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BREAKING THE CYCLE OF REMEDIAL TRAINING:
THE EFFECTS OF GAIN AND LOSS FRAMED MESSAGING
ON TRAINING MOTIVATION AND PERFORMANCE

presented by

Adam John Massman

has been accepted towards fulﬁllment
of the requirements for the

M. A. degree in Psychology

 

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BREAKING THE CYCLE OF REMEDIAL TRAINING:
THE EFFECTS OF GAIN AND LOSS-FRAMED MESSAGING ON TRAINING
MOTIVATION AND PERFORMANCE
By

Adam John Massman

A THESIS
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
MASTER OF ARTS
Psychology

2009

ABSTRACT
BREAKING THE CYCLE OF REMEDIAL TRAINING:
THE EFFETS OF GAIN AND LOSS-FRAMED MESSAGING ON TRAINING
MOTIVATION AND PERFORMANCE
By

Adam J. Massman

The current study examined the effects of gain and loss-framed messages
during training interventions on training outcomes. Quinones (1995) discovered
that remedial training has detrimental effects on various factors that affect
training outcomes such as motivation to learn. A model was developed to
discover if gain and loss-framed messages during training could result in different
levels of motivation and different training outcomes. This study proposed that
gain-framed training would improve fairness perceptions, self-efﬁcacy, and
motivation to learn. These will in turn affected training outcomes (speed of
completing future tasks, accuracy of completing the task, and the transference of
knowledge). Additionally, individual differences such as the ideal versus actual
self (Higgins, 1987) may affect the participants’ ability to receive framed
messages effectively. Replicating the basic design of the Quinones (1995)
study, participants were randomly assigned to two differently framed remedial
training programs (gain and loss-framed training). Results indicated that placing
participants in gain-framed training affects training outcomes differently than loss-
framed training. Implications for training effectiveness research and practice are

discussed.

Copyright by
ADAM JOHN MASSMAN
2009

Acknowledgements

The journey of writing a master’s thesis is one that I did not take alone,
and for those who joined me, I am forever grateful. Those who have been there
by my side for emotional support were just as instrumental as those who were
there for intellectual support during the ten-month adventure.

For intellectual support, I give the most gratitude to my master thesis
chair, Dr. Kevin Ford. His willingness to push me harder and to help me become
a better academic is something that deﬁnes him as a person. I could not ask for
any better thesis Chair. I Offer additional thanks to my other two astute committee
members, Dr. Steve Yelon and Dr. Rick Deshon. These two incredibly talented
academics taught me lessons not only about academia but lessons on how
succeed in my future career. Finally, to three research assistants, Lingyan Ye,
Josh Glowacki, and Melissa Chestara, who worked without any academic or
ﬁnancial recompense, I am overwhelmed by your contributions and time.

For emotional support, there could be an endless amount of individuals
that I could list here. Ultimately, these individuals know who they are but I felt it
necessary to list the people whose support helped me fulﬁll a life dream of
holding a masters degree. Thank you to my partner, Alan Johnson, for his ability
to help me stay focused and relaxed during the stressful times; Krystin Kohls, for
her comedic relief and chart expertise; Julie Strong, for her ingenious technical
tweaks that were needed at critical moments; and finally, Stephanie Corder and
Nicholas Schroeder for their endless encouragement. As I said before, I thank

everyone listed; I hope I can repay them in helping them pursue their life dream.

Table Of Contents

LIST OF TABLES ................................................................................................. vii
LIST OF FIGURES ............................................................................................... ix
INTRODUCTION ................................................................................................... 1
Training Interventions ........................................................................................ 5
Training Choice and Expectations .............................................................. 5
Labeling ...................................................................................................... 7
Training Assignment and Feedback ......................................................... 10
Self-Efﬁcacy .............................................................................................. 14
Motivation ................................................................................................. 16
Fairness Perceptions ................................................................................ 18
Framework for Current Research .................................................................... 19
Prospect Theory ....................................................................................... 21
Framing Effects ........................................................................................ 22

A conceptual model of the study .............................................................. 37
Self-Efﬁcacy .............................................................................................. 41
Additional Exploratory Evaluations ........................................................... 52
METHODS .......................................................................................................... 55
Participants ...................................................................................................... 55
Design .............................................................................................................. 55
Task ................................................................................................................. 55
Procedure ........................................................................................................ 58
MEASURES ..................................................................................................... 63
Learning Assessment/T raining Assessment Test .................................... 63
Actual/Ideal Discrepancy .......................................................................... 64
Fairness Perceptions ................................................................................ 64
Self-Efﬁcacy .............................................................................................. 65
Motivation to Learn ................................................................................... 66
Number of Trials (Speed) ......................................................................... 66
Accuracy ................................................................................................... 66
Reactions to Training ................................................................................ 66
Training Impressions Check ..................................................................... 67
Manipulation Check .................................................................................. 67

Mood Orientation ...................................................................................... 68
Personality ................................................................................................ 68
Analytic Strategy .............................................................................................. 69
RESULTS ............................................................................................................ 72

Descriptive Data .............................................................................................. 72

Main Effect Hypotheses ................................................................................... 73
Moderating Hypotheses ................................................................................... 80
Mediating Hypotheses ..................................................................................... 84
Manipulation and Tone Check ......................................................................... 91
DISCUSSION ...................................................................................................... 94
Summary of the Results .................................................................................. 95
Implications and Directions for Future Research ........................................... 108
Study Limitations ........................................................................................... 116
CONCLUSION .................................................................................................. 117
Appendix A: Consent Form ............................................................................... 119
Appendix 8: Training Assessment Task ............................................................ 120
Appendix C: Training Manual ............................................................................ 134
Appendix D: Training Pamphlet: Gain Framed Participants .............................. 142
Appendix E: Training Pamphlet: Loss Framed Participants .............................. 144
Appendix F: Justice Perceptions Assessment .................................................. 146
Appendix G: Self-Efﬁcacy Assessment ............................................................. 148
Appendix H: Motivation to Learn Assessment ................................................... 149
Appendix I: Actual/Ideal Self Discrepancy Index Assessment .......................... 150
Appendix J: Mood Orientation Assessment ...................................................... 151
Appendix K: Personality Assessment ................................................................ 152
Appendix L: Training Reactions Assessment .................................................... 154
Appendix M: Training Impressions Assessment ............................................... 155
Appendix N: Manipulation Check ...................................................................... 156
REFERENCES .................................................................................................. 157
vi

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

Table 7.

Table 8.

Table 9.

Table 10.

Table 11.

Table 12.

Table 13.

Table 14.

Table 15.

LIST OF TABLES

Combined Means, Standard Deviations, and Reliabilities of
Gain/Loss Reponses .............................................................. 73

Mean, Standard Deviations, and Reliabilities of Gain/Loss
Responses ........................................................................... 75

Inter-Correlations Among Variables ........................................... 76

Regression Analyses Results of the Test of Moderation of
Ideal/Actual Discrepancy at Time 1 on Self-Efﬁcacy ...................... 82

Regression Analyses Results of the Test of Moderation of
Ideal/Actual Discrepancy at Time 2 on Self-Efﬁcacy ...................... 82

Regression Analyses Results of the Test of Moderation of
Conscientiousness on Performance ........................................... 82

Regression Analyses Results of the Test of Moderation of
Openness on Performance ....................................................... 83

Regression Analyses of the Test of Mediation of Self-Efﬁcacy
on Framing and Affective Outcomes ........................................... 85

Regression Analyses of the Test of Mediation of Self-Efﬁcacy
on Framing and Performance (Accuracy) .................................... 85

Regression Analyses of the Test of Mediation of Self-Efﬁcacy
on Framing and Learning ......................................................... 85

Regression Analyses of the Test of Mediation of Self-Efﬁcacy
on Framing and Performance (Speed) ........................................ 86

Regression Analyses of the Test of Mediation of Motivation on
Framing and Affective Outcomes ............................................... 86

Regression Analyses of the Test of Mediation of Motivation on
Framing and Performance (Accuracy) ........................................ 86

Regression Analyses of the Test of Mediation of Motivation on
Framing and Learning ............................................................. 87

Regression Analyses of the Test of Mediation of Motivation on
Framing and Performance (Speed) ............................................ 87

vii

Table 16.

Table 17.

Table 18.

Table 19.

Table 20.

Table 21.

Table 22.

Table 23.

Table 24.

Regression Analyses of the Test of Mediation of Distributive

Justice on Framing and Affective Outcomes ................................ 87
Regression Analyses of the Test of Mediation of Distributive
Justice on Framing and Performance (Accuracy) .......................... 88

Regression Analyses of the Test of Mediation of Distributive

Justice on Framing and Learning ............................................... 88
Regression Analyses of the Test of Mediation of Distributive
Justice on Framing and Performance (Speed) .............................. 88
Regression Analyses of the Test of Mediation of Procedural
Justice on Framing and Affective Outcomes ................................ 89

Regression Analyses of the Test of Mediation of Procedural
Justice on Framing and Performance (Accuracy) .......................... 89

Regression Analyses of the Test of Mediation of Procedural
Justice on Framing and Learning ............................................... 89

Regression Analyses of the Test of Mediation of Procedural
Justice on Framing and Performance (Speed) .............................. 90

Inter-Correlations with Internalized-Framing Participants ............... 102

viii

Figure 1.
Figure 2.

Figure 3.

Figure 4.
Figure 5.

Figure 6.

LIST OF FIGURES

A Conceptual Model Presented by Quinones ................................ 12
A Conceptual Model of the Current Study .................................... 40
The Proposed Interaction of the Ideal/Actual Discrepancy

with Self-Efﬁcacy and Framing ................................................... 45
Representation of the Procedure ................................................ 57
Correlative Map of Signiﬁcant Associations .................................. 95
Resulting Conceptual Model ................................................... 106

INTRODUCTION

Training is a powerful force in determining the success of organizations.
Accordingly, organizational psychologists have nicknamed businesses that are
better able to adapt to changes in society and technology as “learning
organizations” (Noe & Ford, 1992). Unfortunately, training is expensive. It is no
wonder that with over ﬁfty billion dollars spent annually on training (Dolezalek,
2004), that business administrators are increasingly concerned with training
effectiveness (Ford & Weissbein, 1997). Yet, the expense seems to be justiﬁed
as administrators in thriving organizations spend around two thousand dollars per
employee compared to administrators in low performing companies who spend
only six hundred dollars per employee (Dolezalek, 2004).

Thus, administrators can justify the cost of effective training (of. Birdi et at,
2008). Trainers measure training effectiveness in two ways: the amount of
learning and the degree of transfer. Psychologists such as Weiss (1990) note
that learning and transfer are related but each has its distinguishing
characteristics. Weiss states that learning is a “relatively permanent Change in
knowledge or skill produced by experience” (p. 172), while Omrod (1999) states
that transfer is a condition where existing knowledge, abilities, or skills assist the
trainee in the performance of new tasks or affect the next step in the learning
process. Both learning and transfer are essential to training effectiveness.

To be effective, trainers must know what affects learning and transfer.
Baldwin and Ford (1988) have suggested that learning and transfer are affected

by three sets of variables: (1) training program Characteristics, (2) environmental

variables, and (3) trainee characteristics. The objectives and mode of instruction
in a training program will affect learning and transfer, but the workplace
environment in which trainees apply their new knowledge can affect transfer
outcomes. For example, supervisors may provide trainees few opportunities to
demonstrate their learned skills. Finally, trainee characteristics such as
motivation can inﬂuence outcomes (Goldstein & Ford, 2002). A brief overview of
each one of these factors is presented.

Gagne, Briggs, and Wager (1992) described training characteristics as the
events surrounding the training program and the trainee. Campbell (1988)
suggested that training be centered on speciﬁc learning requirements and
learning principles. The ﬁrst principle, identical elements, notes that identical
stimuli and response elements in the training and the transfer setting promote
maximum success (Thorndike & Wood, 1901 ), which Crafts (1935) and
Underwood (1951) demonstrated improved motor and verbal behaviors. The
second factor, general principles, stipulates that training should incorporate the
underlying principles for the training not just the applicable skills. Hendrickson
and Schroeder (1941) demonstrated this principle when they taught their
participants to improve shooting accuracy by learning the principles of physics.
The third, stimulus variability, states that various examples of the trained concept
improve generalizability (Ellis, 1965). The ﬁnal principle states that other
variables affect the design of the training such as feedback, whole versus partial

training, and mass learning (Briggs & Naylor, 1962).

In addition to training characteristics, the work environment can inﬂuence
training effectiveness (Eddy, Glad, 8 Wilksin, 1967). Researchers have
examined environmental factors such as choice of training (Baldwin, Magjuka, &
Loher, 1991 ), organizational support (Tannenbaum, 1997), climate (Goldstein,
1986), managerial support (Cohen, 1990), and task opportunity (Ford, Quinones,
Sego, 8: Sorra, 1992). These work environment factors have direct or indirect
outcomes on training such as improved learning transfer or increased self-
efﬁcacy (Tracey, Hinkin, Tannenbaum, & Mathieu, 2001).

Finally, trainee characteristics have received a signiﬁcant amount of
attention their impact on training outcomes (of. Quinones, 1997; Tracey, Hinkin,
Tannenbaum, & Mathieu, 2001). Several different individual differences have
been found to inﬂuence training outcomes (of. Baumgartel, Reynolds & Patham,
1984; Kozlowski, Gully, Brown, & Salas, 2001 ). One of these Characteristics is
motivation to learn. Mitchell (1982) notes that motivation is the process that
causes persistence in behavior and direction, which leads to the amount of time
an individual invests in their behavior (Naylor, Pritchard, & Illgen, 1980), and that
motivation to learn has been demonstrated to an important factor in the training
success (Mathieu & Martineau, 1997; Tracey, Hinkin, Tannenbaum, & Mathieu,
2001;'Noe & Schmidt, 1986).

Of these three characteristics, Quinones (1995) and Tracey, Hinkin,
Tannenbaum, and Mathieu (2001) note that the effects of trainee Characteristics
need additional research on training outcomes. Of the vast array of

characteristics available to study, motivation to learn and self-efﬁcacy are a few

of the more powerful characteristics. Motivation to learn has been shown to have
an impact on self-efﬁcacy (Gist, Schowerer, & Rosen, 1989), locus of control
(Judge & Bono, 2001), career commitment (Pond, Nacoste, Mohr, & Rodriguez,
1997). Additionally, Ford et al (1998) discovered that self-efﬁcacy inﬂuences the
likelihood of transfer.

Because of these variables, researchers are investigating interventions
intended to affect motivation to learn and self-efﬁcacy, which may in turn
increase training effectiveness (Baldwin & Ford, 1988). Mathieau and Martineau
(1997) and Tannenbaum et al (1991) have demonstrated ways to increase these
two variables. Additionally, Quinones (1995) found instances where pre-training
context has reduced motivation and self-efﬁcacy.

In an effort to continue research in this domain, the focus of this study is to
examine the outcomes of training interventions on motivational variables and
training outcomes. This study examines the extent to which an intervention
during training can inﬂuence motivation, self-efﬁcacy, and attitudes, which in turn
affects training outcomes. This study further examines an intervention to
minimize possible negative impacts on inﬂuence motivation, self-efﬁcacy, and
attitudes.

This document is organized in the following manner. First, training
interventions are reviewed and discussed focusing on trainee choice, trainee
expectations, training labels, training assignments, and training feedback.
Second, theories of training and workplace mechanisms that affect trainee

motivation, self-efﬁcacy, and fairness are explained. Third, an extensive review

of gain and loss labels, and framing effects are presented. Fourth, a conceptual
model that explains ways to improve a potentially negative condition, in this case,

a remedial training assignment is proposed.

Training Interventions

Canon-Bowers, Salas, Tannenbaum, and Mathieu (1995) note that the
context in which the employee learns the information affects the ability for the
knowledge transfer to occur successfully. For example, Rouiller and Goldstein
(1993) discovered by looking at 102 manager trainees of a fast-food Chain that
trainees were not blank slates for knowledge transfer. They uncovered that
positive organizational climates affected the degree to which a learned behavior
transferred to the job. More recently, Heimbeck, Frese, and Keith (2003) and
Joung, Hesketh, and Neal (2006) propose that the work environment and '
individual characteristics are key components to the success of training
interventions. Additionally, employees attend training interventions for different
reasons and motivations. A review of training interventions as it relates to this

study is now presented, starting with training choice and expectations.

Training Choice and Expectations

The actual Choice of training has an impact on transfer. First looking at
voluntary placement, researchers have been interested in whether individual
choice to be in the training session versus involuntarily placement in the training

intervention affects learning and transfer. Facteau, Dobbins, Russell, Ladd, and

Kudish, (1995) found that employees who voluntarily attended training had
increased motivation to learn, which improved long-term training effects. Hicks
and Klimoski (1987)’s study similarly found that training choice mattered as the
trainee’s satisfaction with training and motivation increased when trainees chose
to attend the program.

To demonstrate the effects of choice training, Baldwin, Magjuka, & Loher
(1991) divided 207 trainees into three groups: no Choice of training, choice of
training without receiving that choice, and ﬁnally choice of training and received
that choice in training. Regardless of choice, the content of the course was
identical. Baldwin et al discovered that denying their Choice of training lowered
pre-training motivation to learn and actually decreased learning transfer
compared to those who were either allowed their Choice or were not allowed to
chose at all. Participants who were given the choice of training and then actually
participated in that training had the highest levels of motivation and learning
transfer. Some researchers such as Tannenbaum, Mathieu, and Cannon (1991)
suggest that this is because the expectations of the training were not fulﬁlled, and
this created additional negative outcomes.

With expectation fulﬁllment, trainees are often affected before they begin
their training by internal states such as expectations. Rowold (2007)
demonstrated this effect with a group of new employees. Before beginning a
training session with newly hired employees at a German call center, employees
ﬁlled out a survey regarding their expectations of the training intervention.

Interestingly, expectation fulﬁllment was signiﬁcantly related to subsequent

teaming. Those individuals whose expectations were met during the training
session were more likely to acquire more knowledge during the training session.
In an additional study to link performance on the training task and expectations,
Tannenbaum et. al (1991) hypothesized that Naval recruits who indicated their
expectations were met would perform higher on the training task. With over a
thousand recruits pre-tested for expectations, self-efﬁcacy and training motivation
were highly correlated for those who had indicated their expectations had been
fulﬁlled.

This type of expectation modeling may have some moderating variables
such as anxiety as one study noted. When Hilling and Tattersall (1997)
monitored the training expectations of a course on how to pass a professional
exam, those who expected to perform well on the training performed higher.
However, these researchers also monitored anxiety level. Those who did not
expect to perform as well also had high anxiety; the converse was true as well:

those who expected to perform well had lower anxiety.

Labeling

In addition to the effects of expectations, the effect of training labeling may
have an impact on training outcomes. Martocchio (1992) demonstrated that pre-
training context using labels had an impact on training outcomes. Dividing the
program into “opportunitistic” training group and a “neutral” training group,
Martocchio recruited 79 employees at a large university to undergo computer

training. Evaluations showed that employees who were told that they could

utilize the training as a way to increase their position in their company had higher
scores on computer efﬁcacy, lower computer anxiety scores, and higher learning
scores than those who were placed in the neutral training program. He
concluded that organizations that pay attention to factors that make employees
more excited about learning would enhance the outcomes of their training
programs.

Webster and Martocchio (1993) have further demonstrated the importance
of labeling. When 68 Clerical and administrative employees were asked to
complete a training session designed to improve microcomputer skills, the
researchers divided the employees into two groups: “play” training and “work”
training. The only difference betweenthe training was the label; the courses
were taught in the same way. While overall differences between the groups were
not signiﬁcant, younger employees did respond to the “fun” label factor, resulting
in higher scores on training outcomes for younger employees.

Finally, Webster and Martocchio (1995) continued to demonstrate effects
of labeling of training programs. In a ﬁeld experiment of 143 employees,
Webster and Martocchio divided training into “optimistic,” “realistic,” and “neutral”
training assignments. Each training program was the same; however, how the
experimenters described the program changed to ﬁt the tone of optimistic,
realistic, or neutral. They found that differences in preview labeling had a
signiﬁcant impact on training outcomes. Optimistic previews related to attention
to performance compared to realistic previews. Additionally, realistic preview

labels inﬂuenced post-training reactions more positively than positive previews.

While these studies demonstrate the effects of training labeling, implicit
label training can also inﬂuence training outcomes as seen in aptitude-treatment
interactions (Kanfer & Ackerman, 1989). Aptitude treatment interactions are
effective because the training program is adapted to the different level of ability
for the individual. The individual differences between abilities can be targeted in
this type of aptitude-treatment-interaction training. Besides the costly
implications, assigning individuals to different types of trainings implicitly or
explicitly requires training program labels to be created for both the trainer and
the trainee. Kanfer and Ackerman (1989) revealed that even if the labels remain
neutral, trainees might recognize which individuals are being placed into these
different groups. Consequently, these labels can create differences in
motivations and attitudes, causing an additional “high level of evaluative
baggage” that may have multiple negative outcomes (Quinones, 1995).

The research on labeling effects is not limited to training. Simple cognitive
strategists have recognized the impact of cost and beneﬁt framed messaging on
decision-making strategy (later referred to as gain and loss framing) (Kahneman
& Tversky, 1979). Education specialists, Rosenthal and Jacobson (1968), have
found that children work in accordance to the expectations that are explicitly set
for them much like labels in training, and these labels effect their performance.

From all these studies, training assignment and labels have been
suggested to affect a wide variety of training outcomes. Some researchers

suggest that is because trainees view training assignment as feedback.

Training Assignment and Feedback

Organizations are consistently giving feedback (Balcazar, Hopkins, &
Suarez, 1986). This feedback can come from multiple sources such as a trainer
or a performance review. Each type of feedback has speciﬁc outcomes for an
employee.

One type of feedback comes from the trainer during training. Martocchio
and Webster (1992) demonstrated that sustaining positive feedback throughout a
training session had more beneﬁts on trainee performance than negative
feedback. Additionally, they noted that self-efﬁcacy increased due to the positive
oral and text messages that they received during the training. Martocchio and
Dulebohn (1994) demonstrated a similar effect on 86 employees who received
software training. Participants who received feedback that was based on factors
within the trainee’s control had higher software efﬁcacy.

Not as explicit as feedback during training, performance reviews are a
common way of overt feedback. lndiscreetly, assignment to speciﬁc training
programs can be seen as feedback too. If an employee is performing poorly, he
or she may be assigned to remedial training; if an employee is performing
exceptionally, employees may be sent to advanced training to encourage further
growth for promotion. In essence, training assignment may be seen as
feedback. As demonstrated by several researchers (llgen, Fisher, & Taylor,
1979; Martocchio, 1992), this feedback interacts with the varying levels of

attitudes and levels of motivation. Quinones (1995) proposed and test the idea

10

that training assignment in itself acts as feedback and affects motivation and
training outcomes.

To demonstrate this idea, he had undergraduates from a Midwestern
university learn a military ﬂight simulation. After prescreening the individuals’
knowledge, he assigned them to remedial and advanced training conditions —
however, these randomly assigned assignments were not actually based on the
pre-training performance on the test. He measured several items during both pre
and post-training to see how the actual training assignment affected the
individual: perceived performance (how well the individual thought they did),
expected assignment (where the individual thought they would be placed based
off their pre assessment), pre-training self-efﬁcacy (how well the individual
thought he or she would do in future air ﬂight simulations), fairness perceptions
(how the individual felt about their assignment), motivation to learn (how willing
the participant was to learn the information), learning (how well the individual
mastered the training), behavior (the responses of the individual), performance
(who accurate the individual was in the responses), and reactions (how useful

the training was to the individual).

11

 

 

Training Assignment
Remedial or Advanced _

 

 

 

 

I

Perceived
Performance Attributions

( Fairness Perception Self Efﬁcacy
Distributive and Procedural

Jusﬁce

 

 

 

 

 

 

 

[ Motivation to Learn ]

K I J

Affective

Outcomes Performance
Reactions and Outcomes
Self Eff cacv

Figure 1. A Conceptual Model Presented by Quinones (1995). Participants

enter two different training assignment conditions. Participants will vary on

fairness perceptions, self-efﬁcacy, and motivation to Ieam. These will in tum
affect training outcomes.

