2012. Vol.3, No.12A, 1125-1130
Published Online December 2012 in SciRes (
Copyright © 2012 SciRes. 1125
Appreciative Inquiry: An Effective Training Alternative to
Traditional Adult Learning?
Jillian Webb Day, Courtney L. Holladay
HR-Organization Development, University of Texas M. D. Anderson Cancer Center, Houston, USA
Received September 24th, 2012; accepted October 20th, 2012; accepted November 19th, 2012
While the practice of positive psychology has flourished in the last decade, critics still point to the lack of
intervention-based research and empirical evidence as a shortcoming of this field. Appreciative inquiry
(AI), an intervention with similar theoretical underpinnings as positive psychology, has the capacity to
expand what is known about the impact of positivity in the study of organizations. AI is an intervention
that uses reflective questions of positive experiences to create new opportunities. However, there is scant
research on its effectiveness in real-world settings. This study extends this line of research by evaluating
the effectiveness of AI as an alternative to conventional adult learning methodologies, as well as an ap-
plied example of an intervention based on positive psychology to study organizational change. Results
from changes in pre- and post-assessments following three AI-based interventions, when compared to
those from a control group, suggest AI can lead to significant gains in participants’ perceived attitudinal,
behavioral, and cognitive learning similar to traditional adult learning methodologies. In addition, these
results suggest AI interventions paired with real-world problem solving opportunities yield greater gains
in participants’ perceived confidence to demonstrate positive interpersonal skills. Implications for using
AI in the course of adult learning are discussed.
Keywords: Appreciative Inquiry; Positive Psychology; Adult Learning
The evolution of positive psychology in the last decade has
included an explosion of research regarding how it impacts the
study of organizations and the dynamics within (e.g., positive
organizational scholarship; positive organizational behavior;
positive organizations; Caza & Caza, 2006; Luthans & Youssef,
2007; Seligman & Csikszentmihalyi, 2000). Positive psy-
chologists have explored the circumstances under which work-
places thrive, employees flourish and organizations achieve
success (Caza & Caza, 2006; Fineman, 2006; Gable & Haidt,
2005; Luthans & Youssef, 2007). Though a few argue this ap-
plication of positive psychology is deceptively simple on the
surface, most practitioners agree there is need to balance the
predominant, problem-focused approach to studying organi-
zational dynamics with one that evaluates how positivity and
strengths lead to optimal functioning in this context (Caza &
Caza, 2006; Fineman, 2006; Luthans & Youssef, 2007).
To date, however, a lack of intervention-based research and
reliable measurement exists as areas for opportunity in its ap-
plication to studying organizations (Fineman, 2006; Gable &
Haidt, 2005; Luthans & Youssef, 2007). Appreciative inquiry
(AI) is one intervention that has been used previously to ad-
vance positive psychology in the study of organizations at their
best (Bushe, 2007; Fineman, 2006; Foster & Lloyd, 2007). AI
is an action-research methodology that uses questions to
prompt reflection on past successes as a way of creating new
solutions. At its core, AI is a process that helps individuals to
establish a discourse based on positivity and future possibilities
(Moody, Horton-Deutsch, & Pesut, 2007; Richer, Ritchie, &
Marchionni, 2009; Whitney & Trosten-Bloom, 2010). When AI
is implemented, it typically starts with the collection of positive
stories that capture the organization and its people at their best
(Cooperider & Whitney, 2005). While some practitioners stop
with this step, others leverage this information to “create plans
and processes that encourage and nurture improvised action by
system members” (Bushe & Kassam, 2005: p. 168). This step
usually involves a facilitated discussion to cement new ideas
based on past positive experiences (Bushe & Kassam, 2005).
