional.

However, even erroneous stereotypes can—and do—profoundly affect people’s interaction and

interpersonal relationships (Snyder, 2015). Stereotyping is associated with the concept of

labeling, to influence perception and, in turn, decision making. The famous sociologist Frank

Tannenbaum (1938) said, “The person becomes the thing he (or she) is described as being”

(p.20), adding that “the community expects him (or her) to live up to his (or her) reputation

and will not credit him (or her) if he (or she) does not live up to it” (p. 477- words in italics

added by author).

Stereotypes are therefore relevant to the research goal: to find if, and under what

circumstances, the advisor’s gender (male or female) and advice justification (analytic or

intuitive) influence advice taking. Gender stereotypes, as well as personal beliefs and values,

may affect, to a lesser or greater extent, advisees’ “perception” of the value of the offered

advice, which is what was measured in the conducted experiments.

For Marshall (1993), at the aggregate social level, male values can be characterized by

rationality, analysis, self-assertion, competition, focused perception, clarity, discrimination, and

activity. On the other hand, female values can be characterized by intuition, wholes (i.e. having

a holistic view), emotional tone, cooperation, interdependence, receptivity, acceptance,

awareness of patterns, and synthesizing. The author argues that gender values are qualities to

which both sexes have access. However, through socialization and gender roles, women are

more often grounded at the female pole and men at the male pole, thus supporting gender

stereotypes. Pelham, Koole, Hardin, Hetts, Seah, and DeHart (2005) are aligned with this view

when they argue that, relative to men, women are strongly socialized to trust their feelings and

intuitions.

Eagly, Wood and Diekman (2000) offer a different perspective. They argue that

expectations about men and women necessarily reflect status and power differences. Thus,

cultures feature shared expectations for the appropriate conduct of each sex, and these

expectations foster gender-differentiated behavior. Moreover, social role theory treats gender

roles as a dynamic aspect of culture; it emphasizes the causal impact of people’s beliefs about

the behavior that is appropriate for each sex (Eagly & Wood, 1991). Indeed, gender differences

may occur because experience with hierarchical social structures, in which men have higher

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status, creates expectancies about male and female behavior. In turn, these affect social

interaction in ways that foster behavior that confirms such expectancies (Eagly, 1983).

Yet, Ridgeway and Smith-Lovin (1999) affirm that studies of interaction among peers

with equal power and status show fewer gender differences in behavior. However, they also

point out that most interactions between men and women occur in the structural context of roles

or status relationships that are unequal, thereby perpetuating status beliefs and leading men and

women to recreate the gender system in everyday interactions.

Heilman (2012) focused on the workplace consequences of descriptive gender

stereotypes (designating what women and men are like) and prescriptive gender stereotypes

(designating what women and men should be like). She argues that descriptive gender

stereotypes promote gender bias, because of the negative performance expectations that result

from the perception that there is a poor fit between what women are like, and the attributes

believed necessary for successful performance in male gender-typed positions and roles.

Similarly, prescriptive gender stereotypes promote gender bias by creating normative standards

for behavior, which induce disapproval and social penalties when they are directly violated.

Powell, Butterfield, and Parent (2002) found that although managerial stereotypes place less

emphasis on “masculine” characteristics than in earlier studies published in the 1970s and ’80s,

a good manager is still perceived as predominantly masculine.

Finally, Johnson and Powell (1994) explored differences decisions made by males and

females. They argue that women are often excluded from managerial positions of authority and

leadership due to stereotypes, which have been constructed by observing “non-managerial”

populations at large. However, they conclude that these stereotypes may not apply to managers

because, in the “managerial” sub-population, male and females make decisions of equal quality.

2.5.1 Cognitive Reflection Test

Some studies have defended men are more likely to reflect on their answers than women.

The results suggest that men have a more analytical approach to problem-solving (Bosch-

Domènech, Brañas-Garza, & Espín, 2014; Frederick, 2005; Oechssler, Roider, & Schmitz,

2009). Most have used the Cognitive Reflection Test (CRT) (Frederick, 2005) as a

measurement tool in their methods. It was developed to evaluate people’s response when

solving a small list of problems. Their problem-solving method can be analytical or intuitive.

Questions were made to lead to wrong answers in case the problem solver decides to use an

37

intuitive approach to answer the questions, leading to lower CRT results and indicating an

intuitive way of thinking. For instance, answer the following question:

(1) A bat and a ball cost $1.10. The bat costs $1 more than the ball. How much does the

ball cost? __ cents.

The intuitive answer for most people is $0.10 cents. This is the answer given by System

1 and it is wrong. If people give a second thought, they will realize that the correct answer for

this question is five cents. Even for participants that answer correctly, the first answer that

comes to mind is ten cents. The CRT is short and objective. Please find in Figure 6 below the

entire test that usually can be performed in less than five minutes (Frederick, 2005):

Figure 6 – Questions: Cognitive Reflection Test (Frederick, 2005)

As mentioned, the wrong and intuitive answer for question 1 is ten cents, while the

correct and analytical answer is five cents. For the second question, the wrong intuitive answer

is 100 minutes and the correct answer is five minutes. For the third question, the wrong intuitive

answer is 24 days and the correct is 47 days.

The CRT measures people’s disposition to reflect upon their intuitive answers provided

by System 1. If one gives the first answer that comes up to mind, impulsively, they are very

likely to give the wrong answer. In the CRT, System 2 is more likely to deliver the right answer.

For Frederick’s 2005 paper, the CRT was administered to 3,428 participants in 35

separated studies. They started in Jan 2003 and lasted 26 months. Most respondents were

undergraduate students. Many were from MIT, Princeton Harvard, Michigan State University,

Carnegie Mellon University, Bowling Green University, University of Toledo, and University

of Michigan: Dearborn and Ann Arbor. Some participants answered the CRT online and the

study even included people around a fireworks event in Boston. Subjects received financial

compensation: eight dollars for a forty-five minutes’ questionnaire. Besides the CRT, they also

answered questions about time and risk preferences.

(1) A bat and a ball cost $1.10. The bat costs $1 more than the ball. How much does the

ball cost? ____ cents

(2) If it takes 5 machines to 5 minutes to make 5 widgets, how long would it take 100

machines to make 100 widgets? ____ minutes

(3) In a lake, there is a patch of lily pads. Every day, the patch doubles it size. If it takes 48

days for the patch to cover the entire lake, how long would it take for the patch to cover

half of the lake? ____ days

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The CRT individual result could vary from zero to three. It represents how many

questions the participant answered correctly. The highest result among sampled subjects was

obtained by MIT students: 2.18 as a score mean. Princeton students took the second place with

1.63. The overall mean was 1.24 (Frederick, 2005).

Frederick (2005) found significant higher results for male subjects (1.47) than for female

(1.03) in the CRT (p<0.0001), indicating a higher participation of analytic judgment for men.

In the research, students were tested in other cognitive measures (SAT, SAT verbal, Wonderlic,

NFC and ACT) and there were no differences in tests compared to national averages, except

for a small difference in mathematics skills (SAT math). Usually men present higher scores in

mathematics than women (Benbow & Stanley, 1980; Hedges & Nowell, 1995; Hyde, Fennema,

& Lamon, 1990).

Since the sampling was representative, possible causes for this gender differentiation

were investigated. Attention and expended effort were also evaluated with the Wonderlic

Personnel Test (WPT) to discard them as possible causes. The WPT is a 12-minute, 15-item

test used by the National Football League (NFL) and other organizations to assess intellectual

abilities. In this specific research, women even scored higher than men in the WPT. It is

possible, though, that the significantly higher scores in CRT for men are due to male’s higher

mathematical ability, since the CRT has mathematical content in its questions. Additionally,

motivation can also be a cause for this difference in gender, due to male’s higher interest in

answering mathematical questions (Frederick, 2005).

When researchers investigated errors, women presented frequently the intuitively

expected errors for the CRT questions while men presented more diversified wrong answers.

For instance, in the widget problem, women often answer 100, the intuitive wrong answer;

while men wrongly answer 1, 20 or 500. For every CRT item women had a higher percentage

of wrong intuitive answers to ‘other’ wrong answers than men did. Therefore, these results

suggest that men are more likely to reflect on their answers and less inclined to go with their

intuitive responses (Frederick, 2005).

Oechssler, Roider & Schmitz (2009) also researched using the CRT. They tested 1,250

participants in an online web-based experiment. The results’ mean was right between MIT

(2.18) and Princeton (1.63) students in Frederick’s research: 2.05 correct answers. They also

found a higher result in their research with CRT for men (2.2) than for women (1.7). These

results’ difference was statistically significant (p<0.001). In this study, they paid subjects for

their participation. Subjects were recruited from economics experimental laboratories in Bonn,

Cologne and Manhein. A research limitation, however, is that some participants could have

39

known the CRT, since this test is becoming more and more popular among social researchers

and experiment participants.

Yet, a recent research indicates a potential neuroendocrinological path as a possible

cause for this differentiation. Differences in way of thinking between genders may be related

to prenatal hormone exposure. Researchers argue that exposure to testosterone/estrogens can

predict cognitive reflection. The hormonal exposure was measured by the putative marker for

the relative prenatal testosterone: the second-to-forth digit ratio (2D:4D). Complementing the

study, the CRT was also used as a tool to measure intuitive and analytical thinking process.

This research finding pointed that women could be more intuitive in problem solving approach

than men (Bosch-Domènech et al., 2014).