 

 

 

 

 

 

As indicated the resulting conceptual model in Figure 1, Quinones’ found
that even though 74% of respondents of the training assignment expected to be
in the remedial training condition, there was a non-signiﬁcant relationship
between training assignment and loCus of control (luck attributions). There were,

however, signiﬁcant interactions between effort, task difﬁculty, and training

12

assignment. Individuals who thought their performance on the pre-training test
was due to their own inborn ability and were assigned to advanced training
showed a higher level of self-efﬁcacy than those who were assigned to the
remedial training. In addition, those who had expected to be in the advanced
level of training but were placed in remedial training showed decreased levels of
perceptions of fairness as compared to those individuals who thought they would
be placed in the remedial training and were actually placed in remedial training.
In regards to motivation to learn, highly motivated individuals completed more
trials than those who did not have a high motivation to learn.

In summary, Quinones demonstrated that training assignment, attributions
to assignment, and self-efﬁcacy have important implications for training
interventions. Additionally, he noted that motivation to learn was one of the
strongest variables that linked trainee characteristics to training outcomes.
However, there were virtually no differences in actual performance relevant to
differences in motivation to learn. He suggested that this could be due to a low
power (around 0.3) and that further research needed to be done to decide more
conclusively that motivation to learn has no impact on performance outcomes.

From these studies presented, there are several inﬂuences in training
interventions, especially increasing motivation to learn. From these studies,
framed messaging that includes positive and negative wording may increase the
variables that leading to a change in training outcomes. Based on social
cognitive theory (Martocchio & Webster, 1992; Quinones, 1995), there is a

framework for investigating the effects of framed messages on training. The

13

following section explores three potential mechanisms by which framing of the
training assignment can affect training outcomes. These include self-efﬁcacy,

motivation, and fairness perceptions.

Self-Efﬁcacy

Self-efﬁcacy, as deﬁned by Bandura (1997, p. 3) is known, as “the
judgment of one’s capability to organize and execute the courses of action
required to produce given attainments.” There are three dimensions to self-
efﬁciency: level (how individuals vary in difﬁculty level that they are capable of
performing), strength (the varying levels of conﬁdence in their ability to
accomplish the task), and generality (how the individual generalizes the ability to
do a particular activity across a domain or a similar range of activities). In
general, as individuals succeed in a task in a particular domain, he or she’s self-
efﬁcacy will increase.

Translated to a training context, self-efﬁcacy is a trainee's pre-oriented
beliefs on how well he or she is at the task being trained on or the belief of how
well he or she will learn the information. Messages received before or during the
training - either positive or negative - will help trainees compare their
performance to the established standard (Bandura, 1977). These messages can
affect a trainee’s belief on how they will be able to perform on a task in the future,
(Bandura, 1991) and then these messages translate into the cognitive
representation of different factors such as ability and motivation that will affect

future performance (Bandura, 1986).

14

Self-efﬁcacy has been applied to all different aspects of work
performance: higher self-efﬁcacy has lead to higher job performance (Stajkovi 8
Luthans, 1998). Similarly, Gist and Mitchell (1992) showed that persistence in
achieving a goal in the job is based upon higher self-efﬁcacy scores.

In addition to work performance factors, self-efﬁcacy affects training
outcomes. In studies done with training on computer skills (Gist, Schwoere, 8
Rosen, 1989; Martocchio 8 Webster, 1992), idea generation skills (Gist, 1989),
interpersonal complex skills (Gist, Stevens, 8 Bavetta, 1991), new policy
orientation, or other training tasks (Ford, et. al, 1998), when trainees believe in
their capabilities to transfer the knowledge they are being trained on, they are
more likely to actually transfer the knowledge to the workplace. In a step further,
when individuals believe they are able to change their performanCe with the
training, the trainee feels conﬁdent in his or her abilities to perform at the given
task (Holten et. al, 2000).

Self-efﬁcacy is not linked just to training performance but also to
educational instruction in the classroom (Garcia 8 Pintrich, 1994; Pajares, 1996).
Students who are higher in self-efﬁcacy outperform individuals with lower self-
efﬁcacy in nearly all types of performance tests. Additionally, these students had
more persistence in the face of failure than those with lower self-efﬁcacy
(Schunk, 1989).

These studies show that the level of self-efﬁcacy consistently relates to
various training outcomes. The majority of these studies indeed show that self-

efﬁcacy acts as an antecedent variable, which affects the variables of interests.

15

In regards to training, more researchers are suggesting increasing self-efﬁcacy
should be one of the goals of training and more attention needs to be placed on
how an individual arrives to their level of self-efﬁcacy (Quinones, 1995).

In addition to Bandura (1982)’s four categories of determinates for self-
efﬁcacy (enactive mastery, vicarious experiences, verbal persuasion, and
physiological arousal), Gist and Mitchell (1992) outlined three mechanisms by
which a person can arrive at their pre-training self-efﬁcacy. In the ﬁrst step, an
individual assesses the task and his or her relationship to the task. Secondly, the
individual makes inferences about his or her past performance. Finally, the
individual then evaluates the resources available to complete the task.

Translating these factors in a context of the training assignment,
individuals who receive remedial training will likely assume his or her assignment
is based off past performance, which will lead to lower self-efﬁcacy because of
past poor performance. It is therefore critical to minimize the possible impact on
lowered self-efﬁcacy. In addition to the negative outcomes of self-efﬁcacy in
training, lowered motivation can have an impact on training. A closer look at the
research on the relationship between motivation and training outcomes is now

reviewed.

Motivation

As Ford et al (1992) and Noe (1986) have noted, training effectiveness is
grounded in the trainee’s ability to ﬁrst master the information and then transfer it
to the natural environment. Trainee characteristics have emerged in the ﬁeld of

training research as one of the leading factors of individual outcomes in training

16

effectiveness. All types of individual differences can targeted to increase training
effectiveness such as ability, motivation, and attitudes. Of those, motivation
unlike other individual differences such as self-esteem and ability are malleable
and may be vulnerable to framed-training (Noe, 1986).

The trainee’s motivation to learn has a signiﬁcant impact on the
participants” ability to engage in knowledge transfer. Noe (1986) noted that the
motivation to Ieam is a “trainee’s speciﬁc desire to learn the content of the
training program.” In an attempt to ﬁnd a direct link between motivation and
training outcomes, Facteau et al (1995) found that trainees comprised of
government managers with high motivation to Ieam reported higher transfer than
those who had lower motivation. Mathieu, Martineau, and Tannenbaum (1993)
have also conﬁrmed that trainees with high motivation were more motivated to
learn, which, in turn, affected training effectiveness. In addition, Mathieu 8
Martineau (1997) found that pre-training motivation mediates other
characteristics that would transfer outcomes and Tannenbaum et al (1991)
discovered that pre-training motivation resulted in higher levels of physical and
academic self-efﬁcacy.

Additionally, researchers have suggested that motivation to learn is
related to attitudes about the training. Martocchio (1992) suggested that a
trainee’s pre-training attitudes predicted the ability to learn in a computer-training
task. Those participants who believed the training would be beneﬁcial to their
learning actually learned the information more than those who had a poor attitude

of the training.

17

These studies suggest that motivation to learn is a key component to
training outcomes. However, one component may interact with increasing
motivation, fairness perceptions. Participants of training interventions have self-
assessments of their own performance in the workplace, and this assessment
may be inconsistent with the feedback received from the training assignment. In
such a way, perceptions of lower fairness may then lead to lower motivation to

learn.
Fairness Perceptions

Fairness perception researchers have concluded that there are at least
two distinct types of fairness: distributive (decision outcomes) and procedural
justice (the procedures that helped arrive at the decision outcome). Leventhal
(1976) determined that individuals decide whether a procedure is fair by: (1)
judging whether it follows consistent rules, (2) deciding if the information is
accurate, (3) ensuring there is a safeguard against biases, (4) allowing for
inaccuracies information to be heard, and (5) following commonly agreed upon
terms of moral and ethical standards.

From this perspective, training assignment (remedial or advanced) may be
evaluated for distributive justice, and the perception of how the employer decided
to place the individual in that assignment would be an issue of procedural justice
(Greenberg, 1990). Trainees rate procedural justice to be high if the procedures
used to assign followed the ﬁve rules outlined above. Typically, participants’
judgments of procedural and distributive judgments are often correlated (Folger 8

Greenberg, 1985) however it is possible for participants to see them unequally if

18

they feel the process to arriving at a decision was fair but the outcome was
unfair.

Quinones (1995) stipulated that trainees are not passive to the
assignments that receive. Instead, participants believe they deserve certain
assignments. If they receive the “wrong” type of training based off their own self-
assessment of their performance, they will have decreased perceptions of
fairness for the training. In fact, Quinones found that participants who were
assigned remedial assignment but thought they should be assigned to advance
training reported lower perceptions of fairness. More importantly, he found a
signiﬁcant relationship between their fairness perception and their effort
aﬂﬁbuﬁons.

Fryxell and Gordon (1989) found that fairness perceptions affect training
outcomes. Remedial assignment may indeed change the fairness perceptions of
the individuals, which will then change motivations levels, inﬂuencing other

important training outcomes.

Framework for Current Research
Researchers have demonstrated that placing a trainee into remedial
training and negative feedback can lead to negative training outcomes. What
has received little attention is how to minimize these effects. Researchers from
different ﬁelds have been working to increase the motivation, self-efﬁcacy, and
behavioral changes — several of the variables that Quinones and other
researchers have found remedial training to remove. One strategy to improve

motivation and self efﬁcacy that would ultimately lead to behavioral changes

19

employs a manipulation of how messages are framed during the training by
utilizing key words and phrases that have implications for the individual such as
“gain, “save,” “cost,” etc. In the present study, the extent to which training
outcomes can be altered by providing two types of framed messages were
examined: gain (promotion/beneﬁt) and loss (prevention/cost). Speciﬁcally, gain-
framed messages given during training were hypothesized to remove the
negative impacts on motivation, fairness perceptions, and self-efﬁcacy caused by
the assignment to remedial training.

When individuals hear framed messages, they assess the message in
terms of its personal beneﬁts or costs. In early studies, researchers
demonstrated how problems that were “framed” shape one’s response to a
particular experience. For example, Gomersall and Myers (1966) found that
framing behavioral choices in a positively oriented manner is effective in
changing behavior, improving motivation, and increasing self-efﬁcacy. Further,
Gomersall and Myers assert that framing has been shown to be effective in
various ﬁelds such as medicine, economics, and psychology. In medicine, Toll et
al (2007) discovered that framed messages (a multi-method approach employing
textual messages on cessation pamphlets and verbal messages received during
the cessation program) facilitate increased motivation to quit smoking and
stimulated behavioral changes. In the ﬁeld of economics, Thaler 8 Johnson
(1990) demonstrated how framed messages modify behavioral responses; and in
the area of psychology, Clark and Grote (1998) found that framed messages

change attitudes on relationships.

20

The research is not limited to just these ﬁelds. In fact, framing has many
implications and deﬁnitions for all types of research. In psychology, framing often
refers to how people develop a “particular conceptualization” of an issue or their
cognitive conceptualization of the world around them (Chong 8 Druckman,
2007). lgou and Bless (2007) showed that training is involved in practically all
levels of cognitive processing from encoding, processing, decision making, and
the style in which individuals process information about oneself (lgou 8 Bless,
2007). Simple framing effects were shown in the United State government’s
“VERB. Its What You Do.” campaign. The VERB campaign was a multimillion-
dollar multi-media delivery approach that promoted physical activities in
American children from ages 9 to 13. Essentially the VERB campaign used text
messages in commercials and advertisements that just said simple things like:
“Go play,” “Float,” and “Run.” These simple messages led to behavioral changes
(Huhman, et. al, 2007).

Researchers commonly analyze framing effects in human decision making
and processes involved in deriving decisions. According to Einhorn and Hogarth
(1981), humans do not conform to the rational model of decision-making and
utilize several cues to make decisions. In essence, multiple outside factors can
affect the decisions making process. One of the major sources of research and

theory behind framing is prospect theory.

Prospect Theorv

Prospect theory explains decision-making strategies and how individuals

make choices. The theory suggests that the decision maker organizes

21

applicable information relevant to the decision in two ways: potential beneﬁts or
potential costs (see Kahneman 8 Miller, 1986 for more review on the prospect
theory). The individual has a reference point to evaluate the situations in terms
of beneﬁts or costs (Kahneman 8 Tversky, 1979).

Individuals are more sensitive to costs than beneﬁts when making
decisions, favoring a preference to sure gains rather than a risky alternative of
equal expected value. In a graphical representation, prospect theory takes a S-
shaped function: “When behavioral choices involve some risk or uncertainty,
individuals are more likely to take these risks when information is framed in terms
of relative disadvantages of the behavioral options, that is when the downside of
the situation is made salient. Alternatively when behavioral choices involve little
risk or uncertainty individuals are more likely to prefer these options when
information is framed in terms of the relative advantage, that is, when the upside
of a situation is made salient” (Higgins, 1987; Higgins, 2000).

This graphical representation is related to regulatory focus theory.
Regulatory focus theory explains that there are two different strategies for self-
regulation: one is the pursuit of beneﬁts (or the “avoidance of nongains”) and the
secondary focus is the avoidance of costs/losses (or the “pursuit of nonlosses”)
(Higgins, 2000). Multiple researchers have discussed these two theories as the
conﬂict between promotion and prevention focuses (Brendl, Higgins, 8 Lemm,

1995; Detweiler, Bedell, Salovey, Pronin, 8 Rothman, 1999)

Framing Effects

22

Both regulatory focus theory and prospect theory highlight the importance
of framing effects (Peter 8 Richard, 2008). First formally introduced by
Kahneman and Tversky (1979), framing effects demonstrate that how the
situation is framed, either beneﬁt or cost, will affect someone’s risk behavior. As
the individual enters a beneﬁt-framed situation, the individual seeks rewarding
outcomes. Likewise, when the situation is a cost-framed situation, the individual
avoids risky behaviors (Singh, 1986).

To explore the differences in cost versus beneﬁt, Fox and Dayan (2004)
placed individuals in different frame sets with investing decisions. In beneﬁt-
framed set, participants were told that they had invested money in oil drills and
the shares increased by ﬁfty percent. In the cost-framed set, participants were
told they had invested money in oil drills and shares had decreased by ﬁfty
percent. In both cases, the participants were told that there was also a 50%
chance that no additional oil will be found, which would make them lose all their
shares. Individuals who were in the beneﬁt-framed set perceived the situation as
a ﬁnancial gain unlike those In the cost-framed. Additionally, when the
participant was in the beneﬁt-framed set, the participant was more likely to
continue investing despite the future loss potential. The researchers suggested
that this happened because the participants focused on the description of the
sﬁuaﬁons.

Tversky and Kahneman (1981) have discovered that descriptors can
affect the decision making process because they act as a way to connect the

situation to costs versus beneﬁts. This process is called “decision framing.”

23

Several factors from the descriptors inﬂuence decision-making processes: habits,
personal characteristics, norms, and the formulation of the problem. With several
factors affecting decisions, outcomes from identical situations could be rendered
differently depending on the formulation of the problem.

Thaler (1985) illustrated the importance of the formulation of the problem.
After asking participants to rank the severity of losing a ten-dollar bill versus
losing a ten—dollar movie ticket, participants felt that losing ten dollars was more
detrimental because it was actual money and the ticket was framed as an item
only worth ten dollars. Similarly, Ross and Samuels (1993) renamed the problem
of the Prisoners Dilemma, calling it either the “Wall Street Game” or the “The
Community Game.” Those in the Wall Street game were more likely to defect
than cooperate because of the label regardless of the fact that both games were
idenﬁcaL

One type of cost-beneﬁt framing that uses the formulation of descriptors is
gain (beneﬁt) and loss (cost) framing. Gain-framed messages use beneﬁts and
promotion of beneﬁts to persuade behavioral change; loss-framed messages
highlight costs and prevention of negatives to encourage behavioral change. In a
1999 study, Detweiler, Bedell, Salovey, Pronin, and Rothman asked participants
to watch information on sunscreen safety. They discovered that if an individual is
given the opportunity to make a decision when there was gain-framed messages
(“Protect yourself from the sun and you will stay healthy”), individuals were more
likely to use the sunscreen than if they were presented loss-framed messages

(“Don’t expose your skin to the sun and you will not become sick”). These results

24

were reproduced when looking at smoking cessation programs. In a study with
one hundred seventy smokers (Schneider, Salovey, Pallonen, Mundord, Smith, 8
Steward, 2001), a signiﬁcantly higher amount of smokers were continuously
abstinent in the gain-framed condition than in the loss-framed condition (non-
smokers will live a healthier life versus smokers will die).

Studies like this have conﬁrmed that gain-loss framed messages are
useful in the health industry. More speciﬁcally, when behaviors have relatively
certain outcomes, individuals are more persuaded by gain-framed messages
(Rothman 8 Salovey, 1997). Consequently, if the outcomes are uncertain like
breast cancer, loss-framed outcomes are more persuasive (Schneider, Salovey,
Apanocitch, et. al. 2001). These outcomes have been seen across dozens of
studies, which have been summarized in a meta-analysis.

O’Keefe and Jensen (2007) looked at 93 different studies that identiﬁed
themselves as analyzing the differences between gain and loss framing in health
promotion. The researchers used a literature search, personal knowledge, and
inspection of reference lists to identify the reports that were published up until
August 2006. The studies had three criteria: (1) the study compared gain and
loss framed messages (and did not include any studies that looked at “combined
gain-and-Ioss frame” such as Treiber, 1986), (2) the study had to advocate health
speciﬁc behaviors to prevent disease (disease detection was not included), and
(3) the study had quantitative data that was appropriate for the meta-analysis
(change in attitude or change in behavior were the most common of these

effects).

25

Of the total 21,656 participants, researchers concluded that gain framed
appeals were “more persuasive” than loss-framed messages. However, the
researchers were skeptical of the results for two reasons. First, the computed
correlation of the gain-framed messages to desired behaviors was only 0.03,
which they noted was “not necessarily unimportant or trivial.” Second, large
effect sizes found in hygienic studies overpowered results of gain and loss
framing. However, even looking at these differences without hygienic cases,
there was still a statistical advantage of gain framing. The researchers noted that
gain framing advantages are “exceptionally small and decidedly not general.”

Latimer, Salovey, and Rothman (2007) have responded to this meta-
analysis by echoing several claims by Cesario et al (2004) and Lee and Aker
(2004). First, the meta-analysis only compared medical ﬁelds in regards to
preventing behaviors in convenient, non-targeted conditions. Current research is
attempting to reﬁne framing for the right, appropriate condition as seen in
Rothman, Bartels, Wlashin, 8 Salovey (2006). Second, the reason why dental
hygiene may have results that are more signiﬁcant in gain framing is due to the
tangible, more obvious outcomes (cavities, gingivitis, etc.) whereas cancer
prevention is less tangible (increase likelihood of developing cancer). Third,
many studies included in the meta-analysis showed negative effect sizes which
was a direct result of their being a clear lack of a negative consequence such as
in the studies looking at fruit and vegetable consumption or physical activity

Finally, the effects of gain and loss framing “will remain small to medium

sized.” The reason for this is that gain and loss-framed message effects are “one

26

time shots” at attempting to change someone’s behavior. This is a relatively
huge feat compared to effect sizes seen in the US. Center for Disease Control’s
framed-message VERB campaign (Huhman et al, 2007) because the VERB
campaign had multichannel messaging and had longer effect time intervals.
Therefore, researchers comparing the results against those of the VERB
campaign must take these huge differences into consideration and must also
consider that even small effect sizes in gain and loss framing can make a
practical difference. Rose (1995) suggested that even small effects in a large
group size could have huge implications of change at the population level.

In many domains of framed-messages effects, small to medium effect
sizes have been achieved. The areas that have demonstrated signiﬁcant results
In gain and loss-framed messages are now presented, starting with gain and

loss-framing’s greatest success, health promotion.

Framing Effects in Health Promotion

Gain and loss framing can be used to change attitudes on health.
Researchers propose that frames are effective in leading the population to use
health products when the message is gain framed and paired with a promotion
based message (“good health”); conversely, they noted that loss framed appeals
are the most effective when the message is paired with prevention (“stop
cancer”). Several researchers have demonstrated this effect in such domains as
sunscreen use, cancer screening, dental hygiene, and vaccination.

To promote the use of sunscreen, researchers paired the promotion

message with a gain-framed message: “Enjoy life! Bask in the warm rays of the

27

sun, feeling completely happy. Let SunSkin be a part of your daily routine. Enjoy
Life.” They also paired it with a loss message: “Don’t miss out on enjoying life!
Not being able to bask In the warm rays of the sun may stand in the way of your
feeling completely happy. Let SUNSKIN be part of your daily routine.” In the
prevention-moderated messages, Lee and Aaker (2004) used gain messaging:
“Be safe! Know that you are risk free from sunburns, feeling completely relaxed.
Let SunSkin be a part of your daily routine. Be safe.” They also used loss
framed messaging: “Don’t miss out on being safe! Not knowing you are risk-free
from sunbums may stand in the way of your feeling completely relaxed. Let
SUNSKIN be a part of your daily routine. Don’t miss out on being safe.”
Prevention appeals were more effective with loss framing and promotion was
more effective with gain framing. However, in general, the gain frames were still
more effective in assisting in health promotion, which was not the case in breast
cancer screening.

In reaction to recent increases in breast cancer rates, American and
European governments have attempted to identify the best method to promote
breast cancer screening. Because most women who are screened for breast
cancer are healthy, obtaining a mammogram is actually a psychologically risky
behavior because there is a chance of getting abnormal results. However,
abnormal results are often presented to patients in probabilistic and uncertain
outcomes. Based on these indeterminate outcomes, one hypothesis is that
mammograms should be presented in loss-framed messaging, not gain-framed.

To test this, 130 women were recruited to watch a ﬁfteen-minute video about

28

breast cancer and mammography. The gain-framed individuals watched a video
called “The Beneﬁts of Mammography” and the loss-framed individuals watched
a video called “The Risks of Neglecting Mammography.” There were no
differences between the contents of the videos. However, after 12 months, 66%
of women in the loss-framed set had obtained a mammogram compared to the
52% of women in the gain-framed set (Banks et al, 1995). Schneider et al (2001)
replicated the experiment several years later and found the same results: only
36% of the gain framed women received mammograms as compared to 50%
with the loss framed individuals. Block and Keller (1995) have found similar
results for cervical cancer screening.

Additionally, gain and loss-framed messages have been used in dental
hygiene promotion. Rothman, Martino, Detweiler, and Salovey (1999) looked at
the effects of messaging on undergraduates’ use of mouthwash. One group was
given information about a typical mouthwash - one that removes plaque from
teeth and prevents tooth decay and gum disease. The other group was shown a
new kind of mouthwash — one that detects plague buildup by leaving a
discoloration in the areas that need attention. Arguments were presented in both
gain and loss framed arguments. As expected, participants in the gain-framed
situation were more likely to buy the mouthwash (67%) whereas 47% of the
participants would in the loss-framed situation. The same types of results have
been seen in vaccinations as well.

In a laboratory experiment to test the effectiveness of gain and loss

framed messages encouraging vaccination against the West Nile virus, Bartels,

29

Kelly, and Rothman (2007) used four groups to ensure that it was the message
that actually manipulated behavior and not message content. One set of gain-
Ioss framed participants were told that the vaccine was effective 9 out of 10 times
while the other set of gain-loss framed participants were told that the vaccine was
effective 6 out of 10 times. The results showed that the message (6 out of 10
versus 9 out of 10) had no effect but gain-frame advantage emerged once again
in encouraging behavior to become vaccinated. The researchers concluded that
gain framed appeals optimize persuasiveness.

Gain and loss framing has been also used in chemical addiction cessation
programs. Researchers suggested that gain-framed messaging (beneﬁts of
quitting emphasized) would be more persuasive in encouraging smokers to quit
their addiction than loss-framed (costs of continuing to smoke) messages. To
test this, Toll et al (2007) randomly assigned 258 participants in a clinical trial of
gain and loss-framed conditions where they received video, printed messages,
and written letters, all written in the particular framing. After seven weeks, the
proportion of smokers who remained abstinent was signiﬁcantly higher of the
participants who were in a gain-framed condition as compared to the loss-framed
condition. These type of effects are not limited to health promotion but to other

types of behavior such as consumer behavior.