As an intervention, AI has the capacity to expand what is
known about the impact of positivity by giving practitioners a
tool to implement transformational change and study its out-
comes from a strengths-based model. The growing use of AI
for organizational change has led to critiques that it is a passing
fad, especially since there is little empirical evidence about its
usefulness as an intervention in naturalistic settings (Bushe &
Kassam, 2005; Jones, 2010; Sekera, Brumbaugh, Rosa, & Coo-
perrider, 2006). The studies that have explored this area have
shown AI can lead to meaningful changes in organizations (e.g.,
more favorable perceptions of information sharing practices in
an organization; widely-supported process improvement initi-
atives, such as process changes), as well as that strength-based
inquiries can influence employees’ readiness for change and
overcome initial, often negative reactions to upcoming change
(Bushe & Kassam, 2005; Sekera, Brumbaugh, Rosa, & Coo-
perrider, 2006; Sekerka, Zolin, & Smith, 2009). These studies
though identified the need for additional empirical assessments
about the effectiveness of AI as an intervention, particularly
over longer periods of time in naturalistic settings where practi-
tioners are actively using AI to facilitate organizational change
(Bushe & Kassam, 2005; Sekera, Brumbaugh, Rosa, & Coop-
errider, 2006; Sekerka, Zolin, & Smith, 2009). Additionally,
since AI is based on the idea that the method of inquiry shapes
the information uncovered, there is a need to explore whether
different modes of AI are more effective than others at facili-
tating certain types of change (Sekera, Brumbaugh, Rosa, &
Cooperrider, 2006; Sekerka, Zolin, & Smith, 2009).
This study will expand on the research in this arena by em-
pirically investigating the effectiveness of different modes of
AI when compared to a traditional, problem-based approach to
organizational change in a naturalistic setting. For our purposes,
we define organizational change as it is depicted in Lewin’s
(1951) force field analysis model. In the context of our study,
this means changing employees’ perceptions of their soft skills
from the current state to a more favorable state in which they
are more confident in the relevant knowledge, skills and abili-
ties. Specifically, this study seeks to provide preliminary evi-
dence about the effectiveness of a strength-based intervention,
namely AI, as a training intervention to enhance employees’
soft skills that contribute to the optimal functioning of organi-
zations and workgroups. In doing so, this study seeks to address
one of the criticisms regarding the use of positive psychology
to study organizational change; namely, that there is little re-
search on the efficacy of strength-based interventions in rela-
tion to the predominant problem-based approach of investigat-
ing organizations and their dynamics (Gable & Haidt, 2005;
Luthans & Youssef, 2007).
As previously mentioned, AI is an intervention frequently
associated with and used in the name of positive psychology.
This is primarily because both AI and positive psychology
share some of the same philosophical underpinnings, namely
focusing on strengths first, identifying the conditions under
which people and organizations flourish, and constructing pos-
sible realities with a focus on the positive elements that ener-
gize (Bushe, 2007; Fineman, 2006; Foster & Lloyd, 2007). For
example, asking questions to identify the conditions under
which people have successfully performed in the past is one of
the first steps taken when AI is used (Whitney & Trosten-
Bloom, 2010). This step supports one of the earliest definitions
of positive psychology by Seligman & Csikszentmihalyi (2000):
that it is a “psychology of positive human functioning” (p. 13).
The positive functioning in turn can lead to an exchange of
meaningful dialogue to identify past successes that can help
build a future in which people flourish (Moody, Horton-
Deutsch, & Pesult, 2007; Whitney & Trosten-Bloom, 2010). In
another instance within AI, it is the idea that positivity, spe-
cifically positive inquiry, can lead to transformational change
because the change in inquiry prompts new ways of looking at
old problems and generates new possibilities for the future
(Bushe, 2007, 2010; Whitney & Trosten-Bloom, 2010).
While AI has been used in various organizational settings
such as healthcare (Moody, Horton-Deutsch, & Pesut, 2007;
Richer, Ritchie, & Marchionni, 2009), the few studies that
evaluated its impact were limited by research design. (Bushe &
Kassam, 2005; Jones, 2010; Sekera, Brumbaugh, Rosa, & Co-
operrider, 2006). For example, the field studies by Sekura,
Brumbaugh, Rosa and Cooperrider (2006) and Sekura and
Zolin (2009) randomly assigned participants to study conditions
and gathered information through one-on-one interviews.
