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Purpose The purpose of this study was to investigate if chronological age sparks negative expectancies thus initiating a self-fulfilling prophecy in technology training interactions. Design/Methodology/Approach Data were obtained from undergraduate students (age ≤ 30) paired in 85 trainer–trainee dyads and examined through the actor-partner interdependence model. Trainer and trainee age (younger or older) were manipulated in this laboratory experiment by presenting pre-selected photographs coupled with voice enhancing software. Findings As compared to younger trainees, ostensibly older trainees evoked negative expectancies when training for a technological task, which ultimately manifested in poorer training interactions and trainer evaluations of trainee performance. Implications Identifying a connection between chronological age and negative expectancies in technology training advances our theoretical understanding of sources contributing to older trainees’ poorer performance in workforce training programs. This study provides evidence of a negative relationship between trainees’ chronological age and trainers’ expectations for trainee success and subsequent training evaluations. Such knowledge offers initial support for a “train-the-trainer” intervention through educating trainers on the potential dangers of age-based stereotypes, which could help to reduce age-based performance discrepancies. Originality/Value This is the first study to manipulate age during training thus isolating the influence of age-based stereotypes on training experiences. Given that potential age-related performance decrements in capability and motivation can be eliminated as explanations, this evidence of poorer interactions and outcomes for older workers is critical.
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ORIGINAL PAPER
The Technological Age: The Effects of Perceived Age
in Technology Training
Tracy C. McCausland
Eden B. King
Lindsey Bartholomew
Rachel Feyre
Afra Ahmad
Lisa M. Finkelstein
Springer Science+Business Media New York 2015
Abstract
Purpose The purpose of this study was to investigate if
chronological age sparks negative expectancies thus initi-
ating a self-fulfilling prophecy in technology training
interactions.
Design/Methodology/Approach Data were obtained from
undergraduate students (age B 30) paired in 85 trainer–
trainee dyads and examined through the actor-partner in-
terdependence model. Trainer and trainee age (younger or
older) were manipulated in this laboratory experiment by
presenting pre-selected photographs coupled with voice
enhancing software.
Findings As compared to younger trainees, ostensibly
older trainees evoked negative expectancies when training
for a technological task, which ultimately manifested in
poorer training interactions and trainer evaluations of
trainee performance.
Implications Identifying a connection between chrono-
logical age and negative expectancies in technology
training advances our theoretical understanding of sources
contributing to older trainees’ poorer performance in
workforce training programs. This study provides evidence
of a negative relationship between trainees’ chronological
age and trainers’ expectations for trainee success and
subsequent training evaluations. Such knowledge offers
initial support for a ‘‘train-the-trainer’’ intervention through
educating trainers on the potential dangers of age-based
stereotypes, which could help to reduce age-based perfor-
mance discrepancies.
Originality/Value This is the first study to manipulate
age during training thus isolating the influence of age-
based stereotypes on training experiences. Given that po-
tential age-related performance decrements in capability
and motivation can be eliminated as explanations, this
evidence of poorer interactions and outcomes for older
workers is critical.
Keywords Age stereotypes Expectancy effects
Performance evaluation Bias Distributed learning
environment Self-fulfilling prophecy Actor-partner
interdependence model
Extensive research investigating training designs, methods,
and environments consistently finds that workforce training
programs are beneficial (Tannenbaum and Yukl 1992).
While trainee characteristics have also garnered substantial
empirical attention (Noe 2010), research examining the
influence of trainer characteristics is sparse (Shapiro et al.
2007; Towler and Dipboye 2001). This is concerning be-
cause available evidence suggests that trainer characteris-
tics are determinants of training effectiveness (Holladay
and Quin
˜
ones 2008; Liberman et al. 2011). Importantly for
T. C. McCausland (&) E. B. King L. Bartholomew
R. Feyre A. Ahmad
Department of Psychology, George Mason University, 4400
University Drive, MSN 3F5, Fairfax, VA 22030, USA
e-mail: tracy.c.mccausland@gmail.com
E. B. King
e-mail: eking6@gmu.edu
L. Bartholomew
e-mail: lnb5030@gmail.com
R. Feyre
e-mail: rachel.feyre@gmail.com
A. Ahmad
e-mail: aahmad14@gmu.edu
L. M. Finkelstein
Department of Psychology, Northern Illinois University, Psych.-
Computer Science Building PM 571, Dekalb, IL 60115, USA
e-mail: lisaf@niu.edu
123
J Bus Psychol
DOI 10.1007/s10869-014-9390-5
disadvantaged workers, trainers’ stereotype-based expec-
tations of trainees reduce training effectiveness (Shapiro
et al. 2007). Although organizational psychologists pre-
scribe training to ensure that all workers gain needed
knowledge and skills, the benefits of training may be
unattainable for stereotyped trainees due to trainers’ mar-
red pre-conceptions.
The effect of trainer characteristics may be especially
influential in certain training contexts and demographic
groups. Technology (e.g., software, hardware, internet) is
critical to the success of today’s workplace activities. As
many as 66 % of wage and salaried workers use computers
daily (Bond et al. 2002) and 96 % of employed Americans
utilize some type of new communication technology
(Madden and Jones 2008). Furthermore, technology in-
spired changes (e.g., video conferencing, telecommuting,
and virtual meeting spaces) can reduce traveling expenses,
facilitate distributed team communication, and improve
selection strategies. However, leveraging these potential
benefits is contingent on users’ operating knowledge.
Technology is continually developing and evolving, which
often results in gaps between current competencies and
those that are necessary to survive (and thrive) in a tech-
nology dominated workplace. To reduce such competency
gaps, organizations commonly rely on training programs.
In the context of technology training, chronological age
is likely to be a salient factor and may elicit age-based
stereotypes. A growing body of evidence confirms that
people expect older workers to be unwilling to learn and
resistant to change (Posthuma and Campion 2009). Indeed,
these stereotypes may be based on valid, average group
differences; empirical findings reveal that relative to
younger trainees, older trainees showed poorer perfor-
mance in technological training (Gist et al. 1988; Hickman
et al. 2007; Jenkins and Hoyer 2000; Westerman and
Davies 2000). However, the extent to which such dis-
crepancies in training performance can be explained by
genuine differences in abilities or stereotype-based expec-
tations is unknown.
Thus, the goal of the present investigation is to isolate
the influence of age-based expectations from alternative
factors associated with the aging process (e.g., ability,
motivation) and characteristics common among the current
older cohort (e.g., lack of technology experience). To
achieve this aim, we limit study participation to only un-
dergraduate students (B30) and manipulate perceived age
through pre-selected photos and voice distortion software.
In line with the self-fulfilling prophecy (Snyder and Stukas
1999), we predict participants perceived to be older will be
penalized and ultimately disadvantaged. Furthermore, we
attempt to extend our understanding of self-fulfilling
prophecies more generally by empirically testing the rela-
tive effects of trainer and trainee perceptions. To begin, we
review recent research on the intersection of age, tech-
nology, and training before exploring plausible explana-
tions for average age group differences in training
outcomes. Then, we briefly summarize the self-fulfilling
prophecy literature, and conclude with a justification for
the present investigation.
Chronological Age and Technology Training
Workforce training is regarded as a critical organizational
strategy to prepare employees for the dynamic demands of
today’s workplace (Noe 2010). Unfortunately, previous
research finds significant mean-group age differences in the
acquisition of technological skills during training (e.g., Gist
et al. 1988; Hickman et al. 2007; Jenkins and Hoyer 2000;
Westerman and Davies 2000). Although technology-related
material is speculated to exacerbate age group differences,
these results are consistent with general training perfor-
mance discrepancies (i.e., training not limited to techno-
logical knowledge and skills). In recent meta-analyses,
older trainees showed poorer training performance (r
c
=
-.04; Ng and Feldman 2008), less mastery of the training
material (r =-.26; Kubeck et al. 1996), completed the
final training task more slowly (r = .28; Kubeck et al.