Framing effects on consumer behavior
Gamliel and Herstein (2007) have noticed the predominant advertising
campaigning used by mass merchandisers is gain-framed messaging.

Questioning the effectiveness, the Gamliel and Herstein noted that retailers

30

should be using loss framing because the outcome of not buying their brand is
ambiguous (which follows the theory of gain and loss framing effects). They
conducted an experiment on private brand purchasing (e.g. generic grocery
brand such as Great Value from Wal-Mart) relative to national brands (e.g.
national name brand such as Kellogs) in two contexts: positive frames (“save” on
purchasing) and negative frames (“loss” on purchasing).

Five hundred participants were asked the following question: “Assuming
the price of the private brand is $2.90 for a 1 liter package, would you be willing
to lose $.90 and pay $3.80 for 1 liter package of that National Brand product?” or
“Assuming the price of the National Brand is $3.80 for a 1 liter package, would
you be willing to gain $.90 and pay $2.90 for a 1 liter package of the private
brand?” Participants’ were more likely to buy private brands in the loss
framework than those in the gain framework; that is, they would lose money if
they did not buy this product. This research is consistent with the subjective
nature of framework: when the outcome of not doing the task presented is
ambiguous, loss framework is more effective. These types of effects are related

to the results seen in gain-framed and loss-framed relationships.

Framing effects in relationships

Relationship processing research indicates positive mindsets are
beneﬁcial to relationships, and researchers have questioned the extension of
beneﬁts of gain and loss framed messaging. Berger and Janoff-Bulman (2006),
building from the Clark and Grote’s model (1998), explored the relationship

between appreciation of the partner and how chores were viewed. The

31

researchers took three groups of people: low satisfaction in relationship (the loss
framed group), medium satisfaction, and high satisfaction (the gain framed
group). After placing them in their group using a marital satisfaction survey, the
couples were administered two additional surveys regarding how many chores
and favors they did for their signiﬁcant other and how much they enjoyed doing
these task. The results of the study were parallel to that of gain-framed context.
The “costs” of a relationship, as the researchers called them, such as doing
favors for a spouse (washing their clothes, cleaning their mess), are all
dependent on the frame of the relationship. The costs of the relationship were
more pleasant, “less obligatory,” less routine and mundane, and more enjoyable
when they were gain framed (high relationship appreciation). Conversely, the
cost of relationship was negatively inﬂuencing the individual in loss-framed
relationships (low appreciation). Researchers showed that even the routine tasks
became “positive investments” in the gain framed context. Interestingly, the
partner’s appreciation for these mundane tasks was a basis for regarding these

chores, etc. as investments or inputs for the relationship - not as tasks.

Framing effects in gambling

Gain and loss framing can also affect gambling behavior. Thaler and
Johnson (1990) note that individuals who win after the ﬁrst gamble automatically
enter in the gain-frame and those individuals who lose after the ﬁrst attempt
automatically fall into the loss-frame. They have labeled the subsequent choices
that follow the ﬁrst game as the House Money Effect. The House Money Effect

notes that in prior gains players are more likely to accept future gambles;

32

however in the loss frame (where participants have already lost) participants are
less likely to take risks unless they are given the opportunity to break even. The
House Money Effect happens because the gamblers in the gain frame claim they
are “playing with the house money.”

To demonstrate this effect, Thaler and Johnson simulated the gambling
Choices. Each choice was presented with a prior gain or loss frame (“You have
won/lost X dollars, now chose between gamble A or sure outcome B). Following
the gain, 77% of the subjects were risk seeking (meaning they chose to gamble
instead of the sure outcome) when they were in the gain framework.
Researchers concluded that it was due to the framing of the context that caused
the behavioral differences.

Gain-framing consistently demonstrates positive outcomes from the
domains discussed. Additionally, gain and loss-framed message effects have
been researched in other domains such as personality (Levin, et al, 2002) and
surgical decision making (McNeil, 1982). However, many researchers have
wondered if the response to gain and loss framed messaging is strictly

behavioral or if it has cognitive groundwork.

Framing Effects on Cognitive Processes

Bechara, Damasio, and Damasio (2000) discovered that different parts of
the brain are engaged in decision-making depending on the frameset. To
examine how this works, Gonzalez, Dana, Koshino, and Just (2005) theorized
that framing effects are a direct result of the tradeoff between the cost of the

stimulus in the alternatives (the more costly, the less likely people chose the

33

option) and the “affective value” of the alternatives (the more discomfort felt, the
less likely people chose the option).

In order to understand this framework, researchers followed the work of
Payne and his colleagues who proposed the Cost-Beneﬁt Tradeoff Theory.
Payne et al, (1993) proposed that choice is simply a “compromise between the
desire to make the correct decision and the desire to minimize effort.” In
essence, individuals begin the decision-making process by devising a list of all
the possible alternatives and seeing which decision can expend the minimal
amount of cognitive effort. If the person cannot ﬁnd a good alternative through a
simple alternative, then the individual will commit to a more complicated cognitive
effort.

More speciﬁcally in a gain frame, individuals strive to have to make fewer
compromises and make decisions with as little cognitive ability as possible. For
example, when given a scenario where the participant can save 200 lives, the
decision is easy because there is no cost associated (the decision is effortless)
and the participant "feels” effectively good in what they are doing. Even if the
scenario is risky (for example, the participant has the possibility of becoming
injured but will clearly save hundreds of lives), the participant will still feel
effectively good and thus follow through on the scenario.

Conversely, if the situation changes to a loss frame, the cognitive
processes alter. When a participant is told 400 will die, the participant engages
more cognitive processes as measured by MRI to decide on how he or she will

behave because they are intent on altering the outcome. As a result of the

34

cognitive engagements, participants have been shown to take signiﬁcantly longer
time in making decisions when the message is framed in losses than gain
(Payne, et. all, 1993; Glimcher, 2003).

In addition, fMRl studies have shown that gain and loss-framed decision-
making involves the prefrontal cortex. The prefrontal cortex is associated with
decision-making in affective processes and in risk assessment. Gray, Braver,
and Raichle (2002) proposed that in gain and loss-framed decisions the
prefrontal cortex is more engaged, especially during loss-framed messages
because there were more feelings of displeasure than with the gain frame. To
test this idea, Paulus et al (2001) gave ﬁfteen college students a MRI during 10
different scenarios using the different framing techniques in regards the Asian
disease problem: “Imagine that the United States is preparing for an outbreak of
an unusual Asian disease that is expected to kill 600 people.” The gain-framed
message said that the program would save 200 people while loss-framed
message was that the program would kill 400 people. Both frames were paired
with probability statistics. The researchers were interested in which option they
would chose and their reaction time. The results conﬁrmed most of what the
researchers predicted: participants chose the gain-frame signiﬁcantly more than
the loss frame, and it took more time for participants to make decisions when
there two options were both in loss frames. Additionally, the fMRI revealed that
there was signiﬁcantly cognitive effort expended in loss frames than gain frames

by the activation in the prefrontal and parietal cortices of the brain.

35

Despite the behavioral Changes and cognitive processes, some are
suspicious of the effects of gain and loss-framed messages, claiming that
descriptors used cause participants to respond to the tone of the message, which

causes the cognitive and behavioral reaction.

Power of Descriptors

The way a message is communicated using distinctly different terminology
has been a concern of many researchers (see Dilllard 8 Marshall, 2003;
Rothman 8 Salovey, 1997; Wilson et al, 1988). This has been called the effect of
“descriptor” (Tversky 8 Kaheman, 1981) or the “kernel phrase” (O’Keefe 8
Jenson, 2007). Using the gain-frame experiment with the Asian disease problem
as an example, the gain-framed outcome was framed positively with positive
descriptors: “If Program A is adopted, exactly 200 people will be saved.”
Researchers noted that perhaps they should have worded it as the following: “If
Program A is chosen, 200 people will not die” (Levin, Schneider, 8 Gaeth, 1998).
Similarly, in the loss frame, Program 8 could be redesigned to say, “400 will not
be saved.” This is called “simple negation versus alternative terminology” and
should be considered when exploring the effects of gain-loss framing.

To determine the effect of the terminology, the vaccination study was
replicated to Show differences between the “descriptor formulations,” ensuring
participants were not responding to the tone of the study. In the experiment, the
descriptor formulation was “saving” and “not saving” (versus “dying”) and the

outcome formulation is “losing” and “not losing” (versus “living”). The results

36

replicated the original experiment. Gain framing was still more effective than loss
framing regardless of tone (Levin, Schnedier 8 Gaeth, 1998).

Researchers have repeated experiments to ensure that tone was not the
reason for the behavioral change. Latimer, Salovey, and Rothman (2007) point
out that how the gain and loss framing messages are worded are not nearly as
important as in what domain the framed messages are placed. They conclude
further research to tone of the message is not necessary. However, they note
that research regarding framed messaging in other domains should continue.
One of the domains in which gain and loss-framed messages has not yet been

researched is training.

A conceptual model of the studv

While researchers have established that training assignment may have
detrimental effects, it has yet to be shown how framed messages during the
training affect the way a trainee absorbs the information and perhaps more
importantly, how framed messages may actually enhance or inhibit the effects of
training assignment. Quinones (1995) has demonstrated that assignment to
remedial training has negative outcomes such as self-efﬁcacy, fairness
perceptions, and even training outcomes such as learning and behavior. This
study extended Quinones’ work to examine the effects of framed messages
during training.

Health educators have used framed message to change behaviors, and
they view framed messaging as a driving force for Changing behavior. Trainers

are unsure about how these messages will affect a non-threatening life episode

37

such as training. Given that framed messages have been shown to affect other
domains that involve non-life altering decisions such as economic decision
making, gambling, and relationship perspectives, perhaps the effect of framed
messages do indeed have potential to change behavior and perceptions in
training domains.

In the present study, the researcher assumed the effects of training
assignment are clear and that advanced training participants have a clear
advantage in several areas. Therefore, as demonstrated in the Figure 2 that
follows, all participants were assigned to remedial training to establish the effects
of gain and loss framed messages. Participants were randomly assigned to gain
or loss framed training conditions to evaluate the impact of these messages on
outcomes. The model proposed that gain and loss framed messages would have
the most immediate effect on self-efﬁcacy, fairness perceptions, and motivation
to perform. These results in turn would affect both affective and performance
outcomes. Trainees’ level of self-efﬁcacy would be dependent on each
individuals” own perceptions of their ideal and actual selves. The effect of gain-
framed training was hypothesized to remove the negative outcomes that are
commonly associated with remedial training experienced in the Quinones study.

In keeping with current research, the experimenter carefully removed two
important framing inﬂuences: (1) the distinction between promotion and
prevention and (2) the inﬂuence of descriptors. First, many health-related studies
made the distinction between promotion and prevention such as the sunscreen

study. However, while the effect differences between promotion and prevention

38

are signiﬁcant, the effects are small and, more importantly, limited to studies in
health promotion. Secondly, because this study attempted to look at decision-
making that will not inﬂuence physical health in any direct way, the distinction
between promotion and prevention was not considered in this study.
Additionally, descriptors, as noted previously, have been a concern of some
researchers in framed message. However, as noted by Latimer, Salovey, and
Rothman (2007), descriptors should not be considered and thus not considered
in this study.

Finally, O’Keefe 8 Jenson (2007) have argued in which direction a
particular framing may Change behavior. The results of gain and loss-framed
messaging have dramatically different outcomes depending on the context as
found in the mammogram experiment (Banks et al, 1995) compared to the
mouthwash experiment (Rothman et al, 1999). In certain contexts like the
mouthwash experiment, gain framing has the upper hand in promoting positive
behavioral change, and in other contexts like the mammogram experiment, loss
framing has the advantage. Researchers have theorized that when outcomes
are more tangible (for example, saving 9 lives, reducing plaque by 74%), gain
framing has a Clear advantage and when outcomes are vague (for example,
discovering a disease, “may” save lives, etc), loss framing has the advantage.
Therefore, because this study employed various means of somewhat tangible
outcomes, gain-framed messaging was hypothesized to have the advantage.

With these principles in mind, the study explored the outcomes of

message-framing during remedial training. Researchers have determined that

39

 

 

J

  

J

 

[ Ideal/Actual Self J
Discrefancv

 

Self-Efﬁcacy

 

 

 

 

   
    
 

 

 

Fairness Perception

 

 

 

Gain/Loss—Framed I

Remedial Assignment
AffectIve Outcomes
8 Performance Outcomes

 

 

 

 

 

 

 

Motivation to Learn

 

 

L

 

 

 

 

k

Figure 2. A Conceptual Model of the Current Study. Remedial
training participants are placed into gain framed training, which will
in turn affect self-efﬁcacy, faimess perceptions, and motivation to
Ieam. These variables may affect outcomes.

remedial training has a “cycle” that needs to be broken: participants enter with
lowered motivation to learn and lowered other training variables, which then may
lead to lowered training outcomes. These negative outcomes defeat the purpose
of remedial training. Therefore, the experimenter proposed that after the
participant enters the remedial training, framed-message training might break the
cycle of remedial training. To test this idea, participants randomly were assigned
one of two training conditions: gain or loss. After completing their training,
participants were assessed on self-efﬁcacy, fairness perceptions, and motivation
to learn. Because of the effects demonstrated by Higgins (1987), how
actual/Ideal discrepant individuals interacted with self-efﬁcacy was tested.

Finally, the participants were tested on training outcomes and affective

4O

outcomes. Figure 1 below is a graphical representation of the information
presented above. From these principles, the following section presents the

current study’s hypotheses.

Hypotheses

Because the effects of training assignment have been demonstrated,
hypotheses were formulated based off individual questions to test the effect of
gain and loss framing during the training. These hypotheses included mediating

factors that may come into play during the training.

Self-Efﬁcacy

Self-efﬁcacy, the judgment of “one’s capability to organize and execute the
courses of action require to produce given attainments” (Bandura, 1997), has
continuously played a role in the area of training whether it be in computer
training (Martocchio 8 Webster, 1992), interpersonal skills (Gist, Stevens, 8
Bavetta, 1992), or, as in Quinones’ study, training assignment (1995).
Individuals who were placed in the advanced training showed higher levels of
self-efﬁcacy compared against remedial group individuals attributing their
success to innate ability. In contrast, for participants who thought ability had no
role in their performance, individuals in the advanced group had lower self-
efﬁcacy than those in the remedial group. These ﬁndings conﬁrm that training
assignment does indeed have an impact on self-efﬁcacy. When training
assignment power combines with gain/loss framed training, the effects are not

clear.

41

In a study that may shed some clarity, Krueger and Dickson (1994)
recorded self-efﬁcacy levels when placed in negative and positive feedback
situations. They found similar results to Quinones, who found that self-efﬁcacy
was lower in the negative feedback situation. However, when participants were
then placed in situations with gain and loss gambling situations for an additional
gambling trial, participants’ lowered self-efﬁcacy received from negative feedback
was enhanced by threat of losses (loss framing). Loss-framed messages further
degraded the self-efﬁcacy of the individual. Based off this type of study, self-
efﬁcacy may be related to gain and loss-framed messages in training. Gain/loss-
framed messages may increase the power of the indirect feedback that the
participants receive. Therefore, when a participant receives gain-framed training
in remedial assignment, the trainee may experience higher self-efﬁcacy than
those participants in the loss-framed training.

Q1: Can gain and loss-framed messaging elevate self-efficacy in a

remedial training assignment?

H1: Gain and loss-framed training will affect participants reported self-

eﬁicacy. Participants in gain-framed training condition will report higher

self-efﬁcacy than those participants placed in the loss-framed training

condition.

Motivation to Learn
Previous research has determined that training motivation — that is, a
participants personal desire to learn the skill or knowledge - affects training

outcomes (Colquitt, LePine, 8 Noe, 2000). While participants are able to master

42

the concepts being taught, the failure of the participants to transfer the
knowledge may ultimately lie in their lack of motivation to learn. Multiple studies
have veriﬁed that higher motivation is directly related to higher knowledge
transfer (Mathieu, Martineau, 8 Tannenbaum, 1993; Facteau, Dobbins, Russel,
Ladd, 8 Kudishc, 1995). In research related to sports activities, Kincey, Amir,
Gillespie and Carleton (2006) found that motivation to learn a sports routine was
linked to sports performance. Additionally, in Quinones’ study (1995), he found
that motivation had a signiﬁcant relationship to self-efﬁcacy as a training outcome
especially for individuals in the remedial training. While motivation has been
demonstrated in the role of training assignment, framed messages have yet to be
understood.

The goal of framed messages is to promote a behavioral change. This
has been repeatedly demonstrated in research with health promotion as
demonstrated previously. However, as noted by Rothman and Salovey (1997),
the particular way the framing motivates the behavior is directly dependent on the
context of the desired behavior (health promotion versus relationship contexts).
Very few studies have looked speciﬁcally at the motivation behind the behavioral
change. However, Wilson, Wallston, and King (2006) demonstrated this
interesting effect in their research on motivation to quit smoking. Participants in
their research demonstrated that the threat of losing a positive reward resulted in
more motivational change attitudes than gain-framed messages. Wilson,
Wallston, and King suggested that in certain contexts, like health, loss-framed

messages might be more effective for promoting motivational behaviors.

43

Consequently, the targeted gain and loss-framed messages may also cause
variations in motivation to learn.
Q1: Does gain and loss-framed messaging improve motivation to Ieam in
remedial training?
H2: Motivation to Ieam will vary by framing condition. Those participants
who are placed in the loss-framed training condition will have lower
motivation to Ieam the Na val simulation task than those individuals in the

gain-framed training condition.

Ideal/Actual Self

Researchers have discovered that there is a difference between the
individual’s perceptions of the actual self and the ideal self (Higgins, 1982). The
idea of the actual ideal self has been related to low self esteem when an
individual views their own characteristics are different from their ideal self (mostly
in the negative direction where the real self was below the ideal self). Therefore,
those who have discrepancies (referred to as “discrepant person” In the
literature) are motivated by anything that will help them achieve the ideal self.
As it relates to the current literature on framing, Higgins and Tykocinski (1992)
discovered that actual ideal discrepant persons are more “attentive” to messages
that have loss-type, negative outcomes compared against gain-framed, positive
messages. After using a gain and loss framed instruction on breakfast
consumption, further research by Tykocinski, Higgins, and Chaiken (1994), has
found that these discrepant individuals who read a gain-framed message (which

is type of message used in the Naval training) would actually engage the

 

High
Self

Efﬁcacy . .
HIgh DIscrepancy

— — Low Discrepancy

Low
Self
Efﬁcacy

 

 

Gain Frame Loss Frame

Figure 3. Proposed Interaction of Ideal/Actual Discrepancy with
Self-Efﬁcacy and Framing

individuals low self-esteem, causing a rejection of the gain-framed messages.
Conversely, person with a self-esteem was more receptive and responsive to a
loss-framed message. Translating this into the relationship with self-efﬁcacy and
its impact on the results of the gain/loss-framed training, researchers have found
close ties to self-efﬁcacy. In fact, some researchers have suggested that the
differences among self-esteem and self-efﬁcacy are “psychologically
meaningless” (Meehl, 1990) or “ambient noise” (Lykken, 1968) and “quite small
in magnitude” (Judge, Erez, Bono, 8 Thoreson, 2002). Additionally, discrepant
individual research has shown strong ties to self-efﬁcacy (Eiser, Cotter, Oades,

Seamark, 8 Smith, 1999).

45

In accordance to these ﬁndings, this study assessed the individual’s
ideal/actual discrepancies. As shown on Figure 3, high discrepant individuals will
act in the opposite direction of the proposed self-efﬁcacy hypotheses.

Q3: Do individual differences in ideal/actual self-discrepancies interact

with the power of the gain and loss-framed messages?

H3: The effects of gain and loss-framed training on self-efﬁcacy will vary

depending on the level of ideal/actual self-discrepancy. High discrepant

individuals in the loss-framed message will respond to loss-framed
messages, leading to higher self-efﬁcacy compared to the gain-framed
individuals. Low discrepant individuals will have lower self-efﬁcacy in the
loss-framed message training compared to individuals in the gain-framed

message training.

Fairness Percepﬂms

The issue of fairness is related to motivation to learn. Researchers have
suggested that perceptions of fairness can relate to commitment to the task and
motivation to Ieam (see Folger 8 Konovsky, 1989; Fryxell 8 Gordon, 1989).
Training assignment is a source of fairness feedback, which has been shown to
affect motivation levels. Quinones (1995) found that participants who believed
they should have been placed in the advanced training and were placed in
remedial training showed signiﬁcantly lower levels of fairness compared to those
who were placed in the advanced group. Similarly, those individuals who thought
they did poorly but were placed in advanced training showed high fairness

perceptions.

46

No published studies have strictly looked at fairness perceptions in gain
and loss-framed messages. However, Liberman, Idson, and Higgins (2005)
noted that in contexts such as negotiations, participants would perceive loss-
framed messages as “intensely negative,” and DeDreu (1996) concluded that
framing outcomes in a gain-framed context creates a stronger concern for
distributive justice when paired against losses in the context of co-worker
equality. Utilizing the results of these studies and given framing message cues
of beneﬁts versus cost, participants may experience less negative fairness
perceptions when given gain-framed training compared to loss-framed training.
Q4: Does gain and loss-framed messaging inﬂuence pre-existing the
negative fairness perceptions received from remedial training?
H4a: Perceptions of fairness will be related to framed-messages.
Participants placed in gain framing will have a higher perception of
fairness perceptions (both distributive and procedural justice) to remedial
training assignment than those participants in the loss framed training

condition.

Trainin Outcomes8Trainin Reactions

Training outcomes are tangible behaviors that occur due to the training
intervention. Training effectiveness and outcomes have been measured in
several different ways: reactions to the training, length of retention, behavioral
changes, and results on a task (Kirkpatrick, 1960). Training outcomes will vary

based on framed messages. In this study, both affective and performance

47

outcomes were measured. In regards to affective outcomes, reactions to training
in the gain framed-training will be seen as more positive as it was seen in the
smoking cessations experiment (see Toll et al, 2007). For performance
outcomes, there is little literature to suggest how framing will affect speed,
accuracy, learning, and behavior after a training session. However, based off the
research by Paulus (2001), gain-framed messages facilitate quicker decision-
making. Therefore, in regards to speed, gain-framed messages may help
participants make quicker decisions. Additionally, gain-framed messages in
tangible-outcome behavioral studies have been more effective in changing
behavioral compared to loss-framed messages (Toll et al, 2007). Therefore, in
regards to learning and behavior, gain-framed messages may help participants
learn the information and respond with the appropriate behavior. However, for
accuracy, no studies speciﬁcally test “accurate” decision making when placed in
differently framed messages.

Q5: Do gain and loss-framed messages inﬂuence training outcomes such

as speed, accuracy, leaming, and behavior?

H5a: Framing will be related to speed and accuracy. Participants in gain-

framed training will have higher speed and accuracy compared to

participants in loss-framed training.

H5b: Framing will be related to learning. Participants in gain-framed

training will have higher learning scores over loss-framed training.

H6: Participants will view gain-framed training more positively than those

in loss-framed training.

48

Mediating Hypotheses

Organizational psychologists have recognized the role of mediating effects
in work behaviors (James 8 Brett, 1984). Mediating effects explain, “how
external physical events take on internal psychological signiﬁcance” (Baron 8
Kenny, 1986). When it comes to framing effects, research on the mediating
effects is thin. However, an emerging amount of research has looked at
mediating effects of certain variables on training outcomes.

One of the more popular areas is self-efﬁcacy and the relationship to
training outcomes (Frayne 8 Latham, 1987; Gist, 1989). However, previous
research has not yet tested the mediated effects of self-efﬁcacy on the
relationship between training outcomes and gain/loss-framed messages. In such
way, it was hypothesized that self-efﬁcacy may be a strong predictor of training
success as demonstrated by Gist, Schwoere, and Rosen (1989) and Martocchio
and Webster (1992). However, gain and loss-framed training may activate or
elevate the person’s self-efﬁcacy. This type of hypothesis is linked to the
research by Gomersall and Myers (1966) who demonstrated that a successful
training intervention was due completely because it elevated participants self-

efﬁcacy.

