bi norwegian business school thesis - expertise: what does...

Lars-Kristian Kjølberg (0808977)

Knut Erlend Hjorth-Johansen (0913794)

BI Norwegian Business School – Thesis

- Expertise: What does education give

you? – On education and task complexity and their

moderating effect on expertise

Study Programme:

Organizational Psychology and Leadership

Date of submission:

03.12.2012

Name of supervisor:

Thorvald Hærem

This thesis is a part of the MSc programme at BI Norwegian Business School. The school takes no

responsibility for the methods used, results found and conclusions drawn.

Master Thesis GRA 19003 03.12.2012

Side i

Table of content ABSTRACT ................................................................................................................................... III

ACKNOWLEDGEMENT ............................................................................................................ IV

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

2. THEORETICAL MODEL ......................................................................................................... 3

3. THEORETICAL FOUNDATION ............................................................................................. 4

3.1. DEGREE OF EXPERTISE ........................................................................................................... 4

3.2. EDUCATION ............................................................................................................................ 6

3.3. TASK COMPLEXITY ................................................................................................................. 9

3.4. TASK PERFORMANCE ............................................................................................................ 10

3.5. RISK PROPENSITY ................................................................................................................. 12

3.6. OVERCONFIDENCE ............................................................................................................... 13

3.7. PERCEIVED UNCERTAINTY.................................................................................................... 15

4. METHOD .................................................................................................................................. 17

4.1. PARTICIPANT CHARACTERISTICS .......................................................................................... 17

4.2. SAMPLING PROCEDURES ....................................................................................................... 18

4.3. MEASURES ........................................................................................................................... 19

4.3.1 Expertise ....................................................................................................................... 19

4.3.2. Education..................................................................................................................... 20

4.3.3. Risk propensity ............................................................................................................ 20

4.3.4. Task complexity ........................................................................................................... 21

4.3.5. Perceived uncertainty .................................................................................................. 21

4.3.6. Overconfidence ............................................................................................................ 22

4.3.7. Task Performance ........................................................................................................ 23

5. RESULTS .................................................................................................................................. 23

5.1. MISSING DATA ..................................................................................................................... 23

5.2. ASSUMPTIONS OF MULTIPLE REGRESSION ............................................................................ 24

5.3. DESCRIPTIVE STATISTICS ..................................................................................................... 25

5.4. POST-HOC............................................................................................................................. 30

6. DISCUSSION ............................................................................................................................ 31

6.1. Possible explanation; Education .................................................................................... 32

6.2. Possible explanation; Task complexity ........................................................................... 35

6.3. Post hoc .......................................................................................................................... 36

7. PRACTICAL IMPLICATIONS .............................................................................................. 37

8. LIMITATIONS ......................................................................................................................... 38

9. CONCLUSION ......................................................................................................................... 40


Side ii

10. REFERENCES ........................................................................................................................ 41

APPENDIX 1 ................................................................................................................................. 46

APPENDIX 2 ................................................................................................................................. 48

APPENDIX 3 ................................................................................................................................. 49

APPENDIX 4 ................................................................................................................................. 50

APPENDIX 5 ................................................................................................................................. 52


Side iii

Abstract

The thesis make us of a quasi-experimental design in order to investigate how

education affects expert’s risk propensity, perceived uncertainty, overconfidence

and task performance. The moderating effect of task complexity was considered

for the relationships between expertise and perceived uncertainty, overconfidence

and task performance. In order to demonstrate these effects, data was collected

from 55 Java programmers from global companies located in Norway and

Vietnam. All participation was voluntary. An Internet based survey was

developed and respondents was free to choose when and where to conduct it. The

results suggest that a higher degree of expertise results in higher performance and

less overconfidence on high complexity tasks. Furthermore, the results suggest

that degree of experience is more important than education in perceiving

uncertainty.


Side iv

Acknowledgement

First and foremost, our greatest thanks go to our supervisor Thorvald Hærem, who

has guided us through this endeavor. We also express our gratitude to Gunnar

Bergersen and Jo Hannay who has given us valuable insight and help in gathering

data. Finally, we want to thank our friends and family for their support and

encouragement.

……………………….. …………………………

Lars-Kristian Kjølberg Knut Erlend Hjorth-Johansen


Side 1

1. Introduction

The aim of this thesis is to contribute within the field of expertise. Individuals

with varying degree of expertise will be examined and compared on the basis of

their knowledge acquisition and their path towards the degree of expertise

(Summers, Williamson, & Read, 2004). Individuals with high degree of expertise

differ from other individuals with low degree of expertise in regard to their

superiority. Individuals who are considered experts has specialized knowledge of

the domain and will outperform both novices; which is individuals who has only

commonsense everyday knowledge or prerequisite knowledge assumed by the

domain, and sub experts; individuals that are above the novice level and have

generic, but inadequate specialized knowledge about the domain (Ericsson &

Smith, 1991).

Early discussions of expertise were concerned with the idea of nature vs.

nurture. Nature was coined as the “talent” that an individual naturally possessed

while nurture involved training and being coached towards performing at an

expert level performance in a given domain. The assumption that the prerequisite

for performing at an expert level is genetically transferable has been met with

skepticism; socialization, learning and environmental contributing mechanisms

have proved much greater effect on developing expertise (Ericsson & Lehman,

1996). There has been conducted an extensive amount of research attempting to

capture knowledge and knowledge development among experts within several

different domains, conclusively insight in deliberate practice and training is the

main contribution and the common denominator of this research (Ericsson, 2005).

In contrast to the vast amount of research considering the differences between

novices and experts, we examine individuals with varying degrees of expertise

only. Hereby, we aim to distinguish between different paths towards the superior

performance level within the certain domain representative for where the expert

usually operates (Summers et al., 2004; Ericsson & Lehman 1996; Ericsson &

Smith, 1991). Although research on expertise has been conducted in numerous

domains, such as chess players, physics and sports (Ericsson & Lehman, 1996),

results largely point to the same conclusion; expert performance is acknowledged

as domain specific (Ericsson, 2005; Haerem & Rau, 2007; Sonnentag, Niessen &

Volmer, 2006). Based on the assumption that individuals of expertise should be

able to display their superiority consistently within their domain, it is reasonable


Side 2

to expect it to be scientifically analyzable in controlled settings (Ericsson &

Lehman, 1996).

We base our research in the software industry and use individuals with

expertise within Java programming as research subjects. As research on expertise

has been conducted within many areas, our selection of domain is due to the

potential for measurement adequacy; an important practical implication when

examining the achievements of expert performance (Sonnentag et al., 2006).

Individuals with a high degree of expertise are inclined to engage in forward

reasoning strategies in problem solving, such as software programming were the

solution can be predicted by stable rules termed as the programming language

(Hærem, 2002). Hærem (2002) states that “in this domain the difference between

novices and experts is that experts tend to develop the breadth of the problem

solution first, while novices tend to develop the depth” (p.52). The advantages of

the breadth first strategy are revealed in the high complexity tasks of

programming whereupon the solution often depends on the breadth of alternatives

on previous steps (Anderson, Farell, & Sausers, 1984). A high task complexity

places more demand on the task doer than a task of low complexity by the

increase of the information load, a diversity of the information and the rate of

information change (Campell, 1988; Wood, 1986). This serves as a potential for

determining human performance (Wood, 1986).

This paper aims to contribute to the field of expertise by providing insight

into how educational background is affecting risk, and how both education and

task complexity is affecting, perceived uncertainty, overconfidence, and task

performance among individuals with various degrees of expertise. Our research

question is therefore as follows:

“How does education and task complexity affect expertise in relation to

performance, risk propensity, perceived uncertainty and overconfidence?”


Side 3

2. Theoretical Model

Figur 1

The basis of the model, the independent variable, is individuals that hold varying

degrees of expertise. These individuals are seen in relation to four variables of

interest; risk propensity, perceived uncertainty, overconfidence and task

performance. These relationships are moderated by education. In other words; will

there be differences between those individuals who have relevant educational

background in addition to experience, and those individuals who hold experience

only which knowledge is developed through practice? In addition to this, the

relationships between various degree of expertise and perceived uncertainty,

overconfidence and task performance will be moderated by task complexity.

In the following the theoretical foundation and hypotheses of each variable will be

presented.


Side 4

3. Theoretical foundation

3.1. Degree of expertise

Expertise is hereby defined as degree of technical superiority on a specific set of

representative tasks for a domain (Bergersen, Dybå, Hannay, Karahasanović, &

Sjøberg, 2011; Ericsson & Lehman 1996; Hærem & Rau, 2007).

Theory on expertise is somewhat wide ranged. Pioneering work by de Groot in

1946 examined the expert level of chess players (Ericsson, 2005), subsequently,

numerous of different domains such as music, sports and IT-programming has

been studied, motivated by the means of making training of less skilled

individuals more efficient. Extraction of the knowledge development of an expert

has been a concern with the aim to let students learn the expert’s knowledge

directly instead of rediscover it by them self. The idea of duplicating expertise is

rather optimistic. For individuals at a lower level of knowledge acquisition, the

insight to deliberate practice and training among experts is somehow the most

rewarding contribution; becoming an expert one self just by studying how the

experts obtain knowledge is simply not realistic (Ericsson, 2005).

Previous research in the field of expertise has focused largely on how

experts and novices differ on task performance or comparisons of experts with

different degree of experience. As example, Kendel (1973) found that length of

experience among experts on psychiatric diagnosis did not relate to the validity of

diagnoses given. Summers, Williamson and Read (2004) states that research on

expertise have largely considered the different paths toward the expertise level of

competence relevant to a given domain. Their study compared professional credit

managers who had learned through experience rather than education with credit

managers who had no experience but training in the relevant concepts. Results

showed that education can be a more efficient foundation for developing expertise

than experience only, which might be in accordance to similarities between

education and deliberate practice (Ericsson, 2008).