Moreover, neuroanatomic studies indicated that men and women have differences in

their brains. Using a magnet resonance imaging (MRI) scan, researchers identified that women

and men have different percentages of white and gray matter in different brain areas. They

suggest that this sex difference in the percentage and asymmetry could lead to differences in

cognitive functioning (Gur et al., 1999).

Notwithstanding, some studies fail to find differences in way of thinking and gender

(W. M. Taggart, Valenzi, Zalka, & Lowe, 1997). Experiments with female and male managers

also could not find statistical difference in terms of intuitive/analytical orientation. Female non-

managers were identified as more analytical than male non-managers and more analytical than

female managers (Hayes et al., 2004). In some cases, women may even perceive themselves as

being more analytical than men, going against the traditional belief that women are, in general,

more intuitive (Sadler-Smith, 1999).

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3. METHOD

Historically, there have been some variations in experimental designs for JAS research.

According to Bonaccio and Dalal (2006), JAS can be described as an input-process-output

model. As input, they consider: (i) if decision maker can form an opinion before receiving

advice; (ii) if judge may choose whether to solicit advice; (iii) number of advisors that will

advise judge, and (iv) type of task that is going to be object of decision. In the process, they

consider the interaction between advisee and advisor. In case of more than one advisor,

interaction between advisors is also considered process. As for the output, they include advice

utilization, advisor and advisee confidence and judge’s accuracy post-advice. However, they

do not believe this list is exhaustive. For a comprehensive review in JAS methods, see Bonaccio

and Dalal (2006).

There are different types of task and they are important for JAS research. Some

examples of tasks are: event probabilities (Budescu & Rantilla, 2000) and questions type,

usually with multiple-choice answers (Sniezek & Buckley, 1995). Additionally, response may

be qualitative or quantitative (Bonaccio & Dalal, 2006).

Regarding definition of task type and questions there are two main options. The first

one, called choice task consists of detailing a problem and presenting two alternatives to be

chosen, such as a go/no-go situation or choice between two people or products. Then, subject

would pick one and inform decision confidence; or, alternatively, instead of expressing

confidence, subject may answer his propensity to one of the two given options. Following this

first decision, advice is given and judge will have the chance to review its decision and/or

confidence.

The second type of task is called judgment task and consists of answering some

questions about general issues, such as height or weight of things or people, or to estimate how

much is donated yearly to an institution or an event probability. Following, advisor would

advise decision maker that will have the chance to review initial estimate, and then the variation

on estimates are measured.

In this study, task type was the choice task. Subject had to choose between go/no-go

situations or one out of two alternatives. Another difference in task types is whether there is a

correct answer for it. As this study is focused in managerial decision making, where seldom

there is a right and a wrong option, the preference was for a task which does not have a correct

answer. The downside is that in this kind of experiment, researchers cannot offer financial

incentives for a better answer, since there is not necessarily a better or correct answer.

41

In this research, there was only one advisor giving advice one time only. Thus, this is

the unique interaction between advisee and advisor. The latter is not a real person. The advisor

was described to the subject in the experiment and the pieces of advices were prepared by the

researchers in advance.

Like in previous studies (Tzioti et al., 2014), no definition of intuition or analysis was

provided to participants. They responded based on their own lay interpretations, which are

expected to be based on cognitive assumptions anchored in deeply ingrained societal culture,

beliefs and values. It should be noted that the interpretation process is not available to

consciousness (Samuels, Stich, & Faucher, 2004).

Despite relevant conceptual differences, this research method was strongly based on a

previous research made by Stephanie Tzioti during her PhD and published on the Journal of

Behavioral Decision Making with her co-authors (Tzioti et al., 2014). Furthermore, this

research was suggested as a possible development of theirs to reduce the existing gap in the

decision making and advice taking literatures. Tzioti’s measurement instrument and general

procedures were mostly replicated in this research.

However, different from their research, in this work advisor’s gender was manipulated,

aiming to evaluate how it influences the advice taking process. In Tzioti’s research, they

manipulated two independent variables advisor seniority (junior versus senior) and advice

justification (analytic versus intuitive) and measured to what extent advisee has taken advice

after receiving it from an advisor resulted from this 2x2 experimental design.

In order to increase validity and ensure that people would perceive the gender of the

advisor, in the experiment, the described advisor received a name that is either common for

males or females. Its name was repeated four times and there were pronouns such as his or her

and he or she, reinforcing the advisor gender manipulation.

All in all, decision making is a mature research field with well-established and accepted

methods. Since this research is confirmatory with quantitative methods, hypotheses were tested

using social experiments and ANOVA. This method is widely used in published works in

behavioral decision making and specially in the advice-taking research area (Bonaccio & Dalal,

2006; Gino, 2008; Gino et al., 2012; Gino & Moore, 2007; Gino & Schweitzer, 2008; Gino et

al., 2009; Tost et al., 2012; Tzioti et al., 2014; Yaniv & Kleinberger, 2000).

3.1 SUBJECTS

In this research, subjects were Amazon Mechanical Turk (MTurk) workers and

company professionals. MTurk is a service provided by Amazon since 2005 which aims to link

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service requester, in this case, researchers, to workers. Virtually, anyone who is at least 18-

year-old with access to a computing device connected to internet can be a worker. They perform

tasks called Human Intelligence Tasks (HITs). In this research, the HITs were the experiments

and workers received a financial incentive to participate in the study. Workers were limited to

perform the same experiment only once. Furthermore, each participant could only answer to

one of the two experiment. Participation was anonymous.

MTurk population is at least as representative of the US population as traditional subject

pools. Specially, if compared to subjects recruited in traditional university pools. However,

recently, MTurk pool has been changing. It is becoming more international. For more detailed

information about workers’ profile, there are some published report and analyses (Ipeirotis,

2010; Paolacci, Chandler, & Ipeirotis, 2010; Ross, Irani, Silberman, Zaldivar, & Tomlinson,

2010).

Regarding MTurk workers’ motivations, they reported: primary income source (13,8%),

entertainment (40,7%), and “killing time” (32,3%). They also reported that earning additional

money was an important driver of participation (61,4%) (Horton, Rand, & Zeckhauser, 2011;

Paolacci et al., 2010).

Researchers defend incentives increase subject effort (Camerer & Hogarth, 1999), but

not necessarily accuracy. Requesters can offer as much as they want for HITs. Although, data

quality does not seem to be affected by the payment amount (Mason & Watts, 2010), low

payments reduce the chance of getting workers to HITs. Amazon suggests, as a reference, to

consider the minimum wage per hour in USA when calculating worker’s payments offer.

After finishing activities, requesters may approve or reprove HITs. When HIT is

approved, workers receive payment. If requester reproves a HIT, it is recorded in worker’s

profile. This worker will be less likely to perform future activities because his statistics as

worker are not great. On the other hand, if a requester reproves HITs without a reasonable

motive, it can be punished by workers that communicate themselves in order to boycott the

requester. In this case, the requester will not have workers to perform HITs.

Amazon service allows researchers to use external software to run experiments. In this

specific research, Qualtrics software was used. When accepting to perform a HIT, MTurk

redirects participants through a link directly to the experiment. After performing experiment

activities, subjects will receive an individual randomly assigned code to inform on the Amazon

Turk website in order to finish HIT. Only participants who have done entirely the experiment

receive the code to enter in Amazon website.

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In order to perform a task, workers can search the Amazon Turk website for HITs and

choose tasks they want to perform. They also know in advance, how much money they can

make in each HIT. However, workers can only see HITs which they meet predefined criteria,

for example: country of residence, success completion rate in previous HITs, among others. In

this specific research, only participants from USA were allowed to answer the experiment. This

decision is to reduce the cultural variety and increase results representativeness to USA

population.

MTurk is a popular tool among social researchers for experimental data collection due

its several advantages compared to more traditional recruiting methods (Crump, McDonnell, &

Gureckis, 2013). Some articles have been published in relevant journals, like the Judgment and

Decision Making Journal, using this recruitment service (Eriksson & Simpson, 2010).

However, there are some concerns about using this service for recruiting subjects from the

online labor market (Paolacci et al., 2010).

Among the innumerous advantages, the main ones are: reduced costs in data collection,

a huge amount of data can be collected in a short period of time, no experimenter effect,

avoiding participants cross-talking, among others (Berinsky, Huber, & Lenz, 2011; Crump et

al., 2013; Edlund, Sagarin, Skowronski, Johnson, & Kutter, 2009; Paolacci et al., 2010;

Sprouse, 2011).

Additionally, there are evidences that experiments which have been run both in the

traditional way and with web-based services, such as the MTurk, have not reported significant

different results (Berinsky et al., 2011; Crump et al., 2013; Gosling, Vazire, Srivastava, & John,

2004; Paolacci et al., 2010; Sprouse, 2011).

There are two main concerns about using MTurk. The first one is whether workers are

representative of the desired population. The second one is about overall quality of data

provided by workers. Worker demographics data analysis has proved they are more

representative than traditional pools. Regarding data quality, there is little evidence to suggest

that data collected online is necessarily poorer than data collected from traditional pools

(Gosling et al., 2004; Krantz & Dalal, 2000; Paolacci et al., 2010).

Another downside for online experiments is that subjects usually get less attentive than

participants in labs under supervision (Oppenheimer, Meyvis, & Davidenko, 2009). Like other

internet-based methods, there is also a lack of environmental control (Crump et al., 2013).