1996), and took longer to complete the training program
(r =-.42; Kubeck et al. 1996) than did younger trainees.
Before proceeding, it is important to note, the literature
has been fraught with discrepancies on who should be
considered an ‘older worker.’ One common operational-
ization is a chronological age of 40 or above (e.g., Ng and
Feldman 2008). This decision is consistent with the legal
definition of an older worker as detailed by the U.S. Age
Discrimination in Employment Act (ADEA) of 1967.As
such, in the current investigation we define a younger
worker between the chronological ages of 18 and 30 and an
older worker as 40 years old or above. The literature sub-
sequently reviewed is somewhat consistent with these
definitions; however, specific ages within studies do vary.
As a whole, these findings give rise to an important
question: Why do age-related differences emerge in tech-
nology training? Below we discuss three possible expla-
nations. One plausible explanation is that older workers’
capabilities deteriorate. Based on this rationale, older
workers are not capable of attaining the same level of
performance as younger workers. Indeed, there is a large
body of research linking cognitive (e.g., fluid intelligence)
and physical (e.g., vision) declines with chronological age
(Park and Reuter-Lorenz 2009). Moreover, these changes
are implicated to disadvantage older workers when learn-
ing new skills (e.g., Westerman and Davies 2000).
Specifically, declined fluid intelligence is related to de-
creased cognitive resources, which is linked to slower
J Bus Psychol
123
processing speeds and smaller working memory capacity
(Salthouse 1992; Salthouse and Babcock 1991); however,
these studies include older adults (i.e., up to age 87) who
may no longer be employed. In fact, it is unclear the extent
to which cognitive and physical declines may influence
older workers when they are still active in the workplace
and more specifically during workforce training. Never-
theless, it is possible that these capabilities play some role
in explaining age-related differences in technology
training.
A second likely explanation for older workers’ poorer
training performance relates to previous technology expe-
riences and related psychological considerations. In most
technology-related activities, younger adults report sig-
nificantly more use than older adults as well as more ex-
perience with complex operations and technologies (Czaja
et al. 2006; Olson et al. 2011). Accordingly, prior experi-
ences may influence subsequent beliefs thus impeding
training success. For instance, older adults report less
confidence in their abilities to acquire and retain new skills
and these fears are often more pronounced in technological
domains (Touron and Hertzog 2004). Furthermore, older
adults believe that younger adults view them (meta-
stereotypes) as being technophobic (Finkelstein et al.
2012). In studies examining decisions about technology
adoption, older adults reported lower levels of perceived
ease of use (Arning and Ziefle 2007) and more negative
attitudes (Morris and Venkatesh 2000). Furthermore, age is
negatively related to the desire to acquire novel informa-
tion (Carstensten 1998; Colquitt et al. 2000; Kanfer and
Ackerman 2004). In addition, chronological age negatively
affects individual learning preparedness (e.g., learning
anxiety) to participate in developmental opportunities
(Maurer et al. 2003). Interestingly, there is also evidence to
support that experience interacts with age to predict tech-
nology training performance, such that previous computer
experience eliminates age differences in knowledge and
skill acquisition (Charness et al. 2001).
A third—less common—explanation is that stereotypes
of older adults can account for technology training differ-
ences. Underlying the first two arguments is the idea that
older workforce characteristics—capabilities, experiences,
and/or beliefs—are associated with training outcomes. In
contrast, we propose that age discrepancies may be par-
tially attributable to trainer beliefs evoked from older
employee characteristics. That is, we suggest stereotype-
based expectations are contributing to older trainees’
poorer performance. This idea is not novel (see Landy et al.
1995; Shore and Goldberg 2004), but has yet to receive
empirical examination.
Age stereotypes maintain a prominent role in the aging
literature (Allen et al. 2012). A stereotype is ‘a general-
ization about a group of people in which certain traits are
assigned to virtually all members of the group, regardless
of actual variations among the members’ (Aronson et al.
2010, p. 391). In the context of learning and development,
relevant age stereotypes are summarized by the adage,
‘You can’t teach an old dog new tricks’ (Posthuma and
Campion 2009). Unfortunately, when adding the stipula-
tion that training will be related to technology, negative
expectations are likely to be reinforced (Posthuma and
Campion 2009).
We acknowledge that older trainees’ knowledge and skill
acquisition are likely to be affected by all three explana-
tions, but we focus exclusively on the latter and arguably
most malleable justification. Thus, the primary goal of the
current study is to provide the first empirical investigation
of the role that age stereotypes play in technology training
processes and outcomes. Our theoretical rationale for ex-
pecting age-related stereotypes to impact technology
training is based upon classic research highlighting the
importance of expectations in social interactions.
The Self-Fulfilling Prophecy
Sociologist Robert Merton first coined the term self-ful-
filling prophecy, which is
in the beginning, a false
definition, of the situation evoking a new behavior which
makes the originally false conception come true (Merton
1948, p. 195). Over the past 65 years, Merton’s intuitively
appealing phenomenon has garnered widespread theore-
tical and empirical attention (Snyder and Stukas 1999).
Self-fulfilling prophecies are typically conceptualized as
occurring between two individuals and in three stages
(Darley and Fazio 1980; Jussim 1986; Miller and Turnbull
1986; Snyder and Stukas 1999). Traditionally, one person
is dubbed the ‘perceiver or actor’ and the other is the
‘target or partner.’
First, the actor develops expectations about the partner.
When individuals have had no previous interaction or
knowledge of the other’s reputation, stereotypes are often
cited as the basis for initial expectations (Fiske and Neu-
berg 1990). The maintenance or revisal of these initial
expectations depends on confirmation biases, flexibility of
expectations, and the strength of disconfirming evidence
(Jussim 1986). Second, these expectations guide the actor’s
treatment of the partner. Higher expectations typically
translate into more interaction time (Neuberg 1989; Word
et al. 1974), warmer emotional climates, increased oppor-
tunities to respond, as well as more and better communi-
cation and feedback (Brophy 1983; Jussim 1986; Rosenthal
1994). Finally, if the self-fulfilling prophecy is fulfilled,
expectations are ultimately confirmed. The partner re-
sponds to the actor’s treatment in a way that confirms the
actor’s original expectations. We note that confirmation
J Bus Psychol
123
only occurs when the partner accepts the ‘script’ initiated
by the actor (Neuberg 1994); or rather, when the partner
responds in a consistent manner to the actor’s subtle be-
haviors. To clarify, confirmation may be perceptual (i.e., as
evaluated by the actor) or behavioral (i.e., as evaluated by a
third-party; Snyder and Stukas 1999).
Dyadic Models
The self-fulfilling prophecy occurs between two indi-
viduals; one individual is the ‘perceiver/actor’ and the
other is the ‘target/partner.’ This conceptualization is
characterized by a dominate focus on the actor perspective
(e.g., trainer) at the expense of the partner perspective (e.g.,
trainee; Olson et al. 1996). This is unfortunate because
theoretical and empirical research have largely ignored the
possibility of partner expectations, which may also influ-
ence the interaction. Overlooking partner expectations is
often justified by the argument that actors and partners
differ in terms of power and status (Snyder and Stukas
1999). In fact, it is this power difference that normally
defines the role of the perceiver/actor (has power; e.g.,
teacher, employer) and target/partner (does not have
power; e.g., student, employee; Snyder and Stukas 1999).