In order to better understand underlying cognitive mechanisms among

experts, Sanjram and Kahn (2011) examined the prospective memory; “[cognitive

capability] to remember to carry out delayed intention in fulfilling various task

demands” (Burgess, Veitcha, de Lacy Costello, & Shallice, 2000, as cited in

Sanjram & Khan, 2011 p.428) This was done with the purpose of distinguishing

qualities among experts and novices in the domain of programming. Performance


Side 5

and strategy was investigated among monochrons (individuals who prefer to do

one thing at the time) and polychrons (individuals who prefer to do many things at

the same time) within multitasking operations. Conclusively, cognitive complex

people tend to be more monochromic than individuals with simpler cognition who

tend to be more polychromic. The authors found that expertise is effectively

facilitating the maintenance of the different resources for performing multiple

activities (Sanrjam & Khan, 2011).

Operationalization of programming expertise is often done without

adequate validation as the conceptualization often is based on a manager’s

evaluation of the programmer who is labeling the level of seniority (Bergersen, et

al., 2011). For example, Bergersen et al. (2011) operationalized programmer’s

expertise level in terms of seniority; Arisholm, Gallis, Dybå and Sjøberg (2007)

used the same operationalization in addition to a pretest programming task,

attempting to measure their subjects programming skills, in order to assess the

internal validity of the experiment. By deploying level of seniority as measure of

expertise, the expertness of the individual programmer is hereby not necessarily

captured (Bergersen et al., 2011). Sanjram and Khan (2011) operationalized

programmer expertise as years of experience, which is common for quasi-

experimental designed research within programming and software development

(Sonnentag et al., 2006). The underlying assumption is that expertise develops as

a function of time spent within the domain (Sanjram & Khan, 2011). Further, the

level of formal and academic education within the specific domain indicated the

level of expertise. Previous research by Schmidt, Hunter and Outerbridge (1986)

showed that experience and performance increased linearly within the first five

years, later in time, the relationship seems to flat out. Length of experience does

not necessary relate to a high performance level within programming and software

design (Sonnentag et al., 2006). This supports the assumption by Sanjram and

Khan (2011) that expertise develops as a function of time spent within the

domain. In their study the expert’s experience was, in fact, their progression in

relevant education (Sanjram & Kahn, 2011). Contrary to a merging of education

and experience, we aim to discriminate between Java programmers with highly

relevant education and those with less relevant education, by considering

education as moderation.


Side 6

Building on the definition of expert performance as “consistently superior

performance on a specific set of representative tasks for a domain” (Ericsson &

Lehman, 1996, 277), expertise is hereby seen as a technical superiority within

Java programming (Bergersen et al., 2011; Haerem & Rau 2007). The individuals

that are included in the data collection have varying degrees of expertise within

Java programming. We follow Sanjram and Kahn’s (2011) operationalization of

expertise as years of domain specific experience, which is suitable for quasi

experiments (Sonnentag et al., 2006). The length of experience that is considered

is distinguished from education, which is operationalized s a moderating effect in

the relationship between expertise and the different outcomes. In line with the

assumption that expertise develops as a function of time spent within the domain,

there is no cut off point in the length of experience, to whether individuals are

qualified as experts or not. Individuals will be considered having a varying degree

of expertise by their varying ability to perform domain related tasks; the longer

experience they have in the domain, the higher degree of expertise they have

(Sanjram & Kahn, 2011). This approach opens for the possibility to see whether

different factors affect performance, in addition to the other aspects; risk taking,

perceived uncertainty and overconfidence.

3.2. Education

Education refers to the academic credentials or degrees an individual have

obtained, according to Ng and Feldman (2009) who found that the level of

education is positively related to task performance. This contradicts to Chase and

Simon’s (1973) assumption, that experts’ task-specific knowledge must have been

acquired through experience. The assumption does not embrace that education is

serving as a platform for robust learning (Friedlander et al., 2010). Academia has

possibilities for structured learning, a situation that differs from most self-taught

learning. Friedlander et al. (2010) points to several aspects that foster robust

learning, which in turn develop a better memory capability connected to the

certain knowledge. The following aspects are depicted from the research with the

aim to reveal how education is differing from knowledge developed through

experience.

Friedlander et al. (2010) stresses the importance of the learning

environment as it affects functional and structural changes in the interconnected

cellular networks between neurons (synapses) at a variety of sites throughout the


Side 7

central nervous system. “Memory is a dynamic process where the information

represented is subject to our personal experiences, the context of the learning

environment, subsequent events, levels of attention, stress, and other factors.”

(Friedlander et al., 2010, 415). The learning situation provides the possibility of

active learning where the teacher and the student interact. “There is considerable

neurobiological evidence that functional changes in neural circuitry that are

associated with learning occur best when the learner is actively engaged”

(Friedlander et al., 2010, 417).

Repetition is central to the education context. Repetition of certain

knowledge will produce neuronal pathways that contribute to learning; it leads to

an enormous amount of molecular signals that develops to be more persistent

compared to the briefer knowledge that is less repeated. The plasticity of the brain

and those mechanism described above applies to both young, developing brains,

as well as those with more maturity. The latter occurs in denate gyros of the

hippocampus, but the functional implication of this is to be determined. Moreover,

the brain’s intrinsic reward system plays a major role in reinforcement of learned

behaviors. Connection of one’s learning to previously stored impressions, and

visualization of the learning content, helps the process of storing learning into

memory (Friedlander et al., 2010).

Drawing on this we assume that education is closely related to deliberate

practice where feedback and repetition is central (Ericsson, 2008). Further,

individuals that have reached the level of expertise, with both education and

practice will probably have obtained a higher level of expertise than individuals

that have reached the level of expert with experience based on practice alone,

which in turn may provide better task performance.

Deliberate practice can be defined as training with feedback (Ericsson,

2008). Ericsson and Lehman (1996) state that an individual that has been guided

with deliberate practice will attain a higher level of knowledge acquisition than

those without carefully structured training and practice regimen. Barnett and

Koslowski (2002) argue along the same lines, that one need to understand what

experiences may lead to expert performance, it is not sufficient to simply look at

the amount of experience. Building on this, Ng and Feldman (2009) found that

education is positively related to core task performance and that education became

increasingly important as the complexity in those tasks increased. On tasks of less


Side 8

complexity, however, the authors found that education level was less significant

for performance.

It is found that job experience had greater impact on job related knowledge

than performance at work (Schmidt, Hunter & Outerbridge, 1986). Results from

research suggest that job experience enhance skills, techniques, methods and

psychomotor habits, which in turn improve the performance capabilities

independent of the increase in job knowledge. Further, job knowledge and

performance increase linearly with experience up to 5 years of experience, after

this point in time the relation seems to flat out (Schmidt, Hunter & Outerbridge,

1986). A study by Bergersen and Gustafsson (2011) investigated the relationship

between programming skill and its main antecedents by using Cattell’s investment

theory. The relevance of this study is partially due to the highly competitive and

globalized industry of software production, which is focused on delivering high-

quality software at low cost. The authors predicted that programming knowledge

is the main casual antecedent for programming skill. Tests of cognitive abilities

are frequently utilized in order to recruit and retain highly productive software

developers (Bergersen & Gustafsson, 2011). General mental ability (g) is a central

predictor of performance used under recruitment circumstances (Bergersen &

Gustafsson, 2011; Schmidt & Hunter, 1998). Next, in accordance with Cattel’s

investment theory fluid g is about all new learning and is therefore ubiquitous and

closely related to general mental ability, and in turn working memory, which

relates to consciousness (Sweller, van Merrienboer, & Paas, 1998). On the other

hand crystallized g is about acquired knowledge. The authors found that the

influence of fluid g and experience on skill and job performance was mediated

through knowledge, which is in accordance with Schmidt, Hunter and Outerbridge

(1986), above. Working memory capacity and experience contributes to

programming skill. This relationship is mediated by programming knowledge

which accounts for a large degree of variance in programming skill. Further,

programming experience and knowledge is more often obtained through education

than on the job (Bergersen & Gustafsson, 2011). Sanjram and Khan (2011)

differentiated among their participants by their level of education within the

relevant domain. Novices with a basic course in programming were compared to

advanced students of computer science and engineering. Contrary to the view that

education and experience can be merged (Chase & Simon 1973), Sonnentag et al.


Side 9

(2006) states that years of experience are not necessary related to high

performance within advance software design and programming.

A larger fraction of the effect from education is cognitive which is

contradicting to previous research where it is claimed that only a small portion of

the returns from education is improving the human capital, namely cognitive

abilities (Baron & Werfhorst, 2011). When looking at general cognitive ability

alone, the estimate is that the cognitive component varies between 32 and 63

percent, depending on the country that is analyzed (Baron & Werfhorst, 2011).

This was also supported in 1989 when research showed that training and

experience may be positively related to the ability to structure problems (Garb,

1989).

Based on the theoretical fundament, we believe that relevance of education

should affect different aspects of interest among individuals of varying degree of

expertise as we conceptualize education as a moderating effect, according to the

model presented.

3.3. Task complexity

Our conception of task complexity relies upon contribution from several

researchers. The concept of task and the idea behind task complexity will be

reviewed briefly in order to present the conception.

A task contains three essential components; products, acts and information

cues, according to Wood (1986). Products are defined as measurable results of

acts while acts are simplistically referred to as a pattern of behavior with a certain

purpose. Finally, a task contains information cues that the task performer can use

to identify the required actions or judgments in the process of performing the task

(Wood, 1986). Bonner (1994) subsequently elaborated on this definition. The new

approach of the concepts had a more simplistic appeal; task input, process and

outputs (Haerem & Rau, 2007).