Q7: Does self-efficacy mediate the relationship to successful training

outcomes?

49

H7a: Self-efficacy is hypothesized to mediate the relationship between
gain/loss-framed messages and behavioral outcomes.

H7b: Self-efﬁcacy is hypothesized to mediate the relationship between
gain/loss-framed messages and performance outcomes (speed and
accuracy).

H7O: Self-efficacy is hypothesized to mediate the relationship between

gain/loss-framed messages and learning outcomes.

Another mediating factor hypothesized to effect training outcomes is
motivation. Quinones found many correlational relationships between motivation
and accuracy, behavior, and learning outcomes (1995). More importantly,
researchers have begun to uncover the mediated effects of motivation for
accomplishment. Barrick, Stewart, and Piotrowski (2002) found that motivation
mediated the relationship between personality and job performance in sales
associates. This type of mediated model was hypothesized to predict training
outcomes with gain/loss-framed training.

08: Does motivation to learn mediate the relationship to successful

training outcomes?

H8a: Motivation is hypothesized to mediate the relationship between

gain/loss-framed messages and behavioral outcomes.

H8b: Motivation is hypothesized to mediate the relationship between

gain/loss-framed messages and performance outcomes (speed and

accuracy).

50

H80: Motivation is hypothesized to mediate the relationship between

gain/loss-framed messages and learning outcomes.

Finally, the model hypothesizes that fairness perceptions may mediate
effects for gain/loss-framed messages. Again, Quinones found signiﬁcant
correlational relationships between fairness perceptions and training outcomes
(1995). Similarly, Pillai, Schriesheim, and Williams (1999) found that fairness
perceptions mediated the relationship between transformational leadership and
organizational citizenship behaviors. This type of mediating effect was predicted
to effect gain/loss-framed training outcomes.

Q7: Do fairness perceptions mediate the relationship to successful training

outcomes?

H9a: The perception of fairness is hypothesized to mediate the

relationship between gain/loss-framed messages and behavioral

outcomes.

H9b: The perception of faimess is hypothesized to mediate the

relationship between gain/Ioss-framed messages and performance

outcomes (speed and accuracy).

H90: The perception of fairness is hypothesized to mediate the

relationship between gain/loss-framed messages and learning outcomes.

51

Additional Exploratom Evaluations

In addition to the variables mentioned in the hypothesis, the researcher
measured other variables related to gain and loss-framing: mood orientation and
personality.

Kaheman (2003) and Schwartz (2002) noted that affect may be an
important part of the decision making process. Williams and Voon (1999) and
Shiomura and Atsumi (2001) also noted that mood affects the way individuals
process information. For example, positive mood increases “cognitive ﬂexibility”
and expanded reasoning (Isen, 2000). Conversely, Schwarz (2002) discovered
that negative mood leads to increased attention and expanded cognitive
resources for processing information. Additionally, negative mood relates to
decision-making. For example, when a person experiences a negative mood,
that individual Is more likely to overestimate the likelihood of the chances of a
future negative event and underestimate the chances of a future positive event
(Nyren, lsen, Taylor, 8 Dulin, 1996). For example as Nygren (1998), individuals
given a gambling decision in a positive mood can lead those who typically are
low risk individuals to make higher risk decisions.

In addition to altering decisions, Pinon and Garling (2004) note that
framing may alter mood. Gain framing implies a positive mood, and loss framing
implies a negative mood. For example, a gain frame would increase risk
aversion in gambling behaviors because of mood elevation. Thus, this study

examined the impact of mood by assessing if mood changed after the framed

52

training intervention because an individual’s mood may enhance or inhibit a

person’s receptiveness to the gain or loss framing.

Personality

Levin, Gaeth, 8 Lauirola (2002) studied the relation of personality traits to
framing. They found that framing effects have been correlated with personality
traits as classiﬁed in the Big Five Inventory. Individuals who scored high on
Neuroticism, low on Openness, high on Conscientiousness, and low on
Agreeableness were more likely to have a propensity towards large risk choice
framing, such as choices made with big-stake gambling. Additionally, individuals
who are higher in Conscientiousness are more likely to respond to loss-framed
messages (Levin, et al, 2002). That is, loss-framed messages appear to have
stronger effects on individuals high on conscientiousness as compared to those
who are low in conscientiousness .

In addition to these direct effects, certain features of personality may be
linked to mood as well. Rusting (1998) found that personality traits are correlated
with certain trait-like moods. Based on his meta-analysis, neuroticism,
extraversion, and agreeableness were related to mood states. Therefore, Pinon
and Garling (2004) argue that the effects of personality on framing may be
mediated by mood. Because of these important ﬁndings, this study examines the
role of personality on framing. Individuals who are high on particular personality

qualities such as consciousness or agreeableness may respond to the gain and

53

loss framing in different ways, which may be seen by looking at the relationship

of training outcomes and personality variables.

54

METHODS

Participants
Participants were undergraduate students at a large Midwestern university. The
participants received introductory psychology credit for participating in the
research study. The study originally had 227 participants. However, 41
participants were dropped because they failed to complete both parts of the
experiment. Of the 186 participants (46.2% male; 53.8% female), 47.6% were
CaucasianNVhite, 9.9% were Hispanic/Latino, 15.6% were Asian/Paciﬁc Islander,
16.7% were Black/African American, and 18.7% identiﬁed as other. The average

age of participants was 20.59 (SD = 2.79).

Design
The study used a one-way experimental design using two (2) levels of a between
subjects variable. The manipulated variable was the framed message training to

which the participant was randomly assigned that is gain or loss-framed training.

Task
The focus of this study assessed subsequent attitudes and performance
outcomes by framed messages in training. To assess these outcomes, the
experimenter used the Quinones task. Quinones identiﬁed three necessary
properties to mimic real-world situations: (1) participants must have limited prior
knowledge of the task; otherwise it would create a ceiling effect, (2) the task must

be difﬁcult enough to allow for variation in performance outcomes, and ﬁnally (3)

55

the task must be complex enough to ensure the participant is paying attention to
the task and training.

The experimenter chose the multiple-cue-Iearning task that requires that
participants to make decisions in a simulation. Following the algorithm designed
by the architects of the Navy simulation task, the researcher categorized the
responses as correct or incorrect. Performance on the task was determined by
how well the participant learned how to do the task correctly through training.

The speciﬁc multiple-cue-task was an evolved version of the Naval Air
Defense simulation (Quinones, 1995) called Tandem, which has been used in
testing cognitive abilities and team interactions. The original program was
altered to allow participants to see ﬁve different cues to make a decision instead
of two cues in the original version. In this computerized simulation, researchers
asked participants to command a US Navy vessel. Participants were presented
with a “radar-like” screen, which has 22 different unidentiﬁed targets.
Participants chose whether or not to ﬁre shots at targets based off cues about the
target. In order to determine whether to ﬁre shots at the target, participants had
to ﬁrst decide the type of aircraft they have identiﬁed: air, sub, or surface. Then,
they identiﬁed the classiﬁed of each target: civilian or military. Finally, the
participants classify the intent of the target: peaceful or hostile. From these
decisions, the participant had to either “shoot” or “Clear" the vessel. Each‘trial
had to be completed within four minutes. The simulation tracks the amount of
vessels successfully targeted (the speed of the task) as well as accuracy of the

decisions.

56

I

HPR System

I I

Participants complete online TAT, Personality, 8
Actual/Ideal Discrepancy Assessment

I

l Participants told via email of i

' Participants Recruited through }

Remedial Placement
Blind
Random
Assignment

 

\M

 

 

 

 

 

[ Assigned to Gain ] [Assigned to Loss ]

\l/

Participants work through online training manual
and framed message training pamphlet

I

Participants complete Fairness Perceptions,
Motivation to Learn, Self-Efﬁcacy, Mood, and
Actual/Ideal Discrepancy (Time 2) assessment

It

[ Participants complete the Air Defense Task

 

 

 

Tone, and Manipulation Check Assessments

I

l Participants complete Reactions to Training, Training I

 

Participants Debriefed ]

 

Figure 4. Representation of the Procedure

The researcher scored the outcomes of the task based on how the participants

respond to each target. For each decision, if the participant made it correctly, the

participant received 100 points, and for each decision that was made incorrectly,

the participant lost 100 points. Participants lost points when they allowed vessels

57

to enter their defense perimeter, and gained points for correctly identifying “high

priority” targets, those that were intensely hostile.

Procedure

See Figure 4 for a graphical representation of the procedure. The
experiment was divided into two sessions. The ﬁrst session consisted of the
online pre-assessment, during which the participants were told would what
qualify them for either the remedial or the advanced condition. The participants
were unaware that despite their performance they would be placed in the
remedial training assignment. The researcher revealed their assignment via
email. On the second day, which required an onsite visit to the research lab, the
experimenter reviewed the assignments, and participants were placed into two
different framed training conditions

At the ﬁrst session, the participants read and electronically signed the
consent forms informing them of the nature of the experiment — it was voluntary
and the type of activities that would be administered. See Appendix A for copy of
the consent form. The experimenter informed the participants that they were
helping the research team determine if a particular training design was effective.
The guided computer screens discussed the Air Defense Task and introduced
the task. The researcher then said there were two different types of training
designs they were evaluating. They were told that the performance on the pre-
assessment dictated which of the designs the participant would be evaluating.
The researcher told the participants that past research revealed certain

individuals perform exceptionally well and others do extremely inadequately on

58

the pre-assessment. In addition, the researcher explained that those who
performed poorly on the pre-assessment would be placed in remedial training.
Those who did well on the assessment would be placed in advanced training to
learn how to use the Air Defense Task in more sophisticated ways. The
computer then presented the Training Assigpment Test (TAT), which, as the
experimenter noted, would determine which, training program they would be
assigned (See Appendix B for copy of the TAT). The test included knowledge-
based Naval defense questions and actual simulations used in the trials and the
Actual/Ideal Discrepancy Assessment and the Personalitv Assessment.
Participants were Informed that based on their performance they would be
contacted by e-mail as to which session they were to be assigned. In actuality, all
participants were placed in the remedial training assignment. However, the
experimenter mentioned a fake “Advanced Training” session that would occur
after the Remedial Training to ensure that participants believed the manipulation.
In the second session, participants went to the experiment site in smaller
groups (typically 2-6), and, they were immediately reminded of their results of the
TAT. The course instruction was labeled blatantly with “REMEDIAL” and the lab
room was labeled “REMEDIAL TRAINING.” Participants were randomly
assigned to one of two sessions: one for remedial assignment with gain framed
training and one for remedial assignment with loss framed training. While the
overall instruction and content was the same for both framed training courses,
the way the training was instructed was framed differently depending on framing

assignment, and participants were randomly selected by experimenter prior to

59

participant arrival of their assignment. The instruction began with an emphasis
on the training goals, and the training frame was sustained throughout the
training materials.
In the gain frame, remedial training orientation, participants were
presented with a message:
“You have been selected to participate in this training intervention. This
training intervention will help you succeed in learning the information you
will need to be successful in the simulation task. This training intervention
has been speciﬁcally designed for you because you have several
characteristics that show that you are still learning ﬂight simulation
mastery and may have some areas in which you may want to improve
your knowledge before you engage in the task again. Additionally, you will
be able to save up to nine times more lives than you did in your initial
assessment! After you ﬁnish this training, you will be able to do much
better than you did the last time and you will feel great about yourself.”
The participants were then given the training goals packet, which can be found in
Appendix D, and instructed to how to use the computer-training manual. The
training manual as reproduced in Appendix C was reviewed and any type of loss-
framed messages were removed such as mention of: “failure,” emphasis on
cutting costs, and identifying weaknesses; these phrases were replaced with
neutral messages In the training manual, a special section was added on how to
maintain a positive outlook even if the participant makes a mistake. While the

participants were reading the manual, the instructor guided participants through

60

the task and how to accomplish the Naval simulation task. The instructor stated
each section while the participants were encouraged to follow along with the
training packets provided. During the instruction, participants learned how to use
Naval Courses’ lnﬂection points - that is, how to determine if the scenario
included threatening warships or non-threatening warships. Instructors
emphasized dimensions of the targets and how to determine how these attributes
may interact. For example, a fast vessel going away from your ship is deemed
non-threatening; but when the fast vessel is going towards you or one of your
partners’ vessels it is considered threatening. After participants were familiar
with inﬂection points, they were given additional time to review the manual that
contained instruction on how to use the computer program and how to utilize “hot
keys” which would make the task more smooth if used appropriately. When the
participant ﬁnished reading the manual and listening to the training leader, they
completed a training goals worksheet, which asked them to list ﬁve positive
reasons why they are completing the task.

In the loss frame, remedial training orientation, participants were greeted
with the following message:

“You have been placed in this training program. This training will keep you

failing again in this task. This training program will help you maintain your

ability to continue in this experiment. Additionally, if you do not absorb this

information correctly you will up to kill nine times more people as you did

in your pre assessment. If you do not retain the information that Is

61

presented here, you will have a greater chance of failure in the future on

this task and you may feel sad about that.”
The participants were then given the training goals packet, which can be found in
Appendix E and instructed to how to use the computer-training manual. The
training manual was reviewed and any type of gain-framed messages were
removed such as mention of: “failure,” emphasis on cutting costs, and identifying
strengths; these phrases were replaced with neutral messages (See Appendix D
for Training Pamphlet). While the participants were reading the manual, the
instructor guided participants through the task and how to accomplish the Naval
simulation task. The instructor stated each section while the participants were
encouraged to follow along with the training packets provided. During the
instruction, participants learned how to use Naval Courses’ lnﬂection points —
that is, how to determine if the scenario included threatening warships or non-
threatening warships. Instructors emphasized dimensions of the targets and how
to determine how these attributes may interact. For example, a fast vessel going
away from your ship is deemed non-threatening; but when the fast vessel is
going towards you or one of your partners’ vessels it is considered threatening.
After participants were familiar with inﬂection points, they were given additional
time to review the manual that contained instruction on how to use the computer
program and how to utilize “hot keys” which would make the task more efﬁcient
and smooth it used appropriately. When the participant completed their manual
and ﬁnished listening to the instruction of the training leader, they ﬁlled out a list

on the top ﬁve costs of not completing the task correctly.

62

Before beginning the task, all participants ﬁlled out the Fairness
Assessment, Self-Efﬁcacy Assessment, Mon to Learnﬂessment. Mooc_l_
Orientation Assessment, and Ideal/Actual Discrepancy Measure (Time 2). The
participants then worked on their task. Each participant was allotted 20 minutes
to complete the Air Defense Task. The participants knew they were being rated
on both speed and accuracy of their task performance.

At the end of the task, participants completed the Knowledge Assessment
(Training Assessment Task). ILamianeactions Assessment, Traininﬂ‘me
Assessment, and ManiDplation Check Assessment. Finally, participants were
debriefed and told the details of the study. The experimenter emphasized that
the training assignment was random and had no actual reﬂection on their overall
performance on the TAT. The experimenter asked all participants not to discuss
this experiment with anyone until the experiment had concluded, as not to create

in bias in the experimental data.

MEASURES
The measures are listed in accordance to when the participant completed
the assessment (n = 186). Results of these statistics are described in detail in

the “Results” section.

LiaQInQAssessment/Traﬁing Assessment Test

The Training Assessment Task included a 20-item knowledge test
anchored in 20 ﬁve-option multiple-choice questions, where the participant was
asked about the combination rules for the task simulation. Participants were also

presented with nine targets and asked to give the appropriate response. The

63

same assessment was used after the Navy simulation to serve as a basis of pre
and post knowledge assessment. In Quinones’s study, he found the test to have
a split half reliability of r = .70. The reliability of this study is r =.76. A copy of this

measure is found in Appendix B.

Actual/Ideal Discrepancy

Because of the criticized complexity of the original measure (Tangney et
al., 1998), the current study used a shortened version of Higgins’ original
ideal/actual discrepancy survey (Higgins, Klein 8 Strauman, 1985). Hardin
(1987)’s shortened version of Higgin’s original questionnaire asked participants
to identify up to 5 attributes for their (1) actual self and (2) ideal self. After which,
the participants were asked to rate from 1 to 5 the extent at which each attribute
actually applies (lower combined scores indicated a greater cohesion of the self
and less discrepancies with the self states). Scores ranged from 5 (no
discrepancy) to 25 (highest degree of discrimination) In a study utilizing this
measure, the reliability was a = .85. Additionally, Leonardelli, Lakin, Lynch, and
Arkin (2003) tested speciﬁc predictions of Higgin’s self-discrepant theory utilizing
this measure and found strong evidence of the validity of this measure. The
observed reliability of this study was a = .79 for Time 1 and a = .72 for Time 2. A

copy of the measure can be found in Appendix I.

Fairness Perceptions (measured by Distributive and Procedural Justice)

A twenty-item scale was used to assess the participants’ perceptions of

 

fairness regarding training assignment. Both procedural (10 items) and

distributive (10 items) justice were assessed using a modiﬁed version of

64

Hattrup’s justice measure (1992). Participants rated themselves one to ﬁve on
how well the agreed with the statements. The resulting reliabilities found in the
Quinones study were a = .92 and a = .83, for distributive and procedural justice,
respectively.

Exploratory factor analyses revealed the presence of three distinct factors
in the two justice measures. The presence of a third factor indicated that there
were questions that did not distinctly load to distributive justice or procedural
justice. Only two questions loaded onto the third component. When the two
questions were discarded, internal consistencies for the two justice measured
increased from a = .82 to a = .87 for procedural justice and a = .83 to a = .89 for
distributive justice. However, after re-running the correlative data analyses, the
withdrawal of the two questions had no signiﬁcant impact on the outcomes, and
therefore, the two questions were retained in the original fairness perceptions
measurement. The observed reliability of this study was a = .82 and a = .83, for
distributive and procedural justice, respectively. A copy of the measure can be

found in Appendix F.

Self-Efﬁcacv

Quinones’ pre-training self—efﬁcacy test which was adapted from Hattrup’s
original test (1992) determined a person’s expectation regarding their future
performance on the Training Assessment Test. Anchored in a ﬁve point likert
scale, ten questions were asked. Quinones’ observed reliability was a = .90.
The observed reliability was a = .89. A copy of this measure can be found in

Appendix G.

65

Motivation to Learn

Modeled after Noe and Schmitt (1986), the participant’s motivation to learn
was measured using a ten-item scale. Anchored in a ﬁve point likert scale,
participants reviewed the questions and indicated their level of agreement with
each item. The observed reliability for Quinones was a = .93. This study’s
observed reliability was a = .89. A copy of this measure can be found in

Appendix H.

Number of Trials (Speed)

In order to assess the number of trials the participant completed during
their 20-minute simulation, we accessed the records from the computer. There
were 22 targets per scenario (4 scenarios) in the simulation. The more targets

acquired the better the performance.

Accuracy

The point system described earlier was used to assess the participant’s
accuracy during the computer simulation. Points ranged from +100 to -100 for
each of the trials and the scores were directly related to how close the participant

was to the correct response.

Reactions to Training

Reactions to the quality and usefulness of the training was assessed using
the same 10 item scale that was developed by Quinones (1995), which
demonstrated the utility judgments that underlie training assessments. Anchored

in a ﬁve point likert scale, participants simply answered with their agreement to

66

each statement. The observed reliability of Quinones’ study was a = .80. The
current study’s observed reliability was a = .90. A copy of this measure is found

in Appendix L.

mung Impressions Check

After the verbal messages were given and the manual was read, all
participants completed a post-intervention questionnaire, which evaluated the
different aspects of the messages received during the training and in the training
manual. First, using a 5-point scale, the training program was rated how (1)
believable, (2) interesting, and (3) confusing the training was. Second,
participants were asked to rate their overall impressions of the tone of the
experiment by rating the tone from one (extremely negative) to ﬁve (extremely
positive) (Toll, O’Malley, 8 Katulak, 2007). A copy of this measure is found in

Appendix M.

Manipulation Check

As used in previous gain-framed loss manipulation checks (see Toll,
O’Malley 8 Katalak, 2007), the post-questionnaire asked participants to rate how
heavily the training focused on the beneﬁts or the costs of doing well in the
training program from 1 (It focused heavily on the beneﬁts of completing the task
successfully) to 5 (It focused heavily on the costs of failing at the task). Next,
participants identiﬁed what training assignment group in which they were placed:

advanced or remedial. A copy of this measure is found in Appendix N.

67

Mood Orientation

The basic moods as described by Ekman, Levenson, 8 Freisen (1983) are
happiness, anger, fear, sadness, and surprise. These states can be captured in
the Brief Mood Introspection Scale (Mayer 8 Gaschke, 1988). The BMIS has a
reported reliability of 0.76 to 0.83. Muraven, Tice, and 8 Baumeister (1998)
reported the BMIS as an acceptable measure of mood. As recommended, the
response scales were altered from 4 steps to 7 steps with anchors spaced 2
steps apart. In such a way, reliability increased when the range increased
(Nunnally, 1967). The current study’s observed reliability was a = .81. A copy of

the measure is found in Appendix J.

Personality

Researchers have assessed the Big Five Personality Inventory in multiple
ways since the ﬁrst personality instrument developed in 1917 by Woodworth.
Because other measures are considered ”narrow” in their ability to test
personality, this study utilized the International Personality Item Pool (Goldberg,
1999). The resulting reliabilities of the scale reported by Buchanan, Goldberg,
and Johnson (1999) were a = .74, .84, .88, .76, and .83 for openness,
conscientiousness, extraversion, agreeableness, and neuroticism, respectively.
The current study’s resulting reliabilities were a = .76, .74, .85, .80, and .86. A

copy of the measure is found in Appendix K.

68

Analytic Strategy

The analytic strategy for this study was modeled on the causal relationship
presented in the introduction. Individuals in the both conditions (gain and loss)
were used for the analysis (n = 186).

The ﬁrst set of analyses compared descriptive data of gain and loss
framing messages on the following variables: self-efﬁcacy, motivation to learn,
fairness perceptions, and training outcomes. Even though the effects of framing
were hypothesized to be mediated by these variables, these analyses allowed for
the exploration of any overall main effects that framing might have on training
outcomes. Additional analyses of demographic covariates were not run because
none of the demographics had a signiﬁcant effect on any of the outcome
variables.

During the ﬁrst set of analyses, the researcher ensured reliabilities were
appropriate to continue analyses of relationships. If any of the analyses revealed
sub—standard reliabilities, the researcher examined the scale for validity.
Additionally, the researcher performed an exploratory factor analysis on fairness
perceptions to ensure that the modiﬁed scale retained two distinct measures of
fairness.

The study’s second set of analyses, correlations, calculated associations
between all the variables measured in the study. Participants in the gain-framed
training were coded as 1 and participants in the loss framed training were coded

as 0. Correlations tested for relationships across all variables with special

69

attention paid to the variables of the current study’s hypothesis such as self-
efﬁcacy, fairness perceptions, motivation to learn, and training outcomes.

The third set of analyses involved looking at the moderating effect of the
ideal/actual self and personality. Moderating effects were analyzed using
multiple regression (Cohen 8 Cohen, 1981 ). Presence of an interaction was
determined by performing cross-product terms formed by multiplying the
independent variables together. The targeted dependent variable was regressed
on to the two independent variables as well as the cross product term.
Signiﬁcant regression weights for the cross product term indicated interactions.
Moderating analyses were also conducted for the personality variables of
interest, conscientiousness and openness.

Regression equations were formed using simple-slope formulas in order to
identify the direction of the interaction (Aiken 8 West, 1991). In such a way, the
relationship between the independent variable and the dependent variable can
be seen as a function of the secondary independent variable.

