Teaching Critical Thinking Skills: Ability, Motivation,Intervention, and the Pygmalion Effect

Larry W. Howard • Thomas Li-Ping Tang •

M. Jill Austin

Received: 27 January 2014 / Accepted: 27 January 2014

� Springer Science+Business Media Dordrecht 2014

Abstract Using a Solomon four-group design, we

investigate the effect of a case-based critical thinking

intervention on students’ critical thinking skills (CTA). We

randomly assign 31 sessions of business classes (N = 659

students) to four groups and collect data from three sour-

ces: in-class performance (CTA), university records (ACT,

GPA, and demographic variables), and Internet surveys

(learning and motivational goals). Our 2 9 2 ANOVA

results showed no significant between-subjects differences.

Contrary to our expectations, students improve their critical

thinking skills, with or without the intervention. Female

and Caucasian students improve their critical thinking

skills, but males and non-Caucasian do not. Positive per-

formance goals and negative mastery goals enhance and

decrease improvements of their CTA scores, respectively.

ACT and age are related to pre- and post-test. Gender

(male) is related to pre-test. GPA is related to post-test.

Results shed light on the Pygmalion effect, the Galatea

effect, ability, motivation, and opportunity as signals for

human capital, and business ethics.

Keywords Critical thinking skills � Ability � Motivation �Race � Self-fulfilling prophesy � Priming effect

An investment in knowledge always pays the best

interest.

Benjamin Franklin (1706–1790)

The Organization for Economic Co-Operation and

Development’s ‘‘Programme for International Student

Assessment’’ showed that high school students in the US

ranked 20th in science and 31st in mathematics, among 57

countries (PISA 2009). Only 35 % of eighth graders in

Tennessee achieved proficient in reading, according to the

‘‘2005 report’’ of National Assessment of Educational

Progress. Some of these eighth graders are in college now

and will enter the labor market soon. In the US, our high

school students are no longer the brightest in the world and

are not ready for higher education.

Due to globalization, many multinational corporations

(MNCs) have outsourced their low-skill work to countries

with the lowest labor rates (Xia and Tang 2011). With

technological, cultural, demographic, and economic chan-

ges in the knowledge economy, the metaphor is not

‘‘climbing ladders’’ but ‘‘riding waves’’, according to

David Gergen, Director of Harvard’s Center for Political

Leadership (Coleman et al. 2012, p. 53). College students

today expect to ride seven or eight different waves in their

careers. Educators and executives must enhance creativity,

innovation, R&D, and ‘‘core competence’’ of the corpora-

tion to achieve sustainable competitive advantage (McG-

rath 2013; Prahalad and Hamel 1990). Do students have the

necessary knowledge, skills, and abilities (KSAs) to meet

the new challenges in the twenty-first century?

Among different age cohorts (Baby Boomer, 1946–1964;

Gen-Xer, 1965–1980; and Gen-Yer, or Millennial, after

1980), Gen-Yer’s work-related attitudes, behaviors, and

KSAs are significantly different from those 5 to 10 years ago

(Tang et al. 2012). According to Patricia Albjerg Graham,

We presented portions of this paper at the 27th International Congress

of Applied Psychology, July 11–16, 2010, Melbourne, Australia.

L. W. Howard � T. L.-P. Tang (&) � M. Jill Austin

Department of Management and Marketing, Jennings A. Jones

College of Business, Middle Tennessee State University,

Murfreesboro, TN 37132, USA

e-mail: Thomas.Tang@mtsu.edu

123

J Bus Ethics

DOI 10.1007/s10551-014-2084-0

former dean of the Harvard Graduate School of Education:

‘‘In no instance has academic achievement for all been

widely accepted as the primary purpose of schooling in

America’’ (Graham 2003, p. viii). Since President George

W. Bush signed into law, the No Child Left Behind legis-

lation, in 2001, teachers and administrators have been

accountable for students’ academic achievement, measured

by standardized test scores. Anecdotal evidence suggests

that some teachers attempt to teach students the test, which

causes students to be interested in one thing: What will be on

the test? Due to Hope Scholarship (based on state lottery to

fund college tuition—up to $6,000/year at a 4-year institu-

tion in Tennessee), many first generation college students

have entered public institutions of higher education in recent

years.

Researchers have attempted to identify individual dif-

ferences, training methods, approaches, motivational vari-

ables, and learning culture to enhance critical thinking

skills (Baron and Sternberg 1987; Hammer and Green

2011; Hung et al. 2010; Rodriguez 2009; Tang and Rey-

nolds 1993). Harvard Business School has used case

studies to teach MBA students for decades. Very little

research has investigated the effect of a case-based critical

thinking module on students’ critical thinking skills at the

undergraduate level.

These issues lead us to our three-fold purpose of this

study. The first aim is to explore the effect of a case-based

critical thinking module on university students’ critical

thinking skills (Watson–Glaser Critical Thinking Apprai-

sal, WGCTA or CTA for short, Watson and Glaser 1980).

Using a Solomon four-group design (Solomon 1949), we

assign 31 sessions of business courses randomly to four

groups, collect data from 659 students, and examine the

between-subjects differences (Table 1). Our second aim is

to explore within-subjects changes in students’ CTA scores

from pre- to post-test. Our third aim is to identify students’

abilities and motivational factors that contribute to these

changes in CTA. We collect students’ demographic

variables (age, gender, and race) and official objective

measures of abilities, aptitudes, or performance (ACT—

college admission test score and overall GPA) from the

university’s record office. We employed Internet (online)

surveys to collect students’ motivational goals (Van Ype-

ren 2006), learning modality (Dobson 2009), learning

styles (Felder and Silverman 1988), and values. We offer

the following discoveries. Critical thinking skills depends

on ones’ ability (can do), motivation (will do), and

opportunity (Boxall and Purcell 2007). The pre-test

(priming effect) improves students’ critical thinking skills,

with or without intervention (‘‘opportunity’’). Those who

have high abilities (ACT and GPA) improve their CTA

scores (‘‘ability’’). Positive performance goals and nega-

tive mastery goals enhance and decrease improvements,

respectively (‘‘motivation’’). We offer important theoreti-

cal and empirical contributions to improving critical

thinking skills.

Theory and Hypotheses

Critical Thinking

Bloom et al. (1956) created the six-tiered taxonomy of

cognitive complexity. Anderson and Krathwohl (2001)

revised the taxonomy using the following six verbs:

remember, understand, apply, analyze, evaluate, and cre-

ate. Most students focus on the first three parts of this

cognitive complexity. Critical thinking and creativity

depend on the three more advanced parts of cognitive

complexity: analyzing, evaluating, and creating.