However, the practice of AI can involve individuals sharing
their positive experiences in groups (Cooperrider & Whitney,
2005). Additionally, while these results suggest AI can posi-
tively influence employees’ perceptions on a particular topic
(i.e., readiness for change), they do not measure the changes in
employees’ perceptions as a result of participating in an AI-
based intervention.
Indeed, the lack of intervention-based research is often a
criticism of positive psychology in general. While AI could be
a possible intervention to explore for this reason, the research
on AI can be expanded to empirically evaluate its changes on
employees’ perceptions when compared to the predominant,
problem-based approaches to assess organizational problems
within a naturalistic setting (Bushe & Kassam, 2005; Grant &
Humphries, 2006; Jones, 2010; Sekera, Brumbaugh, Rosa, &
Cooperrider, 2006). Problem-based approaches typically focus
first on identifying the deficit in the organization, then devising
a solution that fixes the problem (Sekerka, Zolin, & Smith,
While a number of studies have demonstrated that AI can
lead to meaningful changes in organizations, there are oppor-
tunities to build upon this research (Bushe & Kassam, 2005;
Jones, 2010; Sekera, Brumbaugh, Rosa, & Cooperrider, 2006).
Our study seeks to address this gap by empirically evaluating
how effective AI is as a training intervention to enhance em-
ployees’ soft skills that contribute to positive organizational
behavior. For that reason, Hypothesis 1 postulates that parti-
cipants’ perceived knowledge, skills and attitudes related to
creating positive interpersonal dynamics will be significantly
higher after participating in AI-based interventions.
As previously mentioned, AI is often critiqued for the lack of
empirical evidence about its effectiveness (Bushe & Kassam,
2005; Jones, 2010; Sekera, Brumbaugh, Rosa, & Cooperrider,
2006). Instead, most of the literature on AI has provided anec-
dotal evidence about the power of this method, as opposed to
empirically evaluating when it works best or moderators influ-
encing the effect of its impact and sustainability over time
(Bushe, 2011; Jones, 2010). Consequently, although AI is
touted as an intervention that can yield results quickly and is
straightforward to implement (Jones, 2010), the sentiment re-
mains from some critics that AI is a passing management fad.
A limited number of studies have empirically evaluated the
impact of AI as a strength-based intervention that can yield
observable positive changes in organizations. Summarizing the
research to date was a meta-analysis that reviewed the con-
ditions under which AI led to transformational outcomes
(Bushe & Kassam, 2005). More recent research has taken a
field approach to examine different inquiry strategies’ (i.e.,
strength- or problem-based) influence on employees’ readiness
for change. The studies found the highest positive effect in
engagement to a change process was reported among groups
that participated in an AI intervention as compared to those
who were engaged through a problem-based approach (Sekera,
Brumbaugh, Rosa, & Cooperrider, 2006; Sekerka, Zolin, &
Smith, 2009). This evidence suggests that strength-based ap-
proaches to organizational change, such as AI, can have a posi-
tive impact on those experiencing the change and their readi-
ness to engage and support it. However, as previously men-
tioned, there are opportunities to expand this line of research by
evaluating how AI impacts organizational change over time
through changes in employees’ perceptions on different matters,
such as their abilities to demonstrate positive interpersonal
skills. Another opportunity to expand this research is to com-
pare how different strength-based approaches, such as the
variations in the delivery of AI, are more or less effective. In-
deed, these are future areas of research identified as needing
exploration (Sekera, Brumbaugh, Rosa, & Cooperrider, 2006;
Copyright © 2012 SciRes.
Sekerka, Zolin, & Smith, 2009).
Our study will address this gap by using pre- and post-as-
sessments to empirically evaluate the impact of different
AI-based training interventions to change employees’ perceived
knowledge, skills and attitudes towards demonstrating soft
skills that contribute to positive organizational behavior. By
using the Kraiger model to measure learning outcomes, this
study expands upon the empirical evidence regarding the im-
pact of AI by evaluating its effectiveness as a training inter-
vention in different modes (Kraiger, Ford, & Salas, 1993). The
Kraiger model is a conceptualization of learning outcomes that
divides them into three categories; affective learning, cognitive
learning and behavioral learning. The first type refers to
changes in participants’ attitudes towards a particular topic. The
second type refers to how much new knowledge is acquired.