In general, researchers agree that MTurk can be used as a tool for recruiting participants

for experimental studies (Crump et al., 2013; Paolacci et al., 2010).

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In MTurk, some specifications were set in order to define subjects: (i) they must have a

95% of approval in previous HITs and (ii) participants should have a history of at least 50 HITs

successfully completed. These precautions are in order to increase the chances of having

subjects that work seriously and are committed to the task.

First, it was collected a sample from MTurk workers. Then, as this experiment was

focused on managerial decision making, and to compare results, it was also collected a sample

of Brazilian company professionals. The company professionals’ observations were collected

online via Qualtrics. Participants received a link from e-mail and answered to only one

condition of one experiment. They were from different Brazilian organizations such as banks,

oil companies, pension funds, among others. Most participants were between 30 and 50 years

old. There were managerial and non-managerial professionals in the sample; including a broad

range of positions, such as analysts, specialists, general managers and executives.

Professionals’ formation and background were broad as well. There were economists, business

administrators, geologists, engineers, accountants, lawyers, and psychologists among others.

They did not receive any financial reward or gift for their participation. It was anonymous and

voluntary.

3.2 PILOTS

Considering that new experimental materials were developed for this research, some

pretests and pilots were run.

The first experiment was a quasirational managerial-decision with both analytic and

intuitive inducing cues. Participants played the role of a product manager and had to indicate

the extent to which they would recommend the launch of a new product (electric boiler) to the

board of directors of the company. For this experiment pretests involved: structure of the text,

its presentation, wording, number of cues in terms of figures and qualitative ones, wording of

advice justification and content, as well as text fluidity, clarity and objectivity to facilitate

understanding of the task. In the first pretests, financial figures were presented to subjects and

they could do some calculations to answer the experiment. However, it was noticed that most

participants did not make any calculations. Thus, instead of only presenting the figures,

financial results were presented as well to make it easier for participants to interpret the

financial figures. The inclusion of the financial results was relevant to the success of the

experiment, as participants knew that the investment return was not certain.

The second experiment was a quasirational managerial decision making with a more

intuitive-induced scenario where intuitive cues prevailed. It involved the visual appraisal of two

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paintings. Participants played the role of an art gallery manager and had to indicate the extent

to which they would recommend one of two abstract paintings (from different artists) to be

exhibited for sale in the art gallery. For this experiment pretests involved: structure of the text,

its presentation, wording of the general text, wording of advice justification and content, text

fluidity, clarity and objectivity to facilitate understanding of the task as well as testing different

pairs of paintings in order to verify their impact on advisees. The selected paintings did not

cause a strong affection or aversion in the respondents, nor were participants indifferent to them.

The paintings in the experiment were chosen in order to foster participants’ interest and

engagement in the task.

Furthermore, in both experiments, some advisor’s names were also tested in the pilots.

They needed to clearly refer to either a male or a female advisor. Additionally, they should not

carry a strong connotation that could cause some noise to the experiment, affecting the

dependent variable – an undesirable effect for research. Thus, some names were tested

quantitatively and qualitatively in order to ensure experiment validity. To avoid advisor gender

confusion, the selected names were commonly used names that are only used for males or

females, but not for both, and that had the same characteristic in Portuguese and English. For

example, Peter and Anna.

During pilots, Brazilian undergraduate students were used to test the experimental

material in Portuguese. Amazon MTurk workers answered the material in English.

Moreover, factors that could affect the dependent variable were also verified, such as

subjects’ gender, education, age and expertise in task. None indicated significant impact on the

dependent variable.

Please find below in Figures 7, 8, 9, 10 and 11; MTurk participants’ demographics data

from pilots. Participants in the experiments were collected from the same subjects’ pool.

Figure 7 - MTurk Pilot – Participants’ Gender

What is your gender?

Frequency Percent

Valid Male 130 40.1

Female 194 59.9

Total 324 100.0

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Figure 8 - MTurk Pilot - Participants' Education

What is the highest level of education you have completed?

Frequency Percent

Valid Less than High School 1 .3

High School / GED 29 9.0

Some College 89 27.5

2-year College Degree 27 8.3

4-year College Degree 128 39.5

Masters Degree 39 12.0

Doctoral Degree 3 .9

Professional Degree (JD, MD) 8 2.5

Total 324 100.0

Those PhDs are likely to be researchers and professors who took the experiment in order

to test the experimental material. It is not expected that PhDs will work as Amazon MTurk

workers.

Figure 9 - MTurk Pilot - Participants' Age

How old are you?

Frequency Percent

Valid 16 to 19 4 1.2

20 to 24 35 10.8

25 to 34 121 37.3

35 to 44 80 24.7

45 to 54 38 11.7

55 to 64 36 11.1

65 or over

9 2.8

Total 323 99.7

Missing System 1 .3

Total 324 100.0

Figure 10 - MTurk Pilot - Participants' Expertise in Product Launch - Experiment 1

What is your experience in product launch?

Frequency Percent

Valid None 213 65.7

Little 65 20.1

Some 30 9.3

A lot 3 .9

Total 311 96.0

Missing System 13 4.0

Total 324 100.0

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Figure 11 - MTurk Pilot - Participants' Expertise - Painting Market - Experiment 2

What is your experience with the painting market?

Frequency Percent

Valid None 188 58.0

Little 90 27.8

Some 28 8.6

A Lot 6 1.9

Total 312 96.3

Missing System 12 3.7

Total 324 100.0

48

4. EXPERIMENTAL MATERIAL & RESULTS

Task properties, advice content, advice justification, advisor’s seniority, and advisee’s

beliefs about the accuracy of the given advice, among other factors, may impact advice taking

(Dalal & Bonaccio, 2010; Hammond et al., 1987; Tzioti et al., 2014). However, it is not clear

how advisees take advice differently when it is given by a male or a female advisor in

managerial contexts.

As previously mentioned, Dhami and Thomson (2012) state that most tasks can be

placed along an analytic–intuitive task continuum, as they have both analytic and intuitive

properties, which are required of most managerial judgments. Hence, in order to test if and in

what circumstances advisor gender and advice justification influence advice taking, the first

experiment was set in a quasirational managerial decision-making scenario with both analytic

and intuitive cues. In such a setting, will analytic justification have more impact than intuitive

justification? Will advisees take advice differently if given by a male or a female advisor? And,

will advice justification affect advice taking differently, depending on whether that advice is

given by a male or female advisor?

4.1 EXPERIMENT 1 – QUASIRATIONAL SCENARIO WITH BOTH ANALYTIC AND

INTUITIVE CUES (QS)

This first experiment, was inspired by an IESE-designed business case (García-Castro,

2011). The quasirational scenario contained both analytic- and intuitive-inducing

characteristics. Participants played the role of a product manager and had to indicate the extent

to which they would recommend the launch of a new product (electric boiler) to the board of

directors of the company.

The scenario presented financial figures, such as estimated investment, sales, price,

product cost, expected return, and minimum return required—characteristics of analytic-

inducing tasks (Dhami & Thomson, 2012). Additionally, there were some intuitive-inducing

cues: the decision maker also had to consider qualitative issues involving consumer behavior,

people’s cultural values, beliefs about the environment, market trends, and competition. These

issues introduced subjective components to the decision. Moreover, participants were told that,

considering the time horizon of the investment (ten years) and general conditions of the

economy, there were uncertainties regarding the effective economic return of the new product.

To verify that this decision would not be perceived as either a male- or female-dominant

task, 100 MTurk workers answered a small presurvey. Respondents did not perceive product

49

management as being a male or a female activity: the average score was 52.7, on a scale where

0 indicated male and 100 female activity. When asked who could perform better as a product

manager, people answered that both men and women could perform equally as well, with an

average score of 50.5 on a scale where 0 indicated men and 100 women. Thus, it seemed

reasonable to assume that this scenario was fairly gender neutral.

Despite the increasing interest in, and recognition of, the importance of intuition in

decision-making literature (Gladwell, 2007; Hogarth, 2010; Kahneman & Klein, 2009), there

is still a strong idea that analytic thinking occupies the superior ground (Bonabeau, 2003;

Dawes et al., 1989; Russo & Schoemaker, 2002). Intuition, on the contrary, is negatively

associated with unreliability and sloppy thinking (Hogarth, 2001). Additionally, analysis is

perceived as a male characteristic, whereas intuition is considered a female attribute (Marshall,

1993; Schein, 1975). Furthermore, Tzioti et al. (2014) suggest that advice justification has a

strong influence on advice utilization. Managers are, in general, trained and recommended to

act per prescriptive and rational ways. Indeed, managers usually prefer analytic thinking (Hayes

et al., 2004).

Thus, considering the following—that (i) analytic thinking has a higher status than

intuitive thinking; (ii) the focus of this experiment was on managerial decision making; and (iii)

managers usually prefer analytic thinking and are trained and recommended to act according to

prescriptive and rational ways—it was expected that participants would prefer analytically

justified advice to intuitively justified advice.

Adding the following considerations—(i) that gender stereotypes can and do affect

people’s interaction; and (ii) that analysis is not perceived as a female attribute but as a male

characteristic—it was also expected that participants would prefer male advice to female

advice.

Considering the previous two effects, we would have a third expected result: advisees

would take more analytic advice from male advisors than from female advisors Therefore, the

hypotheses for this scenario were as follows:

H1: In the context of quasirational tasks, advisees will take more analytically justified

advice than intuitively justified advice.