For university professors, teaching critical thinking is an

important goal (Smith 2003). Scriven and Paul (1987)

stated: ‘‘Critical thinking is the intellectually disciplined

process of actively and skillfully conceptualizing, apply-

ing, analyzing, synthesizing, and/or evaluating information

gathered from, or generated by, observation, experience,

reflection, reasoning, or communication, as a guide to

belief and action. In its exemplary form, it is based on

universal intellectual values that transcend subject matter

divisions: clarity, accuracy, precision, consistency, rele-

vance, sound evidence, good reasons, depth, breadth, and

fairness’’ (http://www.criticalthinking.org/aboutCT/define_

critical_thinking.cfm). Critical thinking refers to higher-

order thinking that questions assumptions and has been

described as ‘‘thinking about thinking’’. We adopt Watson–

Glaser Critical Thinking Appraisal (WGCTA, Watson and

Glaser 1980), one of the oldest and most widely used

critical thinking measures, to assess students’ critical

thinking skills (Bernard et al. 2008). WGCTA has five sub-

domains: inferences, recognition of assumptions, deduc-

tions, interpretations, and evaluation of arguments.

Table 1 Solomon four-group design

Pre-test Intervention Post-test Sample size

CTA 1 CTA 2 n

Group 1 O1 X O2 276

Group 2 O3 X O4 312

Group 3 O5 17

Group 4 O6 54

2 9 2 ANOVA design

Intervention

Yes No

Pre-test Yes O2 O4

No O5 O6

L. W. Howard et al.

123

According to Bernard et al. (2008), it should be viewed as a

measure of general competency and that the subscales

should not be interpreted individually. Critical thinking

guides people to belief and action (Paul 1993) and focuses

on deciding what to believe or do and achieving goals.

Einstein described creativity as combinatorial play. It is

as if the mind is throwing a bunch of balls into the cog-

nitive space, juggling them around until they collide in

interesting ways. People must have sufficient time to create

the balls to juggle and devote to the actual juggling. If balls

that do not normally come near one another collide, the

ultimate novelty of the solution will be greater. Big ideas

take time. Harvard Business School Professor Teresa M.

Amabile (1998) stated: Creativity is not enough in busi-

ness. To be creative, an idea must also be appropriate—

useful and actionable. Creativity has three major compo-

nents: (1) expertise, (2) creative-thinking skills, and (3)

motivation. Innovation depends on ‘‘creativity’’ which is

the generation of those new and useful ideas. Encourage-

ment of creativity, autonomy or freedom, and resources

create stimulants to creativity, whereas pressures and

organizational impediments to creativity are the obstacles.

During downsizing, work environment stimulants to crea-

tivity decrease, while work environment obstacles increase

(Amabile 1988; Amabile and Conti 1999). Having rela-

tively unstructured, unpressured time to create and develop

new ideas may lead to creativity. Time pressure under-

mines creativity. Intrinsic motivation promotes creativity.

We discuss critical thinking skills from the perspectives of

expertise, creative-thinking skills, and motivation below

(cf. Amabile 1998). Performance depends on one’s ability

(can do) and motivation (will do) in a given context

(Bandura 1986; Schmidt and Hunter 1998; Semerci 2011).

We turn to factors related to can do next.

Expertise (Objective Measures)

Expertise contributes to improvements of critical thinking

skills. According to Leonard and Swap (2005), deep smart

is a form of experience-based expertise. It takes about

10 years of experience to develop expertise in one’s field.

Since we deal with university students, we turn to two

objective measures: ACT and GPA. ACT is a curriculum-

based measure for college admission, reflecting students’

abilities, aptitudes, achievements, and academic perfor-

mance, which contribute to critical thinking skills. High

ACT (math and science) scores contribute to students’

success in college. GPA also reflects students’ cumulative

academic achievements and success (course grade).

Asian American students have the highest average

composite score at 22.6, followed by Caucasian students at

22.1, American Indian/Alaska Native students at 18.9,

Hispanic students at 18.7, and African American students

at 17.0. These scores are lower, on average, for racial

minorities. Asian Americans tend to have strong emphasis

on academic performance and achievement, Protestant

Work Ethic, motivation, parents’ involvement, and stu-

dents’ effort as a part of their cultural values (Stevenson

1983; Tang 1990). Males have an average composite score

of 21.2, while females earn 21.0. Critical thinking skills are

associated with academic success (GPA) and fewer nega-

tive real world life events (Butler 2012). An oft-quoted

maxim states: The best predictor of future performance is

the past performance (Schmidt and Hunter 1998). People

with high abilities are more likely to improve their per-

formance than those without. Students with high ACT and

college GPA are likely to improve their critical thinking

skills than those without.

Case-Based Critical Thinking Module

We treat our case-based critical thinking module (Howard

2008) as a teaching tool to improve students’ critical thinking

skills. The objective of the module is to help students

understand the discipline and the logic of critical thinking by

demonstrating the mastery of subject matter content at all

levels necessary to build defensible and rational conclusions

and fulfill the assignment. This offers opportunities for

reflection, collaboration, and critical questioning both the

subject matter and the protocol (Kolb and Kolb 2005). We

developed a case study ‘‘rubric’’ with seven dimensions to

match the critical thinking training module.

Professors graded students’ case study reports regarding

their ability to identify: (1) critical issues in the case, (2) all

stakeholders, (3) theoretical bases in making decisions, (4)

alternatives of various solutions, (5) consequences of

solutions, (6) the decision making process, and (7) the

evaluation of consequences. They incorporated grades of

critical thinking case study as a part of the semester grade

for the course. Although the content of the case study was

not exactly the same, the process of teaching the critical

thinking module and the rubric were the same. Two small

pilot studies were conducted in the spring and fall semesters

of 2006. Each experiment involved students with or without

the training module in two small classes (with about 40

students each). Tentative results showed that students with

the critical thinking module improved their critical thinking

skills, whereas those without did not (Howard 2008).

The Matthew Effect and the Pygmalion Effect

Cohen and Levinthal’s (1990) absorptive capacity theory of

knowledge acquisition asserts that individuals with more

accumulated prior knowledge and strong problem-solving

skills are more likely to recognize and acquire new external

Critical Thinking

123

knowledge, put new knowledge in memory, exploit new

relevant information, recall the information, utilize it in new

settings, and be more creative than those without. Merton

(1968) discussed ‘‘the Matthew Effect’’ and the Nobel Prize

winners in science: The pattern of recognition skewed in

favor of the established scientists—the Nobel Prize winners.