The third type refers to perceived changes in participants’ skills
(Holladay & Quinones, 2008; Kraiger, Ford, & Salas, 1993).
Specifically, we anticipate that the AI-based interventions
will be as effective, if not more effective, in impacting cogni-
tive, behavioral and attitudinal learning outcomes. According to
Hypothesis 2, after controlling for time as a covariate, par-
ticipants’ perceived knowledge, skills and attitudes related to
changes in their soft skills will be significantly greater for those
in the AI-based interventions when compared to those in the
control group.
While our main study objective is to evaluate the effective-
ness of AI interventions, a secondary, exploratory question
exists as to whether the type of AI intervention matters in the
outcomes achieved. Decades of research show training to be
effective in increasing learning outcomes (Alliger, Tannenbaum,
Bennett, Traver, & Shotland, 1997). When it comes to positive
psychology, we anticipate similar learning outcomes to be
achieved; however, it may be the degree of positive outcomes
achieved is dependent on the type of AI intervention. For ex-
ample, it could be that an AI intervention that integrates more
traditional opportunities for practice will be more effective than
the discussion based AI intervention. Thus, we pose the follow-
ing research question: Does the type of AI intervention influ-
ence the learning outcomes achieved?
Participants and Procedures
To evaluate the use of AI-based interventions for training
purposes, as well as to compare their effectiveness to more
traditional training methods, a quasi-experimental design was
employed. These interventions were implemented within the
organization for departmental use with their employees; how-
ever, to assess effectiveness pre- and post-assessments were
developed and administered based on the aforementioned prin-
ciples of Appreciative Inquiry immediately preceding and fol-
lowing the interventions respectively. These assessments were
used to gauge changes in employees’ knowledge, skills and
attitudes relating to creating positive interpersonal dynamics.
These pre- and post-assessments were administered to 497 em-
ployees from July 2011 to July 2012; of these employees, 66%
were women (34% Men) and had an average age of 43 years
old (18% Asian, 26% Black, 15% Hispanic, 33% White, and
9% Other). These employees came from various clinical, busi-
ness and research areas of an academic medical center in
Houston, TX. These employees held job titles ranging from
administrative assistant to clinical nurse to manager with a
majority bachelor prepared. These participants completed the
pre- and post-assessment at the start and end of each intervene-
tion, respectively.
Participants in this study were involved in one of three train-
ing interventions based on the principles of Appreciative In-
quiry or a control intervention based on traditional, classroom-
based adult learning principles. Table 1 provides a more de-
tailed description of these types of interventions and their
length of time. For example, in the AI-only intervention, a pair
of employees started with an interview where they asked the
following question: “At their best, high performing work units
foster a culture of mentoring by creating opportunities to learn
from one another, by willingly sharing information and by pro-
viding feedback that allows individuals to reach their full po-
tential. Thinking about the work units in which you have been
involved, describe one where mentoring was an active part of
the environment”. In another example within the AI training, an
exercise required participants to share with a partner a previous
success at work; their partner listened and then provided feed-
back on the strengths the person demonstrated in their story. As
illustrated, both exercises within the interventions focused on
The study hypotheses related to two sets of variables: 1) the
independent variable—the type of training intervention (AI-
based or control) and 2) the dependent variables—the parti-
cipants’ perceived knowledge, skills and attitudes in creating
positive interpersonal dynamics from pre- and post-assessments.
These dependent measures were chosen due to their alignment
with Kraiger’s model of learning outcomes, specifically tar-
geting training interventions. We also included the duration of
each intervention to control for the impact of time when testing
the relationship between intervention type and differences in
participants’ knowledge, skills and attitudes from pre- and post-
Table 1.
Intervention type and description.