H2: In the context of quasirational tasks, advisees will take more advice from male

advisors than from female advisors.

H3: In the context of quasirational tasks and analytically justified advice, advisees will

take more advice from male advisors than from female advisors.

50

4.1.1 Design

This study addressed how gender and advice justification impact advice taking in a

quasirational organizational task. Each participant received advice from a company colleague

with the same level of seniority as the decision maker (product manager). The design was a 2x2

between-subject factorial, in which gender (male/female) and advice justification

(intuitive/analytic) were crossed.

4.1.2 Procedure

The procedure was followed in accordance with Tzioti et al. (2014). All participants

took part in the experiment online. The task was a go/no-go decision, involving a product

launch. It was based on a case study, which mirrored the real-life Toyota Prius launch decision;

therefore, the task portrayed a realistic and typically common managerial decision.

Respondents played the role of a product manager. Their task was to indicate the extent

to which they would recommend the launch of a new electric boiler called CE-1. Respondents

received a brief description of the scenario on which to base their answers. They were told that

their decision was critical for their career, as the success or failure of the CE-1 would have a

significant financial impact on the company. After reviewing the case information, they had to

indicate on a slide bar the extent to which they recommended the product launch—0 on the left

being “Definitely No-Go” and 100 on the right indicating “Definitely Go”—on an underlying

choice continuum. By default, the slide bar’s initial position was in the middle (50 =

indifference to the options).

Once respondents made their recommendation, they were told that, as this decision was

important, they had the opportunity to receive advice from a company colleague, also a product

manager. The advisor was characterized as male (“Peter”) or female (“Anna”). Each participant

received three pieces of advice from one advisor only, which always favored the product launch.

The pieces of advice were either analytically or intuitively justified, and their informative

content was always the same; only the justification changed. The advice content was as follows:

1. Despite the uncertain economic return, this product will deliver nice publicity and first-

mover advantages. I definitely recommend its launch.

2. CE-1 is going to heavily contribute to the company’s image. People will see us as

innovative, modern, and ecofriendly. It will be a success.

3. People are willing to pay more for efficient and ecofriendly products. There is a trend

and the company must take advantage of this situation now. So, it is a go!

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Advice that was justified intuitively started with: “My intuition says that”, “My feeling

is that”, and “Intuitively, I believe that”, followed by advice contents 1, 2, and 3, respectively.

Advice that was justified analytically started with: “The market research data tell me that”,

“Judging from the competitor analysis, I would say that”, and “If I go with what the consumer

data say”, and was also followed by advice contents 1, 2, and 3, respectively.

After receiving advice, participants had the chance to review their initial decision. Once

more, they used the slide bar to indicate to what extent they would recommend the product

launch. For this second decision, the slide bar’s initial position was also set in the middle.

Finally, participants had an open space for general comments.

4.1.3 Dependent variable

To measure the extent to which the decision maker accepted advice from the advisor, it

was used Advice Taking (Equation 1) as a dependent variable, replicating the formula used by

Tzioti et al. (2014). This is a standard measure (Harvey & Fischer, 1997). It considers the initial

answer from the advisees, their final decision, and the advice received, as presented in the

literature review.

As advice was strongly in favor of launching the product, the Advised Estimate in this

research was considered 100. This dependent variable gauged the degree to which the advisees

reviewed their decision in the direction of the received advice (Tzioti et al., 2014).

4.1.4 Results

No difference between male and female advisees was found in either sample.

4.1.4.1 MTurk sample

347 workers from MTurk (132 men and 215 women) participated in the experiment.

This experiment was initially written in Portuguese and then translated into English, using back

translation to check for accuracy. All experimental materials are available in Appendices A and

B. Sixteen observations that were outliers were removed from this sample. The criterion was

Advice Taking lower than -0.1. These participants did not offer any explanation for this

behavior. Most performed the experiment too quickly. These observations would distort data

analysis. Furthermore, fourteen observations were excluded because their first estimate was

100, identical to the advised decision, thereby yielding an invalid observation. In these cases, it

would not be possible to quantify the extent to which the advisee would utilize the received

52

advice. This is a methodological practice in advice-taking research (Gino, 2008; Tzioti et al.,

2014; Yaniv, 2004a). This data treatment was replicated in all samples. Results are presented

in Figures 12 and 13.

Figure 12 - Quasirational Scenario - MTurk Sample - Descriptive Statistics

Descriptive Statistics

Dependent Variable: Advice-Taking Quasirational Scenario

Advisor Gender Quasirational Scenario Mean

Std. Deviation N

Male Intuitive .3066 .23932 81

Analytic .4298 .29282 76

Total .3663 .27278 157

Female Intuitive .3445 .26010 79

Analytic .3493 .26291 81

Total .3470 .26072 160

Total Intuitive .3253 .24974 160

Analytic .3883 .27982 157

Total .3565 .26651 317

The main effect of advice justification on advice taking was significant: F(1,313) = 4.66,

p = .03. Participants took more analytically justified (M = .39 SD = .28) than intuitively justified

advice (M = .33 SD = .25). The main effect for advisor gender was not significant: F(1,346) =

.52, p = .47. The interaction effect of advisor gender and advice justification was significant:

F(1,313) = 3.98, p = .05. In the male advisor condition, the effect of advice justification was

significant: F(1,155) = 8.38, p < .01. Respondents took more analytically justified (M = .43 SD

= .30) than intuitively justified advice (M = .31 SD = .24). Finally, in the analytically justified

condition, the effect of gender was significant: F(1,155) = 3.23, p = .07. Participants took more

advice from male than female advisors (M = .35 SD = .26). Considering these results,

Hypotheses 1 and 3 were supported. Nevertheless, H2, was not.

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Figure 13 - Mean Advice Taking, Quasirational Scenario (QS): MTurk Sample

Additional statistical results for Experiment 1 – MTurk Sample - are presented in

Appendix C.

4.1.4.2 Professional sample

Of the 137 Brazilian professionals who completed this experiment, 95 were men and 42

were women. Ten observations were removed because the first estimates were equal to 100,

yielding an undefined value for advice. Eight observations were removed due to Advice Taking

lower than -0.1. Results are presented in Figures 14 and 15.

Figure 14 - Quasirational Professionals Sample - Descriptive Statistics

Descriptive Statistics

Dependent Variable: AT_QS

QS_AdJ Mean Std.

Deviation N

Intuitive Male .1741 .27108 34

Female .1748 .23477 34

Total .1745 .25167 68

Analytic Male .3261 .32723 25

Female .2431 .26751 26

Total .2838 .29821 51

Total Male .2385 .30307 59

Female .2044 .24963 60

Total .2213 .27676 119

54

Again, the advice justification main effect was significant: F(1,115) = 4.73, p = .03.

Analytically justified (M = .28 SD = .30) was higher than intuitively justified advice (M = .17

SD = .27). There was no advisor gender main effect: F(1,115) = .66, p = .42. The interaction

effect was not significant either: F(1,115) = .66, p = .41. In the male advisor condition, the

effect of advice justification was significant: F(1,59) = 3.78, p = .056. Analytically justified

advice (M = .33 SD = .33) had a greater impact than intuitively justified advice (M = .24 SD =

.27). In the analytically justified condition, the effect of gender was not significant: F(1,49) =

.99, p = .33. Female advice (M = .24 SD = .27) was similar to male advice. These results

therefore support H1 but not H2 and H3.

Figure 15 - Mean Advice Taking, Quasirational Scenario (QS): Professional Sample

Additional statistical results for Experiment 1 – Professionals Sample - are presented in

Appendix D.

4.1.5 Discussion

In this study, it was found that, in a quasirational managerial-decision setting with both

analytic and intuitive cues, analytic advice outweighed intuitive advice in both samples (H1

was supported). As analytic justification is based on mindful analysis, advisees can obtain

insights regarding the rules and logic that could have guided the advisor to formulate the offered

advice (Tzioti et al., 2014)—which may reduce advice discounting.

55

Nevertheless, the advisor gender main effect was not significant neither in the MTurk

nor in the professional sample (H2 was not supported). In other words, being a male is not

enough to increase advice taking; an analytic justification was required to increase advice

taking.

As expected, in the MTurk sample under the analytic justification condition, male

analytic advice was more valued than female analytic advice (H3 was supported): that is, the

utilization of analytically justified advice boosted advice taking only when the advisor was

male. This is in agreement with expected gender roles and their congruity (Eagly, 1983, 2004)

and could therefore suggest the activation of the male gender stereotype.

Yet, the findings in the professional sample did not show a statistically significant

difference between male and female analytically justified advice (H3 was not supported). This

may be explained by Johnson and Powell’s (1994) assertion that gender stereotypes may not

apply to managers as, in the corporate world, male and females make decisions of equal quality.

Put differently, it could also suggest that the work behavior of men and women can reflect their

organization’s structural environment, rather than the organization members’ gender-role

characteristics (Green & Cassel, 1996). This could signal a mitigation of gender stereotypes,

especially in managerial settings. It has been noticed that gender stereotyping is decreasing

along time (Eagly & Karau, 2002; Finger, Unz, & Schwab, 2010).

In the male advisor condition, male analytically justified advice outweighed male

intuitively justified advice in both samples. This is in line with the greater value that people

attribute to analytic thinking, compared to intuition (Dawes et al., 1989; Kahneman, 2003;

Hogarth, 2010).