Eminent scientists develop a great sense of taste and judg-

ment in seizing significant and important problems, focus on

not just problem-solving but ‘‘problem-finding’’, set their

sights high, display a degree of venturesome fortitude, take

risks, expand their access, maintain their conviction and

prolonged commitment to the issue, and become prophets

who can fulfill their own prophesy. In the US, Harvard,

Columbia, Chicago, MIT, Berkeley, Stanford, Yale, Cornell,

and Princeton have produced the most Nobel laureates

(http://en.wikipedia.org/wiki/List_of_Nobel_laureates_by_

university_affiliation). It creates rich-get-richer and poor-

get-poorer patterns of achievement (Stanovich 1986). Nobel

laureates provide an outstanding role model, instill a creative

fortitude, develop a warm working relationship, bestow a

supportive culture with respect and resources, and inspire

other scientists around them to become creative in organi-

zations (Barsade 2002; Staw and Barsade 1993). Following

these arguments, Gu et al. (2013) explored the relationship

between moral leadership and employee creativity, treated

employee identification with leader and leader–member

exchange (LMX) as two mediators, and collected data from

160 supervisor-subordinate dyads in China (average

age = 29.55). They demonstrated that the relationship

between moral leadership and employee creativity is medi-

ated by not only employee identification with leader but also

leader–member exchange (LMX). Further, employee iden-

tification with leader partially mediates the relationship

between moral leadership and leader–member exchange.

A SMART goal (Specific, Measurable, Ambitious,

Realistic, and Time-bound) becomes a powerful tool to

enhance performance. Setting a ‘‘visible’’ SMART goal may

greatly enhance people’s performance through not only the

Pygmalion Effect (professors expectations are the key to

students performance and development) but also the Galatea

Effect (students self-expectation will help them accomplish

their own goals) (Chen and Klimoski 2003; Eden and Ravid

1982; Tierney and Farmer 2004). Anecdotal evidence sug-

gests that setting a ‘‘visible SMART’’ goal and serving as a

role model (Tang and Liu 2012) may help students not only

enhance their performance and SAT scores but also get

accepted into one of the best universities.1 Recently, Latham

et al. (2010) discussed subconscious goals in the workplace:

when working adults were primed by a backdrop photograph

(a woman winning a race), they wrote significantly more

ideas for a brainstorming task than those without. Further,

employees in a call center raised significantly more money

during a work shift when primed with the same backdrop

photograph (a woman winning a race) than those without.

The priming effect changes behaviors.

In our present study, Solomon Four-Group design

involves a pre-test for Groups 1 and 2. The pre-test sensi-

tizes both students and professors and influences a response

to a later stimulus. Professors received training before the

start of this project. Their expectations of possible

improvement in critical thinking skills may lead to the

‘‘self-fulfilling prophecy’’ (Eden and Rynes 2003). Due to

repetition, or direct priming, later experiences of the same

stimulus will be processed more quickly by the brain. The

automatic activation effect is a pervasive and relatively

unconditional phenomenon. Following the priming effect

(pre-test) and the self-fulfilling prophecy, professors may

consciously and unconsciously promote the importance of

creativity in their courses. We assert that the pre-test helps

students perform better on the post-test.

The Solomon Four-Group design allows researchers to

investigate ‘‘between-subjects’’ differences regarding (1)

the main effect of the intervention—a case-based critical

thinking module (A), (2) the main effect of the pre-test—

CTA 1 (B), and (3) the interaction effect between the

intervention and pre-test (A 9 B) on the dependent vari-

able (CTA 2, post-test). We expect that the case-based

critical thinking module will have an impact on students’

post-test scores. The pre-test (the priming effect) may

enhance students’ post-test scores. Therefore, students with

the combination of the pre-test and the intervention have

the highest post-test scores.

Hypothesis 1 For between-subjects differences, there is a

significant interaction effect between intervention and pre-

test on students’ post-test score: Students with the combi-

nation of the intervention and the pre-test have the highest

CTA 2.

Theory of planned behavior (Ajzen 2001) suggests that

attitudes, social norm, and perceived behavioral control

predict behavior intentions which, in turn, predicts behav-

ior (Chen et al. 2013; Lemrova et al. 2013; Tang and Su-

tarso 2013). Recently, Tang (2014) explored students in a

Principles of Management course and collected data from

multiple sources and at multiple times. Contrary to

expectations, students (average age = 23.29) demonstrate

no significant changes in their perceptions of course work

and their personal values regarding making money and

making ethical decisions from Time 1 (before) to Time 2

(after studying business ethics). Monetary Intelligence

(MI) examines the relationships between money attitudes

1 Parents set a visible SMART goal: ‘‘Look at the Harvard sweatshirt

(your goal) on the wall. You can wear it when you are qualified to

wear it at Harvard’’ (the Pygmalion effect). It takes time to internalize

the vision, obtain good test scores, and get accepted into Harvard (the

Galatea effect).

L. W. Howard et al.

123

(affective love of money motive, stewardship behavior, and

cognitive meaning) and two theoretical appropriate out-

comes. The love of money motive is positively related to

their ‘‘personal values’’ toward making money, but nega-

tively related to making ethical decisions. Interestingly

enough, the negative love of money motive is positively

related to both making ethical decisions in the beginning of

a semester and final course grade. Age is significantly

related to course grade and making ethical decisions.

Gender (male) is positively related to making money, but

negatively related to making ethical decisions. From the

perspective of business ethics, getting Harvard, MIT, Yale,

and Princeton students to contemplate their own ethical

values by recalling the Ten Commandments or signing an

honor code eliminates cheating completely, while offering

poker chips doubles the level of cheating (Aquino et al.

2009; Ariely 2008; Tang 2012). Taken together, individu-

als’ behavior is caused by the interaction between the

person and the environment.

Motivation and Learning Styles

We turn to ‘‘within-subjects’’ changes and investigate

factors that contribute to students’ changes from the pre- to

the post-test. Since goals, values, and attitudes are related

to creativity, motivation and learning styles may contribute

to the improvement of their critical thinking skills

(Rodriguez 2009). Felder and Silverman (1988) studied

students’ different achievement goals for their academic

pursuits (mastery-, performance-approach, mastery-, and

performance-avoidance). Individuals with strong achieve-

ment motivation have high self-efficacy, and high perfor-

mance (Tang and Reynolds 1993). Materialistic students

have lower intrinsic mastery goals but higher extrinsic

performance goals (Ku et al. 2012). Materialistic students,

who (in the having mode) only ‘‘hear’’ and ‘‘memorize’’

words so that they can pass an exam, have lower perfor-

mance, a year later.