With the fundaments of a general task in mind, complexity of the task will

now be explained. Task complexity has the potential to contribute to determine

human performance; it is an important aspect when performance is to be rank-

ordered as it places varying demand on knowledge skills and resources in an

ascending order (Wood, 1986). Summed up; as complexity increases, so does the

demand on the task doer (Wood, 1986). Campbell (1988) characterizes an

increase in complexity as an increase in the information load, the diversity of the


Side 10

information and the rate of information change. This involves the potential use of

multiple cognitive paths to arrive at the end state, the possibility of multiple

desired outcomes, conflicting interdependence among cognitive paths and the

presence of uncertainty (Campbell, 1988). Haerem and Rau (2007) developed a

set of tasks to investigate the difference of knowledge representation and search

strategies between experts, intermediates and novices. The authors made an

important distinction between surface structure tasks, deep structure tasks and

mixed structure tasks. However, for the purpose of our research, we rephrase the

different levels of complexity; surface structure tasks will be phrased as low

complexity tasks, deep structure tasks will be phrased as high complexity tasks.

Further, we concentrate on identifying the differences between low complexity

tasks and the high complexity tasks only. In order to make this type of distinction,

the term critical complexity requires an explanation. Critical complexity is defined

as the complexity embodied in the task resolution path that minimizes the amount

of information processing, which in turn creates the difference between low

complexity tasks and high complexity tasks (Haerem & Rau, 2007). In low

complexity tasks the critical complexity resides in the input and/or the output. For

individuals to complete this kind of tasks a search and analysis of the input and

output is necessary. In order to solve high complex tasks one must focus on the

task process rather than the inputs and outputs in order to solve the task efficiently

(Haerem & Rau, 2007).

Because of the fundamental difference between tasks of high and low

complexity presented above, we believe that task complexity should affect

different aspects of interest that are connected to the tasks directly. Hereby we

conceptualize task complexity as a moderator, according to the model presented.

3.4. Task performance

Ng and Feldman (2009) found a positive relationship between education and core

task performance, which refers to the basic required duties of a particular job. A

core task can be a specific task an expert would conduct within the expertise

domain. The expert holds the declarative and procedural knowledge that is

required for the task to be completed successfully (Ng & Feldman, 2009).

Research by Haerem and Rau (2007) discovered that different degrees of expertise

could lead to different perceptions of task complexity, and in turn, to different


Side 11

performance on task of different complexity. The two fundamental dimensions of

perceived task complexity is task variability (the number of exceptional cases

encountered in the work) and task analyzability (the nature of the search process

that is undertaken when exceptions occur) (Perrow, 1967). A higher degree of

expertise will foster lower perceived task variability and higher perceived task

analyzability (Haerem & Rau, 2007). Moreover, a higher the degree of expertise

will foster a higher performance on complex tasks, this implies that a high degree

of expertise will enhance the performance on complex tasks (Haerem & Rau,

2007). Ng and Feldman (2009) found support for this assumption. They found that

the relationship between education and performance is moderated by job-

complexity. Higher education gave higher performance on complex tasks (Ng &

Feldman, 2009).

Based on this we assume that experts with education and practice will

have achieved a higher level of expertise than experts with only practice, within

the same timeframe, on the grounds of the use of deliberate practice. Our research

subjects have attained their expertise through different paths; nonetheless, they

have all achieved a certain level of technical superiority on representative tasks

for their domain (Bergersen et al. 2011; Ericsson & Lehman 1996; Hærem & Rau,

2007). Based on Ericsson’s (2008) hypothesis; “there is an underlying factor of

attained expertise in a domain, where the majority of the task can be ordered on a

continuum of difficulty”, (p. 989), we believe that because the individuals with

higher degree of experience and high relevance of education have achieved the a

higher expertise level; they should perform better on tasks than individuals with

increasing experience and lower relevance of education. On highly complex tasks

we assume that individuals with more experience will perform better than

individuals with less experience, this is mainly due to the possibility for expertise

to improve by the time spent in the domain (Chase & Simon, 1973). On tasks of

low complexity, where the complexity lies in the input and output, we don’t think

that the increasing level of expertise will not lead to better performance.

H1: The relationship between the degree of expertise and task performance will be

moderated by task complexity.

H2: The relationship between the degree of expertise and task performance will be

moderated by education.


Side 12

H3: Increasing level of expertise, and a high relevance of education will lead to

higher task performance compared to low relevance of education.

H4: A high level of expertise will give higher performance on task of high

complexity compared to a low degree of expertise.

3.5. Risk propensity

We distinguish between three aspects of risk; risk as phenomenon, risk taking and

risk propensity. Risk as a phenomenon is variation in the distribution of possible

outcomes, their likelihoods and their subjective value (March and Shapira, 1987).

Risk taking is the actual behaviors of an individual who have to make a choice

between alternatives of differing risk (Lejuez et al., 2002). We operationalize risk

as risk propensity; an individual´s willingness to take risk (MacCrimmon &

Wehrung, 1990). As a cognitive psychological phenomenon, risk propensity is

seen as distinguishable into two categories, also called systems. These are the

experiential system and the analytic system (Slovic et al., 2004, 2005). This can

relate to the well-known approach to human cognition, described by Kahneman

(2003). Kahneman categorizes cognition into two systems. System one is

reflective and “slow”, system two is intuitive and “fast”, these two systems are

intertwined and works simultaneously, some tasks or situations takes more use of

system one than other situations do. Likewise, risk processing is seen as (1)

intuition like, fast and automatic but vulnerable to manipulation and information

overload (called the experiential system), or (2) assessing, calculative and

dependent of conscious attention (called the analytic system) (Slovic et al., 2004;

Glöckner & Witteman, 2010). Regardless of whether it is the analytic or the

experiential system that is in use in a specific situation, it is argued that the

perception of the judgment criterions is affected by feelings of the situation,

(Slovic et al., 2004, Druckman & McDermott, 2008; Keller, Siegrist & Gutscher,

2006). This means that for an individual to perceive and conduct a judgment upon

a choice that might involve a potential for a negative outcome or that provides an

opportunity to obtain a positive outcome (Lejuez et al., 2002), feelings help

determine the actual choice (Slovic et al., 2004, 2005; Druckman & McDermott,

2008). These feelings are highly individual; they differ from person to person and

are also termed as heuristics (Slovic, 2004).

Our purpose is to examine how differing degree of expertise and relevance

of education will affect individual’s risk propensity. A denominator for the


Side 13

tendency to avoid risk is found to be the length of education; contrary, risk

aversion is fostered by trust in one’s own competences (risk is here seen as

analytic; calculative, assessing and deliberate) (Haerem, Kuvaas, Bakken, &

Karlsen, 2010). Nonetheless, it is not stated that longer education leads to less

trust in one’s own competences; we surmise that this aspects does not necessarily

have any relation. Education has clear similarities to deliberate practice that is a

robust and acknowledged path to a high level of performance (Ericsson, 2008). As

previously mentioned, deliberate practice includes training with feedback and

insight into theories and knowledge that is underlying for a certain topic

(Ericsson, 2008; Barnett & Koslowski, 2002). As education also includes these

aspects, one can assume that individuals who has competence from a deliberate

practice context will develop insight into his/her own knowledge and explore both

what knowledge one has and what one don’t know and by that be less

“convinced” (or less naïve) that one’s competence is sufficient enough to select

the more risky alternative in favor of a safer bet.

Previous research connecting risk and expertise shows that experience

with a task can improve risk judgment associated with completing tasks

(Christensen-Szalanski, Beck, Christensen-Szalanski, & Koepsell, 1983). Studies

have also shown that both experts and lay people tend to overestimate risk; the

estimation of risk is exaggerated and inaccurate. However, experts tend to

overestimate less (Christensen-Szalanski et al., 1983). As we have previously

proposed, that education leads to a higher level of expertise, we believe that a

similar pattern exists between experts with and without education as do between

lay-people and experts.

H5: Increasing degree of expertise, high relevance of education will decrease risk

propensity compared to a low relevance of education.

3.6. Overconfidence

According to Moore and Healy (2008) research on overconfidence has been done

in inconsistent ways in previous studies. They argue that researchers have

operationalized overconfidence in different ways, without distinguishing clearly

between overestimation, overplacement and overprecision. In short;

overestimation is about exacerbated estimation of one’s own actual performance,


Side 14

ability etc.; overplacement is about comparing oneself with others subjectively

and guessing one’s score in comparison to the others; overprecision is about

inaccuracy of one’s belief (Moore & Healy, 2008).

We define overconfidence in terms of overprecision where the accuracy of

one’s estimation is influenced by uncertainty of the task, which in turn will

“produce a subjective probability distribution that is narrower than reality

suggests it ought to be” (Moore & Healy, 2008, p. 505). Examining

overconfidence includes confidence intervals which is estimations provided by

respondents. As illustration, estimating the price of a house from 1,0 – 2,0 million

provides wider confidence intervals that an estimation of the house price as 1,3 –

1,8 million. Even though both estimates has the same midpoint, were the true

price of the house is 1,5 million, the last and most narrow estimate contain more

useful information than the other, regarding its accuracy. “Wider intervals will

generally increase hit rate, all else equal. If experts have higher hit rate than

novices, it may be because they know more about the limits of their knowledge”

(McKenzie, Liersch, & Yaniv, 2008, p.180). It is found that experts had a

midpoint closer to the true value and provided narrower intervals with fewer

errors, which in turn is more informative than wider intervals that increase the hit

rate (Keren, 1987; McKenzie et al., 2008). The virtue of informative estimates,

contrary to high hit rate, is interpreted by Yaniv and Foster (1995, 1997, referred

to in McKenzie et al., 2008) as people’s inherent desire. Jørgensen, Teigen and

Moløkken (2003) coin these intervals as prediction intervals. In their study

participants provided estimations of the effort requirement of certain tasks.

Overconfidence is done mostly in order to compare experts and novices.

These researches have given mixed results (McKenzie, Liersch, & Yaniv, 2008).

Conclusively: “it seems safe to say that experts are overconfident, but it is unclear

how they compare with novices” (McKenzie et al. 2008, p.180). The essence is

hereby that knowledge acquisition leads to overconfidence (Plous, 1993).