Fourth, the model presented a mediating relationship between gain/loss-
framed messages and behavioral outcomes, performance outcomes, and
learning outcomes. A mediation test (James 8 Brett, 1984) was used to check
this proposed relationship. Mediating relationships exist when the variable (in
this case, motivation to learn, self-efﬁcacy, and fairness perceptions were all
proposed to mediate the relationship between framing and a training outcome)
adds prediction of the dependent variable above that which was accounted for by

framing effects. A regression analysis was run for the speciﬁc independent

70

variable (two different ones: gain framing and loss framing) predicting training
outcomes (four speciﬁc outcomes: speed, accuracy, knowledge, and behavior). A
secondary regression was run between the mediating variables and the training
outcomes. These two regression analyses output the raw regression coefﬁcients
and standard errors needed to run the Sobel test of mediation. A Sobel test was
used instead of a bootstrap test due to the large sample size (Preacher 8 Hayes,

2004).

71

RESULTS
The results of this study follow the direction of the analytic strategy. The
researcher states the results in the following order: ﬁrst, the results of the
descriptive data; second, the results of the main effects of framing; third, the
results of the moderating hypotheses, and ﬁnally, the results of the mediating

hypotheses are presented.

Descriptive Data
Table 1 contains the combined means, standard deviations, and scale

reliabilities of the measures for both the gain and loss-framed training participant
responses. Table 2 includes the means and standard deviations of measures for
gain and loss-framed training participant responses. In general, with the
exception of the knowledge test, alias the Training Assessment Test, the scales
showed sufﬁcient reliabilities to proceed with the data analyses. Concerning the
Training Assessment, there appeared to be no concern for ﬂoor or ceiling effects

because the knowledge test showed sufﬁcient range.

72

Table 1
Combined Means, Standard Deviations, and Reliabilities of Gain/Loss Reponses

 

Variable # Items Mean SD Reliability
Procedural Justice . 10 2.90 .57 .82
Distributive Justice 10 2.45 .56 .83
Self-Efﬁcacy 1 0 3.40 .62 .89
Motivation to Learn 10 3.73 .70 .89
Mood 16 78.06 16.10 .80
I/A Discrepancy (Time 1) 5 6.41 3.74 .79
VA Discrepancy (Time 2) 5 8.91 3.65 .72
Training Reactions 10 3.27 .77 .90
Personality (Extraversion) 8 3.54 .74 .85
Personality (Agreeableness) 10 3.94 .61 .80
Personality (Conscientiousness) 9 3.77 .56 .74
Personality (Neuroticism) 8 2.74 .82 .86
Personality (Openness) 8 3.59 .62 .76

Table 3 presents the inter-correlations between all the items that were
studied. Demographic variables were not signiﬁcantly associated with any of the
variables of interest, which halted further investigation of demographic
covariates. Additionally, personality variables had little association with the
variables that were involved in the hypotheses of the current study. However,
there were strong correlations between framing and motivation, distributive
justice and procedural justice, ideal/actual discrepancy measures at Time 1 and
Time 2, and manipulation check items. Further discussion of speciﬁc correlations

follows.

Main Effect Hypotheses
Hypothesis 1 stated that gain-framed training would increase self-efﬁcacy.

The correlation between gain-framed training and self-efﬁcacy indicated a non-

73

signiﬁcant effect of framing on self-efﬁcacy (r = .09, p = .24). The result signiﬁes
a lack of support for Hypothesis 1.

Hypothesis 2 stated that gain-framed training would increase motivation to
Ieam. Framing was signiﬁcantly associated with motivation to learn (r = 23, p <
.01). With the mean of the gain-framed participants as 3.86 (SD = .55) and the
mean of the loss-framed participants as 3.58 (SD = .66), the results indicate that
gain-framed participants had higher motivation to learn than loss-framed
participants. An ancillary test utilizing Cohen’s d indicated that motivation to

learn had a magnitude of d = .40. Thus, Hypothesis 2 is supported.

74

Table 2

Means, Standard Deviations, and Reliabilities of Gain/Loss Reponses

 

Variable Loss Loss SD Gain Gain SD
Mean Mean

Procedural Justice 2.91 .55 2.90 .58
Distributive Justice 2.35 .50 2.53 .60
Self-Efﬁcacy 3.33 .73 3.45 .66
Motivation to Learn 3.58 .66 3.86 .55
Mood 78.94 15.78 77.29 16.41
l/A Discrepancy (Time 1) 6.42 4.3 6.39 3.27
l/A Discrepancy (Time 2) 8.79 3.4 9.01 3.84
Training Reactions 3.15 .78 3.38 .75
Personality (Extraversion) 3.50 .73 3.58 .74
Personality (Agreeableness) 3.94 .62 3.94 .60
Personality (Conscientiousness) 3.78 .55 3.76 .57
Personality (Neuroticism) 2.76 .81 2.72 .82
Personality (Openness) 3.55 .57 3.62 .67
Performance Outcome 19.42 70.8 -7.01 77.7

Hypothesis 4 stated that fairness perceptions of participants who were

placed in the gain-framed training would have lower perceptions of unfairness

(both distributive and procedural measures of fairness) of the remedial training

than those who were in the loss-framed training condition. Framing did

signiﬁcantly affect distributive justice perceptions (r= .15, p < .05). In addition,

participants in the gain-framed training had higher perceptions of fairness as

indicated by the mean of 2.53 (SD = .60) for gain-framed

75

Inter-Correlations Among Variables

Table 3

 

Variable 1 2 3 4 5 5 7
1. Gain/Loss Framing -

2. Gender .017 -

3. Ethnicity -.021 .019 --

4. Age -.024 .066 .094 -

5. Procedural Justice -.012 .080 .046 -.027 (.82)

6. Distributive Justice .154“ -.042 .030 -.005 .543“ (.83)

7. Self-Efﬁcacy .087 -.073 .065 .031 .034 .220“ (.89)
8. Motivation to Learn .225“ -.009 .078 .021 -.054 .040 .329“
9. Mood -.051 .098 .025 .060 .077 .067 .205"
10. I/A Discrepancy (T1) .030 -.053 .009 -.122 -.016 -.042 -.154*
11. HA Discrepancy (T2) -.004 —.105 .065 -.068 .048 .022 -.155*
12. I/A Change .024 .074 -.071 -.050 -.077 -.103 .012
13. Believability .117 .064 -.038 -.089 -.112 -.077 .164“
14, Interesting .095 .067 .021 -.081 -.174* -.140 .240"
15. Confusion -.039 -.014 -.060 -.048 -.124 -.067 .253“
16. Tone .453“ .032 -.092 -.045 -.017 .053 .127
17. Affective Outcome .146* -.022 .008 -.063 -.241** -.183' .190”
18. Knowledge Outcome -.175* .036 .067 .005 .025 .005 .063
19. Performance (Accuracy) -.018 .051 -.016 -.011 -.124 -.086 .038
20. Pre-Assessment -.004 -.070 -.105 -.120 -.064 .020 .135
21. Extraversion .053 .016 -.005 -.009 -.058 -.077 -.035
22. Agreeable -.001 -.045 -.021 .057 .079 .109 -.070
23. Conscien -.018 -.052 -.002 -.036 .086 .144 .016
24. Neuroticism -.022 -.021 .086 .054 -.139 -.130 -.045
25. Openness .057 .046 .014 .017 .019 .010 -.016
26. Performance (Speed) .079 .080 .075 -.012 -.001 .045 -.087

 

NOTE: Reliabilty estimates are listed in parentheses along the diagonal where appropriate

n=187,*p<.05,**p<.01,

76

Table 3 Continued

Inter-Correlations Among Variables

 

8 9 1o 11 12 13 14 15 16 17
1.
2.
3.
4.
5.
6.
7.
8. (.89)
9. .191" (.80)
10. -.041 -.286** (.85)
11. -.078 -230" .421" (.86)
12. .052 -.061 .510" -.566"’* .-
13. .240" .098 .039 -.175* .219" —
14. .412" -.o1o .036 -.019 .084 .503" --
15. .111 .103 -.067 .016 -.073 .258“ .356“ -
16. .192" .079 -.005 -.048 .048 .381" .205" .184“ .-
17. .418“ .076 .038 .043 .022 .579" .682“ .506“ .341“ (.90)
18. .035 .160“ -.062 .102 -.179* -.032 .111 .101 -.001 .063 '
19. .238" .048 -.029 -.058 .035 .059 .119 -033 -.025 .111
20. .052 .141 -.162* -.156* .004 .014 .002 .139 .098 .091
21. .055 -.050 .055 .064 -.003 -.090 -.019 -.042 -.067 -.108 '
22. -.091 -.116 .035 .058 -.034 .024 -.022 -.075 -.134 -.061
23. -.040 .125 .070 -.018 .049 -.099 -.080 .044 -.052 -.088
24. .014 .022 -.118 -.043 -.033 .068 .077 -.084 .078 .087
25. -.014 .058 -.018 .023 -.028 -.077 .046 -.091 .157* -.021
26. -.007 .042 .039 .089 -.048 .009 -.048 -.022 .086 .036

 

77

Table 3 Continued

Inter-Correlations Among Variables

25

 

18 19 20 21 22 23 24
1.

2.

3.

4.

5.

6.

7.

8.

9.

1o.

11.

12.

13.

14.

15.

16.

17.

18. (.69)

19. .142* -

20. .142 -.169* (.47)

21. -.o13 .065 .054 (.85)

22. -.029 -.079 -.033 .141 (.80)

23. .097 -.062 .122 .118 .278“ (.74)

24. .056 .115 .012 -.277** -.240** -.245** (.86)

25. .021 -.001 .074 .333" .186“ .210" -.102 (.76)
26. .064 .163“ -.005 -.009 -.039 -.075 .089 -.014

 

78

participants and 2.35 (SD = .50) for loss-framed participants. A follow up
Cohen’s d metric indicated moderate effect size of d = .32. However, the results
did not indicate such signiﬁcance for procedural justice (r = -.01, p = .88). All
together, these results indicate partial support for Hypothesis 4.

Hypothesis 5a stated that gain-framed training would increase
performance as measured by speed and accuracy on the Naval Simulation Task.
Participants did not signiﬁcantly respond quicker in the gain or loss-frame (r =
.08, p = .29). However, framing was signiﬁcantly related to accuracy (r = -.18, p
< .05), but not in the direction that was hypothesized. In fact, participants in the
loss-framed whose training mean score was 19.42 (SD = 70.82) did signiﬁcantly
better than those in the gain-framed performance whose mean training
performance was -7.01 (SD = 77.73). A Cohen’s d metric indicated that the
signiﬁcant relationship is moderate, d = .30. However, given the large standard
deviation, the researcher investigated the possible presence of inﬂuential
outliers, and found no outliers beyond two standard deviations of the mean.
Analyses revealed that the distribution was slightly negatively skewed, but there
was little evidence for ﬂoor or ceiling effects. Thus, there was partial support for
Hypothesis 5a.

Related to Hypothesis 5a, Hypothesis 5b stated that gain-framed training
would increase Ieaming as measured by a knowledge test. Framing had a non-
signiﬁcant effect on learning (r = .02, p = .83), indicating a lack of support for

Hypothesis 5b.

79

Hypothesis 6 stated that participants would view the training more
positively in a gain-framed training condition. Correlation results indicated that
framing had a signiﬁcant impact on affective outcomes as measured by training
reactions (r = .15, p < .05). As indicated by the mean differences with gain-
framed participants who averaged 3.38 (SD = .75) and the loss-framed
participants who averaged 3.15 (SD = .78), participants in the gain-framed
training reacted more positively to the training, leading to higher affective
outcomes. An ancillary analyses of Cohen’s d metric indicated an effect size of d
= .30. Thus, hypothesis 6 was supported.

Finally, as an exploratory measure, participants were asked to indicate
their mood to determine its role in the Navy simulation task. While there was no
direct relationship with mood from the framing (r = -.05, p = .49), mood did relate
positively to motivation to Ieam, knowledge outcomes, self-efﬁcacy, and the self-
reported rating of the ideal/actual discrepancy. Because these relationships did
not have further relevant implications, the current study did not explore mood
further.

In summary, relating the results to the conceptual model presented in
Figure 1, gain-farmed training did affect participants in several of the proposed

relationships. Moderating relationships are now discussed.

Moderating Hypotheses
Hypothesis 3 stated that the ideal/actual self-discrepancy would moderate
the relationship between gain and loss-framed messages and self-efﬁcacy.

Although the stipulations for moderation were absent, the researcher conﬁrmed

80

that moderating relationships did'not exist. Following the protocol outlined by

Cohen and Cohen (1981), the gain and loss-framed training was multiplied by the

81

Table 4
Regression Analyses Results of the Test of Moderation
of Ideal/Actual Discrepancy at Time 1 on Self-Efﬁcacy

 

 

 

 

 

Variable 8 SE B 13 t
Framinga(A) .15 .13 .11 1.16
Ideal/Actual Discrepancy (IA) -.03 .01 -.16 -2.09**
A x IA -.01 .03 -.02 -.23
Constant 3.46 .06 .00“
2
R = .03
F (3, 181) = 2.02, p = .11
** Signiﬁcant at the p < .05
a Code: 0 = Loss, 1 = Gain
Table 5
Regression Analyses Results of the Test of Moderation
of Ideal/Actual Discrepancy at Time 2 on Self-Efﬁcacy
Variable 8 SE B 8 t
a
Framing (A) .28 .18 .20 1.56
Ideal/Actual Discrepancy (IA) -.03 .01 -.15 -2.01**
A x IA -.03 .03 -.14 -1.07
Constant 3.54 .09 .00*
2
R = .04
F(3,181)= 2.34, p = .08
** Signiﬁcant at the p < .05
a Code: 0 = Loss, 1 = Gain
Table 6
Regression Analyses Results of the Test of Moderation
of Conscientiousness on Performance
Variable 8 SE B IS t
a - _ _ n
Framing (A) 25.73 11.10 .17 2.32
Conscientiousness (CON) 13.09 10.00 .10 1 .31
A x CON -15.98 20.08 -.06 -.79
Constant 5.09 5.54 .92
2
R = .04

F (3, 181)= 2.60, p = .05
** Signiﬁcant at the p < .05

a Code: 0 = Loss, 1 = Gain

82

Table 7

Regression Analyses Results of the Test of Moderation
of Openness on Performance

 

Variable B SE B 43 t
a _ - _ **
Framing (A) 26.30 11.10 .17 2.37
Openness (OPN) 6.66 9.26 .05 .72
A x OPN -29.02 18.78 -.12 -1.55
Constant 5.63 5.54 1 .02
2 = .04

F (3, 181) = 2.70, p > .05
** Signiﬁcant at the p < .05

a Code: 0 = LOSS. 1 = Gain

scores of the ideal/actual discrepancy measures. Self-efﬁcacy was then
regressed onto the gain and loss—framed condition and the ideal/actual
discrepancy measure along with the cross product term. This procedure was
followed with both checkpoints of the ideal/actual discrepancies, before the
training and after the training. Tables 4 and 5 contain the results of this
relationship. The hypothesized interaction was not observed. However, the
ideal/actual discrepancy was signiﬁcantly related to self-reported scores of self-
efﬁcacy.

To test for the interaction of personality with framed messages, in this
case, conscientiousness and openness, the same procedures for Hypothesis 3
were used. Framing was multiplied by scores of the personality variable.
Performance was then regressed onto the gain and loss framed condition and
the personality variable along with the cross product term. Tables 6 and 7

include the results of both conscientiousness and openness. The hypothesized

83

interactions did not appear for either conscientiousness or openness. However,
as reported before, framing did have an impact on performance. These results
taken together do not support the conceptual model. The next set of analyses

assesses the signiﬁcance of potential mediators.

Mediating Hypotheses

Hypotheses 7a, 7b, and 70 stated that the relationship between gain/loss-
framed messages and training outcomes, such as affective, knowledge, and
performance outcomes, would be mediated by self-efﬁcacy. Although
stipulations for mediation were absent, the researcher conﬁrmed the absence of
mediating relationships. Using the methods described by Preacher and Hayes
(2008), a regression analysis was conducted with the gain/Ioss-framing
predicting self-efﬁcacy responses. A secondary regression was conducted with
self-efﬁcacy and the outcome. These two regression analyses yielded the raw
regression coefﬁcients and standard errors needed to run the Sobel test of
mediation (Preacher 8 Hayer, 2004). The results of these tests can be found in
Tables 8, 9, 10, and 11. The results indicate that self-efﬁcacy did not mediate
the relationship between the training condition and affective outcomes as
measured by training reactions (2 = 1.08, p = .28), performance as measured by
accuracy (2 = .82, p = .41 ), performance as measured by speed (2 = .12, p =
.37), and learning as measured by a knowledge test (2 = .45, p = .66). These
results indicate a non-signiﬁcant effect of self-efﬁcacy as a mediating variable,

suggesting a lack of support for Hypothesis 7.

84

Table 8

Regression Analyses of the Test of Mediation of Self-Efﬁcacy
on Framing and Affective Outcomes

 

Variable I!) O; t
Framinga (a) .12 .10 1.19
Framing a (b) .20 .11 1.80
Self-Efﬁcacy .20 .08 2.48”
a Code: 0 = Loss, 1 = Gain
** Signiﬁcant at the p < .05

Table 9

Regression Analyses of the Test of Mediation of Self-Efﬁcacy
on Framing and Performance (Accuracy)

 

Variable , 8 Of t

a
Framing (a) .12 .10 1.19

a _ _ it
Framing (b) 27.75 11.10 2.50
Self-Efﬁcacy 9.00 8.06 1 .12
a Code: 0 = Loss, 1 = Gain
** Signiﬁcant at the p < .05

Table 10

Regression Analyses of the Test of Mediation
of Self-Efﬁcacy on Framing and Learning

 

Variable l3 ox- t
a

Framing (a) .12 .10 1.19
8

Framing (b) .20 1.04 .18

Self-Efﬁcacy .35 .73 .48

a Code: 0 = Loss, 1 = Gain

85

Table 11

Regression Analyses of the Test of Mediation of Self-Efﬁcacy

on Framing and Performance (Speed)

 

 

 

Variable 13 0X t

a
Framing (a) .12 .10 1.19

a
Framing (b) .05 .05 1.19
Self-Efﬁcacy -.04 .03 -1 .29
a Code: 0 = Loss, 1 = Gain
Table 16

Table 12
Regression Analyses of the Test of Mediation of Motivation
on Framing and Affective Outcomes

Variable I!) 0x' t

a i
Framing (a) .28 .09 3.13

a
Framing (b) .08 .11 .79
Motivation .51 .08 5.89“
a Code: 0 = Loss, 1 = Gain
* Signiﬁcant at the p < .01

Table 13
Regression Analyses of the Test of Mediation of Motivation
on Framing and Performance (Accuracy)

Variable It. Ox- t

a i
Framing (a) .28 .09 3.13

a _ _ it
Framing (b) 29.00 11.32 2.56
Motivation 9.38 9.1 1 1.03

a Code: 0 = Loss, 1 = Gain
* Signiﬁcant at the p < .01
** Signiﬁcant at the p < .05

86

Table 14

Regression Analyses of the Test of Mediation
of Motivation on Framing and Learning

 

 

 

Variable III of t
a *
Framing (a) .28 .09 3.13
' a - -
Framing (b) .50 1.03 .48
Motivation 2.70 .84 3.22*
a Code: 0 = Loss, 1 = Gain
* Signiﬁcant at the p < .01
Table 15
Regression Analyses of the Test of Mediation of Motivation
on Framing and Performance (Speed)
Variable l3 ox' t
a *
Framing (a) .28 .09 3.13
a
Framing (b) .05 .05 1.13
Motivation -.01 .04 -.37
a Code: 0 = Loss, 1 = Gain
* Signiﬁcant at the p < .01
Table 16
Regression Analyses of the Test of Mediation of Distributive Justice
on Framing and Affective Outcomes
Variable l3 ox t
a it
Framing (a) .17 .08 2.1 1
a it
Framing (b) .28 .11 2.46
Distributive Justice -.29 .10 -.21

a Code: 0 = Loss, 1 = Gain
** Signiﬁcant at the p < .05

87

Table 17

Regression Analyses of the Test of Mediation of
Distributive Justice on Framing and Performance (Accuracy)

 

 

 

Variable I!» t
0X'

a it
Framing (a) .17 .08 2.11

a _ _ it
Framing (b) 27.23 11.21 2.43
Distributive Justice 4.50 9.98 .45
a Code: 0 = Loss, 1 = Gain
“ Signiﬁcant at the p < .05

Table 18
Regression Analyses of the Test of Mediation
of Distributive Justice on Framing and Learning

Variable l3 0; t

a ..
Framing (a) .17 .08 2.11

3
Framing (b) .35 1.04 .34
Distributive Justice -1.12 .97 -1.15
a Code: 0 = Loss, 1 = Gain
** Signiﬁcant at the p < .05

Table 19
Regression Analyses of the Test of Mediation
of Distributive Justice on Framing and Performance (Speed)

Variable l3 OX' t

a it
Framing (a) .17 .08 2.11

a
Framing (b) -04 .04 .99
Distributive Justice .02 .04 .45

a Code: 0 = Loss, 1 = Gain
*" Signiﬁcant at the p < .05

88

Table 20

Regression Analyses of the Test of Mediation
of Procedural Justice on Framing and Affective Outcomes

 

Variable I3 O)( t

a - -
Framing (a) .01 .08 .16

a it
Framing (b) .22 .1 1 2.01
Procedural Justice -.33 .10 -3.38*
a Code: 0 = Loss, 1 = Gain
** Signiﬁcant at the p < .05

Table 21

Regression Analyses of the Test of Mediation
of Procedural Justice on Framing and Performance (Accuracy)

 

Variable l3 ox- t
a - -
Framing (a) .01 .08 .16
a _ _ it
Framing (b) 26.43 11.07 2.39
Procedural Justice 3.33 9.83 .34

a Code: 0 = Loss, 1 = Gain
“ Signiﬁcant at the p < .05
Table 22

Regression Analyses of the Test of Mediation
‘ of Procedural Justice on Framing and Learning

 

Variable l3 ox- t

Framing a (a) -.01 .08 -.16
a

Framing (b) .13 1.03 .13

Procedural Justice -1 .49 .92 -1.62

a Code: 0 = Loss, 1 = Gain

89

Table 23

Regression Analyses of the Test of Mediation of Procedural Justice
on Framing and Performance (Speed)

 

Variable I3. ox- . t

Framinga (a) -.01 .08 -.16
a

Framing (b) -05 .04 1.07

Procedural Justice .00 .04 -.00

a Code: 0 = Loss, 1 = Gain

Hypotheses 8a, 8b, and 8c stated that motivation to learn would mediate
the relationship between gain-loss framed training and training outcomes such as
affective, knowledge, and performance outcomes. Mimicking the same method
described for the results of Hypothesis 7, two regression analyses were
computed, and the results of these regression analyses can be seen in Tables
12, 13, 14, and 15. The results of the Sobel’s analyses indicated that motivation
to Ieam is a signiﬁcant mediating variable between framing and affective
outcomes measured by training reactions (2 = 2.77, p < .01) and framing and
Ieaming as measured by a knowledge test (2 = 2.25, p < .05). However, results
indicated that motivation to Ieam did not mediate the relationship between
gain/loss-framed training and performance outcomes as measured by accuracy
(2 = .98, p = .32) or speed (2 = -.03, p = .80). These results indicate support for
Hypotheses 8a and 8c but lack of support for Hypothesis 8b.

Finally, Hypotheses 9a, 9b, and 90 stated that fairness perceptions as

measured by distributive and procedural justice, would mediate the relationship

90

to training outcomes. The same procedures as described in Hypotheses 7 and 8
were used here to test mediating variables. Results of the regressions on
distributive justice are presented on Tables 16, 17, 18, 19, 20, 21, 22, and 23.
They indicate that neither distributive justice nor procedural justice mediate the
relationships between gain and loss-framed messages and affective outcomes
as measured through training reactions (2 = -1.71, p = .09; z = .12, p = .90,
respectively), performance as measured by accuracy (2 = .44, p = .66; z = -.12, p
= .91, respectively), performance as measured by speed (2 = .49, p = .63; z = 0,
p = 1.0, respectively), and Ieaming as measured by a knowledge test (2 = -1.01,
p = .31; z = .12, p = .90). The results of these analyses indicate a lack of support
for Hypotheses 98, 9b, and 9c.

Taken together, the results of Hypotheses 7, 8, and 9 indicate partial
support for the conceptual model. The ﬁnal analyses assess the validity of the

experimental procedures.