Learning modalities reflect preference in taking in

information: visual (V), auditory (A), reading–writing (R),

and kinesthetic (K). Some students have a single strong

preference; others may have multiple (multimodal) learn-

ing preferences. More female students have multimodal

learning preferences than males (Breckler et al. 2009). In

descending orders, females preferred visual learning

(46 %), aural/auditory (27 %), read/write (23 %), and

kinesthetic (4 %). Males preferred visual learning (49 %),

read/write (29 %), aural (17 %), and kinesthetic (5 %)

(Dobson 2009). Among modalities, critical thinking may

have a lot to do with visual modality (the highest), but very

little to do with kinesthetic modality. In sports, people

focus on movements of the body. Therefore, when learning

takes place by carrying out physical activities, people use

kinesthetic modality. Following Myers-Briggs type indi-

cator (Myers and McCaulley 1990); we focus on four

learning styles involving sensing (S), intuitive (I), active

(A), and reflective (R) (Felder and Silverman 1988).

Research shows that intuition was associated with Intro-

version. Intuition and perceiving scores were coherently

related to intelligence test scores.

Due to work experiences and actual learning in the real

world of work, non-traditional students, who are older than

the traditional college students, may have higher intrinsic

motivation to perform well and have better grades (Tang

2014). Females attend more lectures and achieve higher

grades in all assessments than males (Halsey et al. 1997;

Horton et al. 2012). Male Jordanian students outperformed

female students on critical thinking skills (Bataineh and

Zghoul 2006). Older male students outperformed younger

ones. Younger female students outperformed their older

counterparts. Students with higher GPAs scored better on

critical thinking measures than those without (Bataineh and

Zghoul 2006).

Hypothesis 2 For within-subjects changes, students

improve their critical thinking skills from CTA 1 (pre-test)

to CTA 2 (post-test).

Hypothesis 3 Students’ objective ability and perfor-

mance measures (ACT and GPA), demographic variables

(sex and age), and subjective measures (motivational goals

and learning modality) are related to CTA.

Method

Participants and Research Design

We conducted this study at a regional state university

located in the southeastern US with 936 full-time faculty

members and 25,000 students. This project was approved

by the Institutional Review Board and supported by Ten-

nessee Board of Regents Faculty Diversity Research Grant

for research materials, senior author’s released time, data

coding, and data analysis. The senior author of this paper

offered two 1-h training sessions to professors who volun-

teered to participate in this research project. He discussed

the research design, a critical thinking module, measure-

ment and administration of the Watson–Glaser critical

thinking appraisal, and the scoring rubric for the case study.

Since professors offered courses to junior and senior

students with different ‘‘contents’’, it was not practical to

adopt one case study for all these classes. Professors par-

ticipated in this study by offering single/multiple sessions

of these courses and/or single/multiple courses. We adop-

ted the Solomon Four-Group design, assigned each of these

Critical Thinking

123

31 sessions/courses (taught by 12 professors) to one of

these four groups randomly, and collected data in two

semesters. In order to investigate within-subjects changes,

we assigned more ‘‘sessions’’ randomly to Groups 1 and 2,

on purpose.

Professors spent approximately 4 h discussing the crit-

ical thinking module and the exact same rubric as a part of

the course, agreed to include students’ rubric scores as a

part of the total semester grade, and collected CTA data for

the pre- and/or post-measure(s) in class. Professors were

asked to teach their classes in a normal manner, except the

intervention and/or additional CTA measure(s). Professors

in Groups 2 and 4 (without intervention) did not use the

case study. Some students may have experienced the case-

study method in other courses.

Juniors and seniors in these selected sessions volun-

teered to participate in this study, signed a consent form,

and provided their student ID number. We used their ID

number to match data from three different sources: (1)

CTA measure(s) in class, (2) demographic variables, GPA,

and ACT scores from the records office, and (3) motiva-

tional goals, learning modality, and learning style from

Internet surveys. Our data from multiple sources help us

avoid the common method variance (CMV) bias (Podsak-

off et al. 2003). We protected students’ identities and

confidentiality, stored data in the first author’s office, and

debriefed students at the end of the semester.

Table 1 shows the Solomon Four–Group design and the

sample size. Among 659 undergraduate business students,

there were 390 (59.2 %) males and 247 (37.5 %) females

(male = 1, female = 0). We obtained data from 472

(71.6 %) Caucasian, 107 (16.2 %) African American, 27

(4.1 %) Asian or Pacific Islander, 11 (1.7 %) Hispanic, and

4 (.6 %) American Indian/Alaska Native. Due to the small

sample size for ethnic groups, we combined all non-white

(non-Caucasian) students into one group.

Measures

We adopted the 40-item short form Watson–Glaser Critical

Thinking Appraisal (Watson and Glaser 1980) (CTA) with

the following five sub-domains: inferences (7 items), rec-

ognition of assumptions (8 items), deductions (9 items),

interpretations (7 items), and evaluation of arguments (9

items) and collected CTA measure(s) in class (Barnett and

Francis 2012). With one correct answer for each item, the

maximum score is 40. Since WGCTA’s subscales should

not be interpreted individually (Bernard et al. 2008), we

adopt the total score as a measure of general competency.

We obtained students’ official age, gender, race, ACT-

English, ACT-Math, ACT-Reading, ACT-Composite,

overall GPA, and total credit hours earned from the uni-

versity’s record office.

We employed online surveys to collect data using very

well established scales: first, there are six items for moti-

vational goals; each compares two of the four scenarios:

mastery-, performance-approach, mastery-, and perfor-

mance-avoidance (Van Yperen 2006). Participants select

one from each pair. Here are two examples: (1) to perform

better than the ‘‘average’’ student vs. not to perform worse

than the ‘‘average’’ student, (2) to perform better than my

usual level vs. not to perform worse than my usual level. We

tallied the sum of these four choices. Second, for learning

modality (VARK), students can circle more than one

answer (you learned best by visual (V), auditory (A),

reading-writing (R), and kinesthetic (K) (Dobson 2009). We

tallied the number of choices selected for 13 items and had a

score for all four modalities. Third, the 22-item learning

styles involve sensing (S), intuitive (I), active (A), and

reflective (R) (Felder and Silverman 1988). Participants

pick one option from each item: Item 1: I understand

something better after I (1) try it out (A), or (2) think it

through (R); Item 2: I would rather be considered (1) real-

istic (S), or (2) innovative (I). These measures were not five-

point Likert-type scales. We cannot calculate Cronbach’s

alpha of our variables (Tang and Austin 2009).