Name Intervention Description Length of Time
Paired interviews and discussions
based on AI questions, coupled
with a team-building exercise
1.5 - 2 hours
Instructor-led classes designed to
identify behaviors that build and
break trust, as well as ways to
rebuild it; incorporated exercises
based on AI principles
4 hours
Interviews based on AI questions
with intent of developing
actionable steps to create
mentoring program
1.5 - 2 hours
Team-building exercises and
instructor-led training class not
designed with AI principles in
1.5 - 3 hours
Note: aData was examined for differences between control conditions; no differ-
ences were observed (p > .05), resulting in their combination for analyses.
Copyright © 2012 SciRes. 1127
Copyright © 2012 SciRes.
Knowledge. This section of both the pre- and post-assess-
ments was comprised of five true/false questions based on the
principles of AI that were objectives in each AI-based interven-
tion. The items were scored 0 (Incorrect) and 1 (Correct), and
then summed to create the measure. A sample item from this
section was “Questions are more powerful than answers”.
Skills. In this part of the pre- and post-assessments, partici-
pants were asked to rate their perceived level of confidence
from 0% - 100% on ten questions that gauged their ability to
demonstrate specific skills related to positive interpersonal
dynamics. A sample question from this section was “How con-
fident are you in your ability to use past successes to generate
new, innovative solutions?” The ten-item scale for both the
pre-assessment (α = .94) and the post-assessment (α = .96) had
an adequate level of internal consistency.
Attitudes. Participants were asked to rate their attitude re-
garding the ability to create positive interpersonal dynamics as
a result of the intervention in which they participated. Parti-
cipants responded to the five items of this section on a 5-point
scale ranging from 1 (strongly disagree) to 5 (strongly agree).
A sample item from this section was “This session will help me
to foster an environment of trust in my team.” Both the pre-
assessment (α = .96) and post-assessment (α = .96) scales for
this measure had an adequate level of consistency.
To evaluate Hypothesis 1, paired t-tests were conducted be-
tween participants’ pre and post assessments to evaluate sig-
nificant changes in perceived knowledge, skills and attitudes
after participating in one of the intervention types. In support of
Hypothesis 1, there were significant gains in participants’ per-
ceived knowledge, skills and attitudes from pre- and post-
scores for each AI-intervention (see Table 2). For example, the
AI-Only intervention showed that participants significantly
increased their knowledge following the training (M = 2.73, SD
= .68) as compared to before the training (M = 2.38, SD = .89; t
= 3.97, p < .01).
Partial support was found for Hypothesis 2. That is, in the
control group, there was no significant difference in partici-
pants’ pre- (M = 2.65, SD = 0.79) and post-knowledge scores
(M = 2.78, SD = 0.84; t = 1.60, p > .05) of perceived know-
ledge, though there was a significant difference for the AI inter-
ventions. However, the AI interventions did not show signifi-
cantly greater gains than the control intervention for skills and
To evaluate the research question, a MANCOVA was per-
formed with the length of time for the three interventions as a
covariate and changes in knowledge, skills and attitudes (mea-
sured as difference scores from pre- and post-assessments) as
the dependent variables. The research question then becomes
whether the type of AI-based intervention differed in terms of
effectiveness compared to each other after adjusting for time as
a covariate. An overall effect was non-significant when exam-
ining the effect of type of intervention (Wilks’ λ = .972, F (9,
1144.08) = .149, p >. 05).
However, a significant main effect for type of intervention
was observed for changes in participants’ skills from pre- to
post-assessment, F (3, 173) = 3.287, p < .05. Significant pair-
wise differences between the gains of participants’ skills were
observed when compared by intervention type; namely, those
who participated in the AI-Teambuilding and AI-Training in-
terventions perceived significantly higher gains from pre- to
post-assessment in their skills to build trust and collaboration
when compared to those who participated in the AI-Only inter-
vention. This finding suggests that participants perceived
greater confidence to demonstrate positive interpersonal skills
when the opportunity to practice was integrated into the inter-
vention beyond the prescribed AI focused discussion.