The results of this experiment were obtained in a quasirational task with both analytic

and intuitive inducing cues. This led us to question whether the standards of advice taking

would change if the quasirational decision-making scenario were more intuitive inducing. In

other words, a scenario where intuitive cues prevailed. In such setting, would intuitive

justification have more impact than analytic justification? And would female advice be more

influential? The next experiment was conducted to address these issues.

4.2 EXPERIMENT 2 – A QUASIRATIONAL MORE INTUITIVE-INDUCED

SCENARIO (IS)

For a more intuitive-inducing managerial-decision scenario, this experiment involved

the visual appraisal of two paintings. Participants played the role of an art gallery manager and

had to indicate the extent to which they would recommend one of two abstract paintings (from

56

different artists) to be exhibited for sale in the art gallery. Participants were told that the

investment in promotional marketing and the launch price of the paintings would be the same,

regardless of what painting was chosen; however, no financial figures were mentioned and there

were no calculations to be done.

In this scenario, the visual component was preponderant as the images of the paintings

were presented to support the participants’ recommendation. Cues were presented

simultaneously and evaluated perceptually. Information was presented in a pictorial way that

induced subjective interpretation. This type of setting also induces intuitive thinking (Dhami &

Thomson, 2012; Hammond, 1988). So, in the analytic–intuitive task continuum, it depicted a

more pronounced intuitive-inducing task than the one in the first experiment.

To evaluate if this scenario would be perceived as gender neutral, 100 MTurk workers

answered a small presurvey. Respondents indicated that this activity was considered gender

neutral: the average score was 57.8, close to the middle of the scale. When asked: who could

perform better, people answered 56.8 (0 being men and 100 women). Thus, it seemed

reasonable to assume that this scenario was fairly gender neutral.

According to Hammond et al. (1987), the key to good judgment is to match task

demands with cognitive style. Thus, intuitive-inducing tasks foster intuitive thinking and

analytic-inducing tasks favor analytic thinking. In accordance with this argument, Tzioti et al.

(2014) proposed that if, under certain conditions, intuition is a good basis for a decision, it

would be imperative that, under such conditions, decision makers follow intuitively justified

advice. Their results showed that for intuitive-inducing tasks, in combination with senior

advisor and junior advisee, following intuitively justified advice can be greater than taking

analytically justified advice. Therefore, in accordance with mainstream literature on decision

making, it was plausible to expect that in a more intuitive-induced scenario, advisees would

take more intuitively than analytically justified advice.

Moreover, there is a shared perception (common sense) that women are more intuitive

than men (Nemecek, 1997). Furthermore, this task involves aesthetics appraisal, sensitivity and

emotion. The gender stereotype usually describes women as being emotional, sensitive and

intuitive (Marshall, 1993; Pelham et al., 2005). Thus, it was expected that in a more intuitive-

induced task participants would take more advice from female advisors than from male

advisors.

Adding up, the previous effects, it was expected that in a more intuitive-induced

scenario, advisees would take more intuitively justified advice from female advisors than from

male advisors. Put differently, the task would favor female advisors and intuitive justification

57

as the visual appraisal of paintings has a strong emotional component. Tzioti et al. (2014)

mentioned this possibility as a recommendation for future research.

Therefore, the hypotheses for this scenario were as follows:

H4: In the context of more intuitive-inducing tasks, advisees will take more intuitively

justified advice than analytically justified advice.

H5: In the context of more intuitive-inducing tasks, advisees will take more advice from

female advisors than from male advisors.

H6: In the context of more intuitive-inducing tasks and intuitively justified advice,

female advisors will be more influential than male advisors.

4.2.1 Design

This second experiment also used a 2x2 between-subjects design, again crossing gender

(male/female) and advice justification (analytic/intuitive). Each participant received advice

from a colleague with the same level of seniority as the decision maker (the colleague also

worked as an art gallery manager).

4.2.2 Procedure

Participants again took part in the experiment online. There was no strong preference

for or aversion to any painting. Respondents were told that their decision was important,

because both their professional future and the gallery’s reputation, as a talent-spotter of new

and promising artists, depended on their recommendation. They also received a brief

description of the scenario on which to base their answers.

After reading the case, participants were asked to indicate the extent to which they

would recommend one painting over the other on a slide bar. The left extreme indicated

“Definitely Painting A” and the right, “Definitely Painting B”, corresponding to 0 and 100,

respectively, on an underlying choice continuum. By default, the initial position of the slide bar

was set in the middle at 50 (indifference to the two paintings).

Following their first recommendation, participants were informed that, as this was an

important decision, they would have the opportunity to receive some advice from a colleague

who also worked as an art gallery manager. Each participant received three pieces of advice

from only one advisor, who always recommended Painting B. After receiving advice, they were

able to review their initial answer. The advisor was described as either male (“Richard”) or

female (“Beatrice”). The pieces of advice were either analytically or intuitively justified and

58

their informative content was always the same; only the justification changed. The advice

content was as follows:

1 Painting B evokes more pleasant and intense feelings and emotions.

2 Painting B has a deeper meaning; therefore, it has more potential to increase in value over

time.

3 Painting A has a good chance of success; however, Painting B communicates more elegance

and sophistication, and thus, has higher chances of success.

Advice that was justified intuitively started with: “My intuition says that”, “My feeling

is that”, and “Intuitively, I believe that”, followed by advice contents 1, 2, and 3, respectively.

Analytically justified advice started with: “The market research data tell me that”, “Judging

from the competitor analysis I would say that”, and “If I go with what the consumer data say”—

also followed by advice contents 1, 2, and 3, respectively.

After receiving the three pieces of advice, participants had to indicate the extent to which

they would recommend one painting over the other on the slide bar. Again, the initial position

of the slide bar knob was set at 50. Lastly, participants had an open space for general comments.

4.2.3 Results

As in the Experiment 1, observations from MTurk workers from the U.S were collected

first. Then, Brazilian company professionals also answered the experiment. No difference

between male and female advisees was found in either sample.

4.2.3.1 MTurk sample

The size of the MTurk sample was 406 workers (195 men and 211 women), each of

whom again received a US$ 0.50 reward for taking part in the experiment. This experiment was

also initially written in Portuguese and then translated into English, using back translation to

check for accuracy. Experimental material is available in Appendices A and B. From this

sample, thirty observations were excluded because their first estimate was 100, identical to the

advised decision, thereby yielding an invalid observation for the research; and, sixteen

observations were excluded due to Advice Taking lower than -0.1. Results are presented in

Figures 16 and 17.

59

Figure 16 - More Intuitive Scenario MTurk Sample - Descriptive Statistics

Descriptive Statistics

Dependent Variable: Advice-Taking Intuitive Scenario

IS_AG Mean Std.

Deviation N

Male Intuitive .3132 .29990 82

Analytic .3616 .32844 90

Total .3386 .31517 172

Female Intuitive .2999 .30034 87

Analytic .3993 .33245 91

Total .3507 .32017 178

Total Intuitive .3064 .29931 169

Analytic .3805 .33009 181

Total .3447 .31733 350

The advice justification main effect was significant: F(1,346) = 4.77, p = .03.

Analytically justified advice (M = .38 SD = .33) was significantly higher than intuitively

justified advice (M = .31 SD = .30). The main effect of advisor gender was not significant:

F(1,346) = .13, p = .72; neither was the interaction effect: F(1,346) = .57, p = .45. In the female

advisor condition, the effect of advice justification was significant: F(1,176) = 4.36, p = .04.

Analytic advice (M = .40 SD = .33) was greater than intuitive advice (M = .30 SD = .30). In the

intuitive condition, there was no significant difference: F(1,167) = .08, p = .77. Male advice (M

= .31, SD = .30) was similar to female advice (M = .30, SD = .30). In the analytic condition,

there was no significant difference either: F (1, 179) = .59, p = .45. Male advice (M = .36 SD =

.33) was similar to female advice. These results therefore do not support H4, H5 and H6.

60

Figure 17 - Mean Advice Taking, More Intuitive-Inducing Scenario (IS): MTurk Sample

Further details about statistical results for Experiment 2 – MTurk Sample - are presented

in Appendix E.

4.2.3.2 Professional sample

The sample of Brazilian professionals who took part in this experiment numbered 190

(110 men and 80 women) and, as in the first experiment, they did not receive any financial

reward or gift for their participation. Seventeen observations were removed because the first

estimate was equal to 100, yielding an undefined value for advice; and, ten observations were

excluded due to Advice Taking lower than -0.1. Results are presented in Figures 18 and 19.

Figure 18 - More Intuitive Scenario - Professional Sample - Descriptive Statistics

Descriptive Statistics

Dependent Variable: AT_IS

IS_AG Mean Std.

Deviation N

Male Intuitive .1959 .20210 41

Analytic .1831 .22885 41

Total .1895 .21465 82

Female Intuitive .2088 .28854 39

Analytic .3423 .33609 42

Total .2780 .31928 81

Total Intuitive .2022 .24651 80

Analytic .2637 .29738 83

Total .2335 .27450 163

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The advice justification main effect was not significant: F(1,159) = 2.05, p = .15.

Analytic (M = .26 SD = .30) and intuitive (M = .20 SD =.25) advice justification were not

statistically different. The main effect of advisor gender was significant: F(1,159) = 4.2, p =

.04. Female advice (M = .28 SD = .32) was more valued than male advice (M = .19 SD = .21).