Manipulation and Tone Check
As expected, no differences in general evaluations of the messages

emerged from self-reported measures. The gain and loss-framed training were ~
rated equally believable (r = .12, p = .11), considering that the mean of the gain
frame rating was 3.23 (SD = .96) while the mean of the loss frame was 3.01 (SD
= 1.12). Similarly, participants did not have differing perceptions of confusion in
the training (r = -.04, p = .60), considering the mean of the gain frame was 2.65
(SD = .84) while the mean of the loss frame was 2.74 (SD = 1.04); and

participants found both of the trainings equally interesting (r = .10, p = .20),

91

considering the mean of the gain frame 3.31 (SD = 1.19) while the mean of the
loss frame was 3.08 (SD = 1.26). Additionally, there was no signiﬁcant difference
between the two groups’ understanding of their placement in remedial training:
98% of loss-framed participants indicated they were in remedial training and
100% of gain framed participants indicated they were in remedial training.

However, consistent with the objective of this study, differences emerged
on speciﬁc manipulation checks. As indicated by the signiﬁcant correlation (r =
.39, p < .01), participants in gain-framed training rated the training as focusing
more on beneﬁts of completing the Navy simulation successfully (M = 2.19, SD =
.95), whereas participants in the loss-framed training condition rated the training
as focusing on the costs of completing the Navy simulation incorrectly (M = 4.34,
SD = 1.40).

In addition, differences emerged when participants were asked to list
personal goals of completing the training. In an additional qualitative check, the
researcher coded responses to the training goals checklist that was part of the
Training Pamphlet. Participants’ goals, which serve as an indication of
internalization for the training condition, were rated from -2 to +2 on the degree to
which the respondent’s goals indicated a gain or loss-framed goal. Two
additional research assistants trained in the coding scheme also coded the goals,
resulting in a high level of agreement (k = .77). Results from the coding indicated
that the majority of participants internalized the training condition: 87% of gain-
framed participants reported a gain-framed goal and 76% of loss-framed

participants wrote a loss-framed goal. The average rating for loss participants

92

was -.15 (SD = 1.19) while the average rating for gain framing was 1.18 (SD =
1.24).

Finally, the overall tone of the video was rated as positive in the gain
framed training (M = 3.65, SD = .84) than the loss framed training (M = 2.71, SD
= 1.01 ). These results support the conceptual model’s strength in manipulating
the participants to internalize the gain and loss-framed messages. The

implications and limitations of these results are now discussed.

93

DISCUSSION

The purpose Of the present study was to extend the research on ways to
increase training effectiveness when the instruction is labeled remedial. The
general question asked to what extent do certain types of messages received
during remedial training inﬂuence training outcomes. The speciﬁc question
asked to what is the effect of gain and loss-framed messages on multiple training
variables and training outcomes.

As demonstrated in Figure 2, the author developed a conceptual model
that illustrated the hypothesis: the enhancement of motivation to learn, self-
efﬁcacy, and fairness perceptions through framed messages affect training
outcomes of remedial training. The model also proposed that an individual’s
ideal/actual discrepancy interacts with self-efﬁcacy. Finally, the model included a
number of individual characteristics that mediate the relationship between
framing and training outcomes. Now, a revised conceptual is introduced.

The discussion of this study is divided into four parts. First, there is a
summary of the study results combined with a graphical representation of the
connections between the variables and outcomes. Second, there are
implications of the study results along with a discussion of future research. Third,
there is a revised conceptual model, reﬂecting on how well the proposed model
ﬁt with the results of the study. Finally, there is a discussion of the limitations of

the study.

94

Procedural

   
   

 

 

 

' -24“
“3““ Affective
.54“ % Outcome
.1 , * D' ‘b . 1 ,,
Framed . Istri utive -. 8 _19.
.23” '42 Self-Efﬁcacy -.15* VA Discrepancy
.21** [-.29"
Motivation to .19“ Mood
Learn
_ a
'13 .24" .16*
.45“ ,,
Perform. .14 Knowledge
Pre- ”17. (Accuracy) Outcome
Assessment
.1 *
Tone 6 Openness

* p < .05, ** p < .01, Note: l/A Discrepancy at Time 1 (Time 2 had similar relationships)

Figure 5. Correlative Map of Signiﬁcant Associations. As pictured, framed
messages had several direct and indirect pathways to outcomes.

Summary of the Results

As displayed in Figure 5, framing had several direct effects on outcomes.
But, framing also took several indirect pathways to training outcomes. The
following discussion describes those pathways and explains why certain
pathways did not appear.

Structural Hypotheses. Hypotheses 1, 2, 4, 5, and 6 stated there would
be a main effect of framing on training variables and training outcome. The
current study found support that gain-framed training elevated motivation to learn
and distributive injustice compared against those in the loss-framed condition. In
addition to proximal outcomes such as increased motivation, gain-framed training

also had a main effect on training reactions as compared to those in the loss-

95

framed training. Conversely, loss-framed participants performed more accurately
than those in the gain-framed training condition.

Hypothesis 1, that noted that self-efﬁcacy would be inﬂuenced by framing,
was not supported. These results may have occurred because self-efﬁcacy was
already relatively high (M = 3.40, SD = .62). Additionally, the relationship of
framing to self-efﬁcacy may be more indirect as there is evidence to suggest that
participants were willing to increase their knowledge and skills. The current
study did ﬁnd a connection between motivation to Ieam and self-efﬁcacy (r = .33,
p < .01), and association between framing and motivation to learn (r= .23, p <
.01). Given that framing is related to motivation and motivation is related to self-
efﬁcacy, the current study does not explicitly rule out the possible effects of
framing on self-efﬁcacy - just that, it was not a direct effect. Researchers should
continue to examine the effects of framing and self-efﬁcacy, an area that is rarely
explored, and should develop additional hypotheses to analyze indirect pathways
from framed messages to increased self-efﬁcacy.

The hypothesis that framing would affect procedural was also
unsupported. DeDreu (1996)’s ﬁnding about justice perceptions associated with
framing explains the results of the current study. He suggested in framed
contexts participants are increasingly aware of distributive justice, rather than
procedural justice. If this is true, then participants in the current study may have
been less concerned about procedural justice, which would account for the non-

signiﬁcant difference in procedural justice. In either framed condition, trainees

96

tended to feel that they were placed in remedial training unfairly (M = 2.90, SD =
.57 for procedural justice; M = 2.45, SD = .57).

Finally, several hypotheses stated that framing would be related to
performance outcomes. Four measures of performance outcomes were used to
test these hypotheses: (1) knowledge assessment, behavioral performance
including (2) speed and (3) accuracy, and (4) affective outcomes measured by
training reactions. Accuracy in performance and training reactions were related
to framing, while speed in performance and knowledge were not.

It is not surprising that speed was not related to the framing-focused
training because of the training program itself. Individuals in both training
conditions were not explicitly given speed goals nor were they taught how to
complete the simulation more quickly. It is even plausible to think that if the
trainees completed the training correctly and learned the information as it was
presented that they actually would spend more time, rather than less time,
completing each trial. Because of the overall focus on quality over quantity, the
results are not surprising. However, future research should explore the effect of
adding framed messages to goals that are related to speed and timing.

Additionally, participants in the gain-framed training did not Ieam, as
measured by a knowledge test, signiﬁcantly better than those participants in loss-
framed training; and those who did perform better were not in the gain-framed
condition. There may be ﬁve reasons for the lack of evidence for these proposed

relationships.

97

Knowledge Outcome Literature. First, researchers such as Lee and Aker
(2004) and Toll et al (2007) have used gain and loss-framed messages to
demonstrate behavioral change and attitude change. However, there is little
evidence from the framing literature to suggest that framed messages affect
knowledge outcomes speciﬁcally. This may be the case in the current situation;
perhaps framed messages cannot inﬂuence knowledge transfer. Researchers
should explore the relationship between framed messages and knowledge
transfer.

Lack of Control Group. Second, training may have equally improved
knowledge regardless of condition. Accordingly, Kanfer 8 Ackerman (1989) and
Winne (1996) have reported that reminders of goals throughout training have an
impact on training outcomes. Perhaps the training itself increased knowledge.
Descriptive data does suggest that the training itself did affect knowledge
transfer. Participants on average increased their knowledge outcome by 5.12
points (SD = 6.53). Given that there was no control group to compare both
conditions against, it is difﬁcult to assess if the framed training universally
assisted in knowledge transfer, or if the training itself was the ultimate knowledge
enhancer. Therefore, researchers should consider including a control group with
neutral messages to determine if the mere presence of framed messages has an
impact on knowledge outcomes.

Negative Transfer Problem. Third, the training evaluation procedure may
have presented a negative transfer problem. That is, the program, Tandem, may

been overly complicated. So much so, that the participants were unable to

98

clearly demonstrate their assimilation of the knowledge presented in the training.
For example, the trainees had to “right mouse click” certain items while “left
mouse click” other items in addition to holding down the mouse click for a certain
duration to ensure the participant was able to understand what was happening in
the program.

Additionally, the interface utilized a MS-DOS type environment which is
very unfamiliar to the participants who are accustomed to sleek, user-friendly
gaming interfaces present on popular gaming systems by Nintendo, Sony, and
Microsoft. Because the program is so complicated, researchers who use the
Tandem program sometimes run their participants through over 20 trials to
ensure the participant has a clear understanding of what is happening.
Therefore, the presence of difﬁcult transfer environment may in itself presented
an issue related to performance outcomes. Future researchers should attempt to
replicate this study using a less complicated training system.

Arousal and Attention Theory. Fourth, the reason for the loss-framed
advantage for performance outcomes may come from theories about arousal and
attention. First, as indicated by Payne et al (1993), individuals spend more time
on loss-framed messages. Likewise, they have increased cognitive activity when
processing loss-framed messages, which takes additional processing time.
Researchers have found links between time and accuracy (Maag, 1993). Thus,
a preliminary consideration between loss-framed messaging and outcomes may

have to do with the amount of time spent on processing the information. Other

99

theories may come into play regarding the success of loss-framed performance
outcomes.

Second, researchers such as Pashler (1998) and Triesman (1964) have
examined the role of attention and arousal. They have speculated that, given the
enormous amount of information that each individual encounters, there has to be
something that guides what is given attention. Known commonly as a “cocktail
party effect,” unattended stimuli receive no further attention while attended stimuli
receive full attention. Other theorists such as Deutsch and Deutsch (1963)
suggest that all stimuli are given attention. Both types of theories have been
grounded in neuropsychological models, which are far too complex for the extent
of the current study. Nevertheless, the theories on attention lead to interesting
interpretations of loss-framed messages. Given that they are grounded in
additional processing time and that they emphasize a beneﬁt lost, perhaps the
presence of a loss message arouses the senses to sustain greater attention.

The loss-message may arouse attention from the senses for two reasons.
First, loss-framed messages may engage “fear” which is demonstrated by loss-
framed messages such as, “If you don’t ﬂoss, you will contract disgusting gum
diseases.” These fear-induced messages as researched by Baron, Logan, Lilly,
Inman, and Brennan (1994) and Meijnders, Midden, and Wilke (2001) evoke
greater processing time and may indeed lead to behavioral changes, such as
performing better on the task of the current study.

The second reason for loss-framed advantage in performance measures

may come from the “negativity bias” (Cacioppo 8 Gardner, 1999). Researchers

100

have discovered the gain and losses are “psychologically asymmetrical” because
persons are sensitive to losses. Individuals wish to avoid all possible losses.
Because of this, information presented in terms of losses are detected at much
lowered levels than gain-framed messages. For example, it is likely for an
individual to perceive a phrase as “loss” oriented because of the presence of just
one word. Conversely, it may take several “gain” framed words for an individual
to process that phrase as gain-framed.

These two reasons, fear and bias, lead O’Keefe and Jensen to conclude
that loss—framed messages lead to longer processing than gain-framed
messaging. Again, this extended attention may lead to higher behavioral
outcomes and should be focus of future research. This, however, may be altered
if the participant can internalize the framing.

Intemalization Of Framing. Finally, the participants in the framed conditions
may not have internalized the framed conditions fully. Although a high level of
participants internalized the responses, there were still 34 participants who did
not report training goals that related to framed-training; instead they reported,
“non sense” goals such as, “I will save the environment” and “I need a new
pencil.” As a result, a post-hoc analysis was run. Participants who did not report

a gain or loss-framed message

101

Table 24
Inter-Correlations with lnternalized-Framing Participants

 

Variable 1 2 3 4 5 6 7 8
1 Gain/Loss Framing

2 Procedural Justice -.04

3 Distributive Justice .13 .58“

4 Self-Efﬁcacy .10 .03 .23“

5 Motivation to Learn .30“ -.06 .08 .35“

6 Affective Outcome .20“ -.17" -.10 .23" .38“

7 Knowledge Outcome .42“ -.13 -.03 .04 .27“ .13

8 Performance (Accuarcy) -.17" .04 .06 .04 -.07 -.02 .01

9 Performance (Speed) .09 .00 .04 .05 .18“ .03 .22“ .07

 

*‘. Correlation is signiﬁcant at the 0.01 level (2-tailed).
*. Correlation is signiﬁcant at the 0.05 level (2-tailed).

goal were dropped from the correlation analysis. Results suggest that
internalization Is an important part of training outcomes, as indicated by
signiﬁcant differences in the relationships.

As Table 24 states, the knowledge outcome becomes signiﬁcantly related
to framed training when those who did not internalize the training goals are
removed (r= .42, p < .01). As indicated by the differences between participants
in the gain-framed condition who averaged at 15.26 (SD = 4.75) and the loss-
framed participants who averaged at 10.32 (SD = 5.99), participants in the gain-
framed training had higher knowledge outcomes than those in the loss-framed
training. In fact, the association becomes the biggest effect size, d = .75, for the
current study. These results suggest that in order for framing to be effective on
training outcomes, participants must internalize the framed condition in which

they are placed.

102

Other training outcomes became signiﬁcantly correlated when non-
internalized participants are removed. Of interest, speed related to both
motivation (r = .18, p < .05) and knowledge outcomes (r = .22, p < .05). Again,
both results revealed that participants who had higher motivation also performed
trials quicker and had higher knowledge outcomes. These results contradict
previous ﬁndings by Quinones (1995) who noted that performance outcomes
were unrelated to motivation when participants are placed in different training
assignments.

The remaining results are similar to the results when all of the 187
participants are considered. Yet again, motivation to learn mediated the
relationship between framing and knowledge outcomes, 2 = 1.86, p < .05.
However, procedural justice, distributive justice, and self-efﬁcacy did not mediate
the relationship between framing and knowledge outcomes (2 = -1.15, p = .25 for
procedural justice, 2 = -.67, p = .80 for distributive justice, and z = .01, p = .99 for
motivation to Ieam). Additionally, there were no signiﬁcant effects of openness
(IS = .03, p = .77) or conscientiousness (8 = .01, p = .86) as moderators between
framing and knowledge outcomes as reported by their non-signiﬁcant
standardized regression coefﬁcient for the interaction term. The implications of
these ﬁndings are discussed later.

Moderating Hypothesis. The current study proposed that an individual’s
ideal/actual discrepancy would moderate the relationship between framed
messages and self-efﬁcacy. Support for Hypothesis 3 was not found. However,

the ideal/actual discrepancy measure was related to self-reported self-efﬁcacy.

103

There are several possible reasons for non-signiﬁcant effects of the
moderator. First, Tykocinski, Higgins, and Chaiken (1994) noted that framing
engages self-esteem differences in ideal/actual discrepant individuals, not self-
efﬁcacy speciﬁcally. Although it was argued that the differences between self-
esteem and self-efﬁcacy are “psychologically meaningless,” future research
should explore if self-esteem instead of self-efﬁcacy would differentially affect the
messages of gain and loss messages.

Secondly, the sample had a relatively low discrepancy level with no
individuals reporting a discrepancy higher than 18 (measure ranged up to 25).
For example, at the ﬁrst measure of the ideal/actual discrepancy, participants
were relatively low in this discrepancy with a reported mean of 6.41 (SD = 3.73).
These relatively low scores make it difﬁcult to see how individuals who are high
on the ideal/actual discrepancy interact with framed training.

In addition to the lack of support for the interaction with the ideal/actual
discrepancy, there was no evidence that personality interacts with framed
messages. Future research should attempt to further investigate the interactions
of personality and framed messages in a training context, as this is the ﬁrst
attempt to look at personality’s interaction with framed messages in training.
While the moderating analyses were not supported, the mediated hypotheses
were partially supported.

Mediating Hypotheses. The current study proposed there would be
several mediating effects of self-efﬁcacy, motivation to learn, and fairness

perceptions on the relationship between gain and loss framed-messages and

104

training outcomes. The study found that motivation to learn, which mediated the
relationship between framing to training reactions and knowledge outcomes.

The lack of support for self-efﬁcacy as a mediator may come from the fact
that the current study did not effectively increase self-efﬁcacy through framed
messages. As Gist and Mitchell noted (1992), a trainee arrives to their self-
efﬁcacy level through three steps: (1) assessment of the task as it relates to his
or her ability, (2) assessment of past performance on the task, and (3)
assessment of the available resources to complete the task. The current study
may not have presented individuals with all three steps to elevate self-efﬁcacy
level through framed messages. First, the study used a task that was unfamiliar
to the trainee. As a result, the participant had limited abilities. Second, the
researcher told the trainee that they had already failed to complete the pre-
assessment satisfactorily. Therefore, the assessment of past performance does
increase self-efﬁcacy. The ﬁnal piece of a trainee’s self-efﬁcacy, the available
resources, was the only part of the self-efﬁcacy that framed messages actually
targeted. Therefore, the current study only used one of the steps to target self-
efﬁcacy through framed messages.

Researchers Should address how self-efﬁcacy can be speciﬁcally targeted
through framed messages utilizing all three levels of Gist and Mitchell’s theory of
a trainee’s self—efﬁcacy. For example, framed messages could be included to
reveal the actual training assignment. As Gist and Mitchell (1992) noted,
participants assess self-efﬁcacy when they evaluate the task based on past

performance. If participants had received the assessment results in terms of

105

framed messages rather than the neutral message they received, perhaps their
self-efficacy would have interacted with framed messages.

In addition to the lack of the mediating effect of self-efﬁcacy, there was a
lack of support for fairness perceptions as a mediator on training outcomes. As
noted, gain framed training did increase perceptions of fairness. Additionally,
fairness perceptions were related to training reactions. However, there was no
mediation. The lack of mediation is similar to Quinones’ results (1995).

Conclusions: Consider all the relationships that were supported in this
study and note the revised conceptual model in Figure 6 that shows that framed
messages directly affect training outcomes, motivation to learn, and fairness
perceptions. Figure 6 displays the results for gain-framed training only because
loss-framed training did not havethe same type of signiﬁcant relationships.

As shown in Figure 6, the framed messages trainees hear as they enter
remedial training have a direct impact on their performance. More speciﬁcally,
they respond to gain-framed messages by perceiving the training they received
more positively. In the loss-framed condition which is note shown in Figure 6,
trainees react to loss-framed messages by performing more accurately on the

task.

106

gain framed-training to promote achievement of affective goals. Likewise,
trainers may employ loss-framed training to affect the behavioral outcomes of
training. Further, if a trainer is able to persuade trainees to internalize gain-
framed training, the trainer may help learners to achieve several training goals
such as increased motivation to learn, increased fairness perceptions, increased
knowledge outcomes, and increased affective outcomes.

Second, researchers may extend the ﬁndings by continuing to examine
the unique role that gain and loss-framed messages have on behavioral
outcomes. As indicated in the current study, researchers may pursue the idea
that gain and loss framed messages have distinctly different and independent
effects and applications. For example, loss-framed messages increased
accuracy performance. However, gain-framed messages increased motivation to
learn. This is not to say that loss-framed messages had no effect on motivation
to learn or that gain-framed messages had no effect on performance outcomes —
just that, they did not work together.

Gain and Loss-Fragiiﬂgptcomes Mismatch. To reiterate, both gain and
loss-framed messages seem to act differently depending on the context. In
examining the results of this study we may note an interesting paradox: gain and
loss-framed messages independently affected two variables that should be
related, motivation to learn and performance. This relationship creates
interesting research questions.

Theoretically, other things being equal, higher motivation to learn leads to

higher performance outcomes than lower motivation to learn (Mathieu, Martinue,

109

Implications and Directions for Future Research

The results of this study are consistent with the research on remedial
training and framed messages: remedial training has negative training outcomes,
and gain and loss-framed messages inﬂuences an individual’s behavior.
Consequently, the results of this study have implications for practice and
research related to training effectiveness especially for remedial training and
framing research.

Training Effectiveness. Three outcomes of this study conﬁrm Quinones’
training effectiveness results. First, Quinones (1995) indicated that remedial
assignment is a continuous method Of informal feedback that has the detrimental
effect of negating the effectiveness of the remedial assignment. The results of
this study agree with Quinones’ results, suggesting that remedial training
assignment affects training effectiveness. Second, Quinones’ (1995) participants
in remedial training reported less fairness compared to those participants in the
so—called advanced training condition. Here too, participants reported relatively
lower fairness, suggesting that remedial training leads to lowered perceptions of
fairness. Third, one of the strongest effects in Quinones’ study was that framing
affected motivation to learn, which was found in this study as well. These three
outcomes have implications for trainers and researchers.

First, trainers may apply the results of the current study to stop the
negative effects of remedial training. For example, trainers may use gain-framed
messages to increase motivation to learn, to decrease perceptions of unfairness,

and to persuade trainees to see training as beneﬁcial. Trainers may also use

108

 

     

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Figure 6. Resulting Conceptual Model for Gain-Framed Participants Only. After
participants are given selected gain-framed training, participants experience
higher motivation to Ieam, higher perceptions of fairness, and higher selected
performance outcomes.

Additionally, responding to gain-framed messages trainees increase
motivation to Ieam and simultaneously decrease perceptions of unfairness for the
placement into remedial training. While there are no direct effects of framing on
self-efﬁcacy, a trainee’s motivation to learn is related to self-efﬁcacy, which was
also related to the ideal/actual discrepancy. Despite these outcomes, the
ideal/actual discrepant individual does not have any direct relationship to the

framing.

107

8 Tannenbaum, 1993). However, this was not the case in this study; motivation
to Ieam and performance outcomes were not related. Interestingly though, the
results are consistent with Quinones’ work on training assignments, in which
performance outcomes were not related to motivation to learn. To explore the
reason for this result, researchers who use this study as a model for investigation
should use other measures of performance that are historically linked to
motivation to learn, leading to research the question: can other measures of
performance capture an increase in motivation to learn?

If researchers conclude that a different performance measure does not
result in signiﬁcant results, perhaps this gain-loss—frame resulting mismatch may
be due to from speciﬁcity of training goals. Detweiler, Bedell, Salovey, Pronin,
and Rothman (1999) indicate that the goal has to be tangible and speciﬁc for
gain-framed messaging to be more inﬂuential than loss framing. Othenivise,
vague outcomes are more inﬂuences by loss-framed training. This relationship
was demonstrated for health screening such as mammograms (Banks et al,
1995), and is demonstrated in the present study. While this study presented
examples of what would happen if a participant failed to complete the training
accurately, participants had little “connection” to that speciﬁc outcome (i.e. the
majority of participants had no personal association with doing well in a navy task
because they have no intention of joining the Navy). Specifying that participants
will “take more lives” or “feel badly” may fall within the “vague outcome,” in which
loss frame is best utilized. Researchers should make training goals speciﬁc in

order to see if this may inﬂuence the effect of gain-framed messages, leading

110

researchers to answer the question: Does creating speciﬁc training goals itself
have an affect on gain and loss framed messages?

Agvanceg Training. While the research leads trainers and researchers to
conclusions about how to conduct necessary remedial training, researchers have
not analyzed advanced training. Quinones (1995) identiﬁed three types of
training: Advanced, Remedial, and “Neutral”/Control. This study identiﬁed clear
training outcome differences for messages received during remedial training.
Researchers should consider expanding the investigation of gain and loss
framing to advanced training conditions and including a control condition that
utilizes neutrally framed messages.