Proponents of positive psychology have advocated for its ex-
ploration within organizations. One particular intervention, AI,
relies on the tenants of positive psychology to help individuals
within organizations achieve outcomes affecting the organi-
Table 2.
Descriptive statistics and paired t-test results.
Pre-Test Post-Test
Variable Intervention N
Mean SD Mean SD
AI-Only 105 2.38 0.89 2.73 0.68 3.97*
AI-Training 150 2.69 0.88 2.94 0.80 3.55*
AI-Teambuilding 140 2.57 0.98 2.85 0.83 3.28*
Control 102 2.65 0.79 2.78 0.84 1.60
AI-Only 105 85.76 12.62 89.44 11.25 7.11*
AI-Training 150 87.60 9.81 93.56 7.49 9.83*
AI-Teambuilding 140 83.89 12.33 90.62 9.80 9.59*
Control 102 83.53 13.39 89.14 12.90 7.23*
AI-Only 105 4.02 0.69 4.22 0.71 4.20*
AI-Training 150 4.31 0.62 4.57 0.59 5.35*
AI-Teambuilding 140 3.94 0.70 4.23 0.64 5.37*
Control 102 4.14 0.70 4.40 0.82 4.48*
Note: *p < .01.
zation’s success. Such an intervention moves beyond traditional
organizational interventions that focus on finding problems in
processes and pointing to performance issues by instead recog-
nizing positive performance and accomplishments. This redi-
rected focus assumes that subsequent learning outcomes can be
similar if not greater when considering successes.
The present study demonstrated that AI-based interventions
can in fact achieve attitudinal, behavioral, and cognitive learn-
ing similar to traditional trainings. Further, the present study
showed that AI interventions integrating a skills based applica-
tion achieved greater learning than an AI discussion based in-
tervention. This may have resulted from the possibilities par-
ticipants were encouraged to explore as a way of practicing the
skills taught during these interventions. In considering these
possibilities, both hypothetical and actual in nature, participants
were empowered to practice these skills before leaving the
classroom. This likely increased participants’ perceptions that
these skills were mastered. Similar findings from post-medical
education suggest experiential learning enhances the skill de-
velopment of participants over lecture-based learning because
they can practice and refine them in a safe setting (Shreeve,
2008; Smits, de Buisonjé, Verbeek, van Dijk Metz, & ten Cate,
Another focus of our study was to compare differences in
learning outcomes between the AI-interventions and the control
group. Our results only showed significant differences in the
knowledge gained by those who participated in the AI-inter-
ventions. In other words, participants learned more about AI
because they participated in the AI-based interventions. These
results are to be expected since the AI-interventions were de-
signed based upon the principles of AI while the control
group’s intervention was not.
Participants in this study were not randomly assigned to the
various interventions. However, previous studies exploring the
effectiveness of AI indicated a need to empirically evaluate it
under different circumstances, such as while being practiced
over longer periods of time in controlled naturalistic settings
(Bushe & Kassam, 2005; Sekera, Brumbaugh, Rosa, & Coo-
perrider, 2006). While we cannot conclusively demonstrate the
effectiveness of AI under the study’s current conditions, our
findings suggest through our quasi-experimental design that
AI-based interventions result in learning outcomes comparable
to more conventional, problem-based training interventions,
even when controlling for time. These results can serve as a
springboard for future research to evaluate the effectiveness of
AI under other study parameters.
The present study did face unequal sample sizes across con-
ditions that in turn may impact these results. Because the prob-
ability of a Type 1 error increases with this imbalance, rela-
tionships may have been falsely detected (Zimmerman, 2004).
However, the consistency of the results across multiple mea-
sures, time and interventions points to the unlikelihood of the
findings being strictly due to this error.