Nonetheless, we extend this by assuming that individuals at expertise level that

have a high degree of relevant education possess greater meta-knowledge about

one’s own knowledge and capabilities than self-taught individuals at the same

expertise level. This is due to the similarities between education and deliberate

practice where feedback is central, and should decrease overconfidence (Plous,

1993). Further, the lack of feedback within software development and

programming makes it difficult to learn from experience (Jørgensen et al., 2003),


Side 15

and the same accounts for the programming tasks that are deployed for the data

collection were no feedback will be given throughout the conduction. Therefore

our assumption is that experts without education will possess more

overconfidence than experts who has relevant educational background.

An individual who sets wider intervals will be considered less

overconfident than those who set narrower intervals in correspondence with

his/her hit rate, estimation accuracy and the actual performance relative to the

time/effort used (McKenzie et al., 2008; Jørgensen et al., 2004). “If the hit rate is

lower than the confidence level, we observe overconfidence” (Jørgensen et al.,

2004, p. 81).

Nonetheless, one should be aware of interpreting low estimation results as

poor estimation skills (Jørgensen et al., 2004). Complex technology and

development of innovative software solutions has built-in uncertainty and

problem specification have to be decided during the design process. This

complexity can originate deviations between the expert’s estimate of time

consumption and the actual use of time (Sonnentag et al., 2006). A task that does

not require much time, such as easy tasks, is usually evoking overestimation of

time necessary to complete the task and underestimation of performance (Moor &

Healy, 2008).

H6: Increasing degree of expertise, a high relevance of education decrease

overconfidence, compared to low relevance of education.

H7: A high level of expertise will reduce overconfidence on tasks of high

complexity, compared to a low level of expertise.

3.7. Perceived uncertainty

Perceived uncertainty is hereby understood as perception of a task; more

specifically the perception of a certain task’s complexity (Hærem & Rau, 2007).

Perceived uncertainty can be seen in contrast to risk propensity that is an inherent

and general inclination to take risk (Lejuez et al., 2002). Perceived uncertainty is

relating directly to the task and is including perception of the task’s complexity by

two dimensions; the perceived analyzability and the perceived variability (Hærem

& Rau, 2007).


Side 16

Perrow (1967) defined perceived task analyzability as the nature of the

search process that is undertaken when exceptions occur, such as unfamiliar

stimuli encountered during a task. The search process is dependent on whether the

task is previously learned or programmed. If the task is highly programmed, the

search is logical, systematic and analytical, while if the task is not previously

learned and thus un-programmed, the search is based on chance and guesswork

(Haerem & Rau, 2007; Perrow, 1967). On the other hand, perceived variability is

defined as the number of exceptional cases encountered in the work (Haerem &

Rau, 2007). Haerem and Rau (2007) found that the higher the degree of expertise,

the lower the degree of perceived task variability and the higher the perceived task

analyzability, which in relation to perceived uncertainty would indicate that the

higher the degree of expertise, the lower the perceived uncertainty.

Following the reasoning that deliberate practice contributes to expertise,

defined as superior performance (Ericsson & Lehman 1996), and has similarities

with education by the magnitude of domain related feedback (Ericsson, 2008),

perceived uncertainty will possibly be affected by this element. Education may

provide insight into theories and knowledge that is underlying for the certain topic

(Ericsson, 2008; Barnett & Koslowski, 2002), which in turn may lead to an insight

to one’s own knowledge in such a way that one’s limitations is also understood.

Broadness in knowledge acquisition may also lead to an assessment of what

theory should be deployed (Friedlander et al., 2011) in a certain task-solving

situation. Because education also can be considered as experience (Sanjram &

Kahn, 2011), and that amount of experience predicts the level of expertise (Chase

& Simon, 1973), we assume that individuals at a low level of expertise and

relevant education will have less perceived uncertainty. This is assumed because

relevant education at lower degree of expertise may provide acquaintance to

theories and knowledge that contributes to feeling of certainty when meeting

relevant tasks, the experience with these theories may simply evoke too

conclusive (Plous, 1993; Slovic et al., 2004). Individuals at low levels of expertise

without relevant education might not have developed these heuristics (Solvic,

2004). Deeper insight to underlying theories is hereby not present because it

develops as a function of time spent in the domain (Friedland et al., 2001;

Sonnentag et al., 2006). When individuals have relevant education and a longer

experience, the deeper insight should be present. We assume that perceived

uncertainty will increase when facing domain related tasks.


Side 17

As these dimensions serve as a foundation for operationalizing perceived

uncertainty we hypothesize as follows:

H8: At low degree of expertise, high relevance of education leads to less

perceived uncertainty than less relevant education, while at high degree of

expertise, high relevance of education leads to more perceived uncertainty than a

low relevance of education.

H9: As the degree of expertise increase, perceived uncertainty will decrease on

tasks of high complexity, compared to a low level of expertise.

4. Method

4.1. Participant characteristics

Participants were selected in accordance to a technical superiority on tasks of Java

programming. With regard to its properties, Java programming was chosen as the

domain to where participants should hold various degree of expertise. This

criterion does not set clear requirements for which to be qualified to participate.

With this in mind, we aimed to reach individuals with experience within Java

programming. Arenas that were assumed to relate with individuals of high Java

programming competence was mapped. There were mainly three different kinds

of Java-related arenas that were contacted for this purpose: Internet forums,

public- and private sector. The broad range of individuals that were targeted and

invited to participate was considered as contributing; the survey that was

developed constrained the possibility for non-experts, or of target competency, to

slip in. In other words, the survey required a certain level of competence to be

conducted. The strategy of selecting individuals with varying degree of expertise,

among those without this ability, is in accordance with, but yet still nuancing the

approach by Sonentag et al. (2006). In their case, an expertise level task requires a

certain level of competence to be completed successfully, in our case a varying

degree of completeness was allowed. While Sonentag et al. (2006) term

individuals at expertise level as those who hold abilities to complete the task; we

conceptualize these individuals as those who have the ability to conduct the tasks

with a varying degree of completeness. This opens for the possibility to reveal


Side 18

how individual differences can lead to varying task performance, where the

degree of completeness equals to performance.

As the participants were considered holding varying degrees of expertise

by their ability to conduct the survey, there were no set requirements to the past

experience that the individuals held. As a part of the survey, those who

participated were self-reporting their education and their experience with software

development and Java programming. The method was chosen based on the

intention to collect participants with varying educative and experientially

backgrounds. The inviting procedure was conveyed without constrains in regards

to geographical areas, previous performance level, age or gender. Overall we

invited individuals from forums, organizations and seminars connected to Norway

in addition to one company located in Vietnam. 18 participants had Vietnam as

platform and was paid 15 euro per hour, 15 participants had Norway as platform.

There was no control of gender or age throughout the data collection.

4.2. Sampling procedures

Throughout the spring 2012, plans for collecting data were established. The data

collection tool that we used was developed in collaboration with both Technebies

and our supervisor. By use of Qualtrics and a downloadable application, a survey

consisting of a self-reporting part and three programming tasks was established.

Participation in the survey did only require a computer with an internet

connection, an Internet browser, and a Java development environment installed.

The survey could be conducted without any particular appearance at any certain

place, there were no restrictions to where and when to conduct the survey, which

took about 1,5 – 2 hours to complete. The advantage of this task solving setting is

that the difference in expertise arises in a natural way rather than being

manipulated in the laboratory (Keren, 1987). All data was collected between June

and November 2012. All participation where to be done individually and there

was possible to take breaks between each of the three tasks.

In order to collect participants we invited individuals by contacting

organizations in public and private sector that related to Java programming in

Norway and Vietnam. Further, we held a presentation of research for attendants in

a course holder firm that educated Java programmers at advanced level in

Norway, we were given permit to send invitation letters to several companies


Side 19

within private and public sector also in Norway, and we presented the research for

attendants at a Java seminar located in Oslo. Further, some Internet forums with a

Norwegian platform were approached. Individuals who wanted to participate sent

us an email or wrote their email address on a list. A link to the survey was sent out

to these email addresses. Totally seventy links were sent out to emails that was

collected through the different sources. All participants were guaranteed

anonymity. We intended to collect data from 50 respondents; the sampling

procedure resulted in totally 55 respondents were 52 contributed to measure risk

propensity and 33 respondents answered most of the survey. This will be

explained into detail under results.

4.3. Measures

4.3.1 Expertise

Measuring the expertise construct we applied a formative approach, as we believe

the measured variables cause the construct, i.e. the construct is not latent. (Hair,

2010). According to Diamantopoulos & Winklhof (2001) content specification,

indicator specification and indicator collinearity are critical to successful index

construction. The content of the construct is supposed to represent the combined

general software development experience and Java-programming specialization.

Each subject answered a 13 item questionnaire regarding their software

development experience, both general and Java-programming specific, and about

their estimation skills (Appendix 1). 3 items regarding estimation skill were

dropped because they did not fit the content specification.

The 10 remaining items were putt through a principal component analysis

and revealed a 3 factor structure where 2 seemed to reflect the content

specification good, thus upholding indicator specification. The factors extracted

were named Length of experience, consisting of length of total programing

experience, length of total Java programming experience and number of project

roles held, and Specialization of experience, consisting of current consecutive

length of Java programming, percent of work day used on coding and self-

reported Java expertise. Both factors were subsequently combined into the

experience construct.

As collinearity amongst formative items can be problematic (Hair, 2010,

Diamantopoulos & Winklhof, 2001) the indicators were regresses on the expertise

construct (Diamantopoulos & Winklhof, 2001) and then inspected for collinearity.


Side 20

The regression proved that collinearity was not a problem as all the tolerance

levels was above the recommended cut-off value of .10 and all VIF values were

below 5 (Diamantopoulos & Winklhof, 2001; Gripsrud, Olsson & Silkoset, 2004;

Hair, 2010).

4.3.2. Education

In order to measure the relevance of education the responds self-reported on

length and type of education. The respondents could choose between 14 different

categories of education, based on the categorization from Samordna Opptak

(unifying unit of education in Norway). In addition they reported the length of

their education within each category. Each respondents educational background

was then ranked from 1-7 based on type and length of education (Appendix 2),

where 1 indicated lowest relevance to Java-programming and 7 indicated most

relevance to Java-programming. We made the ranking of education categories by

evaluating the aspects that has similarity to Java programming.