Indeed, power is a critical variable to fulfilling prophecies.
Copeland (1994) manipulated the amount of power (over
the target) granted to the actor and found that high power
(but not low power) actors elicited behavioral confirmation
from their partners. In the current context, the trainer, un-
questionably, has power because he or she has complete
control over the design and delivery of training and, ac-
cordingly, is of primary interest. However, to assume the
trainee has no influence on the training interaction may be
misleading. In fact, this assumption has been labeled
pseudounilaterality (Kenny et al. 2006).‘‘perceiver/actor’
Therefore, the secondary goal of the current investiga-
tion is to provide an empirical test of the presumption that
the partner perspective (trainee) influences the trainer–
trainee relationship less than the actor perspective (trainer).
Indeed, there is evidence suggesting that the partner plays a
role in the emergence of self-fulfilling prophecies. For
example, Snyder and Haugen (1995) examined the moti-
vations of a partner by manipulating partner instructions.
Results suggested that behavioral confirmation was less
likely to occur when participants were instructed to acquire
knowledge than when participants were motivated to have
a smooth interaction. This implies that the target’s goal can
influence actor-partner interactions and ultimately ex-
pectancy confirmation.
By extension, we argue that trainee expectations are
relevant. Just as trainers form expectations about trainees
based on trainee characteristics, we suggest that it is
equally likely that trainees form expectations about trainers
based on trainer characteristics. These expectations could
possibly influence the interpretation of trainer behavior.
For instance, after the trainee forms beliefs about the
trainer he or she then searches for supporting evidence.
Accordingly, otherwise ambiguous (or neutral) trainer be-
havior may assume a decidedly positive or negative inter-
pretation depending on the trainee’s initial orientation (i.e.,
a confirmation bias). By acknowledging that targets also
bring expectations to training interactions, we investigate
each stage of the self-fulfilling prophecy from the trainee
perspective (i.e., trainer age is manipulated) as well.
In the present study, when appropriate, we use a bidi-
rectional analytic model to assess the reciprocal effects of
the variables related to interactions and confirmation for
distinguishable dyads (trainer–trainee); also known as the
actor-partner interdependence model (APIM; Kenny et al.
2006). The reciprocal effects are estimated through actor
and partner effects. The actor effect is the how one partner’s
score on a predictor variable influences his or her own
outcome variable and the partner effect is how one partner’s
score on a predictor variable influences the outcome vari-
able of his or her partner. Fundamental to this type of dyadic
analysis, is the possibility of examining the actor and
partner effects for both the trainer and trainee, simultane-
ously. In addition, subsequent probing of interactions (akin
to simple effects analysis) can reveal which effect (actor or
partner) for which training role (trainer or trainee) is
meaningful. Thus, the current analytic model permits a
more comprehensive assessment of trainers’ and trainees’
influence in a dyadic relationship than has previously been
conceptualized and analyzed in the self-fulfilling prophecy
literature.
Research Hypotheses
Following Merton’s original definition and subsequent
empirical support of the self-fulfilling prophecy, we ex-
amine all three stages (expectations, interaction, and con-
firmation) from the trainer perspective (e.g., trainee age is
manipulated) and trainee perspective (e.g., trainer age is
manipulated).
Stage 1: Expectations
When individuals have no previous experience nor infor-
mation about their partner with whom they are required to
interact (i.e., stranger), people are likely to rely on available
cues to inform initial judgments. One such cue is age. In
training, age stereotypes are typically negative, such that
older individuals are perceived to have a lower ability to
learn and higher resistance to change (Posthuma and
J Bus Psychol
123
Campion 2009). From the trainer perspective, these findings
strongly support that older trainees will be evaluated more
negatively than younger trainees. Formally, we hypothesize:
Hypothesis 1 Compared with trainers of ostensibly
younger trainees, trainers of ostensibly older trainees will
have lower expectations of trainee success.
Extant age stereotype research offers less guidance for
how ostensibly older trainers are perceived. Because age
stereotypes are stronger in technological domains
(Posthuma and Campion 2009), we suspect that stereotypes
of older employees—regardless of training role—are likely
to be incompatible with technology-related success and
therefore will also be negative. Formally, we hypothesize:
Hypothesis 2 Compared with trainees of ostensibly
younger trainers, trainees of ostensibly older trainers will
have lower expectations of the trainer.
Stage 2: Interaction
Next, we anticipate that expectations will give rise to in-
terpersonal expectancy-consistent behaviors. That is,
training expectations will positively influence evaluations
of training for both the trainer and trainee perspectives.
Specifically, higher expectations of the trainee or trainer
will be associated with higher evaluations of training.
Thus, we predict:
Hypothesis 3a An actor’s expectation for training will
positively influence both the actor’s and partner’s eval-
uation of training.
An important requirement of dyadic data analysis is that
the dyadic variable is collected from both the trainer and
the trainee (i.e., reciprocal; Kenny et al. 2006). An alter-
native indicator of the training interaction is the instruc-
tional quality as rated by independent judges; however,
because this is considered a ‘one sided’ variable, a be-
tween-groups analysis is more appropriate. As such, we
hypothesize:
Hypothesis 3b Compared with trainers of ostensibly
younger trainees, trainers of ostensibly older trainees will
have poorer instructional quality.
Hypothesis 3c Compared with trainees of ostensibly
younger trainers, trainees of ostensibly older trainers will
have poorer instructional quality.
Stage 3: Confirmation
Finally, the prophecy concludes when a partner responds to
an actor’s treatment in a manner that confirms the actor’s
original expectations. In line with the self-fulfilling
prophecy, poorer training interactions are likely to result in
worse performance as rated by the partner. Therefore,
Hypothesis 4a An actor’s evaluation of training will
positively influence both the actor’s and partner’s perfor-
mance in training.
An alternative indicator of performance is trainee suc-
cess on the final training task and, in line with the rationale
described above, we hypothesize:
Hypothesis 4b Compared with trainers of ostensibly
younger trainees, trainers of ostensibly older trainees will
have poorer performing trainees.
Hypothesis 4c Compared with trainees of ostensibly
younger trainers, trainees of ostensibly older trainers will
perform worse.
Role of the Perceiver and Target
To gain a better understanding about the relative effects of
the trainer and trainee perspective, we are also interested in
directly comparing them. In other words, does the per-
spective of the trainer or the perspective of the trainee
account for more variance in training interactions and
confirmation? Considering there is almost no research di-
rectly comparing the two perspectives in this context, we
draw from previous arguments highlighting the importance
of power and status to the fulfillment of the self-fulfilling
prophecy (Snyder and Stukas 1999) and propose:
Hypothesis 5 The trainer’s actor and partner effects will
contribute more to the prediction of training outcomes
(training interactions and performance) than the trainee’s
effects.
Method
Participants
In total, we collected data from 198 undergraduate students
attending a mid-Atlantic public university. The unit of
analysis was the trainer–trainee dyad, which resulted in an
original sample size of 99 dyads. As a result of our ma-
nipulation check (discussed later) and participants’
chronological age, 14 dyads were removed.
1
Therefore, our
final sample consisted of 170 undergraduate students or 85
dyads. Participants identified as 62 % White, 15 % Asian,
6 % Hispanic, 9 % African American, and 8 % other. All
1
One trainee and four trainers reported ages of 31 or older. This
resulted in five dyads being removed. The manipulation check, which
will be discussed in more detail, resulted in the deletion of nine dyads.
Therefore, a total of 14 dyads were removed.