Another potential limitation is the generalizability of the re-
sults from this exploratory study to different industries. While
other studies have been conducted in healthcare settings to
evaluate the use of AI (Moody, Horton-Deutsch, & Pesut, 2007;
Richer, Ritchie, & Marchionni, 2009), to the authors’ know-
ledge, no assessment has been developed based on these princi-
ples nor has one been tested to evaluate the effectiveness of
AI-based interventions for training purposes (Bushe & Kassam,
2005; Jones, 2010). Since this exploratory study was conducted
in a healthcare organization, these results may not be character-
istic of the types of gains participants in other industries could
achieve if AI-based training interventions were used. However,
these results provide some of the first quantifiable evidence
about the effectiveness of AI as a training intervention. To ex-
tend the generalizability of these results, similar studies will
need to be conducted in other industries and in organizations of
different sizes.
Past research on the successes and gains from using this
methodology are based on anecdotes and stories, including
subjective opinions and perceptions (Bushe, 2011). The lack of
quantifiable, measurable data to support the effectiveness of AI
is one of the major criticisms associated with this methodology.
Consequently, some researchers have compared AI to a “man-
agement fad”—an idea which gains popularity quickly without
hard evidence of its effectiveness (Jones, 2010).
The present study provides some evidence to the idea that
AI-based interventions are as effective as conventional adult
training methodologies in developing interpersonal skills for
the workplace, providing support for the plethora of anecdotes
highlighting its effectiveness. The structure of learning in the
three AI-based interventions was similar to problem-based
learning, an approach to adult training, in that actual experi-
ences formed the basis of learning in small, interactive groups
lead by a facilitator (Gijbels, Dochy, Van den Bossche, &
Segers, 2005; Smits, de Buisonjé, Verbeek, van Dijk Metz, &
ten Cate, 2003). Research from post-medical education has
shown problem-based learning can be as effective, if not more,
in comparison to lecture-based learning at fostering long-term
learning and changes to performance, such as interpersonal
skills (e.g., Lin, Lu, Chung, & Yang, 2010; Schmidt, van der
Molen, & te Winkel, 2009; Shreeve, 2008; Smits, de Buisonjé,
Verbeek, van Dijk Metz, & ten Cate, 2003). Our study expands
upon this area of research by providing empirical support for
the use of AI as an alternative approach to problem-based
learning, albeit with a different focus; namely, evaluating past
successes as a source for new solutions instead of focusing
exclusively on problems.
Appreciative Inquiry is an approach focusing on past succe-
sses to influence future success with the underlying assumption
that a positive approach can lead to positive outcomes. The
present study observed that AI interventions can achieve learn-
ing outcomes similar to traditional training methods, suggesting
its effectiveness from an empirical perspective. The benefit to
relying on positivity in the intervention is that employees are
likely to leave it energized, which could have downstream effects
on the climate of the organization, whereby employees could
see the organization as one where possibilities are explored
rather than one where problems are scrutinized. Such positivity
ultimately benefits the organization in its retention of those
employees and changes implemented by those employees.
Copyright © 2012 SciRes. 1129
Alliger, G. M., Tannenbaum, S. I., Bennett, W., Traver, H., & Shotland,
A. (1997). A meta-analysis of the relations among training criteria.
Personnel Psychology, 50, 341-358.
Bushe, G. (2007). Appreciative Inquiry is not (just) about the positive.
OD Practitioner, 39, 30-35.
Bushe, G. (2011). Appreciative inquiry: Theory and critique. In D. Boje,
B. Burnes, & J. Hassard (Eds.), The Routledge Companion to Or-
ganizational Change (pp. 87-103). Oxford: Routledge.
Bushe, G. R., & Kassam, A. F. (2005). When is appreciative inquiry
transformational? A meta-case analysis. Journal of Applied Behav-
ioral Science, 41, 161-181. doi:10.1177/0021886304270337
Caza, B. B., & Caza, A. (2008). Positive organizational scholarship: A
critical theory perspective. Journal of Management Inquiry, 17, 21-
30. doi:10.1177/1056492607305907
Cooperrider, D., & Whitney, D. (2005). Appreciative inquiry: A posi-
tive revolution in change. San Francisco: Berrett-Koehler.
Fineman, S. (2006). On being positive: Concerns and counterpoints.
Academy of Management Review, 31, 270-291.
Foster, S. L., & Lloyd, P. J. (2007). Positive psychology principles
applied to consulting psychology at the individual and group level.