Researchers may ask: Will participants respond the same way to framed-
messages in an advanced training condition? Given that the variables motivation
to Ieam, and accuracy in performance, were stronger in opposite conditions - that
is, a gain-framed condition paired in a remedial training context - researchers
may consider how gain-framed conditions paired in advanced training contexts
will affect participants.

Additionally, if researchers used a control group they could answer
questions such as how framed messages affect knowledge outcomes. For
example, with a control group, researchers could compare neutral messages to
both gain and loss-framed messages to see if framed messages produce
relatively greater knowledge in relatively shorter times. Researchers could also
ﬁnd overall effect sizes that framed messages have on behavioral outcomes

utilizing a standard Cohen’s d equation.

111

Training Context. In addition to different training labels, researchers
should look at the context, which these labels can create. Researchers have
suggested that trainers should be aware of training context and devise strategies
to increase pre-training self—efﬁcacy, motivation to Ieam, etc. These tasks can
add substantial time to the training curriculum. The current study presents a way
to blur the line between training context and training design. The pre-training
exercises that once required separate sessions can be incorporated into the
training design easily without the time and ﬁnancial cost. Framed training
automatically includes ways to increase motivation to learn and increase fairness
perceptions simply through the way the messages are framed.

Motivation to Learn. The results of the present study are partially
consistent with past ﬁndings. Tannebaum et al (1991) demonstrated the
importance of motivation to Ieam on training effectiveness. Individuals who have
increased motivation to learn tend to master more of the training information
presented, which translates into increased knowledge and behavioral transfer. In
the current study, participants who had increased motivation also performed
better on the Navy Simulation Task. However, motivation to Ieam was not
associated with knowledge outcomes. Therefore, the study only partially
supported the connection between motivation to learn and performance
outcomes. Nonetheless, motivation to learn did have positive effects in the
current study.

Nevertheless, because of the relationship between motivation to learn and

performance outcomes, Quinones (1995) noted that trainers should be aware of

112

factors such as training assignment that decrease motivation to learn. The
results suggest that, by utilizing framed messages, trainers can overcome the
reduction of motivation to learn. Framing may lead to higher levels of motivation
to Ieam, which was indeed one of the strongest impacted variables of the framing
conditions.

Researchers should take this ﬁnding further to examine to what extent
does motivation to Ieam vary by framed condition. Given that motivation to learn
did not extend its relationship to performance outcomes, this leaves an
interesting relationship that needs further explanation. Researchers should take
note that framing does increase motivation to Ieam but should determine how
much motivation is needed to actually affect performance outcomes when
attempting to manipulate motivation levels through gain-framed training.

Fairness Perceptions. This study represents an early attempt to
determine the role of gain and loss-framed messages on fairness perceptions.
Taken alone, fairness perceptions do not seem to have an impact on behavioral
outcomes. But as Quinones (1995) demonstrated on training outcomes,
perceiving a training program as fair indeed affects training outcomes and can
lead to behavioral changes. The results assists in alleviating the negative
outcomes associated with poor fairness perceptions in training assignment
(Quinones, 1995), again helping to break the cycle of negative outcomes that
remedial training presents. As presented earlier, remedial training and “special”

training interventions will continue but again, by simply utilizing targeted

113

messages trainers may have a way to assist in altering perceptions - at least
with distributive justice.

However, our study failed to present a way to alter procedural justice
perceptions. Procedural justice is deﬁned by the procedures that helped
individuals (in this case, the researcher) arrive to decision outcomes (placement
into the remedial assignment). Given that gain-framed messages appear to
assist in helping alleviate perceptions of distributive injustice, researchers should
analyze pathways In which gain framing may also affect procedural justice.

Framing Effects. While the intent of the study was to capitalize on
established theories on framing to break the cycle of negative outcomes of
negative outcomes, the current study added an interesting theoretical question
on framed theory. That is, do participants need to internalize framed messages
in order for the framed effects to be effective? This was seen especially with the
knowledge outcomes in the current study.

The idea of internalizing framed behavior is not a new one. Toll et al
(2007) asked participants to list reasons they should complete a smoking
cessation program. However, the researchers did not report any descriptive
statistics on the rate of internalization nor did they indicate how reported goals
related to behavioral outcomes. Other studies that were discussed for the
literature review did not indicate any type of reporting of framed goal orientation.
Therefore, the current study represents one of the ﬁrst studies to analyze the role

of internalization on behavioral outcomes.

114

While the current study measured internalized framed by the self-reported
goals of completing the training successfully, future researchers should analyze
other methods of ensuring internalization has happened. Once this method has
been validated, researchers can pursue additional means as to which
internalization affects outcomes of framed interventions, including training.

Combination of Gain and Loss Effects. In addition to internalization, the
present study’s somewhat conﬂicting results, concluding that gain-framed training
is better for affective outcomes while loss-framed training is better for
performance outcomes, leads to an interesting research question. That is, could
the effect of gain and loss-framed messages be combined in some way?

As mentioned before, Quinones (1995) did utilize both messages in his
training curriculum. However, this was done unintentionally and without
planning, which should not discredit his research because gain and loss-framed
messaging is present in everyday language. Therefore, an important extension
of this research study would be determining if certain parts of the training coded
with different types of framing would result in both performance and behavioral
outcomes. For example, the training curriculum that deals with feeling conﬁdent
about the training may be coded with gain-framed messaging while the
curriculum that deals with increasing performance in the simulation may be
coded with loss-framed messaging. In such a way, the power of both gain and

loss-framed messaging would be utilized in a strategic way.

115

Study Limitations

This study has limitations which must be taken into account when
interpreting the results and designing future studies. First, a college student
population provides only limited generalizability to the older populations, which is
typical of the workforce. The current study is one of few studies that actually
utilized gain and loss-framed in a college sample. An older population may react
differently to gain and loss-framed messages in training. However, given that
several studies have looked at gain and loss-framed messages in an older
population and successfully demonstrated Changed behavior, researchers can
believe that the results presented in the current study will generalize to other
populations.

Second, the present study utilized a task that was highly unfamiliar to the
participants. Future studies should use remedial training for skills that are more
familiar to participants such as an ofﬁce task or a generic educational domain
such as math or verbal skills. The reasoning for picking an unfamiliar task was to
limit inﬂuence of previous knowledge on training outcomes. Experience could be
an added factor into predicting success and should be accounted for in follow up
studies. Having more experience with a task may enhance or inhibit gain-framed
messages on the effects of motivation to learn, self-efﬁcacy, and fairness
perceptions.

Third, the training course was a short one-hour session, which is an

uncommon practice of most organizations. Goldstein (1993) noted that training

116

sessions typically last at least a half-day and may continue for several days after
that. However, with such extended training courses, extended motivation is
required and as such, this study captured participants’ motivation to learn before
it became ﬂeeting. However, given that framing had the strongest effect on
motivation to learn, additional research may look to see how motivation to learn

interacts with time of the training program.

CONCLUSION

This study is an effort to generate knowledge to help trainers mitigate the
negative outcomes of remedial training such as low performance outcomes and
reduced motivation to learn. From this work researchers and practioners may see
a way to confront the problem: using targeted gain and loss-framed messages.
The results indicate that gain-framed messaging increase motivation to learn and
loss-framed messaging increases accuracy in performance. However, this study
used a shortened, simpliﬁed training program that should be expanded to ensure
the results can be replicated in more realistic training situations.

Although the evidence does not present a clear, universal direction for
trainers, it would seem sensible that, depending on the type of training, trainers
could use gain-framed messaging for affective training and utilize loss-framed
messaging for performance training.

Additionally, although performance outcomes are generally favored by
Fortune 500 companies over affective outcomes, research on affective

implications for the organization should be recognized. For example, Babakus,

117

Yavas, Karatepe, and Avci (2008) found that performance as measured by
service quality was mediated by affective variables such as organizational
commitment and job satisfaction. This type of relationship is consistent with
other types of research that suggest job satisfaction can be linked to employee
turnover (Mobley, 1977). In such a way, there is much to be said about the
importance of affective outcomes. So much so, perhaps affective outcomes may
produce long-term effects on performance outcomes. While current researchers
continue to examine the long-term effect of affective outcomes and its
relationship to performance outcomes, the current study offers an array of
solutions to the negative outcomes of remedial training.

Future research should focus on expanding the role of framing to other
training domains such as continuous Ieaming and other training labels such as
advanced training. In such a way, trainers and researchers can continue to
break the negative outcomes that remedial training presents and can utilize the

power of framed messages in training.

118

Appendix A: Consent Form
AIR DEFENSE TASK TRAINING STUDY

This research study investigates the effectiveness of a training program in
improving performance on a computer task. Over two sessions, you will be
asked to complete an electronic Training Assignment Test as well as a few other
measures. This ﬁrst session, which is online, will last approximately thirty
minutes. You will then be asked to sign up for a subsequent session in which
you will be assigned to a training program. You will then be given an opportunity
to work on the Air Defense Task. This second session should last approximately
one hour.

Your participation in this research study is strictly voluntary and you may refuse
to answer any questions which you may ﬁnd inappropriate without any penalty.

In addition, you can discontinue the experiment if you feel it necessary to do 80.
Your individual results in this study will be CONFIDENTIAL. You will be assigned
a unique eight-digit number, which will appear on all questionnaire forms. The
experimenter will only report the ﬁnal data in an aggregate form, which does not
allow any particular individual to be identiﬁed.

There are no foreseeable risks associated with participating in this research
study. Your name and information will remain conﬁdential. Your privacy will be
protected to the maximum extent allowable by law. The data will be saved for the
duration of the study and will only be accessible by the primary investigator and
two additional researchers. You may refuse to participate in certain procedures
or answer certain questions. Participation is completely voluntary. You may
choose not to participate at all and may discontinue your participation at any time
without penalty or loss of beneﬁts.

If you have concerns or questions about this study, such as scientiﬁc issues, how
to do any part of it, or to report an injury, please contact the Adam Massman at
512-876-7691, or by e-mail, massmana@msu.edu, or by regular mail at 302
Psychology Building, MSU, East Lansing, MI 48824. If you have questions or
concerns about your role and rights as a research participant, would like to obtain
information or offer input, or would like to register a complaint about this study,
you may contact, anonymously if you wish, the Michigan State University's
Human Research Protection Program at 517-355-2180, Fax 517—432-4503, or e-
mail irb@msu.edu or regular mail at 202 Olds Hall, MSU, East Lansing, MI
48824.

I have read the consent form and choose to participate in this study:

Participant will electronically click “I consent” or “I decline to consent”

119

The following test measures your understanding of the material, which you have
read regarding the Air Defense Task. Your responses to this test will determine
which training program you will be assigned to on the subsequent session.
Those scoring above average on this test will be assigned to Advanced Training.
Those scoring below average on this test will be assigned to Remedial Training.

PART I: Mark the correct answer for each of the questions below. Make sure to

Appendix 8: Training Assessment Task

mark your answers on this form as well as the computer form.

1) 1.5 Mhz represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

2) +20 degrees of angle represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

3) 5 miles outside the corridor represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

4) 35 degrees of direction represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

120

5) Class 10 radar type represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

6) 400 miles per hour represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

7) 30,000 foot altitude represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

8) 20 meters represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

9) 25 miles outside the corridor represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.
A target that is out of the possible range of values.

10) 850 miles per hour represents which of the following?

A non-threatening target.

A somewhat threatening target.

A very threatening target.

A target on the border between two threat levels.

121

11)

12)

13)

14)

15)

16)

e) A target that is out of the possible range of values.
Class 1 radar represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(I) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

-12 degrees of angle represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

0) A very threatening target.

d) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

80 miles for range represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

0) A very threatening target.

d) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

Which of the following combinations represents a more threatening target?

a) slow and land radar targets

b) high ﬂying and fast targets

c) descending and inside trafﬁc corridor targets

d) outside the trafﬁc corridor and high ﬂying targets
e) close and descending targets

Which of the following combinations represents a more threatening target?

a) slow targets with weather radar

b) high ﬂying and fast targets

C) descending and inside trafﬁc corridor targets
d) small targets with weapons radar

e) close and ascending targets

Which of the following combinations represents a more threatening target?
a) slow and land radar targets

b) high ﬂying and fast targets
C) descending and inside trafﬁc corridor targets

122

d) fast targets headed toward the group
B) close and ascending targets

17) Which of the following combinations represents a more threatening target?

a) slow and land radar targets

b) high ﬂying and fast targets

c) descending and inside trafﬁc corridor targets

d) outside the trafﬁc corridor and low ﬂying targets
e) close and ascending targets

18) All else equal, which of the following is/are characteristic(s) of a
threatening target?

a) Low ﬂying targets.

b) Military targets.

C) Targets with weather radar

d) 2 of the above are characteristics of a threatening target.
9) 3 of the above are characteristics of a threatening target.

19) All else equal, which of the following is/are characteristic(s) of a
threatening target?

a) Targets that are high ﬂying.

b) Targets with weapons radar.

c) Targets that are fast and inside the trafﬁc corridor.
d) Targets that are descending and close.

9) Targets that are large and descending.

20) All else equal, which of the following is/are characteristic(s) of a
threatening target?

a) Targets that are large.

b) Targets with weather radar.

c) Targets that are fast and coming straight in.

d) Targets that are small and inside the trafﬁc corridor.
e) Targets that are ascending and close.

123

PART II: For the following targets, make the appropriate decision regarding the
defensive posture which you should take. Mark your answer on both forms.

m
SPEED: 172 miles per hour (mph)
ALTITUDE: 10,248 feet
SIZE: 10 meters
ANGLE: -14 degrees
lFF: 1.3 Mhz
DIRECTION: 4 degrees
CORRIDOR STATUS: 29 miles
RADAR TYPE: Class 2

RANGE: 31 miles

YOUR DECISION (circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

124

11g
SPEED: 321 miles per hour (mph)
ALTITUDE: 26,605 feet
SIZE: 41 meters
ANGLE: 9 degrees
lFF: .7 Mhz
DIRECTION:21 degrees
CORRIDOR STATUS: 19 miles
RADAR TYPE: Class 4

RANGE: 108 miles

YOUR DECISION (circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

125

 

ﬁg;
SPEED: 291 miles per hour (mph)
ALTITUDE: 14,321 feet
SIZE: 21 meters
ANGLE: -6 degrees
lFF: .7 Mhz
DIRECTION: 9 degrees
CORRIDOR STATUS: 21 miles
RADAR TYPE: Class 6

RANGE: 101 miles

YOUR DECISION (circiet
(1) IGNORE
(2) MONITOR
(3) WARN
(4) READY
(5) DEFEND

126

SPEED: 591 miles per hour (mph)
ALTITUDE: 14,016 feet
SIZE: 19 meters
ANGLE: -6 degrees
lFF: 1.3 Mhz
DIRECTION: 9 degrees
CORRIDOR STATUS: 11 miles
RADAR TYPE: Class 7
RANGE: 43 miles

YOUR DECISION (Circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY
5)DEFEND

127

#_25;
SPEED: 172 miles per hour (mph)
ALTITUDE: 10,248 feet
SIZE: 14 meters
ANGLE: -14 degrees
lFF: 1.6 Mhz
DIRECTION: 4 degrees
CORRIDOR STATUS: 23 miles
RADAR TYPE: Class 2

RANGE: 31 miles

YOUR DECISION (circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

128

m
SPEED: 281 miles per hour (mph)
ALTITUDE: 14,018 feet
SIZE: 21 meters
ANGLE: -7 degrees
lFF: .7 Mhz
DIRECTION: 10 degrees
CORRIDOR STATUS: 21 miles
RADAR TYPE: Class 6
RANGE: 106 miles

YOUR DECISION (circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

129

£27;
SPEED: 595 miles per hour (mph)
ALTITUDE: 14,937 feet
SIZE: 19 meters
ANGLE: -4 degrees
lFF: 1.3 Mhz
DIRECTION: 9 degrees
CORRIDOR STATUS: 21 miles
RADAR TYPE: Class 4

RANGE: 49 miles

YOUR DECISION (circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

130

i12_8:_
SPEED: 597 miles per hour (mph)
ALTITUDE: 16,115 feet
SIZE: 18 meters
ANGLE: -6 degrees
lFF: 1.3 Mhz
DIRECTION: 19 degrees
CORRIDOR STATUS: 21 miles
RADAR TYPE: Class 4
RANGE: 109 miles

YOUR DECISION (circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

131

SPEED: 345 miles per hour (mph)
ALTITUDE: 6,525 feet
SIZE: 14 meters
ANGLE: —10 degrees
lFF: 1.7 Mhz
DIRECTION: 7 degrees
CORRIDOR STATUS: 0 miles
RADAR TYPE: Class 1

RANGE: 75 miles

YOUR DECISION (circle):
(1) IGNORE

(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

132

 

m
SPEED: 299 miles per hour (mph)
ALTITUDE: 16,768 feet
SIZE: 18 meters
ANGLE: -6 degrees
lFF: 1.3 Mhz
DIRECTION: 9 degrees
CORRIDOR STATUS: 23 miles
RADAR TYPE: Class 2

RANGE: 31 miles

YOUR DECISION (circle):
(1) IGNORE
(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

133

 

Appendix C: Training Manual

Scenario Background

You are the Captain of a US. Navy Aegis-class cruiser. Your ship is part of a
task force currently stationed in a military zone. You have received reports of
enemy contacts and your Ship is on alert. You are seated on the bridge of your
ship where you can receive information from all your ship's sensors on your radar
screen. Yourjob is to Shield your ship from hostile enemy vessels and to avoid
destroying peaceful vessels.

Your ship is in the center of the radar scope on your screen. Surrounding your
ship are a number of asterisks called "targets." The sensors on your ship provide
”you the information you need to classify these targets according to their
characteristics. First, each target can be classiﬁed as being Type Air, Sub, or
Surface indicating the target is an aircraft, submarine, or surface ship. Second,
the Class of each target can be Civilian or Military. Finally, the Intent of each
target can be Classiﬁed as either Peaceful or Hostile. You must decide what
action your ship should take toward each target by deciding the Type, Class, and
Intent of each target.

Hooking Targets

In the lower right corner of your radar, you see the Hooked Track #. Each of the
targets on your radar is assigned a track number. When you "hook" a target, by
placing the mouse pointer on the target and clicking the left button, the Hooked
Track # changes to correspond to the target number. When you gather
information from your ship’s sensors, that information will be given for the target
you currently have hooked. Each target retains the same track # throughout the
simulation.

Using Menus

The menus are operated with the right mouse button. Click the right mouse
button on a menu to display it. Press and hold down the right mouse button to
display the information gathered by your sensors. The sensor information is
displayed in the lower right comer of your radar.

Making Cue Decisions

On the far upper right corner of your radar, you see OPER, TYPE, CLSS, and
ITNT. These are pull-down menus which allow you to gather the information from
your sensors you need to make the Type, Class, and Intent decisions. However,
your sensors are not 100% reliable and may tell you the requested information is
unknown or they may even provide you conﬂicting information. However, at least
two cues will always be accurate. Given this fact, you may increase your
decision-making efﬁciency by gathering only the minimum amount of information

134

 

necessary to make a correct decision. You can always make the best decision by
choosing the option indicated by the majority of cues.

Only one of the three pieces of information you can get for a decision may be
ambiguous, so the maximum pieces of information you need to gather is three. If
the ﬁrst two pieces of information you gather agree, you do not need to gather
any more information to make a correct decision. If you gather two pieces of
information and one indicates one decision but the other indicates a different
decision, you need to get a third piece of information to make a decision.

The ﬁrst three items in the TYPE, CLSS, and ITNT menus provide you the
information to make the (1) Type (lD_Air/SublSurface), (2) Class (lD_Civilianl
Military), (3) Intent (lD_Peaceful/Hostile), and (4) Engage (Engage_Shoot/
Clear). There are rules to follow to interpret each piece of information and make
the correct decision for each target. This manual contains all those rules in the
sections on the following pages. Memorizing those rules will help you in the
simulation.

As you make these decisions, the targets will change shape and color to reﬂect
your decisions. Do not worry about this.

Decision Rules Overview
The ﬁrst ﬁve items in the TYPE, CLSS, and ITNT menus provide you the
information to make the following decisions:

(1) Type (lD_Air/Sub/Surface)
(2) Class (lD_Civilian/Military)
(3) Intent (lD_PeacefullHostile)

You must make the decisions in this order.

As you make these decisions, the targets will change shape and color to
reﬂect your decisions. Do not worry about this.

There are rules to follow to interpret each piece of information and make
the decision for each target. This manual contains all those rules
in the following sections. NOTE INFORMATION WAS DELTED

The TYPE menu allows you to gather the information needed to determine
whether the target is an aircraft, a submarine, or a surface vessel, that is,
whether the target is Air/Sub/Surface. The table below lists the pieces of
information needed to make the target Type decision and the rules for
interpreting each piece of information needed to make the Type decision.

AIR
Speed is >35 knots

135

Altitude/Depth is >0 feet
Communication Time is 0 - 40 3.

SURFACE

Speed is 25 - 35 knots
Altitude/Depth is 0 feet
Communication Time is 41 - 80 s.

SUB

Speed is 0 - 24 knots
Altitude/Depth is <0 feet
Communication Time is 81 - 120 3.

Example of TYPE decision

We will use information about target Speed to illustrate the rules for making the
ID_Air/Sub/Surface decision. The Speed of a target tells you how fast the target
is moving. This helps you make the decision about the target's Type; that is,
whether the target is an aircraft, submarine, or surface vessel. The meaning of
the Speed values are shown below.

Speed > 35 knots indicates the target is an aircraft (Type = Air)
Speed 25-35 knots indicates the target is a surface vessel (Type=Surface).
Speed 0—24 knots indicates the target is a submarine (Type=Sub).

For example, if the Speed value is 115 knots, the target you have hooked is
traveling at 115 knots. Since this value is greater than 35, it indicates the target
Type is Air. Since your sensors may give incorrect information, you must gather
as much information as you need to make the best decision possible. Also, the
Speed of a target allows you to estimate how far the target will travel on your
radar screen during the simulation. This can help you judge which targets need
to be acted on quickly and which targets can be dealt with later. When you are
ready to make the Type decision, choose ID_Air/Sub/Surface from the bottom of
the Type menu. A list of choices appears in a menu on the lower right corner of
your radar screen and you select your decision by clicking on it with the right
mouse button.

The CLSS menu allows you to gather the information needed to determine the
Class of the target, that is, whether the target is Civilian, Unknown, or Military.
The table below lists the pieces of information needed to make the target Class
decision and the rules for interpreting each piece of information needed to make
the Class decision.

ClVlLIAN

Intelligence is Private

Direction of origin is Blue Lagoon
Maneuvering pattern is Code Foxtrot

136

UNKNOWN

Intelligence is Unavailable
Direction of origin is Unknown
Maneuvering pattern is Code Echo

MILITARY

Intelligence is Platform

Direction of origin is Red Sea
Maneuvering pattern is Code Delta

Example of Class Decision

We will use information about target Intelligence to illustrate the rules for making
the target Class decision. The Intelligence of a target tells you the type of
intelligence sensors possessed by the target. This helps you make the decision
about the target's Class; that is, whether the target is a Civilian or Military target
or whether the target's Class is Unknown. The meaning of the Class values are
shown below.

Intelligence Private indicates the target Class is Civilian
Intelligence Unavailable indicates the target Class is Unknown.
Intelligence Platform indicates the target Class is Military.

For example, if the Intelligence is listed as Private, the intelligence sensors of the
target you have hooked are Private. This value indicates the target Class is
Civilian. Since your sensors may give incorrect information, you must gather as
much information as you need to make the decision. ”ID_Cileilitary" at the
bottom of the CLSS menu allows you to make your decision about the Class of
the target you have hooked. When you are ready to make Class decisions,
choose ID_Civ/Military. A list of choices appears in a menu on the lower right
corner of your radar screen and you select your decision by clicking on it with the
right mouse button.

Target Intent

The ITNT menu allows you to gather the information needed to determine the
Intent of the target; that is, whether the target is Peaceful, Unknown, or Hostile.
The table below lists the pieces of information needed to make the target Intent
decision and rules for interpreting each piece of information needed to make the
Intent decision.