J Bus Psychol
123
participants were age 30 or younger. More specifically, 85
students (27 males and 58 females) assumed the role of the
trainer (M
age
= 19.43, SD = 1.79) and 85 students (33
males and 52 females) assumed the role of the trainee
(M
age
= 19.57, SD = 2.09). Participation was voluntary
and all participants received course credit for their time and
effort.
Design
The design was a 2 (trainer age: younger or older) 9 2
(trainee age: younger or older) between–subjects factorial
design.
Materials
Manipulated Age
Photographs Immediately preceding the training inter-
action, each participant was shown a pre-selected pho-
tograph (on the computer screen) of his or her alleged
training partner. Photos were selected based upon the re-
sults of Pilot Test #1, which we explain below.
Using the photo bank from the Center for Vital
Longevity Face Database (Minear and Park 2004), 24
Caucasian photos, with neutral facial expressions, were
selected as possible options. The photos represented an
assortment of individuals that varied in chronological age
(younger: 15–30 and older: 45–60) as well as sex (male and
female). Initially, eight graduate students and younger
working professionals (age range from 21 to 26 years old)
volunteered to rate all 24 possible photos; however, due to
the difficulties attributed to securing agreement from eight
people, we randomly sampled the ratings from two males
and two females (N = 4). All photos were rated on at-
tractiveness (1 = extremely unattractive; 7 = extremely
attractive) and age group (1 = 15–30 years old;
2 = 30–45 years old; 3 = 45–60 years old; 4 = 60–75
years old).
The first criterion for selection was high levels of inter-
rater agreement on both dimensions of age and attractive-
ness. Given the focus of our study, it was especially
important to ensure that individuals generally agree on the
chronological age of the individuals portrayed in the pic-
tures. We selected r
wg
to index interrater agreement because
the scoring responses contained a meaningful order, as
compared to nominal data. Following the syntax of
LeBrenton and Senter (2008) we calculated r
wg
for each
photo and each dimension. Only photos that reached a
r
wg
= .70 for both dimensions—attractiveness and age—
were considered. In addition, because previous research
suggests that physical attractiveness can play a role in the
self-fulfilling prophecy (Snyder et al. 1977), the second
criterion for selection was level of attractiveness. We ex-
cluded photos that were rated very high (M
attractiveness
C 4.5)
and very low (M
attractiveness
\ 3) as well as photos that
showed high disagreement (SD
attractiveness
[ 1). These cri-
teria resulted in 16 remaining photos. From these options we
then selected two photos for each condition (younger and
older) and sex (male and female) to avoid any biases that
may be introduced as a result of idiosyncrasies from indi-
vidual photos. These decisions yielded 8 photos in total (see
Fig. 1 for sample photos).
Communication Trainer–trainee dyads communicated via
headsets utilizing the audio capabilities of Skype. To
guarantee that participants never accidently saw the real
appearance of their training partner, the video display was
permanently disabled.
Voice Pilot Test #2a was conducted to determine the
believability of the age manipulation (pre-selected digital
photo). For the current purpose, participants (N = 4) were
undergraduate students attending a mid-Atlantic public
university and volunteered in exchange for course credit.
Participants were assigned to the older trainer and older
trainee condition. After training was complete, research
assistants asked, ‘‘Do you have any questions or suspicions
about the experiment?’ All participants reported that they
did not believe the manipulation because the photo pre-
sented before training did not match the voice during
training. To address this issue, we implemented voice
changing software (MorphVOX Pro) during the interaction.
Participants were unaware that their photo and voice were
being altered.
A follow-up study (Pilot Test #2b) was conducted to
determine the believability of the revised age manipulation
Fig. 1 Sample of manipulated photographs used to portray the
trainee and trainer as younger or old for male and female (Minear and
Park 2004)
J Bus Psychol
123
(pre-selected digital photo combined with voice altering
software). Again, four (different) undergraduates attending
a mid-Atlantic public university volunteering in exchange
for course credit were assigned to the older condition and
remained blind to this manipulation. None of the par-
ticipants reported any suspicions during the post-training
interview. Consequently, the voice altering software was
paired with the pre-selected digital photo for the ‘older’
condition
Technology Training
Experimental Task The training task was to record and run a
personalized Excel Macro. A macro is ‘a bunch of com-
mands grouped together as a single command to accomplish
a task automatically’’ (Stahl 2010, p. 1). For instance, if the
goal is to bold text, instead of pressing the B button on the
Home tab in Microsoft Office a default macro is ‘Ctrl ? B.’
To our knowledge, this task has not been used in previous
research; although, it is extremely relevant to common, IT
workplace activities and many people do not know how to
personalize a macro (as will be demonstrated later).
Trainer Training Participants assigned to the trainer role
received various documents and underwent standardized
training. First, trainers received a typed overview of ‘‘What’s
a macro and why you should care.’ Next, trainers viewed
approximately 16 min of an Excel Macro video tutorial (Jelen
2009). Due to timing and ability considerations, training only
included portions of two topics: (1) recording a macro and (2)
running a macro. The standardized trainer training was se-
lected based upon satisfying certain features of the instruc-
tional design (Gagne
´
et al. 1992;Noe2010). For example,
learning objectives were stated at the beginning of each
training segment and participants were provided very clear
guidance. To supplement, trainers received typed notes on the
topics covered in training.
Standardized Training Tutorial Effectiveness To deter-
mine the effectiveness of trainer training, we conducted
Pilot Test #3. Participants were 142 undergraduate students
(M
age
= 20.35, SD = 4.15; 33 males and 109 females)
from a mid-Atlantic public university and volunteered in
exchange for course credit. A one-way ANOVA deter-
mined that there were no significant differences between
the groups (control, trainer, and trainee) on pre-training,
excel performance (Welch’s F (2, 174.35) = 1.26,
p [ .05).
2
In order to determine the effectiveness of the
standardized training tutorial, we compared trainers’ post-
training macro test performance to the control groups’
macro test performance. After receiving the standardized
training tutorial, the trainer condition (M = 7.91,
SD = 1.21) performed significantly better on the macro
test than the control condition (M = 5.53, SD = 1.43) who
had not received standardized training (t(215.17) =
-12.46, p \ .001, r = .65).
Procedure
Participants volunteered for an experimental session lasting
approximately 90 min (the trainer) or 60 min (the trainee),
respectively. Following Shapiro et al. (2007), participants
arrived at two staggered time points in order to guarantee
that trainer–trainee pairs never saw their training partner
face-to-face, as well as to allow sufficient time for the trainer
to complete the trainer training. The experiment consisted of
three basic phases: pre-training, training, and post-training.
Pre-training
Upon arrival, regardless of condition, researchers told
participants the purpose of the study was to examine dis-
tance learning through computer-mediated training inter-
actions. Next, a digital photograph was taken of each
participant to enhance credibility of the manipulation.
After this, participants were ushered into separate rooms
(with a personal computer) to prevent training dyads from
possibly seeing each other. Then, participants completed a
Microsoft Excel knowledge exam.
At this point, protocol differed depending on participant
condition. For the trainer role, participants received trainer
training. This included reading an overview of ‘What’s a
macro and why you should care’ as well as viewing a
video tutorial of recording and running a macro. Upon
completion of trainer training, trainers’ macro performance
was evaluated (macro test and macro syntax). Please note
that trainers’ macro performance was assessed prior to the
trainer–trainee interaction.
Both participants were then presented with a digital
photo of their alleged training partner; half of the par-
ticipants were randomly selected to the ‘older’ condition,
even though the chronological age of all participants was
30 or below. All participants were blind to their randomly
assigned condition and participants were unaware of the
age manipulation until after completion of the study.