The interaction effect was significant at a p-level of .1: F(1,159) = 3, p = .085. In the female

advisor condition, advice justification was significant: F(1,79) = 3.61, p = .06. Analytic

justification (M = .34 SD = .34) was higher than intuitive justification (M = .21 SD = .29). In

the intuitive condition, there was no significant difference: F(1,167) = .05, p = .81. Male advice

(M = .20, SD = .20) was similar to female advice (M = .21, SD = .29). In the analytically

justified condition, the effect of gender was significant: F(1,81) = 6.33, p = .01. Participants

took more advice from female than male advisors (M = .18 SD = .23). These results therefore

support H5, but not H4 and H6.

Figure 19 - Mean Advice Taking, More Intuitive-Inducing Scenario (IS): Professional Sample

Further details about statistical results for Experiment 2 – Professionals Sample - are

presented in Appendix F.

4.2.4 Discussion

The first hypothesis of this experiment, H4, where it was expected that in a more

intuitive-induced scenario, intuitive justification would outweigh analytic justification was not

confirmed in either sample. Intuitively justified advice was not superior to analytically justified

62

advice. In the MTurk sample, analytic justification outweighed intuitively justified advice. This

may be due to the focus of the experiment being on managerial decision making. Traditional

approaches to management often focus on analysis and ignore the intuitive approach required

in some situations (Hayes et al., 2004)—the idea that analytic thinking is superior (Dawes et

al., 1989; Russo & Schoemaker, 2002) to intuition, with its negative associations (Hogarth,

2001) is prevalent. Additionally, managers usually prefer analytic to intuitive thinking (Hayes

et al., 2004). This could have led to a higher advice taking for analytically justified advice than

intuitivelly justified advice in the MTurk Sample. Despite the result obtained in the MTurk

sample, in the professional sample there was no statistical difference between intuitive (M =

.20) and analytic justification (M=.26), which could be due to small sampling size (p-value =

.15).

The main effect of advisor gender, where it was expected that female advice would outweigh

male advice was only verified in the professional sample. Females are considered to be more

sensitive and better at judging tasks that involve aesthetics and emotion, and are also thought

to have a more holistic view (Marshall, 1993). Femininity is associated with the ability to

experience and express emotions (Broverman, Vogel, Broverman, Clarkson, & Rosenkrantz,

1972; Fischer & Manstead, 2000). Moreover, paintings are art products that can evoke certain

emotions (such as aesthetic ones) that result from an appraisal process (Desmet, 2003;

Machajdik & Hanbury, 2010). Therefore, it is arguable that this could explain why, in the

professional sample, female advice was more utilized than male advice in this more intuitive-

inducing task. This could also suggest an activation of the female gender stereotype in terms of

emotional and aesthetic awareness. MTurk workers may not have made these associations,

leading to no difference in advice taking between male and female advisors. This could be

explained by Ridgeway and Smith-Lovin (1999) who, as discussed earlier, showed that there

are fewer gender differences in behavior among those of equal power and status. In this second

experiment, advisor and advisee had equal power and status: they were colleagues, and both

worked as managers in an art gallery.

The lack of full support for the previous hypotheses; consequently, led to not supporting

H6. Participants did not take more intuitively justified advice from female advisors than from

male advisors. There was no significant difference between these two conditions. In spite of the

fact that (1) advisees may perceive women as more intuitive and (2) it was a more intuitive-

induced scenario, advice taking did not reflect a possible preference for female intuitive advice.

This may be due to the absence of additional cues in the advice-taking context that hinted at the

accuracy of the advisor’s intuition, as in this second experiment, it was clear that both advisor

63

and advisee shared the same position; and, thus, the same level of seniority. This was a

significant difference from Tzioti et al. (2014)’s research. They pointed out that one advisor

was junior and the other was senior. In their case, subjects attributed seniority/expertise to the

senior advisor, leading to a higher advice taking for this senior advisor.

Finally, in terms of the female advisor condition, advisees in both samples accepted

more advice from female advisors when the justification was analytic instead of intuitive. A

good manager is still perceived as having predominantly “masculine” values, such as analysis—

regardless of the manager’s sex (Gardiner & Tiggemann, 1999; Powell et al., 2002). Although

analysis is considered a male value, values are still qualities that both sexes can have access to

(Marshall, 1993).

64

5. FINAL CONSIDERATIONS

Despite the discussion about which cognitive way of thinking brings the best results,

this work addressed a related but different perspective: how the advisor’s gender and advice

justification influence advice taking. In the two conducted experiments, advice justification and

advisor gender were manipulated.

In the first experiment, involving a go/no-go quasirational managerial decision with

both analytic and intuitive inducing cues, it was identified that decision makers were more

likely to follow analytically justified than intuitively justified advice. Advisees accepted male

analytic advice more than female analytic advice in the MTurk sample. In the second

experiment, concerning a more intuitive inducing quasirational managerial decision, advisees’

preference for intuitively justified advice was still no greater than for analytically justified

advice. When advice was analytically justified, advisees in the professional sample utilized

more advice from female advisors than from male advisors.

These findings allow us to infer that, depending on the advisees’ profile (MTurk workers

or professionals) and providing that advice justification is analytic, quasirational scenarios with

both analytic and intuitive cues seem to foster the utilization of advice offered by male advisors,

whereas more intuitive-inducing settings seem to favor female advisors. Thus, it seems that

analytic justification is more valued in most managerial situations, including in more intuitive-

inducing tasks. This corroborates Heilman, Block, Martell, and Simon (1989), who suggest that

successful managers are characterized as logical, analytic, and objective. Green and Cassel

(1996) argue that the work behavior of men and women is shaped by the (male/analytic)

domination of opportunity and power structures, rather than by the organization members’

individual characteristics. Moreover, the gendered cultural perspective suggests that it is “male”

values that define appropriate behavior for managers (Gardiner & Tiggemann, 1999). This

would explain the preponderance of analytic justification in advice taking, even when the task

is more intuitive induced. However, a study by McMackin and Slovic (2000) found that in an

intuitive task, explicit reasoning degraded judgment quality. It is worth mentioning that their

study dealt with individual decision making and not with advice taking.

Traditional approaches to management focus on analysis, planning, and systematic

decision making, and have tended to ignore the intuitive approach required in many situations

(Hayes et al., 2004). This fosters an organizational discourse where rationality and analytic

thinking are valued, while intuition is often dismissed. However, Mintzberg (1976), Isenberg

(1991), and Lank and Lank (1995), among others, argue that much executive work involves

65

speculative data, high uncertainty, and immediate action, rather than reflection and planning.

In fact, managers need to rely heavily on a mix of intuition and disciplined analysis, whether

acknowledged or not.

The second experiment was a quasirational more intuitive-induced task. In such cases,

according to mainstream decision making literature, decision makers are induced to think

intuitively. Notwithstanding, in this scenario, intuitively justified advice did not outweigh

analytically justified advice. This might be due to the fact that individual decision making and

advice taking may not follow the same rule. Individuals may judge and decide intuitively in

intuitive inducing scenarios. However, our results in experiment 2 allow us to infer that when

taking advice, decision makers may still prefer to take analytically justified rather than

intuitivelly justified advice. We could speculate that this might be due to different reasons, such

as : (1) decision makers have no access to the internal rationale that support the accuracy of

the advisor’s intuition; (2) when the expertise and experience or status of advisor and advisee

are the same, decision makers’ intuitive judgment might be overcome by the advisor’s

analytically justified advice.

This study can also be analyzed in terms of expected gender roles and their congruity

(Eagly, 2004). This can explain why MTurk workers valued male more than female analytic

advice in the first experiment. By the same token, professionals valued female more than male

analytic advice in the intuitive-inducing task. People may perceive men as being more capable

of giving advice when there are analytic cues. Conversely, women may be perceived to perform

better than men in intuitive-inducing tasks that awaken emotion and feelings, even though the

advice still has to be analytically justified.

People often take advice because of their accountability to others (Kennedy, Kleimutz,

& Peecher, 1997). Possibly, a decision based on analysis and logic is easier to justify, in

hindsight, if there are negative outcomes. Arguing that a person followed somebody else’s

hunch to make a decision may not be perceived as sound managerial judgment. In collective

imaginary, intuition still has, to some extent, mystical, magical, and spiritual connotations,

weakening its power as a valid source of judgment.

Moreover, in our research, different from Tzioti et al (2014)’s work, participants did not

take, intuitively justified advice more than analytically justified advice in any condition. This

might be due to experimental conditions, as Tzioti et al (2014) explicitly manipulated seniority,

pairing a senior advisor with a junior advisee. Consequently, in their case, participants attributed

expertise to the advisor and took more intuitively justified advice. Another difference between

our and Tzioti et al (2014)’s research is regarding subjects. Possibly, this could have also led to

66

differences in the dependent variable. Professionals and MTurk workers may be less open to

intuitively justified advice. Specially professionals are trained and expected to act rationally

and analytically. Thus, undergraduate students could be more open to intuitively justified advice

than professionals or MTurk workers.

Finally, the two experiments demonstrated the importance of the interplay between

advisor gender, advice justification, and task characteristics in advice taking.

5.1 LIMITATIONS

There were no incentives for participants to give their best possible answer. This was

due to the experimental conditions and tasks. Since there was not a right or wrong answer,

incentives for a better answer could not be offered.

Moreover, this study’s validity is subject to sample characteristics. Other cultures will

probably present different results, since cultural capital influences directly intuition and

decision-making.