Results

Descriptive Statistics

Table 2 shows: all ACT scores were significantly related to

students’ pre-test (CTA 1) and post-test (CTA 2) scores and

total GPA. The ACT Composite score (21.93) in this study

was slightly higher than the overall national average (21.1)

in 2008. Pre-test scores were significantly related to post-

test scores, supporting the reliability of CTA in general.

Male students tended to have lower GPA but higher pre-

test scores than females. White students tended to have

higher ACT scores and pre-test and post-test scores than

their non-white counterparts. Older students tended to have

lower ACT scores and higher total credit hours than their

younger counterparts.

We investigated five objectives variables (i.e., ACT

English, math, reading, and composite and overall GPA)

across four groups using a multivariate analysis of variance

(MANOVA). We found no significant differences across

four groups (MANOVA: F = 1.29, p = .203, Wilks’

lambda = .961, partial eta square (effect size) = .013). Our

random assignment was successful regarding these objective

measures. This gives us confidence examining the possible

(1) between-subjects differences across four groups using

MANOVA/ANOVA (Huberty and Olejnik 2006), (2)

within-subjects changes using paired samples t tests, and (3)

L. W. Howard et al.

123

factors contributing to the differences using regression and

SEM in three separate steps.

Step 1: Between-Subjects Differences

Our first objective is to explore effects of an intervention and

a pre-test on university business students’ critical thinking

skills (CTA 2 scores). Results of our 2 9 2 analysis of

variance (ANOVA) showed that the main effect of critical

thinking module (F(1, 505) = 1.107, p = .293, partial eta

squared = .002), the main effect of pre-test (F(1,

505) = 1.470, p = .226, partial eta squared = .003), and

the interaction effect between the two on CTA 2 (F(1,

505) = 1.459, p = .228, partial eta squared = .003) all

failed to reach significance. Our results did not support

Hypothesis 1. In addition, one-way analysis of variance

(ANOVA) across four groups showed that there were no

significant differences in post-test scores (F(3, 505) = .664,

p = .574, partial eta squared = .004): Group 1: 25.64

(SD = 5.26, n = 210), Group 2: 25.52 (SD = 5.19,

n = 228), Group 3: 23.76 (SD = 4.59, n = 17), and Group

4: 25.52 (SD = 5.89, n = 54). We compared post-test

scores between Groups 1 and 2 while controlling pre-test

scores (ANCOVA) found no significant difference

(F(1,383) = .013, p = .910, partial eta squared = .000).

Gender and Race

MANOVA results showed that there were significant dif-

ferences in student achievement scores (ACT-English,

ACT-Math, ACT-Reading, ACT-Composite, and Overall

GPA) across race (white vs. non-white) (F(5,

481) = 6.918, p = .001, Wilks’ lambda = .933, partial eta

squared = .067). White students had higher scores than

non-white students in ACT English (white = 22.42 vs.

non-white = 20.20), ACT-math (21.86 vs. 20.09), ACT-

reading (22.66 vs. 20.48), and ACT-composite (22.43 vs.

20.45), and overall GPA (2.94 vs. 2.73). Significant dif-

ferences in achievement scores (ACT-English, ACT-Math,

ACT-Reading, ACT-Composite, and Overall GPA) across

gender (MANOVA: F(5, 481) = 9.854, p = .001, Wilks’

lambda = .907, partial eta squared = .093) revealed that

females had higher scores than males on ACT English

(female = 22.40 vs. male = 21.53) and overall GPA (3.00

vs. 2.82).

Due to gender and race differences, we applied 2 9 2

ANOVAs to examine the same effects on CTA 2 across

gender and race separately. The two main effects (inter-

vention and pre-test) and the interaction effect on CTA 2

failed to reach significance for male students (F(1,

297) = .518, p = .472, partial eta squared = .002; F(1,

297) = .832, p = .363, partial eta squared = .003; F(1,

297) = 1.487, p = .224, partial eta squared = .005) and

for female students (F(1, 193) = .548, p = .460, partial eta

squared = .003; F(1, 193) = .597, p = .441, partial eta

squared = .003; F(1, 193) = .548, p = .460, partial eta

squared = .003). The two main effects and the interaction

effect were not significant for white students (F(1,

365) = 1.396, p = .238, partial eta squared = .004; F(1,

365) = 1.815, p = .179, partial eta squared = .005; F(1,

365) = 1.233, p = .268, partial eta squared = .003) and

their non-white counterparts (F(1, 136) = .006, p = .941,

partial eta squared = .000; F(1, 136) = .006, p = .941,

partial eta squared = .000; F(1, 136) = .087, p = .769,

partial eta squared = .001). All ANOVA results for the

whole sample and for gender and race failed to reach sig-

nificance. Next, we identify possible within-subjects

changes and focus on Groups 1 and 2 exclusively.

Table 2 Means, standard deviations, and correlations of major variables

Variable M SD 2 3 4 5 6 7 8 9 10 11

1. Age 23.27 4.58 .07 .04 -.13** -.10* -.11* -.14** -.05 .18** .04 .05

2. Gender .61 .49 .08* -.10* .07 -.06 -.00 -.19** .01 .14** .07

3. Race .72 .45 .21** .21** .21** .25** .12* .10* .24** .28**

4. ACT English 21.88 4.42 .65** .71** .89** .41** .09 .45** .45**

5. ACT Math 21.41 3.75 .54** .82** .41** .04 .40** .41**

6. ACT Reading 22.11 4.41 .86** .36** .02 .46** .45**

7. ACT Composite 21.93 3.46 .44** .04 .51** .50**

8. Total GPA 2.91 .56 -.00 .22** .27**

9. Total Credit Hr. 100.79 35.31 .13** .12**

10. CTA 1 25.15 5.39 .70**

11. CTA 2 25.51 5.27

N = 659. Gender: Male = 1, Female = 0; .61 = 61 % male. Race: White = 1, non-White = 0; .72 = 72 % White/Caucasian. CTA 1: critical

thinking appraisal pre-test. CTA 2: critical thinking appraisal post-test. * p \ .05, ** p \ .01

Critical Thinking

123

Step 2: Within-Subjects ‘‘Changes’’ from Pre- to Post-

Test

Results of paired samples t test (Table 3) suggested that overall,

students (Groups 1 and 2 combined) had significantly better post-

test scores (25.82) than their pre-test scores (25.03)(t(385) = -

2.780, p = .001). For students with the intervention, their post-

test scores (25.83) were better than their pre-test scores (25.08)

(t(188) = -2.323, p = .021). For those without the interven-

tion, their scores improved more significantly from pre-test

(24.99) to post-test (25.81) (t(196) = -3.091, p = .002).