Concluding the pre-training phase, both participants rated
their expectations of the trainer or trainee, respectively.
Training
In order to investigate how perceived age and subsequent
expectations influence the quality of training, trainers were
2
The data violated the assumption of normality and we therefore
report Welch’s F; however, we note that the parametric analysis
revealed the same conclusion.
J Bus Psychol
123
given complete control over the content, style, and length
of training. Only trainers had access to the training notes
and were instructed to teach the trainee to record and run a
macro to the best of his or her ability. Trainers were in-
structed to terminate training when the trainee knew ev-
erything necessary to record and run a macro. Trainees
were also informed that the trainer had complete control
over training. A Microsoft Office 2007 Word document
and Excel workbook were open on both of the participants’
screens. After instructions, participants were connected via
audio headsets using Skype and training began. To reiter-
ate, those assigned to the ‘older’ condition also had their
voices altered to enhance believability.
Post-training
When the trainer deemed training to be complete, training
ended and audio capabilities were disabled. Participants in
the trainer condition immediately completed evaluations of
the training experience and the trainee. Participants in the
trainee condition completed macro performance measures
(macro test and macro syntax) and then concluded with
evaluations of the training and trainer.
Post-training Interview
After completion of the study, research assistants con-
ducted a brief post-training interview to serve as a ma-
nipulation check. As in Pilot Test #2, all participants were
asked ‘Do you have any questions or suspicions about the
experiment?’ Seven participants in the trainer condition
and three participants in the trainee condition reported
suspicions and stated that they did not believe the age
manipulation. Most of the suspicions were attributed to the
manner of speech (e.g., overusing words such as ‘like’
and/or ‘whatever’’). This resulted in the removal of nine
dyads from the final analysis (two of the suspicious indi-
viduals were in the same dyad).
Measures
The following experimental measures were collected prior
to the onset of training.
Excel Knowledge
Basic Microsoft Excel knowledge was assessed through a
5-item exam. Items were selected from a pre-existing
evaluation (Penn State 2008a). Response options varied as
a function of each question with only one answer being
correct. Example items include, ‘‘True or False? Additional
worksheets can be inserted into an Excel workbook if more
sheets are needed’ and ‘‘What can be inserted into a cell in
Excel?’
Stage 1: Expectations
Trainer Expectancy A questionnaire consisting of 3 items
(see Shapiro et al. 2007) was used to assess the trainer’s
expectations of trainee success (a = .81). Participants re-
sponded on a 7-point, Likert-type scale consisting of 1
(strongly disagree)to7(strongly agree). An example item
is ‘I expect the trainee will succeed.’
Trainee Expectancy A 10-item scale, adapted from Sha-
piro et al. (2007), measured trainee’s expectations of the
trainer. Responses to these items ranged from 1 (strongly
disagree)to7(strongly agree). Example items include ‘I
expect the trainer to be encouraging’ and ‘I expect the
trainer to communicate well.’ Coefficient alpha for this
scale was .93.
Stage 2: Interaction
The remaining experimental measures were collected after
the completion of training.
Trainer Training Evaluation The trainer’s evaluation of
training was assessed by a 9-item survey (Shapiro et al.
2007). Participants rated their training experience using a
7-point scale ranging 1 (not very)to7(extremely). Sample
items for this scale are ‘How effective was the training?’
and ‘How satisfied were you with the training you re-
ceived?’ This scale had an internal consistency reliability
of .94.
Trainee Training Evaluation The trainee’s evaluation of
training was measured using a 9-item survey (Shapiro
et al. 2007). Participants rated their training experience
using a 7-point scale ranging 1 (not very)to7(extremely).
Sample items are ‘How effective was the training you
received?’ and ‘How useful was the training you re-
ceived?’ This scale had an internal consistency reliability
of .93.
Instructional Quality All training interactions were
recorded and later transcribed by research assistants. Based
upon trainer training, 14 dimensions (with 26 items) were
identified to be critical for comprehensive macro training.
For example, ‘Did the Trainer mention not to have spaces
in the macro name?’’ and ‘‘Did the Trainer give an example
of a macro?’ Each item was rated using a dichotomous
scoring option (0 = no; 1 = yes). Twenty transcripts were
then coded by two independent raters. Cohen’s Kappa was
computed for each item. Upon discussion between the
J Bus Psychol
123
raters, four dimensions were dropped because of redun-
dancy and lack of clarity, leaving 10 dimensions (with 22
items). All disagreements were discussed and resolved and
then the two raters reviewed another 20 transcripts. This
process was repeated until excellent agreement (j [ .81;
Landis and Koch 1977) was reached for each item. The
remaining transcripts were divided equally and coded in-
dependently between the two trained raters.
Stage 3: Confirmation
Trainer Performance Trainer performance was captured
by the trainee’s evaluation of the trainer. The trainee’s
evaluation of the trainer was assessed using a 10-item
measure (Shapiro et al. 2007). Response options ranged
from 1 (strongly disagree)to7(strongly agree). Example
items from this scale include ‘The trainer was motivating’
and ‘The trainer was responsive.’ Coefficient alpha for
this scale was .87.
Trainee Performance Trainee performance was evaluated
by the trainer’s evaluation of the trainee using a 10-item
survey with responses ranging from 1 (strongly disagree)
to 7 (strongly agree). Example items from this scale in-
clude ‘The trainee put forth effort’ and ‘The trainee lis-
tened well.’ Coefficient alpha for this scale was .88.
Macro Performance An alternative measure of perfor-
mance was objective and operationalized by aggregating the
macro test and macro syntax scores (see below) to capture
declarative and procedural knowledge, respectively.
Therefore, possible macro performance ranged on a scale
from 0 (novice performance) to 22 (expert performance).
Macro Test Declarative macro knowledge was assessed
through a 10-item exam. Based upon the material covered
in trainer training, we selected items from a pre-existing
evaluation (Penn State 2008b). Response options varied as
a function of each question and only one answer was
correct. Sample items include ‘Which of the following is
not a concern when recording macros?’ and ‘True or
False? Macro names contain spaces.’
Macro Syntax The procedural macro assessment was
developed for the current study. Based upon trainer training
and macro syntax assessment instructions, six dimensions
were identified to be relevant and important for properly
recording and running a macro. Examples include ‘‘Is there
a macro name?’ and ‘Is there a macro description?’ Each
dimension was rated using a dichotomous scoring option
(0 = no; 1 = yes). Twenty cases were coded by two in-
dependent raters. Cohen’s Kappa was computed for each
criterion and all values were above .90, suggesting
agreement was almost perfect (Landis and Koch 1977).
Moreover, both raters agreed that the identified criteria
adequately covered the domain of interest suggesting that
additional revisions to the scoring rubric were not neces-
sary. Because agreement was almost perfect, the remaining
syntax was divided equally and coded independently by the
trained raters. Please note that macro performance was
collected for both the trainer and trainee; however, this
variable was used differently depending on the role. For the
trainer, macro performance was conceptualized as a control
variable and for the trainee macro performance was con-
ceptualized as an outcome variable.
Results
Means, standard deviations, and zero-order correlations
among all study variables are reported in Table 1.
We present findings in sequential order (expectations,
interaction, and confirmation) as well as simultaneously
consider the trainer and trainee perspective for each stage.
Stage 1: Expectations
After the presentation of the manipulated photograph, par-
ticipants immediately rated their expectations of their partner.