4.3.3. Risk propensity

For risk propensity we used a 4 item measure from Calantone, Garcia, and Dröge

(2003) as a basis. The questionnaire was originally aimed at risk propensity within

strategy planning for development of new products. We rephrased these questions

in order to be applicable to the domain of programming, intentionally maintaining

the essence of the questions. The measure was also extended with a 5th question

to better represent risk propensity in software development (Appendix 3). The

rewritten measure was exposed to principal component analysis in order to

establish construct validity.

Before conducting the analysis we assessed the correlation matrix for

adequacy of factor analysis. Several criterions were used. First, according to Hair

(2010): ”a strong conceptual foundation is needed to support that a structure does

exist” (p.105). The validation by Calantone, Garcia, and Dröge (2003) suggests

that there in fact is a strong conceptual foundation. Second, it is recommended

that the sample size should be minimum 50 and have a 10:1 ratio or more, which

is met as there is 52 (N = 52) cases for 5 variables giving a sampling ration of

11:1 (Hair, 2010). Third, as recommend by Tabachnick and Fidell (2001), the

correlation matrix was inspected for coefficients greater than .30, but this was

somewhat disappointing as only one coefficient had a value in over .30. Fourth,


Side 21

and finally, both the Bartlett’s test of sphericity (Bartlett, 1954), and the Kaiser-

Meyer-Olkin (KMO) measure of sampling adequacy (Kaiser, 1970; 1974) was

used to assess the dataset for factor analysis. The Bartlett’s test of sphericity was

not significant (p = .072) , the KMO, however, was above the lowest value

recommended by Hair (2010) (.50) reaching .563, albeit this is rated as miserable

(Hair, 2010), indicating the factorability of the correlation matrix.

A Principal Component Analysis with Variamax rotation revealed 2

factors, of which 4 questions loaded on the first factor, while a single item, item 3,

loaded on the second factor. The item was removed from the factor analysis and a

one factor solution emerged, which can be viewed in table 3.

Finally, a reliability analysis revealed that the 4 items derived had low

reliability, a coefficient alpha of only .453, which is regarded as unacceptable

(Hair, 2010). Despite these disappointing results, we chose, based on the

theoretical foundation of the risk scale, to summarize the items into one factor and

proceed with regression analysis.

4.3.4. Task complexity

Task complexity was operationalized by use of three tasks of varying degrees of

complexity. The task with medium complexity was developed by Arisholm and

Sjøberg (2004) and is in their research used as a pretest task in advance of four

tasks of increasing complexity named “coffee machine tasks”. The task of low

complexity was the third of the coffee machine tasks. The task with high

complexity was developed by Bergersen and Gustafsson (2011). Each of the three

tasks that we deployed had a level of complexity dissimilar from the others. The

tasks was given to the respondents in a sequence were the task of less complexity

first and the one with most complexity last. The complexity increased within the

same dimension, which means that the same programming language was in use in

all tasks, but the requirement of this language was increasingly complex. They

were coded as 1 – 3 were 1 was low complexity and 3 was high complexity.

4.3.5. Perceived uncertainty

The Perceived uncertainty measure is based on a combination of two measures

developed by Haerem (2002), perceived task analyzability and perceived task

variability. Each dimension consists of 4 items in the form of questions where the

respondents rate their perceptions on a scale of 1-7. Both dimensions were


Side 22

rewritten to reflect the task domain of Java programmers, and in addition

perceived task variability was extended with a fifth question to fully capture the

dimension (Appendix 4).

To establish the construct validity of the scales a principal component

analysis was conducted, but before conducting the analysis we assessed the

correlation matrix for adequacy of factor analysis. Several criterions were used.

First, according to Hair (2010), ”a strong conceptual foundation is needed to

support that a structure does exist” (p.105). The validation by Hærem (2002)

suggests that there in fact is a strong conceptual foundation. Second, it is

recommended that the sample size should be minimum 50 and have a 10:1 ratio or

more, which is met as there is 94 (N = 94) cases for 9 variables giving a sampling

ration of 10,44:1 (Hair, 2010). Third, Tabachnick and Fidell (2001) recommend

an inspection of the correlation matrix for coefficients greater than .30. The

correlation matrix revealed several coefficients over .30. Fourth, and finally both

the Bartlett’s test of sphericity (Bartlett, 1954), and the Kaiser-Meyer-Olkin

(KMO) measure of sampling adequacy (Kaiser, 1970; 1974) was used to assess

the dataset for factor analysis. The Bartlett’s test of sphericity was significant (p <

.05) and the KMO was above the recommended value of .60 reaching .763 ratet

by Hair (2010) as middling, indicating the factorability of the correlation matrix.

The validity of the scales was tested using a principal component analysis.

The analysis revealed that the third item in the analyzability scale loaded

negatively with both scales and was as a consequence the item was removed.

After the removal the factor analysis revealed two dimensions as predicted.

The reliability was calculated based on all respondents’ perceptions of all

three tasks as this is a repeated measure. The reliability coefficient alpha for the

analyzability dimension was .756 and for the variability dimension it was .902

which is above the recommended cut-off point of .70 and regarded as acceptable

(Hair, 2010). The result is presented in appendix 4.To create the Perceived

uncertainty variable, we summed each dimension and added them together. As the

two dimensions theoretically and conceptually opposites, the task analyzability

dimension was reversed before summing the two dimensions.

4.3.6. Overconfidence

Participants were asked to estimate most likely how complete they thought task

solution would be (Appendix 5). As with perceived uncertainty this was a


Side 23

repeated measure where the estimation was done after specifications for each of

the three tasks was given, and before participants got the opportunity to start

solving the task in question. To calculate the overconfidence we used the mean

relative error (MRE), |actual – estimated| / estimated, which is an accuracy

measure used to calculate under- and over confidence (Jørgensen,& Sjøberg,

2003).

4.3.7. Task Performance

Task performance is a repeated measure that was measured by a system-generated

calculation of the completeness of each task. A higher degree of completeness was

interpreted as better task performance, and given as performance score.

5. Results

5.1. Missing data

We conducted a missing value analysis (MVA) to detect missing data in our

dataset. 96 variables and 55 cases giving a total of 5280 data points were included

in the analysis. Of the 96 variables 84 had 1 or more missing values. These 84

variables had a total of 1237 (23,43 %) data points missing. Further inspection of

the data revealed that of the 55 respondents only 34 (62,96%) had downloaded

the application needed to solve the programming tasks. This is not a missing data

process that be classified as ignorable, nor is it data missing at random and as

such, action needed to be taken (Hair, 2010). Because the measure of Risk

propensity was included in the initial questionnaire and therefore was answered

prior to downloading the task solving application a decision was made to divide

the dataset into two sets. One dataset contained all of the 55 respondents used to

analyze Risk propensity, and one dataset containing the 34 that had downloaded

the task solving application used to analyze Task performance, Perceived

uncertainty and Overconfidence. These datasets were then further scrutinized for

the identification of additional missing data.

In the dataset consisting of 55 respondents, 3 respondents (5,45%) only

completed parts of the survey, and quit without answering the questions regarding

risk propensity. Although, according to Hair (2010), there is no specific rule of

thumb about when to delete respondents we saw the need for the removal of these

3 as they did not contribute at all to the dependent variable. Consequently they


Side 24

were removed from the sample. Of the reminding respondents in the dataset, there

were no missing data, giving a sample of 52 respondents.

In the dataset consisting of 34 respondents, 1 respondent (2,94% of the 34)

did not attempt to solve any of the programing tasks and was subsequently

removed from the dataset. Furthermore, 4 respondents did not solve the high

complexity task or answer the questions related to perceived uncertainty nor

overconfidence on this task and one respondent did not answer the analyzability

items for the low complexity task. This gives a total of 1,62% missing data in the

dataset. As this is a repeated measure, by variable this represents 4,04% for

Performance and Overconfidence respectively and 5,05% for Perceived

uncertainty. Little's MCAR test (Chi-Square = 102.434, df = 121, Sig. = .888)

indicated that the data was indeed MCAR (Hair, 2010). The imputation method

used was the complete case approach, although this method has several

disadvantages the method was used as the extent of missing data was sufficiently

low and the sample was large enough to warrant it (Hair, 2010).

5.2. Assumptions of multiple regression

According to Hair (2010) there are 4 assumptions for multiple regressions;

linearity of the phenomenon, constant variance of the error term, independence of

the error terms, and normality of the error term distribution. In order to assess

these assumptions we inspected the residuals for both datasets.

Inspection of the dataset with 52 respondents revealed that the equation

met the assumptions concerning linearity of the phenomenon, constant variance of

the error term, and independence of the error terms, however the assumption of

normality of the error term distribution was not met. Several transformation

techniques (Hair, 2010) were tried to correct this, unfortunately with

disappointing results. As such the variable was used in its original form.

The inspection of the dataset with 33 respondents revealed that all three

regression equations met the assumptions of linearity and independence of the

error terms. The assumptions about constant variance of the error term and

normality of the distribution were, however, not met for either of the equations.

Several transformation techniques have been utilized to accommodate this

shortcoming of the data, but unfortunately the data showed best fit in their

original, untransformed form. (Hair, 2010). It is, however, argued that this


Side 25

problem of nonnormality becomes smaller when the sample is larger than 50

(Hair, 2010).

Another important assumption for utilizing regression analysis is that the

variables do not correlate to a large extent. Multicollinearity leads to shared

variance between variables, decreasing their ability to predict the dependent

variable in question, as well as the ability to decipher their individual effects

(Hair, 2010). As we are analyzing interaction effects we centered the independent

variable as recommended by Aiken & West (1991) to avoid multicollinearity on

both datasets. Furthermore we ran multicollinearity statistics on all four regression

equations in order to reveal if it would still be a problem. However none of them

showed any problems with multicollienarity, as all variables had tolerance levels

above the recommended cut-off value of .10, with the majority above .90, and

VIF-values below 2.0, with the majority between 1.0 and 1.5 (Hair, 2010).