Results supported our Hypothesis 2. Due to possible differences

regarding gender and race, we conducted additional analyses.

With or without the intervention, female students improved their

critical thinking skills (t(143) = -4.347, p = .000), but male

students did not (t(240) = -1.431, p = .154). With or without

the training, white students improved their critical thinking skills

(t(288) = -3.520, p = .001), but non-white students did not

(t(96) = -1.461, p = .147).

Step 3: Factors contributing to Changes of Critical

Thinking Skills

Changes (Better Vs. Worse)

Comparing post-test with pre-test, only 35.5 % of our

students performed ‘‘equally well or better’’, whereas

64.5 % of them performed ‘‘worse’’. We used VARK

(visual (V), auditory (A), reading-writing (R), and kines-

thetic (K), Dobson 2009), academic achievement goals,

and academic motivation to predict post-test scores and

examined the possible differences in these variables

between better performers and worse performers (Table 4).

Students wanted to ‘‘perform better than the average stu-

dent’’ (negative Performance Avoidance) improved their

critical thinking scores (R = .158, R2 = .025, DR2 = .025,

DF (1,158) = 4.021, p = .047, beta = -.158, effect

size = .026). Cohen’s (1988) f2 effect sizes of .02, .15, and

.35 are termed small, medium, and large, respectively

(Courville and Thompson 2001). Those expected to per-

form worse than their usual level (negative ‘‘Mastery

Approach’’), used reflective style in their thinking, and

kinesthetic cognitive style performed worse.

From Pre- to Post-Test

We developed a parsimonious structural equation model

(SEM) to examine the relationship between pre- and post-

test while controlling for gender, age, ACT, GPA, learning

modality, and motivational goals (Groups 1 and 2 com-

bined) (v2 = 126.29, df = 48, p \ .001, v2/df = 2.63,

IFI = .98, TLI = .95, CFI = .98, RMSEA = .05) (Fig. 1).

Age and ACT were significantly related to both CTA 1

(age = .16, ACT = .53) and CTA 2 (.08, .18). Gender

(male) was significantly related to pre-test (.11), whereas

GPA was related to post-test (.09) (ps \ .05). The rela-

tionship between ‘‘visual modality’’ and CTA 2 (.07)

approached significance (p = .087). The pre-test was sig-

nificantly related to post-test (.59).

Multi-group SEM

To examine within-subjects changes and between-subjects

differences simultaneously, we treat intervention (with

(Group 1) vs. without (Group 2)) as a moderator in a multi-

group SEM (v2 = 177.47, df = 96, p \ .001, v2/

df = 1.85, IFI = .98, TLI = .94, CFI = .97,

RMSEA = .04). Results for Group 1 (with intervention)

showed that both age and ACT were significantly related to

pre- (.17, .59) and post-test (.11, .28). Gender (male) was

only related to pre-test (.16). Kinesthetic modality was

mildly/negatively related to CTA 1 (-.12, p = .071). The

pre-test was significantly related to post-test (.48). Group 2

(without intervention) results showed that age and ACT

were both related to the pre- (.18, .47) and the post-test

(.14, .14). GPA was only related to the post-test (.18). Pair-

wise comparison of the parameters of the pre- to post-test

relationship (within-subjects changes) for Groups 1 (.48)

and 2 (.64) (between-subjects differences) showed that the

difference approached significance (Z = 1.824, p [ .05).

Table 3 Paired samples t test results

CTA

1

CTA

2

t df p

Groups 1 and 2

combined

25.03 25.82 -3.780 385 .000***

Group 1 (with intervention)

Whole 25.08 25.83 -2.323 188 .021*

Gender

Female 23.94 25.10 -2.425 71 .018*

Male 25.83 26.31 -1.118 115 .266

Race

White 25.84 26.65 -2.202 140 .029*

Non-White 22.83 23.42 -.855 47 .397

Group 2 (without intervention)

Whole 24.99 25.81 -3.091 196 .002**

Gender

Female 24.00 25.76 -3.721 71 .000***

Male 25.56 25.83 -.890 124 .375

Race

White 25.93 26.81 -2.805 147 .006**

Non-White 22.14 22.78 -1.293 48 .202

N = 659. Group 1: n = 276, Group 2: n = 312. CTA 1: pre-test.

CTA 2: post-test

* p \ .05, ** p \ .01, *** p \ .001

L. W. Howard et al.

123

Thus, the relationship between the pre-test and the post-test

is slightly higher for Group 2 than for Group 1. Taken

together, results in this section supported Hypothesis 3.

SEM and Multi-group SEM for the Change of CTA

We used the same variables to examine the change score

from the pre-test to the post-test (Groups 1 and 2

combined) (v2 = 123.50, df = 48, p \ .001, v2/df = 2.57,

IFI = .97, TLI = .95, CFI = .97, RMSEA = .05). Only

GPA was significantly related to CTA change (.12)

(Fig. 2). Similarly, in a multi-group SEM (v2 = 175.72,

df = 96, p \ .001, v2/df = 1.83, IFI = .97, TLI = .95,

CFI = .97, RMSEA = .05), only GPA was related to the

change score for Group 2 only (without the training mod-

ule) and other results were non-significant.

Table 4 Step-wise multiple regression analysis

Variable R R2 DR2 DF df p Beta Effect size

Same or better performer (Groups 1 and 2 combined, CTA 2 – CTA 1 [ 0)

1. Performance avoidance .158 .025 .025 4.021 1,158 .047 -.158 .026

Worse performer (Groups 1 and 2 combined, CTA 2 – CTA 1 \ 0)

1. Mastery approach .259 .067 .067 12.369 1,172 .001 -.275 .072

2. Reflective .363 .132 .065 12.768 1,171 .010 .255 .070

3. Kinesthetic .393 .155 .023 4.575 1,170 .034 .151 .024

Fig. 1 A model of critical

thinking skills: from pre-test to

post-test

Critical Thinking

123

Discussion

There is no ‘‘between-subjects difference’’ in this study.