To reiterate, dyad members had still not interacted; therefore,
we analyzed the effect of perceived age (younger or older) on
trainer and trainee expectations separately. Because ability
may affect expectations and behavior, we controlled for
trainer macro performance when investigating trainer expec-
tations and we controlled for trainee excel performance when
investigating trainee expectations. These pre-training perfor-
mance measures were selected because they were adminis-
tered immediately preceding the training interaction. In order
to include the appropriate covariate in the between-group
analysis, we conducted two, one-way analyses of covariances.
Trainer Perspective
Supporting Hypothesis 1, after controlling for trainer
macro performance, a main effect of perceived trainee age
emerged on expectations for the trainee such that trainers in
the perceived older trainee condition had lower training
expectations (M = 5.07, SD = 1.07) than in the perceived
younger trainee condition (M = 5.55, SD = .86), F(1,
80) = 4.24, p = .04, partial g
2
= .05.
Trainee Perspective
Failing to support Hypothesis 2, there was not a main effect
of perceived trainer age on expected trainer success. There
was no statistical difference between trainers in the
J Bus Psychol
123
perceived older trainer condition (M = 5.57, SD = .92)
and the perceived younger trainee condition (M = 5.24,
SD = 1.03), F(1, 82) = 2.83, p = .10.
Stage 2: Interaction
The instructional quality between the trainer and trainee is
a dyadic construct and inherently multilevel because the
instructional quality emerges from the interaction between
two individuals (lower level unit), nested within a dyad
(higher level unit). The actor-partner independence model
(APIM; Kashy and Kenny 2000; Kenny 1996; Kenny et al.
2006) facilitates this type of examination by retaining the
individual level measures and treating them as nested
within dyads. Furthermore, the APIM permits clear esti-
mation of bidirectional effects within a dyad; or rather,
how one partner’s score on a predictor variable influences
his or her own outcome variable (actor effect) and the
outcome variable of his or her trainee (partner effect). In
addition, both actor and partner effects can be investigated
simultaneous for both dyad members (i.e., trainer and
trainee). See the work of Kenny and colleagues for a de-
tailed discussion of dyadic analysis (e.g., Kashy and Kenny
2000; Kenny 1996; Kenny et al. 2006).
Pre-analytic Considerations
One pre-analytic consideration pertains to the distin-
guishability between the dyad members (Kenny et al.
2006). Because these data contain theoretically meaningful
training roles (trainer and trainee), as opposed to inter-
changeable positions (e.g., coworkers, identical twins) the
current data is distinguishable. As such, training role was
represented by a dummy variable (-1 for the trainee, ?1
for the trainer).
Second, empirical support is necessary to justify dyadic
analysis. Because a standard design with distinguishable
dyads was utilized, one must assess the nonindependence
of dyad members’ scores on the outcome variables through
the Pearson product moment correlation. Indeed, for the
reciprocal variables of training evaluation and macro per-
formance were significantly related (r = .21 and .35, re-
spectively) using the recommended two-tailed, p = .20
threshold (Cook and Kenny 2005; Kenny et al. 2006).
A third consideration relates to the type of variables in
the analysis. All the study variables in the current inves-
tigation were treated as mixed variables because there is
variation between and within dyads. The one exception is
training role, which is a within-dyad variable because the
sum of the two participants’ scores is the same for every
dyad.
We applied multilevel analysis using APIM (Kenny
et al. 2006) syntax for SPSS. The data were structured
pairwise and the dyad was considered distinguishable.
Furthermore, for the remainder of the analyses, trainer
macro performance and trainee excel performance were
controlled for because both of these variables were con-
ceptually relevant. For example, if the trainer does not
Table 1 Descriptive statistics and correlation coefficients for study variables
M SD 1 2 3 4 5 6 7 8 9 10 11 12
Background
1. Perceived trainer age
2. Perceived trainee age -.06
3. Trainer excel test 3.38 0.93 -.12 -.09
4. Trainee excel test 3.59 0.92 .08 .18 -.12
5. Trainer macro perf. 15.10 3.42 -.05 -.11 -.03 -.05
Stage 1: expectations
6. Trainer expectancy 5.30 0.98 .01 -.22* -.02 -.05 .20
7. Trainee expectancy 5.41 0.98 .17 -.08 -.10 -.13 -.04 .03
Stage 2: interaction
8. Instructional quality 14.38 2.86 -.09 -.02 .31** -.11 .16 .05 -.02
9. Trainer training eval. 5.08 1.22 .03 -.21 .14 -.12 -.05 .29** .08 .22
10. Trainee training eval. 5.16 1.33 .07 -.12 .14 .06 .01 -.07 .15 .29* .35**
Stage 3: confirmation
11. Trainer performance 5.63 0.86 -.05 -.18 .02 .14 .07 .00 .28* .18 .20 .65**
12. Trainee performance 6.00 0.87 -.04 -.16 .06 -.03 -.09 .29** .11 .14 .54** .33** .21
13. Trainee macro perf. 13.0 3.86 .04 -.01 -.12 .02 .31 ** .08 -.12 .18 .02 .28** .02 -.02
N = 85 for all variables except for training quality (N = 76). Perceived age (0 = younger and 1 = older). Eval. Evaluation, Perf. performance
* p \ 0.05 (2-tailed), ** p \ 0.01 (2-tailed)
J Bus Psychol
123
understand the material then it is unlikely that he or she
will be able to teach it. Likewise, if the trainee does not
know basic information about a related technological task
then irrespective of the trainer’s instruction, the trainer may
not be able to teach the training task because the trainee
lacks fundamental knowledge. Note, however, that the in-
clusion or exclusion of these control variables did not ul-
timately affect the patterns of effects. All predictor
variables were grand mean centered (Kenny et al. 2006).
The analyses for the training expectations (Hypothesis
3a) are summarized in Table 2. We hypothesized that the
training expectations of a dyadic partner would influence
his or her own evaluation of training and that of the partner
as well. However, only the actor effect for the trainer was
significant (p \ .01). In other words, the trainer’s expec-
tation of the trainee significantly influenced his or her
evaluation of training. For Hypotheses 3b and 3c, we did
not find any evidence of between-group differences for
instructional quality.
Stage 3: Confirmation
Results evaluating Hypothesis 4a are summarized in
Table 3. We anticipated that the evaluation of training
would affect his or her own performance (as rated by the
partner) and that of the partner as well. Only the actor and
partner effect for the trainer was significant (p \ .05). For
Hypotheses 4b and 4c, we did not find any evidence of
between-group differences for trainees’ performance on the
final training task.
Perceiver-Target Perspective
We anticipated that the trainer’s perceptions would influ-
ence training outcomes more than the trainee’s perceptions.
Following the custom hypothesis syntax of Kenny’s two-
intercept model (Kenny et al. 2006), we conducted tests
directly comparing actor and partner effects as a function
of training role (trainer or trainee) for instructional quality
and performance (see Table 4). As evidenced by the
significant, positive actor and partner effects for perfor-
mance, results suggest that trainer perceptions exert greater
influence on outcomes than trainee perceptions. Thus,
Hypothesis 5 was partially supported.
Discussion
The present paper contributes to the diversity, training, and
self-fulfilling prophecy literatures by being the first em-
pirical study to investigate whether perceived age evokes
trainer and trainee negative expectations that influence
training interactions and outcomes. Although support for
the self-fulfilling prophecy has been replicated over the
past 60 years, the present investigation expands previous
knowledge by examining the influence of chronological
age and accounting for trainees’ expectations using the
APIM.