The experiments were not repeated with the same participants to check for external

reliability (test-retest). Respondents might answer differently the second time because after

having participated the first time, they might wish to alter the image they felt they made

(Coolican, 1990).

Despite the pretests, subjects might have associated men as being more capable of

offering advice regarding boilers and women regarding paintings. The tests tried to capture if

the scenarios were gender neutral. However, participants might have responded the pretest

being “politically correct”. The continuum had men (or male activity) in one extreme and

women (or female activity) in the other extreme, so they might have answered close to middle

in order not to seem sexist. Nevertheless, when participants took the experiment, they were just

exposed to the scenario and they might have reacted according to their values, beliefs and

stereotypes.

The frequent exposure to gender expected roles may shape people’s beliefs. For

example, although boys and girls do not perceive much difference in mathematical abilities

among them, they believe that male adults are better than female adults in mathematics. On the

other hand, children believe that female adults and young girls have better reading skills than

male adults and young boys, respectively. These beliefs were tested among 398 French children

with both direct and indirect measures (Martinot, Bagès, & Désert, 2012). It is possible that

children’s stereotypical belief about mathematical abilities among adults may be a consequence

67

of the massive presence of men in occupations that require good mathematical abilities such as

science or engineering (Ceci & Williams, 2011; Martinot et al., 2012).

Besides science, there are other industries and professions that are male dominated. For

instance, men are majority in oil and heavy machinery industries and professions such as auto

mechanic, firefighter, janitor and plumber. While in industries such as cosmetics and

professions like librarian, kindergarten teacher and nurse there is a higher feminine

participation, at least in many western countries (USA, 2014; Wilbourn & Kee, 2010).

Participants may have perceived Experiments 1 and 2 as male and female tasks,

respectively. In this sense, participants might have attributed, through gender stereotypes,

expertise to males or females for the tasks in each experiment.

To the best of my knowledge, there are no reliable tools to verify exactly where, in the

task continuum index, fall the two scenarios we designed; the extremes of the continuum being

intuitive and analytic induced. Therefore, although the author tried to follow qualitatively what

theory states about task characteristics to induce thinking modes, it is not possible to assert how

much the first scenario was quasirational and balanced or middle of the road, and the second

scenario quasirational and more intuitive-induced.

Lastly, in connection with samples of MTurk workers, it should be mentioned that they

are likely experienced in answering experiments; therefore, they may have experience in

experimental research. In cases like the Cognitive Reflection Test, many workers report they

have already done it before, biasing results. Notwithstanding, this research experimental

material was entirely developed by the researchers and had never been used before.

68

5.2 CONTRIBUTIONS, IMPLICATIONS, AND FUTURE RESEARCH

To the best of my knowledge, this is the first study that examines the impact of the

advisor’s gender and advice justification in different managerial decision settings. In advice-

taking literature, observations are usually collected from students. As this study focused on

managerial decisions, it included independent samples collected from MTurk workers and

professionals: a valuable contribution to the research field.

Specifically, when comparing male and female advisors in the quasirational scenario, it

was found that in the MTurk sample, male analytically justified advice was more influential

than that of the female. Conversely, in the professional sample from the more intuitive-inducing

task, female analytically justified advice was more valued than the male analytically justified

advice. This could suggest the activation of male and female gender stereotypes: in each

scenario, gender stereotypes seem to be active in one sample, but not in both. Consequently,

these results might signal a slow, ongoing process leading, in the long run, to the mitigation of

gender stereotypes. It has been noticed that gender stereotyping is decreasing along time (Eagly

& Karau, 2002; Finger, Unz, & Schwab, 2010). As a matter of fact, social role theory treats

gender roles as a dynamic aspect of culture (Eagly et al., 2000). A secondary finding, which

can be used as a basis for future research (as expressed confidence was not measured in this

study), is that advice taking among professionals was significantly lower than among MTurk

workers, both in the quasirational scenario (F(1, 433) = 399.4, p < .001), and in the intuitive

scenario (F(1,511) = 401.3, p < .001). In the first experiment, professionals’ advice-taking mean

was .22 (SD = .27), while the MTurk workers’ mean was .36 (SD = .28). In the second

experiment, professionals accepted less advice (M = .23, SD = .27) compared to MTurk workers

(M = .34, SD = .32). Although samples were from different cultures, this difference in advice

taking is possibly also due to professionals’ higher self-confidence. This might explain their

lower acceptance of advice (Tost et al., 2012).

Although there is some literature regarding similarities between advisee and advisor;

and their impact on advice taking, (Gino, Shang & Croson 2009), our study did not find any

significant gender difference in advisees. Nevertheless, when the author was running some

pilots and participant’s gender was asked before the task, some advisees’ gender differences

arose. When this question was moved to the end of the experiment, differences vanished. The

experiment was conducted asking advisee’s gender at the end of the experimental material.

Another research also found no significant difference in advisees’ gender. Tost et al.

(2012) researched about power and competitiveness; and did not find any gender difference in

69

advisees. Brooks et al (2015) research focus was on advice seeking and confidence. They did

not find any advisee’s gender difference either. Gino (2008) investigated the effect of advice

cost in its use. Again, no evidence of gender differences in advisees. Finally, a study regarding

anxiety and advice seeking and taking verified if advisees’ gender would have any impact in

the dependent variable (Gino, Brooks & Schweitzer, 2012). Once more, no significant

difference.

For future research, scholars may look for some priming effect or activation of gender

role expectations. It happened during pilots when advisee’s gender was asked prior to the task.

Here there may be a potential for future research. How male and female decision makers feel

about their own gender when taking advice. Are men more confident? Does it depend on the

industry or task characteristics (more analytic or intuitive induced task)?

Researchers could also try to identify if there is an intuitive-inducing task where

intuitively justified advice has a greater impact than analytically justified advice, regardless of

the advisor’s seniority. Moreover, further studies could try to explore different tasks and

advisors along with different characteristics (such as race, sexual orientation, religion, and even

advisors’ names). For instance, when dealing with cuisine or fashion, people might place more

value on advice given by an advisor with a French or Italian name.

Lastly, future studies might consider experiments in intercultural settings. The

dependent variable could vary depending on what cultures are being considered. For instance,

if researchers were to manipulate advisor gender and run the same experiment in a Nordic and

an Arab country, results might vary substantially, owing to inherent sexism and expected gender

roles.

A practical message for managers and consultants to take away is that, in managerial

decision making, advisors should be aware how, in general, analytic justification is more

utilized than intuitive justification. Therefore, analytic justification will likely lead to more

advice utilization. This work contributes with some initial insights to the advice-taking research

field by introducing advisor gender into the equation. Of course, there is still much to learn.

70

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84

APPENDIX A – EXPERIMENTAL MATERIAL – PORTUGUESE

93

APPENDIX B – EXPERIMENTAL MATERIAL - ENGLISH

101

APPENDIX C – EXPERIMENT 1 – STATISTICAL RESULTS – MTURK SAMPLE

Figure 20 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking

Tests of Between-Subjects Effects

Dependent Variable: AT_QS

Source

Type III Sum of Squares df

Mean Square F Sig.

Partial Eta Squared

Noncent. Parameter

Observed Powerb

Corrected Model

,626a 3 .209 2.992 .031 .028 8.976 .804

Intercept 40.505 1 40.505 581.032 .000 .650 581.032 1.000

QS_AG .036 1 .036 .515 .474 .002 .515 .186

QS_AJ .324 1 .324 4.655 .032 .015 4.655 .694

QS_AG * QS_AJ

.278 1 .278 3.983 .047 .013 3.983 .636

Error 21.820 313 .070

Total 62.738 317

Corrected Total

22.445 316

a. R Squared = ,028 (Adjusted R Squared = ,019)

b. Computed using alpha = ,10

Figure 21 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking - Female Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_QS_FA

Source

Type III Sum of Squares df

Mean Square F Sig.

Partial Eta Squared

Noncent. Parameter

Observed Powerb

Corrected Model

,001a 1 .001 .013 .908 .000 .013 .102

Intercept 19.255 1 19.255 281.520 .000 .641 281.520 1.000

QS_AJ .001 1 .001 .013 .908 .000 .013 .102

Error 10.807 158 .068

Total 30.069 160

Corrected Total

10.808 159

a. R Squared = ,000 (Adjusted R Squared = -,006)

b. Computed using alpha = ,10

102

Figure 22 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking - Male Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_QS_MA

Source

Type III Sum of Squares df

Mean Square F Sig.