The two main effects of the case-based intervention and

pre-test as well as the interaction effect (interven-

tion 9 pre-test) have no significant impact on critical

thinking score at the end of the semester. However, sig-

nificant ‘‘within-subjects changes’’ show that given the

‘‘opportunity’’, students improve their critical thinking

skills from pre- to post-test, with or without the cased-

based critical thinking module. With or without the inter-

vention, both female and white (Caucasian) students

improve their critical thinking scores, but their male and

non-white counterparts do not. Critical thinking skills

depend on both ‘‘can do’’ (ability) and ‘‘will do’’ (moti-

vation) in a given situation. Those who have high ‘‘per-

formance goals’’ (negative Performance Avoidance)

improve their CTA scores from Time 1 to Time 2. How-

ever, students with negative ‘‘Mastery Approach’’ goals,

reflective style, and kinesthetic modality decrease their

CTA scores form Time 1 to Time 2. Furthermore, age and

ACT scores are significantly related to both CTA scores at

Time 1 and Time 2. Gender (male) was significantly

related to CTA at Time 1 (pre-test), whereas GPA was

related to CTA at Time 2 (post-test). The pre-test was

significantly related to post-test. We offer the following

theoretical, empirical, and practical implications. We focus

on the ‘‘can do’’ part first.

Can do

Students’ age and ACT are significantly related to both the

pre- and the post-test. Gender (male) is related to the pre-test,

whereas GPA is related to the post-test. These findings

provide several important clues: ACT scores are more

strongly related to critical thinking skills than GPA, overall.

Age is related to critical thinking skills. Females and older

students may take their academic work more seriously than

Fig. 2 A model for the change

of critical thinking skills

L. W. Howard et al.

123

their counterparts. Males tend to have higher pre-test scores

than females; but gender is not related to post-test. Since

females improve their critical thinking skills, but males do

not, it is plausible that many male students are careless in

taking their post-test because it is not a part of their final

semester grade. Only students with high GPAs seem to take it

seriously and have high post-test scores, reflecting the Gal-

atea effect. GPA is significantly related to the changes of

critical thinking skills. These findings reinforce the idea that

high GPA students care about the improvement of critical

thinking skills. ACT scores (English, math, reading, and

composite) are related to overall GPA and critical thinking

scores (pre- and post-test). Pre-test scores are significant

predictors of post-test scores. Only 35.5 % of these students

improve their critical-thinking skills in one semester, but

64.5 % of students perform worse, indirectly supporting the

2005 National Assessment of Educational Progress.

Will Do

On the one hand, those who have a strong competitive spirit of

‘‘outperforming the average student’’ (online survey) actually

improve their critical thinking scores (in class). On the other

hand, for those who do not expect to master their skills, or

expect to ‘‘perform worse than their own usual level’’ (nega-

tive ‘‘Mastery Approach’’ goals), their performance of critical

thinking skills decreases. Our results support the theories of

goal setting (Locke and Latham 1990), self-efficacy (Bandura

1986; Tang and Reynolds 1993), and creativity (Amabile

1998). Professors, parents, and executives must expect stu-

dents and employees to succeed in academic or organization

settings, respectively. Eden and Rynes (2003) stated: ‘‘If you

expect success, your likelihood of achieving it is increased.

You become a prophet who can fulfill your own prophesy’’ (p.

683). This works in the opposite direction, too: Don’t expect to

fail (Eden and Rynes 2003)! Taken together, both ‘‘can do’’

(ability) and ‘‘will do’’ (motivation) contribute to performance

improvement (Merton 1968). Reflective thinking style and

kinesthetic cognitive style contribute to their worse perfor-

mance. Critical thinking is not related to kinesthetic modality.

Visual learners may have the potential to do better on their

post-test. Our results also support the notion that students’

attitudes (online survey results) predict their own behaviors in

class (changes of their CTA scores), both positively and

negatively. The theory of planned behavior works not only in

the area of academic success and achievement but also in the

field of business ethics (e.g., Tang and Chen 2008; Tang and

Liu 2012; Tang and Sutarso 2013).

To retain2 students, improve their levels of ‘‘can do’’,

‘‘will do’’, and ‘‘opportunity’’ simultaneously, and facilitate

their success in higher education, professors need to

remove all road blocks/barriers, offer additional second-

and third-round training in subsequent curriculum, and

provide additional resources/assistance (advising, tutoring,

and mentoring) to improve their abilities (can do). It is

important to provide counseling to cultivate a strong cul-

ture for success and personal motivation (self-esteem and

self-efficacy) (will do) to enhance their critical thinking

skills. Educators’ most exciting joy of this learning and

education process is to help students ‘‘improve’’ their skills

and help them ‘‘realize’’ their potential. The easy way out

is to do nothing.

The Pygmalion Effect and the Galatea Effect

We treat pre-test as our priming effect. Our results, the

‘‘can do’’ and ‘‘will do’’ parts mentioned above, support the

Pygmalion effect and the Galatea effect. People are con-

sciously and unconsciously influenced by their external

environment and learning culture in organizations (Ban-

dura 1986; Hung et al. 2010; Liu and Tang 2011; Milgram

1974). Scholars and professionals may practice the Pyg-

malion effect and Galatea effect, set a ‘‘visual SMART

goal’’, and apply techniques, such as a backdrop photo-

graph (a woman winning a race) to boost employees’ self-

efficacy (Tang et al. 1987a, b), enhance creativity (generate

more ideas for a brainstorming task), performance (raise

more money in a call center) (Latham et al. 2010), and

solve real work-related problems (Tang et al. 1987a, b,

1989). These changes may enhance students/managers’

abilities and motivation to accept expectations which in

turn, may promote their own self-expectation to become

successful individuals.

The best predictor of future performance is the past

performance (‘‘can do’’). To enhance people’s critical

thinking skills, creativity, and innovation in organizations,

managers must select individuals with high objective per-

formance measures and abilities or special expertise in

content area (not just the ones with diplomas). A diploma

in higher education only signals existing human capital.

However, specific objective measures of higher education

(ACT, GPA, and CTA) produce the signal (diploma) and

endow an individual with human capital which is accu-

mulated through experience and education (Kroch and

Sjoblom 1994; Serneels 2008). Students’ academic per-

formance is important for the allocation to job levels and

for jobs in dynamic science-based industries, in particular

(Luo et al. 2009).

Unlike personality and attitudinal scales, most people

cannot fake good on an objective measure (ability, aptitude,

or achievement measure), but can fake bad quite easily.

Students’ rubric scores are included as a part of the class

grades. Since students’ pre- and post-test scores do not count

2 Only about 48 % of students graduated from the university within

5 years.

Critical Thinking

123

as a part of the grades, 65 % of them perform worse on the

post-test. One possible explanation is that some of them did

not pay enough attention and/or exert a lot of effort when

they took the CTA measure at the end of the semester. These

students’ behavioral patterns may be different from fulltime

employees (Gu et al. 2013). Employees’ work activities and

performance may have significant impacts on their perfor-

mance appraisals which, in turn, may have an impact on their

compensation and promotion opportunities (Milkovich et al.