In the current study, all trainers were instructed to train a
trainee on a technological task that is relevant to workplace
activities. Age of the trainee and the trainer were ma-
nipulated through the use of pre-selected digital photos and
voice distortion software. In this way, we accomplished the
first goal of this investigation: to isolate the effect of age-
based stereotypes as an explanation for age-based
Table 2 Results for training expectations predicting training
evaluation
Parameter Estimate SE df
Trainee 5.23** .15 78.67
Trainer 5.11** .12 81.04
Actor effect-trainee .16 .15 77.86
Actor effect-trainer .34** .13 81.04
Partner effect-trainee -.12 .15 78.92
Partner effect-trainer .06 .13 81.04
** p \ 0.01
Table 3 Results for training evaluation predicting performance
Parameter Estimate SE df
Trainee 5.88** .09 80.48
Trainer 5.76** .08 81.03
Actor effect-trainee -.02 .07 80.59
Actor effect-trainer .25** .07 83.29
Partner effect-trainee .07 .07 82.63
Partner effect-trainer .26** .07 81.29
** p \ 0.01
Table 4 Comparison of trainee and trainer perspective
Parameter Estimate SE df
Training expectations
Main effect of role -.11 .15 78.17
Actor effect .18 .20 134.65
Partner effect .18 .20 136.23
Performance
Main effect of role -.12 .14 81.10
Actor effect .28** .10 161.12
Partner effect .19* .09 161.01
Contrasts were performed by subtracting the Trainee score from the
Trainer score (i.e., Trainer - Trainee); a positive estimate value
means that the trainer exerted a greater effect
* p \ 0.05. ** p \ 0.01
J Bus Psychol
123
differences in training performance. After viewing a photo
of their training partner, trainers provided lower expecta-
tions of (ostensibly) older trainees than of ostensibly
younger trainees. This suggests that recent findings on age-
specific stereotypes (e.g., Finkelstein et al. 2012) have im-
plications for training interactions.
We allowed trainers to control the pace, content, and
method of training to allow potentially biased trainer be-
havior to manifest. From the perspective of the trainer, the
results suggest that lower expectations resulted in poorer
evaluations of training. From the perspective of the trainee,
however, we found no evidence that expectations influ-
enced training interactions.
We concluded our examination by investigating the
third phase of self-fulfilling prophecies—confirmation.
From the perspective of the trainer, lower instructional
quality resulted in lower trainer performance (as rated by
the trainee) and lower trainee performance (as rated by the
trainer). Furthermore, instructional quality was sig-
nificantly correlated with trainee macro performance
(r = .28, p \ .05).
Evaluations from both the trainer and trainee suggest
that seemingly older trainees are disadvantaged during
training. However, objective measures of training suggest
otherwise. The instructional quality of training, as rated by
independent judges (blind to condition) and by macro
performance, revealed no significant differences due to
trainer perceptions of trainee age. This finding is encour-
aging because although participant evaluations point to
noteworthy disadvantages, objective evaluations suggest
participants were able to overcome these stereotype in-
duced barriers.
In summary, manipulated trainee age did ultimately
result in some forms of confirmation, suggesting that the
self-fulfilling prophecy did occur. However, because ob-
jective criteria did not support differences as a function of
trainee age, we caution against overstating the implications
of the current study. Additionally, in line with previous
conceptual arguments, it was the trainer’s (rather than the
trainee’s) perspective that dictated training outcomes.
Theoretical and Practical Implications
Substantial research reveals age differences in technology
skill attainment during training (e.g., Hickman et al. 2007;
Westerman and Davies 2000). We sought to extend pre-
vious findings by investigating Why do age-related dif-
ferences emerge in technology training? After reviewing
the literature, three possible explanations surfaced. In
comparison to younger trainees’: (1) older trainees’ possess
lower cognitive and physical capabilities; (2) older trai-
nees’ have fewer technology experiences as well as lower
expectations for technology training success; and (3) older
trainees’ elicit negative trainer beliefs, which will
negatively affect training outcomes. This study focused
exclusively on the last explanation. To isolate age-stereo-
types from older adult characteristics (e.g., age capabilities
and age meta stereotypes), only participants under the
chronological age of 30 years old and younger were con-
sidered. This approach represents the first effort to ex-
perimentally investigate the role of age stereotypes in
training interactions. Our results reveal a complex interplay
between age-based stereotypes and training processes and
outcomes.
A dominant theme emerging from early age stereotype
research was ‘think old-think bad.’ Accordingly, com-
pared to younger workers, older workers are seen as less
flexible (Vrugt and Schabracq 1996), creative (Butler 1975;
Simonton 1990), sociable (Braithwaite 1986), open-minded
(Harris 1975), and having less potential for development
(Finkelstein et al. 1995). However, a recent review chal-
lenged this overly pessimistic conclusion by revealing that
positive age stereotypes exist too. For example, older
workers are also viewed as more stable, dependable, loyal,
and less likely to miss work (Posthuma and Campion
2009).
Although encouraging, these positive age stereotypes
are not related to training experiences and thus, at the time
of hypothesis development, we had no reason to believe
that older workers may actually be perceived favorably.
Only recently, does a more positive perception of older
workers emerge in training (Finkelstein et al. 2012). Across
qualitative and quantitative studies, Finkelstein and col-
leagues found that stereotypes of the most senior workers
are largely positive (younger workers reported 60 % to be
positive; middle-aged workers reported 85 % to be posi-
tive). Of particular relevance to a training context are
‘experienced and ‘knowledgeable age stereotypes.
Consistent with other scholarly age-stereotypes research
(Cuddy and Fiske 2002; Cuddy et al. 2005; Fiske et al.
2002), the findings from the current study suggest that
participants viewed ostensibly older workers participating
in training activities through mixed lenses. On one hand the
‘lower ability to learn’ and ‘resistant to change’ stereo-
types appeared to influence trainer expectations about
trainee success, such that ostensibly older trainees were
penalized. On the other hand, the ‘experienced’ and
‘knowledgeable’ stereotypes appeared to influence trainee
expectations about trainer effort, such that ostensibly older
trainers were rewarded, slightly.
This research also contributes to the understanding of
self-fulfilling prophecies. Our findings reinforce previous
work (Shapiro et al. 2007) by showing that trainers’
negative stereotypes elicited from trainee characteristics
can manifest in poorer quality training (i.e., the interaction
J Bus Psychol
123
stage). In addition, our data suggest trainers’ ‘‘lower ability
to learn’ and ‘resistance to change’ stereotype (negative)
overrode the ‘work ethic’ stereotype (positive). Trainers
may be expressing subtle negative behavioral cues (Barr
and Fleck 1995; Fazio et al. 1995), although we were not
able to capture the specific features in the current study.
Another implication, in line with the self-fulfilling
prophecy, is that when trainers infer negative stereotypes
this can also emerge in only select training outcomes.
Further extending the contributions of the present study
is its simultaneous examination of both the trainee and
trainer perspective. Previous investigations of self-fulfilling
prophecies commonly ignore the expectations of the ‘tar-
get/partner’ (Olson et al. 1996). Moreover, assumptions
that the perceivers/actors (trainers) will be more influential
drivers of self-fulfilling prophecy effects than tar-
gets/partners (trainees) are—until now—untested. By di-
rectly comparing these factors using the APIM, we provide
the first empirical evidence that perceivers do indeed play a
larger role than targets in affecting interactional outcomes.