Partial Eta Squared

Noncent. Parameter

Observed Powerb

Corrected Model

,595a 1 .595 8.379 .004 .051 8.379 .892

Intercept 21.265 1 21.265 299.298 .000 .659 299.298 1.000

QS_AJ .595 1 .595 8.379 .004 .051 8.379 .892

Error 11.013 155 .071

Total 32.669 157

Corrected Total

11.608 156

a. R Squared = ,051 (Adjusted R Squared = ,045)

b. Computed using alpha = ,10

Figure 23 - Statistics Quasirational Scenario – MTurk Sample - Advice Taking - Intuitive Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_QS_IJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,088a 1 .088 1.360 .245 .006 1.360 .317

Intercept 22.971 1 22.971 356.736 .000 .599 356.736 1.000

QS_AG .088 1 .088 1.360 .245 .006 1.360 .317

Error 15.390 239 .064

Total 42.266 241

Corrected

Total

15.477 240

a. R Squared = ,006 (Adjusted R Squared = ,001)

b. Computed using alpha = ,10

103

Figure 24 - Statistics Quasirational Scenario - MTurk Sample - Advice Taking - Analytic Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_QS_AnJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,254a 1 .254 3.292 .072 .021 3.292 .564

Intercept 23.804 1 23.804 308.484 .000 .666 308.484 1.000

QS_AG .254 1 .254 3.292 .072 .021 3.292 .564

Error 11.961 155 .077

Total 35.887 157

Corrected

Total

12.215 156

a. R Squared = ,021 (Adjusted R Squared = ,014)

b. Computed using alpha = ,10

104

APPENDIX D – EXPERIMENT 1 – STATISTICAL RESULTS – PROFESSIONAL

SAMPLE

Figure 25 - Statistics Quasirational Scenario - Professional Sample - Advice Taking

Tests of Between-Subjects Effects

Dependent Variable: AT_IS

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,683a 3 .228 3.141 .027 .056 9.423 .819

Intercept 8.807 1 8.807 121.518 .000 .433 121.518 1.000

IS_AG .301 1 .301 4.155 .043 .025 4.155 .650

IS_AdJ .148 1 .148 2.048 .154 .013 2.048 .414

IS_AG *

IS_AdJ

.218 1 .218 3.008 .085 .019 3.008 .533

Error 11.524 159 .072

Total 21.093 163

Corrected

Total

12.206 162

a. R Squared = ,056 (Adjusted R Squared = ,038)

b. Computed using alpha = ,10

Figure 26 - Statistics Quasirational Scenario - Professional Sample - Advice Taking - Female Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_FA

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,361a 1 .361 3.655 .060 .044 3.655 .599

Intercept 6.141 1 6.141 62.237 .000 .441 62.237 1.000

IS_AdJ .361 1 .361 3.655 .060 .044 3.655 .599

Error 7.795 79 .099

Total 14.415 81

Corrected

Total

8.155 80

a. R Squared = ,044 (Adjusted R Squared = ,032)

b. Computed using alpha = ,10

105

Figure 27 - Statistics Quasirational Scenario - Professional Sample - Advice Taking - Male Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_MA

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,003a 1 .003 .072 .789 .001 .072 .112

Intercept 2.946 1 2.946 63.197 .000 .441 63.197 1.000

IS_AdJ .003 1 .003 .072 .789 .001 .072 .112

Error 3.729 80 .047

Total 6.678 82

Corrected

Total

3.732 81

a. R Squared = ,001 (Adjusted R Squared = -,012)

b. Computed using alpha = ,10

Figure 28 - Statistics Quasirational Scenario - Professional Sample - Advice Taking - Analytic Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_AnJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,526a 1 .526 6.330 .014 .072 6.330 .802

Intercept 5.728 1 5.728 68.977 .000 .460 68.977 1.000

IS_AG .526 1 .526 6.330 .014 .072 6.330 .802

Error 6.726 81 .083

Total 13.022 83

Corrected

Total

7.252 82

a. R Squared = ,072 (Adjusted R Squared = ,061)

b. Computed using alpha = ,10

106

Figure 29 - Statistics Quasirational Scenario - Professional Sample - Advice Taking - Intuitive Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_IJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,003a 1 .003 .053 .818 .001 .053 .109

Intercept 3.273 1 3.273 53.222 .000 .406 53.222 1.000

IS_AG .003 1 .003 .053 .818 .001 .053 .109

Error 4.797 78 .062

Total 8.071 80

Corrected

Total

4.801 79

a. R Squared = ,001 (Adjusted R Squared = -,012)

b. Computed using alpha = ,10

107

APPENDIX E – EXPERIMENT 2 – STATISTICAL RESULTS – MTURK SAMPLE

Figure 30 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking

Tests of Between-Subjects Effects

Dependent Variable: AT_IS

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,552a 3 .184 1.841 .139 .016 5.522 .604

Intercept 41.234 1 41.234 412.445 .000 .544 412.445 1.000

IS_AG .013 1 .013 .130 .719 .000 .130 .122

IS_AJ .476 1 .476 4.765 .030 .014 4.765 .703

IS_AG * IS_AJ .057 1 .057 .567 .452 .002 .567 .194

Error 34.591 346 .100

Total 76.739 350

Corrected Total 35.143 349

a. R Squared = ,016 (Adjusted R Squared = ,007)

b. Computed using alpha = ,10

Figure 31 - Statistics More Intuitive Scenario – MTurk Sample - Advice Taking - Female Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_FA

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,439a 1 .439 4.361 .038 .024 4.361 .668

Intercept 21.745 1 21.745 216.158 .000 .551 216.158 1.000

IS_AJ .439 1 .439 4.361 .038 .024 4.361 .668

Error 17.705 176 .101

Total 40.039 178

Corrected Total 18.144 177

a. R Squared = ,024 (Adjusted R Squared = ,019)

b. Computed using alpha = ,10

108

Figure 32 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking - Male Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_MA

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,100a 1 .100 1.011 .316 .006 1.011 .264

Intercept 19.541 1 19.541 196.735 .000 .536 196.735 1.000

IS_AJ .100 1 .100 1.011 .316 .006 1.011 .264

Error 16.886 170 .099

Total 36.701 172

Corrected Total 16.986 171

a. R Squared = ,006 (Adjusted R Squared = ,000)

b. Computed using alpha = ,10

Figure 33 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking - Intuitive Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_IJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,007a 1 .007 .083 .774 .000 .083 .114

Intercept 15.872 1 15.872 176.206 .000 .513 176.206 1.000

IS_AG .007 1 .007 .083 .774 .000 .083 .114

Error 15.043 167 .090

Total 30.916 169

Corrected Total 15.050 168

a. R Squared = ,000 (Adjusted R Squared = -,005)

b. Computed using alpha = ,10

109

Figure 34 - Statistics More Intuitive Scenario - MTurk Sample - Advice Taking - Analytic Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_AnJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,064a 1 .064 .587 .445 .003 .587 .197

Intercept 26.196 1 26.196 239.877 .000 .573 239.877 1.000

IS_AG .064 1 .064 .587 .445 .003 .587 .197

Error 19.548 179 .109

Total 45.824 181

Corrected Total 19.612 180

a. R Squared = ,003 (Adjusted R Squared = -,002)

b. Computed using alpha = ,10

110

APPENDIX F – EXPERIMENT 2 – STATISTICAL RESULTS – PROFESSIONAL

SAMPLE

Figure 35 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking

Tests of Between-Subjects Effects

Dependent Variable: AT_IS

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,683a 3 .228 3.141 .027 .056 9.423 .819

Intercept 8.807 1 8.807 121.518 .000 .433 121.518 1.000

IS_AG .301 1 .301 4.155 .043 .025 4.155 .650

IS_AdJ .148 1 .148 2.048 .154 .013 2.048 .414

IS_AG *

IS_AdJ

.218 1 .218 3.008 .085 .019 3.008 .533

Error 11.524 159 .072

Total 21.093 163

Corrected

Total

12.206 162

a. R Squared = ,056 (Adjusted R Squared = ,038)

b. Computed using alpha = ,10

Figure 36 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking – Female Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_FA

Source

Type III Sum of Squares df

Mean Square F Sig.

Partial Eta Squared

Noncent. Parameter

Observed Powerb

Corrected Model

,361a 1 .361 3.655 .060 .044 3.655 .599

Intercept 6.141 1 6.141 62.237 .000 .441 62.237 1.000

IS_AdJ .361 1 .361 3.655 .060 .044 3.655 .599

Error 7.795 79 .099

Total 14.415 81

Corrected Total

8.155 80

a. R Squared = ,044 (Adjusted R Squared = ,032)

b. Computed using alpha = ,10

111

Figure 37 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking - Male Advisor

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_MA

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,003a 1 .003 .072 .789 .001 .072 .112

Intercept 2.946 1 2.946 63.197 .000 .441 63.197 1.000

IS_AdJ .003 1 .003 .072 .789 .001 .072 .112

Error 3.729 80 .047

Total 6.678 82

Corrected

Total

3.732 81

a. R Squared = ,001 (Adjusted R Squared = -,012)

b. Computed using alpha = ,10

Figure 38 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking Analytic Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_AnJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,526a 1 .526 6.330 .014 .072 6.330 .802

Intercept 5.728 1 5.728 68.977 .000 .460 68.977 1.000

IS_AG .526 1 .526 6.330 .014 .072 6.330 .802

Error 6.726 81 .083

Total 13.022 83

Corrected

Total

7.252 82

a. R Squared = ,072 (Adjusted R Squared = ,061)

b. Computed using alpha = ,10

112

Figure 39 - Statistics More Intuitive Scenario - Professionals Sample - Advice Taking - Intuitive Justification

Tests of Between-Subjects Effects

Dependent Variable: AT_IS_IJ

Source

Type III

Sum of

Squares df

Mean

Square F Sig.

Partial

Eta

Squared

Noncent.

Parameter

Observed

Powerb

Corrected

Model

,003a 1 .003 .053 .818 .001 .053 .109

Intercept 3.273 1 3.273 53.222 .000 .406 53.222 1.000

IS_AG .003 1 .003 .053 .818 .001 .053 .109

Error 4.797 78 .062

Total 8.071 80

Corrected

Total

4.801 79

a. R Squared = ,001 (Adjusted R Squared = -,012)

b. Computed using alpha = ,10