5.3. Descriptive statistics

Table 1 presents the descriptive statistics and correlations among the variables in

the sample consisting of the 52 respondents that answered the initial questionnaire

regarding background and risk propensity. This sample was used solely for testing

the hypothesis regarding risk propensity. Note that Education Rank is the

education dimension and is coded 1-7 depending on relevance of education. The

hypothesis regarding risk propensity, H6, gain no preliminary support as none of

the relationships between the variables are significant.

Table 1

Means, Standard Deviations, and Intercorrelcations of Degree of expertise,

Education rank and Risk propensity.

Variable M SD 1 2 3

1 .XP1XP3 .0000 .81550 -

2. Education Rank 4.19 1.609 .032 -

3. Risk propensity 3.2837 .92624 .091 .137 -

*p <.05. **p<.01.

Table 2 presents the descriptive statistics and the intercorrelations among the

variables in the sample consisting of 33 respondents.


Side 26

Table 4 indicates that Task Complexity correlates negative with Performance (r =

-.465, p <.01) and Overconfidence (r = -.399, p < .01), giving some preliminary

support for H1, and H4 and H7. Furthermore, Education Rank is negatively

correlated with Perceived uncertainty (r = -.268, p < .01), giving some preliminary

support for H8.

Table 2

Means, Standard Deviations, and Intercorrelcations of the independent and

dependent variables

Variable M SD 1 2 3 4 5 6

1. Degree of

expertise

.0000 .809 -

2. Task Complexity 2.00 .821 .000 -

3. Education Rank 4.273 1.609 -.030 .000 -

4. Performance 72.549 39.715 .185† -.465** -.016 -

5. Overconfidence 16.641 35.098 .158 -.399** -.027 .875** -

6. Perceived

Uncertainty

2.066 .928 -.315** -.068 .061 -268** -.145 -

† p < .10. *p <.05. **p<.01.

Hypothesis testing

Table 3 presents the results of the regression analysis.

Columns 1, 3 and 5 of table 5 indicates a significant main effect of degree

of expertise on performance, perceived uncertainty and overconfidence (b =

9.959, p < .05, b = -.310, p < .01 and b = -7.493 p < .10 respectively). Note that

when regressing the independent variable and the moderators on perceived

uncertainty we control for performance as performance and perceived uncertainty

had a significant negative correlation (p <.01) and seemed to a have significant (p

< .10) main effect on perceived uncertainty. Column 2 and 6 indicates some

significant interaction effects between degree of expertise and task complexity on

performance (b = 11.510, p < .05) and overconfidence (b = -10.722, p < .05),

though there are no significant interaction effects between degree of expertise and

education rank on neither performance nor overconfidence leaving H2, H3 and H6

not supported. Column 4 indicates a significant interaction effect between degree

of expertise and education rank on perceived uncertainty (b = .129, p < .10), but


Side 27

no significant interaction effect between degree of expertise and task complexity

on perceived uncertainty leaving no support for H9.

To test the reminding hypotheses, we needed to analyze the interaction

effects of each hypothesis. Two regression lines are plotted for each dependent

variable, one for high level of the moderator, and one for low level of the

moderator (Aiken & West, 1991).

Table 3

Summary of regression analysis for variables Performance, Perceived

uncertainty and Overconfidence

Performance Perceived uncertainty Overconfidence

Dependent

Variable 1 2 3 4 5 6

Constant 71.323**

(3.574)

71.008**

(3.525)

2.087**

(.090)

2.087**

(.090)

-17.578**

(3.300)

-

17.871**

(3.253)

Expertise 9.959*

(4.411)

.204

10.554*

(4.376)

.216

-.310**

(.116)

-.273

-.282*

(.116)

-.248

7.493†

(4.072)

.174

8.050*

(4.039)

.187

Task

complexity

-23.172**

(4.426)

-.473

-

23.711**

(4.368)

-.484

-.012

(.132)

-.010

-.031

(.132)

-.027

-17.572**

(4.086)

-.406

-18.075*

(4.031)

-.418

Education

Rank

.373

(2.281)

.015

.314

(2.252)

.012

.036

(.057)

.061

.042

(.057)

(.071)

-.637

(2.106)

-.029

-.691

(2.078)

-.031

Performance

(control)

-.005†

(.003)

-.219

-.006

(.003)

-.238

Expertise x

Complexity

11.510*

(5.377)

.191

.043

(.140)

.031

10.722*

(4.962)

.201

Expertise x

Education

Rank

1.126

(2.454)

.041

.106†

(.063)

.167

1.061

(2.264)

.044

R2 .258 .295 .149 .176 .190 .232

N

94 94 93 93 94 94

F 10.545** 7.462** 2.967** 3.105** 7.118** 5.367**

Note. The regression parameter appears above the standard error (in parentheses).

†p<.10. * p < .05. ** p <.01.


Side 28

Fig. 2 plots the effects of degree of expertise on performance for high and

low task complexity. It indicates, as hypothesized, that a higher degree of

expertise results in higher performance on high Task Complexity (slope p <.01),

thus supporting H4. The slope plotting degree of expertise against performance on

low complexity tasks indicates almost no difference in performance along the

expertise continuum and furthermore, the slope is not significant (p = .643).

Figur 2

The risk propensity variable was tested based on the theoretical foundation

even though it did not have sufficient construct validity. The result, however was

somewhat disappointing, with no main effect of expertise on Risk propensity, and

the interaction term far from significant, leaving H6 unsupported.

Figure 3 plots the effects of degree of expertise on overconfidence for high

and low task complexity. The slope for high task complexity indicates that as

degree of experience increase, the overconfidence decreases. This is supported by

the significant slope (p < .01), and thus H7 is supported. On low complexity tasks,

there seem to be a small decrease in overconfidence, the slope is however not

significant (p = 0,654).


Side 29

Figur 3 (Note that the scale for overconfidence is reversed for easier interpretation)

Figure 4 depicts the effect of degree of expertise on perceived uncertainty

moderated by relevance of education. It indicates that at a low degree of expertise

high level of relevant education will have reduced perceived uncertainty

compared to a low level of relevant education, albeit a very small difference.

Furthermore, it indicates that this relationship changes as the degree of expertise

increases. Even though perceived uncertainty decrease from low degree to high

degree of expertise, it decreases more for low relevance of education than high

relevance of education showing that at a high degree of expertise, a higher

relevance of education results in more perceived uncertainty than low relevance of

education. The slope for low education rank is significant (p < .01), while the

slope for high education rank, however, is not significant (p = 0,495). This leaves

H8 only partially supported.

Figur 4


Side 30

5.4. Post-hoc

As the factor analyses of the items making up the expertise construct revealed two

factors with seemingly different properties of expertise we wanted to explore

whether there were any interaction effects between the two. We tested this by

regressing the experience variables, and the interaction between the two, on the

dependent variables. Only Perceived uncertainty proved significant and the results

are presented in table 4.

Column 1 indicate significant main effects by both experience variables on

Perceived uncertainty, and column 2 also show a significant interaction between

the two (b = -.262, p < .01). Figure 1 plot the effects of length of experience for

high and low degree of specialization on Perceived uncertainty.

Table 4

Perceived uncertainty

Dependent Variable 1 2

Constant 2.071**

(.080)

2.146**

(.075)

Length of

experience

-.539**

(.083)

-.590

-.473**

(.077)

-.518

Specialization of

experience

.192*

(.085)

.205

.366**

(.088)

.391

Length of

experience x

Specialization of

experience

-.262**

(.061)

-.403

R2 .317 .431

N

94 94

F 21.315** 22.981**

† p < .10. *p <.05. **p<.01.

Fig.5 indicates that programmers with a high level of specialized experience have

higher perceived uncertainty throughout the length of experience continuum

compared to programmers with lover degree of specialized experience.


Side 31

Figur 5

6. Discussion

In this thesis, we have investigated how education and task complexity are

influencing individuals of various degree of expertise. Four outcome variables of

relevance have been considered, namely risk propensity, perceived uncertainty,

overconfidence and task performance. We have included two separate moderators

in our research; education, that is seen as moderating the relationship between

expertise and all of the four outcome variables; and task complexity as moderator

of the relationship between expertise and perceived uncertainty, overconfidence

and task performance. Task complexity is not included as a moderator of the

relationship between expertise and risk propensity because risk is operationalized

as an individual’s general and intrinsic tendency to choose a risky option, which

differ from risk taking behavior as such (Lejuez et al., 2002). Hereby, risk

propensity is not seen in relation to any certain tasks, which is contrasting to the

other variables that are related to the tasks used in the survey.

We hypothesized the following for the moderating effect of education:

A high relevance of education should contribute to better task performance when

expertise increases, which should be contrasting to the contribution from

education of low relevance. A high relevance education should contribute to a

decrease in both risk propensity and overconfidence when the expertise increases.

A high relevance of education should lead to less perceived uncertainty when

individuals have a low degree of expertise, and more perceived uncertainty when

the degree of expertise is high. For the moderating effect of task complexity, the


Side 32

following was hypothesized: Higher degree of expertise should provide better

performance on tasks of high complexity, compared to a low degree of expertise.

Higher degree of expertise should decrease overconfidence when the task has a

high complexity, compared to a low level of expertise. Higher degree of expertise

should contribute to a decrease in perceived uncertainty when the tasks have a

high complexity, compared to a low degree of expertise.

The results indicate that both task complexity and education plays a role in

some of the aspects in which individuals with varying degrees of expertise are

approaching and performing domain specific tasks. For the moderating effect of

task complexity, our expectation was supported for task performance and

overconfidence, but not with for perceived uncertainty. For the moderating effect

of education, the expectation that a high relevance of education should foster more

perceived uncertainty among individuals of high expertise was not supported.