In conclusion, trainer beliefs did affect training experi-
ences and certain outcomes, which hold important impli-
cations for organizations: Solely attributable to perceived
age, older employees may be receiving poorer training and
thus may not be acquiring the necessary skills to be suc-
cessful on the job. This in turn, ultimately, limits older
employees’ full potential within the organization. In re-
sponse, organizations may want to educate trainers in
‘training the trainer’ programs about the potential conse-
quences of self-fulfilling prophecy effects, especially with
respect to age in a technological context. Although we only
examined this possibility in a training environment, it is
possible that similar effects are occurring in other domains
(e.g., selection, performance appraisal, and interpersonal
interactions with supervisors, co-workers, and customers).
Limitations and Future Research Directions
Several limitations may constrain the generalizability of
the current findings, but these also present interesting av-
enues for future research. First, our sample was composed
of undergraduate students. From one perspective, this is
strength because we wanted to infer the implications of
expectancies based on age stereotypes rather than capa-
bility declines associated with chronological age and/or
attitudes and beliefs associated with aging. From a different
perspective, employed individuals may not be as likely to
succumb to self-fulfilling prophecies as undergraduate
students because of additional factors (e.g., a desire to
deliver the best training possible regardless of initial ex-
pectations in order to improve organization’s talent level).
In rebuttal of this argument, there is evidence to suggest
that working adults fall victim to self-fulfilling prophecies
as well (e.g., Kierein and Gold 2000). Nevertheless, we see
value in replicating the current design using working adults
as the trainers and undergraduate students as the trainees to
continue examining the role of age stereotypes in tech-
nology training. Additionally, future research should in-
vestigate the role of age-metastereotypes with actual older
and younger employees and non-technological subject
matter.
A second limitation is that training occurred virtually
and in a one-on-one training session. Future efforts should
investigate these research questions in classroom-based
training. Grounded in the original work of Rosenthal and
Jacobsen (1968) and their efforts to examine the self-ful-
filling prophecy with elementary students in the classroom,
we anticipate that these findings would be reproduced and
potentially even more pronounced because classroom
situations increase the demands placed on the instructor.
Research finds that self-fulfilling prophecies are most
likely to occur when cognitive resources are reduced (e.g.,
when individuals are distracted; Biesanz et al. 2001; Harris
and Perkins 1995). Another research avenue would be to
determine if age bias is limited to technology training tasks
or training more generally.
A third noteworthy factor is that our data did not find
differences in objective training performance. Furthermore,
the significant differences that did emerge were small in
magnitude. Nevertheless, we argue that the effects are
meaningful given they emerged in a context (training) that
induced a knowledge-acquisition orientation, rather than an
interpersonal-facilitation orientation, which has been
shown to attenuate the effects of self-fulfilling prophecies
(Snyder and Haugen 1995). In addition, we found evidence
for behavioral confirmation (trainees assigned to the older
condition rated trainers more poorly), as opposed to just
perceptual confirmation (trainers evaluated ostensibly older
trainees more negatively) suggesting that trainers deliver
worse training to stereotyped trainees. Finally, small effect
sizes are common to self-fulfilling prophecy research
(Jussim and Harber 2005; Madon et al. 1997); however,
even if effect sizes are seemingly trivial, over time, these
effects can translate into meaningful organizational con-
sequences and thereby warrant attention (Martell et al.
1996).
Finally, it is unclear from the current study at exactly
what chronological age stereotypes regarding the perfor-
mance of older workers in technological tasks become
salient, and what other age-based characteristics might
moderate that expectation. For example, is it chronological
age itself, or how older one appears (social age), perhaps in
comparison to others (relative age), or due to their interests
or current life events (Pitt-Catsouphes et al. 2010) that
triggers the ‘technologically-challenged’ stereotype?
J Bus Psychol
123
Future research will need to consider these variables in
order to gain a more comprehensive understanding of the
influence of age stereotypes in various interactions.
Conclusions
By 2018, 24 % of the United States workforce will be
composed of individuals age 55 or older (U.S. Bureau of
Labor Statistics 2008). The aging workforce can be at-
tributed to a number of health, societal, and economic
trends. Since 1950, life expectancy has increased by ap-
proximately 12 years and is currently estimated at 78 years
older (Arias 2012). Additionally, retirement is no longer an
all-or-nothing event. Workers may transition out of the
workforce more slowly through part-time or ‘bridge em-
ployment’’ (Kim and Feldman 1994). Lastly, unpredictable
economic conditions may require working later in life.
Combined, these trends have resulted in the most age di-
verse workforce in modern history and this shift does not
represent a temporary flux, but a new standard.
Increases in age diversity are simultaneously occurring with
dramatic increases in the use of technology. Consequently, the
workplace is transforming into a domain that is almost un-
recognizable from the past decade. To remain competitive,
organizations must adopt a continuous learning philosophy
(Noe 2010) so that the knowledge and skills of employees
‘keep pace’ with ever evolving technological capabilities. The
most common activity from this philosophy is, perhaps, formal
training. Research suggests that training, when implemented
and tailored appropriately, produces noteworthy gains
(Aguinis and Kraiger 2009;Noe2010). Unfortunately, the
benefits derived from training may be guarded by additional
barriers for certain workers.
At the onset of this study, the authors posed the fol-
lowing question: Why do age-related differences emerge in
technology training? Likely, there are multiple factors
contributing to the full explanation (e.g., mean-level de-
clines in cognitive functioning and fewer technology-re-
lated experiences), but the current investigation ignores
these employee characteristics and focuses exclusively on
trainer beliefs evoked from older employee characteristics.
This study finds, when lacking relevant information to base
expectations, trainers may utilize widely available stereo-
types, which translate to poorer training quality and ulti-
mately facilitate expectancy confirmation serving to
disadvantage stereotyped employees. This finding is par-
ticularly concerning because training is intended to elim-
inate competency gaps, not contribute to them. Differing
performance levels can then justify future personnel deci-
sions (e.g., promotion, training, and performance ap-
praisal), which, once again, penalize stereotyped
employees. In short, researchers and practitioners alike
must devote future efforts to better understand the role of
stereotypes in workplace interactions and outcomes.
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Background: Both rapid technological changes and (self-)ageism are pervasive challenges of the 21st century, potentially impacting older adults' everyday functioning, health, and well-being. This systematic literature review aimed to synthesize scholarly evidence to determine the associations between everyday information and communication technology (EICT) usage and (self-)ageism as well as potential moderators. Methods: A systematic search was performed in 8 academic databases, covering the timeframe from January 1995 to January 2021. Following the Preferred Reporting Items for Systematic Reviews Meta-Analyses guidelines, a total of 15 articles met the inclusion criteria and were involved in the analysis. The standardized National Heart, Lung, and Blood Institute's (NHLBI) quality assessment tools were used for risk bias. Results: Several studies demonstrated significant associations between EICT usage and stereotype embodiment (n=8), stereotype threat (n=2) and age discrimination (n=3). Age (group), gender, and motivation were examined as potential moderators. Discussion: This review provides initial evidence on the associations between (self-)ageism and EICT usage. It highlights the importance of positive subjective aging perceptions for active EICT usage in older adults, but also emphasize the detrimental consequences of ageism in EICT learning settings and technology design on older persons' willingness and ability to use EICT. Further ecologically valid and methodologically sound research is needed to better understand both the nature and direction of the association between EICT usage and (self-)ageism.
... Study 3 is also considered a somewhat small sample. We offer that this limitation is somewhat mitigated as our sample size exceeds the criteria proposed for dyadic analyses (Kenny et al., 2006;Kreft & De Leeuw, 1998;Maas & Hox, 2005) and is consistent with previous research (e.g., Brouer et al., 2016;McCausland et al., 2015;Mitchell et al., 2015;Song et al., 2020;Williamson et al., 2017). As noted earlier, we also had sufficient power to detect small, medium, and large effect sizes. ...
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