2014). In the competitive world of work, educators, man-

agers, and practitioners must increase their awareness of

critical thinking skills, foster work environment stimulants,

reduce work environment obstacles to creativity (Amabile

and Conti 1999), create a sea change of creative cultures in

academic and business environments, provide opportunities

for success, set higher goals, motivate all individuals to

perform better, and improve their critical thinking skills.

These issues deserve further attention in future empirical

research.

Tang and Tang (2010) identified good apples and bad

apples, using the propensity to engage in unethical behavior

measure (Tang and Chen 2008). After the ethics intervention

(a chapter on business ethics and corporate social responsi-

bility), good apples became better, but bad apples became

worse. After the ethics intervention, students have no signif-

icant changes in their perceptions of course work and their

personal values regarding making money and making ethical

decisions (Tang 2014). Females have higher ethical decision

making orientation than males, consistently overtime. From

the theory of Monetary Intelligence perspective, individuals

with negative love of money have high priority to make ethical

decisions and make the grade (final academic achievement).

These findings support the notion that materialistic students

have lower intrinsic mastery goals but higher extrinsic per-

formance goals and lower grades a year later (Ku et al. 2012).

Thus, the materialistic values, peer pressures, and the subject

social norm in the society may have an important impact on

students’ academic interests and behaviors. Taken together

(Ku et al 2012; Tang 2014; Tang and Tang 2010, and this

study), implementing ethics intervention to enhance ethical

intentions (Tang and Chen 2008) and ethical decision making

may be more difficult than improving critical thinking skills.

Age is also related to both high CTA scores and making ethical

decisions. When students become more mature, they may

become wiser and more ethical individuals in their academic

journey. Future researchers may want to adopt the theoretical

framework of Monetary Intelligence (Tang and Sutarso 2013)

in studying critical thinking skills.

Limitations

We acknowledge that we collected our data from a sample

of students in one semester and from only one state

university. We do not suggest that results of our student

sample can be generalized to students in other universities,

in other regions, cultures, countries, or employees in

organizations, i.e., the issue of external validity. We

obtained convenient data from students in 31 sessions

(classes) taught by 12 professors. We assigned each ses-

sion/class to one of the four groups at random with more

sessions in Groups 1 and 2, in particular, in order to

identify possible within-subjects differences. Our random

assignment of sessions to these four groups was reasonably

successful based on our MANOVA results of students’

ACT and GRE scores. Our sample size for the whole study

was reasonable (N = 659) in experimental studies.

We believe that our case-based critical thinking inter-

vention was successfully implemented by professors who

volunteered to participate in this study. Although the

content of the intervention was not exactly the same due

to course materials, the process and the rubric were

exactly the same. Our non-significant between-subjects

differences might be caused by students’ exposure to the

case studies in other courses already: The case study

training module was not completely new to some stu-

dents. Future researcher may want to use one single

training module to control the intervention of the study.

Although researchers with larger samples, fewer predictor

variables, and large effect sizes may have less sampling

error using regression approach, readers should read our

results with caution.

In this study, students’ ACT composite score, 21.93, is

slightly higher than the national average (21.1). The pre-

test (priming effect) sensitizes both students and profes-

sors’ awareness of critical thinking skills. Our pre-test may

provide important vocabulary, serve as a practice, help

students remember, understand, apply, analyze, evaluate,

and create new learning, knowledge, and critical-thinking

skills which enhance their post-test scores. Due to the self-

fulfilling prophecy, both students and professors may

expect to have higher performance from the pre-test to the

post-test. These factors may contribute to not only our non-

significant interaction effect on between-subject differ-

ences but also significant within-subjects changes.

We obtained data from three different sources: (1)

critical thinking skills in classes, (2) objective measures

and demographic variables from the university’s record

office, and (3) students’ online surveys. Our results are not

artificially inflated by the common method variance bias.

Although students improve their critical thinking skills,

with or without intervention, the case study interven-

tion and the pre-test (the priming effect) may serve one

additional and important function—the ‘‘opportunity’’ to

learn. When working adults are primed by a backdrop

photograph—a woman winning a race, their actual per-

formance improves significantly (Latham et al. 2010).

L. W. Howard et al.

123

Future researchers need to explore this issue carefully,

investigate students and faculty’s thinking styles and sub-

jective perceptions regarding critical thinking skills

empirically, control variables meticulously, and test our

present findings among students in other majors, colleges,

institutions, and regions.

Conclusion

We find no between-subjects difference. Our novel, sig-

nificant within-subjects changes from the pre-test to the

post-test suggest: Students improve their critical thinking

skills, with or without intervention (‘‘opportunity’’). Criti-

cal thinking skills are significantly related to students’

abilities (ACT) and academic achievements (GPA in col-

lege) (‘‘ability’’). Overall, only 35.5 % of students

improved their critical thinking skills in one semester.

Further, only female and white students enhance their

performance, but male and non-white students do not. ACT

scores and age are related to both pre-test and post-test.

Students with high GPA are concerned about their post-test

and improve their critical thinking scores. Those who have

a strong competitive goal orientation of outperforming the

average student perform better, whereas those who expect

to perform worse than their own usual level of performance

and use reflective and kinesthetic learning styles perform

worse (‘‘motivation’’). Although many students obtain

their diploma (signaling existing human capital), only those

who have both the ability (can do) and motivation (will do)

and make the best use of all the resources (opportu-

nity) improve their critical thinking skills and reveal their

true human capital. To compete successfully in the twenty-

first century, scholars and executives in MNCs need to set a

goal and provide opportunities to enhance critical thinking

skills for students in higher education and managers in the

work place. Our results provide important implications for

scholars and executives in the competitive world market

and make significant contributions to the studies of higher

education and ethics.

Acknowledgments The authors would like to thank the Tennessee

Board of Regents (TBR) Faculty Diversity Research Grant for the

financial support of this research project. We would like to thank

Laura Buckner, Kimball Bullington, David Foote, Amy Hennington,

Daniel Morrell, Richard Mpoyi, Donald Roy, Earl Thomas, Joe G.

Thomas, Cliff Welborn, Rachel Wilson, and Yu Amy Xia for their

participation in this project, Toto Sutarso, Jo Ann Nolan Batson,

Rachel Clark, Whitney Sewell, Ashleigh Raby, Caitlin Lee, and Ivy

Strohm for their assistance, Thomas Brinthaupt for his encourage-

ment, and Ruth Howard for her support. This paper is dedicated to

Larry W. Howard, the principal investigator (PI) of this TBR project,

who passed away on March 12, 2009. This content represents the

research efforts of the authors and does not represent the views of the

Tennessee Board of Regents.

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