However, we found support for the expectation that perceived uncertainty should

decrease with low relevance of education when expertise increases. With regard to

task performance, risk propensity and overconfidence we found no support. In the

following, possible explanations will be presented for each of the two moderators

and the relationship in which they were expected to moderate.

6.1. Possible explanation; Education

Despite that expertise is domain specific (Ericsson & Lehman, 1996; Ericsson,

2005; Haerem & Rau, 2007; Sonnentag et al., 2006) and that relevant education

should enhance knowledge strength in the certain domain, which in turn should

improve task performance (Ng & Feldman, 2009), our results does not confirm

this relationship. According to theory, feedback should provide an increase of task

performance (Barnett & Koslowski, 2002). Even though the educational context is

providing solid feedback throughout knowledge acquisition (Friedlander et al.

2011), which consequentially should mean that domain related feedback is given

more consistently to those having a more relevant educational background,

increased task performance is not the case, according to our results. Drawing on

this, experience might be considered as a more dependable predicator for task

performance, were high complexity programming tasks are current. This is

confirming Chase and Simon’s (1973) assumption, that task-specific knowledge at

an expertise level must have been acquired through experience. One may assume

that education should be considered merged with experience (Sanjram & Kahn,


Side 33

2011). Further, a possible explanation for why this might be the case, despite that

feedback situations should give a higher level of expertise (Barnett & Koslowski,

2002), is that knowledge development, independent of learning context, is

increasing linearly with experience in a period of five years before it flattens out

(Schmidt et al., 1986). This do moreover supplement the consideration of

education merged with experience (Sanjram & Kahn, 2011); whether or not you

have relevant education does not matter, it is the length experience, up to five

years (Schmidt et al., 1986) that counts. One can also reflect on whether education

and feedback can provide a better type of knowledge acquisition than a self-taught

approach to the topic were strict rules and formality is the major characteristic

(Jørgensen et al., 2003). Possibly, the domain is not suitable for education to be

utilized throughout with regard to task performance.

Overconfidence is related to the domain directly (Moore & Healy, 2008)

and we hypothesized that more relevance of education should lead to less

overconfidence (inaccuracy of one’s belief (Moore & Healy, 2008)) based on the

same reasoning as previously; that relevant education should provide insight to

knowledge also about what one does not know, and thereby decrease

overconfidence about how well one will perform. Our results do not support this

relationship. A possible explanation is that years of education might be more

appropriate seen as years of experience (Sanjram & Kahn, 2011), which in fact

tells us that the relevance of the education is not affecting relationship. Hereby, if

you possess more relevant education, it accounts only as more years of

experience. In line with this reasoning, the main advantage of education, namely

feedback (Friedlander et al., 2011, Ericsson, 1996), is not particularly present in

software development and programming (Jørgensen et al., 2003) were forward

reasoning is used in problem solving (Hærem, 2002). Thereby, education in this

domain does not provide the insight into the meta-knowledge, and individuals

with expertise tend to be overconfident (McKenzie et al., 2008).

Following the reasoning that deliberate practice contributes to expertise,

and has similarities with education by the magnitude of domain related feedback

(Ericsson, 2008), perceived uncertainty will possibly be affected by this element.

Education may provide insight into theories and knowledge that is underlying for

the current topic (Ericsson, 2008; Barnett & Koslowski, 2002), which in turn may

lead to an insight to one’s own knowledge in such a way that one’s limitations is

also understood. Broadness in knowledge acquisition may also lead to an


Side 34

assessment of what theory should be deployed (Friedlander et al., 2011) in a

certain task-solving situation. Because education also can be considered as

experience (Sanjram & Kahn, 2011), and that amount of experience predicts the

level of expertise (Chase & Simon, 1973), we assumed that individuals at a low

level of expertise and education of low relevance will have less perceived

uncertainty. This is assumed because education of low relevance at lower degree

of expertise may provide acquaintance to theories and knowledge that contributes

to a general feeling of certainty, (Plous, 1993; Slovic et al., 2004). Individuals at

low levels of expertise with relevant education might not have developed these

heuristics (Solvic, 2004). In our case, we can predict this direction, but without

significant findings on this relationship this cannot be concluded. When the

individual possess low relevance of expertise and a low level of education, deeper

insight to underlying theories will be absent by the lack of both experience and

education (Friedland et al., 2001; Sonnentag et al., 2006).

Perceived uncertainty relates directly to the task and is including

perception of the task’s complexity by two dimensions; the perceived

analyzability and the perceived variability (Hærem & Rau, 2007). Our expectation

was met with regard to low level of education relevance and its influence on

perceived uncertainty when the level of expertise is low. This tells us that

individuals with a lower level of expertise will possess more uncertainty when

relevance of education is low, when they approach programming tasks. Following

the assumption from Sanjram and Kahn (2007), that education and experience can

be merged, and seen as equally contributing, a possible explanation of the result

can relate to Plous' (1993) argumentation, that knowledge and information cues

affects perception of the situation. Plous (1993) argues that trivial information

about a situation increase the feeling of certainty about a specific case. Based on

this, the low relevance of education at a lower level of expertise may provide

information cues that are fostering a higher level of perceived certainty.

The reasoning is that when a programmer with low relevance of education

is solving programming tasks, her/or his knowledge from education has

assumable introduced her/him for knowledge that enhances the feeling of

certainty, not in depth knowledge that contribute to more meta knowledge about

one’s weakness within the domain. Moreover the increasing degree of expertise

can be considered as confirming to one's performance, which in turns foster more

certainty were logic and straight forward reasoning is present (Hærem, 2002).


Side 35

Our findings on risk propensity show no significance in the regression nor

for the measure of this construct. This will be discussed under limitations.

6.2. Possible explanation; Task complexity

For the moderating effect of task complexity, we found support for the

expectation that a high level of expertise should lead to increasing performance on

tasks of high complexity, compared to low level of expertise. The same pattern,

with an opposite direction, applies for overconfidence; high level of expertise

leads to less overconfidence when complexity of the task is high, compared to a

low level of expertise. The expectation that a higher level of expertise should

decrease perceived uncertainty on tasks of high complexity, compared to a low

level of expertise was not supported by our results.

Expertise is in our research operationalized as length of experience in the

domain, which in this case is Java programming. We theorized that expertise

develops as a function of time spent within the domain (Sanjram & Kahn, 2011);

longer experience would lead to more domain related knowledge (Schmidt,

Hunter & Outerbridge, 1986), which in turn is an antecedent for programming

skill (Bergersen & Gustafsson, 2011). Because tasks of higher complexity put

more demand on knowledge skills at the task doer (Wood, 1986), the level of

expertise should play an increasingly important role for these tasks to be perform

well. The confirming results tells us that individuals that whit longer experience in

the domain has the ability to use multiple cognitive paths (Campbell, 1988) and

forward reasoning strategies were the breadth of the problem solution is to be

developed (Haerem, 2002), which is needed for solving programming tasks of

high complexity. This distinguishes them from individuals with low experience.

When individuals are overconfident, termed as overprecision, their

estimate will produce a narrower probability distribution than what is the reality

(Moore & Healy, 2008). We found that a high level of expertise reduces the

overconfidence, compared to a low level of expertise, where the individual is

asked to estimate her/his completeness of a given task. As the task of a higher

complexity puts an increased demand on the task doer (Wood, 1986), the

experience should play and increasing role for the prediction of how complete one

is able to solve the current task; this despite that it seems safe to say that experts

are overconfident (McKenzie et al., 2008) and that software programming lacks

contributing feedback (Jørgensen et al., 2003). The results confirm that more


Side 36

experience provides the availability for repetition of the stable rules, termed as

programming language that is deployed when solving programming tasks

(Haerem, 2002), which is fundamental for learning by experience (Freidlander et

al., 2011). Conclusively, with regard to our results, more experience improves

perception of ones own capability in relation to a certain task by the end state at

tasks of high complexity (Campbell, 1988).

Perception of uncertainty relates directly to the complexity of a certain

task (Hærem & Rau, 2007). Because more experience provides a more solid

platform for repetition and internalization (Freidlander et al., 2011) of the stable

rules that is used for programming (Hærem, 2002), we expected that individuals

with higher expertise should figure a more logical, systematic and analytical

approach when meeting tasks of high complexity (Hærem & Rau, 2007; Perow,

1967). This implies that a high level of expertise should reduce perceived

uncertainty, however our results did not support this assumption. A possible

explanation can be that even though software programming deploys stable rules

and the same programming language (Hærem, 2002), which in this domain is

Java, the structure of the tasks that are used in our survey may not match with the

experience respondents have. Hereby, individuals might have tended towards an

approach with more insecurity, chances and guesswork, which naturally evoke

perception of uncertainty (Hærem & Rau, 2007), which in turn will affect

participants to report more perceived uncertainty.

6.3. Post hoc

The finding that specialization moderates length of experience is

interesting, especially seen in light of the possibility that participants of the survey

might have been confused when self-reporting about education and length of

experience, which was intended to be distinguished. As it turns out degree of

specialization interacts with length of experience in much the same way as we

anticipated that education would interact with experience, however the findings

regarding high relevance of education was inconclusive. As degree of

specialization is seen as how long the current Java development period is, how

much time is spent coding and self-rating of Java-expertise, day to day Java

programming seem to be important in perceiving uncertainty. The specialization

may contribute to the development of the meta-knowledge that we expected that

relevant education should provide.


Side 37

7. Practical Implications

Education among individuals with varying degree of expertise seems to be of less

relevance than expected. Transferring this to the real life scenario, we assume that

recruitment settings are a situation in which the contribution of the research may

be of interest. With regard to education and its influence on the different aspects

presented, our results points towards the same aspect, that relevant education may

be considered as a part of the experience within the domain of programming.