PEACEFUL
Countermeasures is None
Threat Level is 1
Response is Given

UNKNOWN

137

Countermeasures is Unknown
Threat Level is 2
Response is Inaudible

HOSTILE

Countermeasures is Jamming
Threat Level is 3

Response is No Response

Example of Intent Decision

We will use information about target Countermeasures to illustrate the rules for
making the target Intent decision. Countermeasures tells you whether your
sensors have detected the presence of defensive countermeasures on the target.
This helps you make the decision about the target's Intent; that is, whether the
target's Intent is Peaceful, Unknown, or Hostile. The meaning of the Intent values
are shown below.

Countermeasures = None indicates the target Intent is Peaceful
Countermeasures = Unknown indicates the target Intent is Unknown
Countermeasures = Jamming indicates the target Intent is Hostile

For example, if Countermeasures = None your sensors indicate the target you
have hooked has no countermeasures equipment and the Intent of the target is
Peaceful. Since your sensors may give incorrect information, you must gather as
much information as you need to make the decision.

"ID_PeacefullHostile" at the bottom of the ITNT menu allows you to make your
decision about the Intent of the target you have hooked. When you are ready to
make the Intent decision, choose ID_Peaceful/Hostile. A list of choices appears
in a menu on the lower right corner of your radar screen and you select your
decision by clicking on it with the right mouse button.

Final Engagement

After making the Type, Class, and Intent decisions for a target, you may decide
what action the USS Intrepid should take toward that target. The actions you may
take toward a target are "Shooting" the target or "Clearing" the target from your
area. You decide whether to Shoot or Clear targets from your area according to
the Intent of the target. If the Intent of a target is Peaceful, you should Clear the
target from your area. If the Intent of a target is Hostile, you should Shoot the
target.

"Engage_Shoot/Clear" at the bottom of the OPER menu allows you to make your
Engage decision in the menu. When you are ready to make the Engage decision,
choose Engage_Shoot/Clear. A list of choices appears in a menu on the lower
right corner of your radar screen and you select your decision by clicking on it

138

 

 

with the right mouse button. Targets disappear from your radar after you Engage
them so you cannot change your decision after Engaging a target.

When you are engaging the target by clicking on the right mouse button, you can
click and hold the right mouse button to receive information that will tell you
whether you have engaged the target correctly, and whether each of your three
subdecisions (Type, Class, Intent) were correct.

Zooming

The distance or radius in nautical miles (NM) currently displayed by your radar is
in the lower left corner of your screen. You may enlarge that radius by clicking on
the Zoom_Out function on the OPER menu. Each time you click on Zoom_Out,
the current viewing range of your radar increases. Since threatening targets may
appear outside your current viewing range, you may need to click on Zoom_Out
one or more times to check for targets outside your current radius. Each time you
click on Zoom_ln, your radar's current viewing range decreases. Since Zooming
Out can make targets close to your ship difﬁcult to see, you may need to Zoom
back in to clearly see these targets. Finally, you may need to Zoom_Out and
Zoom_ln periodically to locate dangerous targets close in and far away.

Pop-Up Targets

When you begin the simulation, several targets will already be visible. These
targets may be either inside or outside your current viewing range. Other targets
may appear during the simulation. These are called pop-up targets. Pop-up
targets may appear during the simulation for different reasons. Occasionally your
sensors may fail to detect a target because the sensor is broken or because
weather conditions are hampering its ability to operate correctly. However, some
targets use their Countermeasures to fool your sensors and cloak themselves
from your view. They may continue to cloak themselves until they are near your
defensive perimeter and then they appear and assume an attack proﬁle. Your
adversaries know about your defensive perimeters and sometimes pop-up just
outside them as they prepare to launch weapons. You cannot tell the difference
between targets that are deliberately hiding themselves from your view and
friendly targets your sensors failed to see because of bad weather or equipment
without checking the characteristics of the target. It is important to be aware of
pop-ups because they can change the situation very quickly.

Defensive Perimeters

Your Aegis-class cruiser is assigned to defend the ﬂeet-~you are the "eyes and
ears" for the entire task force. You have been assigned 2 perimeters to defend.
The inner perimeter is at 10 NM, and is a threat zone for your ship. The outer
perimeter is at 256 NM, and is the threat zone for the task force. It is a matter of
policy that no targets should enter these zones. Under the current rules of
engagement, forty "penalty" points are subtracted for any targets that appear in
or enter these defensive zones. Once a target penetrates a perimeter, you have
lost the points and cannot regain them by engaging the target. You must engage

139

targets before they cross the perimeters. You should also know that under rules
of engagement the penalties could be much more serious than they are now.

Marker Targets

It is important to keep an eye on both your perimeters. Your inner perimeter
located at 10 NM is easy to see, but your outer perimeter at 256 NM is invisible
and is thus more difﬁcult to watch. Pop-ups often appear just near the outer
perimeter and head right for it. Other long-range targets will also head directly for
your perimeter. One way to defend your outer perimeter is to Zoom Out to 256
NM. Look for targets that are just at the edge of your 256 NM display. These
"marker" targets will help you ﬁnd the invisible boundary. Hook a marker so it
turns green, and note its location. Then zoom out to 512 NM. The marker target
will now be about 1/2 way to the edge of your screen, highlighted in green, and
will give you a good idea of where your invisible defensive perimeter is located.
Markers at different locations around the 256 NM perimeter can be used to help
identify the full penalty circle. Targets close to the outer perimeter should be
checked to make sure they don't penetrate.

Prioritization Strategies

In the heat of battle, lots of pop-ups and other targets near your defensive
perimeters can be a real problem. You need a way to ﬁgure out which targets
close to the defensive perimeters should be engaged right away, and which ones
you can get to later on. You need a strategy to prioritize the targets.

One thing to notice is that some of the targets nearest the perimeter are going
very fast and are likely to penetrate - very fast targets that are close to the
perimeter are the highest priority. Other targets are going so slow that they will
take a long time to get close enough to the perimeter - they may never even
cross it. You can deal with the fastest and closest targets right away, and get the
others later if they become more of a threat.

An effective way to prioritize targets is to just check the speed cue for targets
' nearest the perimeter. Then you can engage the fastest and closest targets right
away, and deal with others later when necessary.

Making Trade-Offs

Since you can only focus on one defensive perimeter at a time, certain situations
may force you to "trade-off" Engaging targets at the two perimeters. You can
make these trade-offs based on the exchange of allowing targets to cross the
perimeters and how many targets will cross one perimeter while you are focusing
on the other perimeter. For example, fast targets will threaten your defensive
perimeters soonest. It can be to your advantage to Engage several fast targets
approaching a less costly perimeter than one target approaching a more costly
perimeter.

14o

A few targets close to your inner perimeter may be able to distract you from
many more targets near the outer perimeter. Even though these targets may
appear less costly, the cumulative effect of many intrusions could be quite
devastating. You need to keep checking your outer perimeter to keep from
getting caught by surprise. Lots of high priority targets deserve attention. In some
cases, you may even have to "make a trade-off" by letting a target penetrate the
inner perimeter in order to defend many more targets on the outer boundary.

Results Overview

The simulation generates a lot of information, one of which is a score indicating
how well you followed the rules of engagement governing target classiﬁcation,
target engagement, target prioritization, and defending your defensive
perimeters.

TYPE, CLSS, ITNT, & Engagement Decisions

The simulation generates a lot of information, one of which is a score indicating
how well you followed the rules of engagement governing target classiﬁcation,
target engagement, target prioritization, and defending your defensive
perimeters.

Your score depends on how well you use information from your sensors to
decide the Type, Class, and Intent of targets and on whether you make the
correct Final Engagement Decision for targets. You must correctly make all four
decisions (i.e., Type with ID_Air/Sub/Surface menu choice, Class with the
ID_Civilian/Military menu choice, Intent with the ID_PeacefuI/Hostile menu
choice, and Final Engagement with ENGAGE_Shoot/Clear menu choice) to
successfully Engage a target. When you Engage a target, the score computed
will then increase or decrease depending on whether you made correct decisions
about the target. If you correctly make each of those decisions for a target, you
receive 100 points. If you make any of those decisions incorrectly, you lose 100
points. '

Marker Targets
10 points will be added to your score for each of the marker targets that you hook

High Priority Targets
10 points will be added to your score for each of the high priority targets that you
correctly prosecute

Inner/Outer Defensive Perimeters

You will lose 40 points whenever a target enters either of your two defensive
perimeters

141

 

 

 

you are much more likely to succeed. Because people have completed our
training program, dozens of lives have been saved in the ﬁeld.

After you complete this task, our team is conﬁdent that:

1. You will be more successful than the majority of those in remedial training
and are likely to be placed in more advanced training programs
afterwards.

You will score high on your simulation.

You will have a greater chance of total success when actually engaging in
the task.

You will save dozens of people from dying in the simulation.

You will feel more conﬁdent when you attempt this task.

You will help the US Navy in the simulation task.

You will see this training as a way to succeed in the future and as a way to
step into more advanced scenarios in the future.

39’9”? 9°!”

Please ensure that you are following along carefully with the instructor and the
training manual because if you do not, it will cost you many points in the game.
Start succeeding. Start making the successful decisions!

Begin Your Online Training

You may now begin your online training manual. You will follow along with the
instructor in learning how to use the program. When you are ﬁnished with the
program, please turn the page.

Training Goals Checklist

Before beginning the simulation, please identify ﬁve (5) beneﬁts of succeeding on
this task (either from the training session or personal reasons why failing on this
task could cost you).

 

 

 

 

QPWN.‘

 

143

 

Appendix D: Training Pamphlet: Gain Framed Participants

Welcome

You have been selected to participate in this training intervention. This training
intervention will help you succeed in learning the information you will need to be
victorious in the simulation task. This training intervention has been speciﬁcally
designed for you because you have several characteristics that show that you
are still learning ﬂight simulation mastery and may have some areas where you
may want to ask questions before you engage in the task again. Additionally,
you will be able to save up to nine times more lives than you did in your initial
assessment! After you ﬁnish this training, you will be able to do much better than
you did the last time and you will feel great about yourself. Our team has found
that below average performance leads to less positive self-images about
performance on this task. The goal of this training program is to promote your
Ieaming so you can gain total mastery of the information.

Our research team is evaluating the effectiveness of this particular training
program to improve performance in future simulations. This type of simulation is
used in actual Naval training and your evaluation of this training program is
important.

Scenario Background

You will encounter the same type of information you were presented in your pre-
assessment. However, here we will equip you with the skills so you may
succeed and promote your accurate execution of the task.

In order to accomplish the task correctly, you should imagine yourself as the
Captain of a US. Navy Aegis-class cruiser. Your ship is part of a task force
currently stationed in a military zone. You have received reports of enemy
contacts and your ship is on alert. You are seated on the bridge of your ship
where you can receive information from all your ship's sensors on your radar
screen. Your job is to save as many ships and lives as possible and to avoid
misdirecting missiles to peaceful vessels.

Your ship is in the center of the radarscope on your screen. Surrounding your
ship is a number of asterisks called "targets.” The sensors on your ship provide
you the information you need to classify these targets according to their
characteristics. First, each target can be classiﬁed as being Type Air, Sub, or
Surface indicating the target is an aircraft, submarine, or surface ship. Second,
the Class of each target can be Civilian or Military. Finally, the Intent of each
target can be classiﬁed as either Peaceful or Hostile. You must decide what
action your ship should take toward each target by deciding the Type, Class, and
Intent of each target.

Training Goals

Our team reminds you that if you hold your own reasons for completing this
training, you will have a greater chance of success. With our training program,

142

 

 

Appendix E: Training Pamphlet: Loss Framed Participants

Welcome

You have been placed in this training program. This training will keep you failing
again in this Navy defense task. This training program will help you maintain
your ability to continue in this experiment. Additionally, if you do not absorb this
information correctly you will up to kill up to nine times as more people as you did
in your pre-assessment. If you do not retain the information that is presented
here, you will have a greater chance of failure in the future on this task and you
feel sad about your performance. Our team has found that below average
performance leads to negative self-images about performance on this task. The
goal of this training program is to prevent you from failing again on the task.

Our research team is evaluating the effectiveness of this particular training
program to prevent failure in future simulations. This type of simulation is used in
actual Naval training and your evaluation of this training program is important.

Scenario Background

You will encounter the same type of information you were presented in your pre-
assessment, which prevented you from being part of the advanced training.

In order to accomplish the task correctly, you should imagine yourself as the
Captain of a US. Navy Aegis-class cruiser. Your ship is part of a task force
currently stationed in a military zone. You have received reports of enemy
contacts and your ship is on alert. You are seated on the bridge of your ship
where you can receive information from all your ship's sensors on your radar
screen. Yourjob is to prevent your ships from being destroyed by hostile enemy
vessels and to avoid demolishing peaceful vessels.

Your ship is in the center of the radarscope on your screen. Surrounding your
ship is a number of asterisks called "targets." The sensors on your ship provide
you the information you need to classify these targets according to their
characteristics. First, each target can be classiﬁed as being Type Air, Sub, or
Surface indicating the target is an aircraft, submarine, or surface ship. Second,
the Class of each target can be Civilian or Military. Finally, the Intent of each
target can be classiﬁed as either Peaceful or Hostile. You must decide what
action your ship should take toward each target by deciding the Type, Class, and
Intent of each target.

Training Goals

Our team reminds you that if you do not hold your own reasons for completing
this training, you will have a greater chance of failure. Without our training
program, you are more likely to fail. Because people do not complete this task,
dozens of lives have been lost in the ﬁeld.

144

After you complete this task, our team is conﬁdent that:
1. You will have the minimal skills to be “at par” with the rest of your
teammates in this training.
You will have a lower total point loss.
You will be at less of a risk of complete failure when actually engaging in
the task.
You will prevent dozens of people from dying in the simulation.
You will feel less of a failure when you attempt this task.
You will do less harm in the simulation task.
You will see this training as a way to prevent you from failure.

was» 9N

Please ensure that you are following along carefully with the instructor and the
training manual because if you do not, it will cost you many points in the game.
Stop failing. Stop making the wrong decisions.

Begin Your Online Training

You may now begin your online training manual. You will follow along with the
instructor in learning how to use the program. When you are ﬁnished with the
program, please turn the page.

Training Goals Checklist
Before beginning the simulation, please identify ﬁve (5) costs of failing on this

task (either from the training session or personal reasons why failing on this task
could cost you).

 

 

 

 

 

@PPN.‘

 

145

 

Appendix F: Justice Perceptions Assessment

Using the scale below indicate your level of agreement with the following items.
These items are intended to measure what you feel were the main causes for
your performance during the Training Assignment Test. Do not answer how you
think you are expected to answer. Answer in an honest fashion.

1 = Strongly Disagree.

2 = Disagree
3 = Neither agree nor disagree
4 = Agree

5 = Strongly Agree

Procedural Justice

1. The test used to make training assignments was not reliable and valid
indicator of my ability to perform on the Air Defense Task.

2. The test used to make training assignments is an unfair test of a person’s

true ability to perform the Air Defense Task.

Using the Training Assignment Test to make assignments was unfair.

4. The procedure was used to make training assignments included
consistent standards for assigning people.

5. The assignment procedure obtained accurate information about each
person's abilities to perform the Air Defense Task.

6. The assignment decisions were inﬂuenced by things, which should have
not been considered.

7. Under the circumstances, the process used to decide training
assignments were fair.

8. l have strong doubts that the Training Assignment Test really measures a
person’s ability to perform the Air Defense Task.

9. I feel other procedures should have been used to make training
assignments.

10.The Training Assignment Test should not have been used to make
training assessments.

.03

Distributive Justice
1. I believe that the decision to assign me to my assigned training
assignment was a fair one.
I deserved to be assigned a different assignment.
I think it is unfair that l assigned to my training assignment.
4. I seriously question my assignment to this group.

pore

146

 

S"

8.

9.

The decision to assign me to this training assignment was not a fair one.

I would disagree with anyone who tried to tell me that the decision to send
me to this training assignment was a fair one.

Given my performance on the task, I think my training assignment is really
unjustiﬁed.

I consider the decision to assign me to this training assignment to be a fair
outcome.

I would have made the same training assignment In my own case.

10. I am really disappointed about my training assignment.

147

 

 

Appendix G: Self-Efﬁcacy Assessment

Using the scale below indicate your level of agreement items. These items are
intended to measure your conﬁdenCe in performing the Air Defense Task. Do not
answer how you think you are expected to answer. Answer in an honest fashion.

1 = Strongly Agree

2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree

5 = Strongly Agree

1.

I feel conﬁdent in my ability to perform the Air Defense Task effectively.

2. I think I can eventually reach a high level of performance on the Air

8.
9.

Defense Task.

I am sure I can perform this task effectively in a relatively short period of
time

I don't feel that I am as capable to perform the Air Defense Task as other
people.

On average, other people are probably much more capable of performing
this task as I am. '

I am a fast learner for these types of tasks, in comparison to other people.
I am not sure I can ever reach a high level of performance on this task, no
matter how much practice and training I get.

It would take me a long time to learn how to perform this task effectively.

I am not conﬁdent that I can perform this task successfully.

10.l doubt that my performance will be very adequate on the Air Defense

Task.

148

 

 

Appendix H: Motivation to Learn Assessment

Using the scale below indicate your level of agreement with the following items.
These items are intended to measure how motivated you are to Ieam the
material presented in the course to which you have been assigned. Do not
answer how you think you are expected to answer. Answer in an honest fashion.

1 = Strongly disagree

2: Disagree
3= Neither agree nor disagree
4= -Agree

5= Strongly agree

I am motivated to use the skills emphasized in this training program.

I will try to perform as well as I can on the Air Defense Task.

I want to improve my performance on the Air Defense Task.

I am going to put forth a lot of effort during the Air Defense Task.

I am going to blow of the Air Defense Task.

I did not expect to pay much attention to the material presented during this
training program.

I am very unmotivated to do well on the Air Defense Task.

If I get frustrated or discouraged during the Air Defense Task, I will utilize
the strategies Ieamed in the training.

9. l have no desire to increase my performance on this task.

10. I really could care less about my performance on the Air Defense Task.

9’9‘5‘9’5’.‘

9".“

149

Appendix I: Actual/Ideal Self Discrepancy Index Assessment

You will be asked to list two different types self:
0 Your “SHOULD selfz" Traits that you think you ought to possess; the type of

person you have a duty, obligation, or responsibility to be; the traits you are
morally obligated to possess.

0 Your “IDEAL selfz” Traits that you would IDEALLY like to possess; the type of
person you wish, desire, or hope to be

0 Here is an example of how the ideal and should selves are different: I may
hope to be rich someday, being rich may be a goal I have for myself, but I do
not think I have a duty or a moral obligation to be rich. So, rich would be a
word that describes the type of person I ideally want to be, but it is not a word
that describes the type of person I think I should be.

Please list the characteristics of the type of person YOU would ideally like to be;
the type of person you WISH, DESIRE, or HOPE to be.

I ideally want to be...

Own Ideal 1.

 

Own Ideal 2.

 

Own Ideal 3.
Own Ideal 4.
Own Ideal 5.

We would now like you to answer some questions about each of the qualities you
have listed. Please go back and indicate how much you think each of the
qualities actually describes or applies to you at this time by writing the
appropriate number next to it:

 

1 = Does not describe me at all
2 = Describes me slightly

3 = Describes me somewhat

4 = Describes me well

5 = Completely describes me

150

 

L T n “c I... 'l l.‘ l l‘ 1' I

Appendix J: Mood Orientation Assessment

Using the scale below indicate your level of agreement with the following items.
These items are intended to measure how motivated you are to Ieam the
material presented in the course to which you have been assigned. Do not
answer how you think you are expected to answer. Answer in an honest fashion.

1 = Deﬁnitely do no feel

2 = Somewhat do not feel
3 = Very slightly do not feel
4 = Do not feel or feel

5 = Very slightly feel

6 = Somewhat feel

7 = Deﬁnitely feel

Happy
Lively

Loving
Caring
Calm
Content
Active
Peppy

. Jittery
10. Nervous
11.Grouchy
12. Fed up
1 3. Tired

14. Drowsy
1 5. Gloomy
16. Sad

®@N@@PPN#

151

 

 

Appendix K: Personality Assessment

Here are a number of characteristics that may or may not apply to you. For
example, do you agree that you are someone who likes to spend time with
others? Please indicate the extent to which you agree or disagree with each

statement using the following scale:

1 = Strongly disagree

2 = Disagree
3 = Neither agree nor disagree
4 = Agree

5 = Strongly agree

I See Myself as Someone Who

Is talkative.

Tends to ﬁnd fault with others.

Does a thorough job.

ls depressed, blue.

Is original, comes up with new ideas.
ls reserved. .

Is helpful and unselﬁsh with others.
Can be somewhat careless.

9. Is relaxed, handles stress well.

10. Is curious about many different things.
11.Is full of energy.

12. Starts quarrels with others.

13. Is a reliable worker.

14.Can be tense.

15. Is ingenious, a deep thinker.

16. Generates a lot of enthusiasm.

17. Has a forgiving nature.

18.Tends to be disorganized.
19.Worries a lot.

20. Has an active imagination.

21 .Tends to be quiet.

22. Is generally trusting.

23.Tends to be lazy.

24. Is emotionally stable, not easily upset.
25. Is inventive.

9°.“.°’.°‘:“.°°.N7‘

152

26. Has an assertive personality.

27.Can be cold and aloof.

28. Perseveres until the task is ﬁnished.

29. Can be moody.

30. Values artistic, aesthetic experiences.

31 . ls sometimes shy, inhibited.

32. Is considerate and kind to almost everyone.
33. Does things efﬁciently.

34. Remains calm in tense situations.

35. Prefers work that is routine.

36. Is outgoing, sociable.

37. Is sometimes rude to others.

38. Makes plans and follows through with them.
39. Gets nervous easily.

40. Likes to reﬂect, play with ideas.

41. Has few artistic interests.

42. Likes to cooperate with others.

43. Is easily distracted.

44. Is sophisticated in art, music, or literature.

153

Appendix L: Training Reactions Assessment

Using the scale below indicate your level of agreement with the following items.
Do not answer how you think you are expected to answer. Answer in an honest
fashion.

1 = Strongly Disagree

2 = Disagree

3 = Neither agree nor disagree
4 = Agree

5 = Strongly agree

I found the training program to be very useful.
I liked the training program

I’m glad I attended this training course

The training course was a waste of time

I didn’t get anything out the training program

I learned very interesting and useful information during the training course.
The training course was well prepared

I should never been made to attend the training program.

I think the training program made me better able to perform the Air
Defense Task.

10. My performance on the Air Defense Task was completely unaffected by
what I learned in the training course.

90939999.”?

154

 

Appendix M: Training Impressions Assessment

Please respond to the scale for the impressions of the training. Do not answer
how you think you are expected to answer. Answer in an honest fashion.

1. Using the scale that follows, how believable was your training you received
today?

1 = Very Unbelievable

2 = Unbelievable

3 = Neutral

4 = Believable

5 = Very believable

2. Using the scale that follows, how interesting was your training you received
today?

1 = Very uninteresting

2 = Uninteresting

3 = Neither interesting or uninteresting

4 = Interesting

5 = Very interesting

3. Using the scale that follows, how confusing was your training you received
today?

1 = Very confusing

2 = Confusing

3 = Neither confusing or clear

4 = Clear

5 = Very clear

4. Using the scale that follows, what was the overall tone of the training your
received today?

1 = Very Negative

2 = Somewhat Negative

3 = Neither positive or negative

4 = Somewhat Positive

5 = Very Positive

155

Appendix N: Manipulation Check

Using the scale below indicate your level of agreement with the following items.
Do not answer how you think you are expected to answer. Answer in an honest
fashion.

1. Using the scale that follows, how heavily did your training focus on the beneﬁts
or costs of doing well in the program?

1 = It focused heavily on the beneﬁts of completing the task successfully

2 = It focused somewhat on the beneﬁts of completing the task successfully

3 = It had equal focus on the costs and beneﬁts of completing the task
successfully

4 = It focused somewhat on the costs of failing at the task

5 = It focused heavily on the costs of failing at the task

2. Please circle what group you were assigned to:
1. Advanced

2. Remedial

3. No assignment

156

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