Consulting Psychology J ournal: Practice and Research, 59, 30-40.
Gable, S. L., & Haidt, J. (2005). What (and why) is positive psychology?
Review of General Psychology, 9, 103-110.
Gijbels, D., Dochy, F., Van den Bossche, P., & Segers, M. (2005).
Effects of problem-based learning: A meta-analysis from the angle of
assessment. Review of Educational Research, 75, 27-61.
Grant, S., & Humphries, M. (2006). Critical evaluation of appreciative
inquiry. Action Research, 4, 401-418.
Holladay, C. H., & Quinones, M. A. (2008). The influence of training
focus and trainer characteristics on diversity training effectiveness.
Academy of Management Le arn ing and Education, 7, 343-354.
Jones, R. S. P. (2010). Appreciative inquiry: More than just a fad?
British Journal of Healthcare Management, 16, 114-122.
Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive,
skill-based, and affective theories of learning outcomes to new
methods of training evaluation. Journal of Applied Psychology, 78,
311-328. doi:10.1037/0021-9010.78.2.311
Lewin, K. (1951). Field theory in social science. New York: Harper &
Lin, C. F., Lu, M. S., Chung, C. C., & Yang, C. M. (2010). A compari-
son of problem-based learning and conventional teaching in nursing
ethics education. Nursing Ethics, 17, 373-382.
Luthans, F., & Youssef, C. (2007). Emerging positive organizational
behavior. Journal of Man age men t, 33, 321-349.
Moody, R., Horton-Deutsch, S., & Pesut, D. (2007). Appreciative in-
quiry for leading complex systems: Supporting the transformation of
academic nursing culture. Journal of Nursing Education, 46, 1-6.
O’Daniel, M., & Rosenstein, A. H. (2008). Professional communication
and team collaboration. In Hughes, R.G. (Ed.), Patient safety and
quality: An evidence-based handbook for nurses. Rockville, MD:
AHRQ Publication.
Richer, M. C., Ritchie, J., & Marchionni, C. (2009). “If we don’t do
more, let’s do it differently!”: Using appreciative inquiry to promote
innovative ideas for better health care work environments. Journal of
Nursing Management, 17, 947-955.
Schmidt, H. G., van der Molen, H. T., & te Winkel, W. W. R. (2009).
Constructivist, problem-based learning does work: A meta-analysis
of curricular comparisons involving a single medical school. Educa-
tional Psychologist, 44, 227-249. doi:10.1080/00461520903213592
Sekerka, L. E., Brumbaugh, A., Rosa, J., & Cooperrider, D. (2006).
Comparing Appreciative Inquiry to a diagnostic technique in organ-
izational change: The moderating effects of gender. International
Journal of Organization Theory and Behavior, 9, 449-489.
Sekerka, L. E., Zolin, R., & Smith, J. G. (2009) Be careful what you
ask for: How inquiry strategy influences readiness mode. Organiza-
tion Management Journal, 6, 106-122. doi:10.1057/omj.2009.15
Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychol-
ogy: An introduction. American Psychologist, 55, 5-14.
Shreeve, M. (2008). Beyond the didactic classroom: Educational mod-
els to encourage active student involvement in learning. Journal of
Chiropractic Education, 22, 23-28. doi:10.7899/1042-5055-22.1.23
Smits, P. B., de Buisonjé, C. D., Verbeek, J. H., van Dijk, F. J., Metz, J.
C., & ten Cate, O. J. (2003). Problem-based learning versus lec-
ture-based learning in postgraduate medical education. Scandinavian
Journal of Work, Environment and Health, 29, 280-287.
Whitney, S., & Trosten-Bloom, A. (2010). The power of appreciative
inquiry (2nd ed.). San Francisco: Berrett-Koehler.
Zimmerman, D. W. (2004). Inflation of Type 1 error rates by unequal
varianaces associated with parametric, nonparametric and rank-
transformation tests. Psicológica, 25, 103-133.
Copyright © 2012 SciRes.