Hereby, job applicants with a certain length of experience within the domain may

be seen equal as those with the same length of relevant education when task

performance and overconfidence is considered. Nonetheless, when individuals

have low relevance of education, those who have a shorter length of experience

tend to possess higher perception of uncertainty when facing programming tasks,

than more experienced programmers. This might potentially have implications for

organizations recruiting for contexts in which chance taking must be at a

minimum level. These contexts can be software programming of medicine

equipment such as x-ray machines etc. were faults are of detrimental

consequences.

The findings of this research may also be of interest for those who

consider taking further education in software programming. The cost of time and

money attending a course may be evaluated against the possibility of learning

through experience, for instance at a current work situation were trial and error

provides knowledge development. Our results show that education does not

provide better task performance; neither do deeper insight into one’s own

knowledge development, which might stem from the stable rules and the high

predictability that this domain are characterized by (Hærem 2002). Because of this

we do not intend to generalize the finding to other domains.

As experience has been proven to be more important on tasks of high

complexity, with regard to performance and overconfidence, this may imply that

recruitment and interest of investing in young talent should not be at the expense

of retainment of more mature programmer.


Side 38

8. Limitations

Both practical and theoretical limitations may have affected the results of this

study and it is therefore necessary to point to some of these issues.

Sample wise there may have been a problem with the different participants

coming from different cultures. This was not controlled for but considering that a

large part of the sample came from Vietnam this may be a limitation. Looking at

Hofstedes (1983, 1994) research on differences in national cultures, there is

profound differences between western and Asian cultures. Looking at Norwegian

and Vietnamese cultures in specific, as they made up most of the sample, there are

noticeable differences in uncertainty avoidance, with Vietnamese culture being

much more inclined to avoid uncertainty. This weakness could have been reduced

by the use of participants originating in the same geographical area.

Another limitation is the lack external validity of the expertise measure.

We did establish external validity o the expertise construct as we did not have a

reflective measure to create a MIMIC model as recommended by Diamantopoulos

& Winklhof (2001) and Hair, (2010).

A third limitation to the study was the instrument used to measure risk

propensity. The rephrasing of Calantone, Garcia, and Dröge (2003)’s measure

failed to meet the construct validity level required for validating the rephrasing

aimed towards programming, and furthermore it proved far from significant in the

regression analysis. It was measured in the dataset consisting of 52 respondents,

giving a ratio of 13:1 for the 4 questions chosen to represent risk propensity after

the factor analysis, and as such the sample size needed for validation is met (Hair,

2010). Conclusively, the adjustment of the tool for measuring risk propensity does

not apply for programmers and other options should be explored to find a proper

measure for risk propensity amongst programmers.

Participators might have understood education as a part of their experience

and thereby reported their length of experience including the length of education.

The possibility of this mistake is present despite attempts to phrase the questions

probing this in a manor not easily misunderstood, and may have influenced the

expertise measure with ambiguity.

A wide range of companies with relevance to the current competency was

represented. Despite the vast amount of connections used in this purpose, the

actual outcome in numbers of respondents to the survey was moderate. Several of

the companies that engaged in a dialog for establishing a two-way contribution,


Side 39

the current companies were given information about the reward of providing

respondents, but unfortunately the cost of participating seemed to be too high. The

reward was individual feedback on the test, which in fact is insight to how the

respondents would perform on a validated test program for certification of Java

developers. We believe that this was the main drawback that prevented

individuals from participate; time consumption off work with job related activity

might not be too appealing. This is further strengthened by the large number of

paid participants from Vietnam, compared to the amount of voluntary participants.

There are also several limitations to how the experiment was conducted

The experiment in is self was two-fold, first one answered a survey about

background information and risk propensity, then one had to download an

application to complete the programming task. As showed in the missing values

analysis, this in itself was enough to make participants not complete the

experiment. Furthermore, by conducting the experiment over the Internet, there

was no possibility to control the task environment, thus the environment the

respondent was in could have influenced the outcome.

Together the limitations mentioned leads to serious threats to internal

validity. Because of the lack of experimental control it is difficult to whether

extraneous variables influenced the outcome. According to Singelton & Straits

(2010) this threatens the internal validity of the study because it makes it harder to

establish a causal link between the independent and the dependent variables.


Side 40

9. Conclusion

The aim of the research was to develop insight into how education and task

complexity affects individuals of varying degree of expertise. Through an

experiment we investigated risk propensity, perceived uncertainty, overconfidence

and task performance in order to reveal tendencies that were affected by education

and the complexity of tasks. The domain that was chosen for the investigation was

Java programming. Our results indicate that education plays a minor role in how

these individuals perceives uncertainty of the tasks, how overconfident they feel

and how they perform when solving these tasks. Education influenced individuals

with low degree of expertise by their perception of uncertainty, which means that

those that had less experience and low relevance of uncertainty, felt more certain

when facing tasks of software programing than those with higher degree of

expertise. We conclude that for this domain, education may be considered as

merged with experience. The theoretical argumentation indicates that our findings

do not necessarily apply for other domains, distinguishable from the current. On

the other hand, we found that individuals with a high degree of expertise had

better performance and lower overconfidence, compared to individual with a low

degree of expertise when solving tasks of high complexity. Summed up, we

conclude that whether or not you have education, it is the length of experience

within the domain that matters the most.


Side 41

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Side 46

Appendix 1

Expertise

Please state the length of your experience ( in years and months; for example,

enter 2 years, 6 months for two and a half years)

Years Months

Total programming experience (All programming languages

that you use):

Total Java programming experience:

Your current continuous work period of programming (all

programming languages that you use). If you are currently not

in a period of programming, please enter 0:

Your current continuous work period of Java programming. If

you are currently not in a period of Java programming, please

enter 0:

Self-reported skill assessment

Please indicate your skills in programming (all programming languages that you

use)

On a scale from 1-10, where 10 is Best and 5 is average, I assess my skills in

programming to be:

Please indicate your skill in Java programming:


Java programming to be:

Please asses your skill in estimating the number of work hours needed in

software development projects.


estimating the number of work hours needed in software development projects to

be:

How many software development projects have you participated in? Nr. (0-50)

Number of projects

Which software development project rolles have you held? Please tick the


Side 47

applicable boxes:

Junior developer

Intermediate developer

Senior developer

Administrator / Project leader

During an average working day, how much time do you, as a programmer spend

on:

Coding (percentage)

Project planning (percentage)

Other (percentage)


Side 48

Appendix 2

Rating Rating

År År

0,5-1 1-2 3 4 5

Aesthetics, Art and Music 1 1 1 1 1

Farming, Fishing, and Veterinarian 1 1 1 1 1

History, Religion and Philosophy 1 1 1 1 1

Physical Education, Sports and Outdoor Activities 1 1 1 1 1

Information Technology and Computer Science 3 4 5 6 7

Law, and Police education 1 1 1 1 1

Teacher education 1 1 2 2 2

Pedagogical education 1 1 1 1 1

Education_Math, and Science 2 2 3 4 5

Media-, Library- and journalistic education 1 1 2 3 3

Medicine, Dentistry and Health and Social care 1 1 1 1 1

Tourism 1 1 1 1 1

Social Science and Psychology 1 1 2 3 3

Technology, (civil) engineering and architecture 2 2 3 5 6

Language and literature 1 1 2 3 3

Economy and administration 1 2 3 4 4

OTHER, please enter type here

OTHER_Length


Side 49

Appendix 3

Risk propensity

Table 3

Item Factor loading

1 In order to save time when programming at work, I do quick fixes to code, without a deeper understanding of the underlying faults

.786

2 When programming at work, I focus on speed over accuracy, since errors and faults will be detected and fixed later

.607

4 When programming at work, I have a sensation of boldness and wide impact on the system under development

.562

5 Close to shipping date, I fix as many faults as possible, in order to provide a better software product for delivery to the customer, even when there is insufficient opportunity to regression test these fixes.

.510

Eigenvalue 1.563 Prc. of Variance 39.065 Coefficient Alpha .453

Extraction method: Principal component analysis N=52 Items = 4


Side 50

Appendix 4

Perceived uncertainty

Measured on a scale 1-7 to indicate degree of agreement with each item.

Table 1

Items Component

Perceived task analyzability 1 2 1 I think I will be able to follow well-defined stages

or steps to solve this task. -.032 .908

2 I can solve this task using a methodology or a series of steps that I have used in other occasions.

-.091 .681

3 When I read the task description, a mental picture formed in my mind that will guide med while completing the task.

.002 .861

Perceived task variability 1 To what extent did you encounter problems you

were unsure about while solving the task? .889 .080

2 To what did you come up against unexpected factors while completing the task?

.894 .062

3 To what extent do you feel that your solution is different from how you anticipated it to be before solving the task?

.766 -.232

4 To what extent do you feel that your solution is unstructured, hard to describe, or unclear?

.820 -.105

5 To what extent did you find that it was difficult to identify a solution to the task description?

.920 -.066

Eigenvalues 3,752 2.059 Pct of variance 46.905 25.732% Coefficient Alpha .902 .756

Extraction method: Principal component analysis Rotation method: Varimax rotation with Kaiser Normalization, N=94 Items: Perceived task analyzability = 3 Perceived task variability = 5

Perceived Task Analyzability

1. To what extent do the requirements reflect structured tasks?

2. To what extent do you feel that the requirements can be solved by use of a

certain method?

3. To what extent do you feel that there are fundamental similarities between the

responses to these requirements?

4. To what extent do you feel that you have a mental picture to guide you in

responding to the above requirements?


Side 51

Perceived Task Analyzability

1. To what extent did you come across problems about which you were unsure

while responding to these requirements?

2. To what extent did you come up against unexpected factors in responding to the

above requirements?

3. To what extent do you feel that your solutions were vague and difficult to

anticipate?

4. To what extent do you feel that it is difficult to identify a solution to the

requirements?

5. To what extent did you find that it was difficult to identify a solution to the task

description?


Side 52

Appendix 5

Overconfidence

1. How complete do you think you can solve the task (within the time limit)? (In

percentage)

bi norwegian business school thesis - expertise: what does...

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