BUSA 208 C20W: Project Paper Instructions and Scoring CriteriaOverview:
Each student will prepare a case study reflecting a “real life” statistical issue. The issue
should relate to a business topic of the student’s choosing and approved by the instructor.
As part of the project each student will be required to locate two peer-reviewed, businessrelated articles. Each student will then have to design an offshoot study around the same
topic as the one used in the articles. Students will not have to actually conduct a study,
only design a study.
Each student will be required to turn in a 5-7 page paper that discusses their study and
should reflect analytical thinking and application of course material.
The project will be worth one hundred (100) points.
Articles:
Although not a requirement, I suggest you utilize the CSI Library to locate your businessrelated research articles. The CSI Library is located here: http://www.csi.edu/library/.
Once you’re on the CSI Library homepage, the “Find Articles” section is found along the
left-hand side of the screen. The “ERIC” or “PsycARTICLES” databases would both be
good places to look for articles; however, feel free to peruse any of the other sections as
long as the articles you find are: 1) business-related, 2) from a peer-reviewed publication
from within the past twenty years, and 3) use research/statistical methods to address a
problem. If you have any questions about an article you found or are having difficulty
finding articles, please email me, call me, or stop by my office and I will assist you. I’ve
included a sample article of the type/quality I’m looking for next to the project
instructions on Canvas; you may not use this article for your project, it’s for illustrative
purposes only. Please have me approve your articles before beginning on your project!
Paper:
Each student will be required to submit a project paper during finals week (due by 10:00
p.m. on Thursday, July 25th). No late papers will be accepted.
The paper should be between 5-7 pages, double-spaced with 1 inch margins along the top,
bottom, left, and right sides, and APA style. The type should be either Arial with 11-point
font or Times News Roman with 12-point font.
1
BUSA 208 C20W: Project Paper Instructions and Scoring Criteria
The paper is worth 100 points and should be organized as follows:
Introduction:
In 2-3 pages, describe the purpose of your project
(i.e. what was the topic, why did you pick this topic,
what do the research articles that you read indicate
about the topic, how did they guide you to your
hypothesis, etc.).
Method:
In 1-2 pages, using what you learned in this class,
describe how you would conduct your study (i.e.,
who would be the participants, what materials
would you use, how would you conduct your study,
etc.).
Analysis Plan:
In ½-1 page, using what you learned in this class,
describe what statistical test(s) you would use to
analyze your data (i.e. if you would do a survey how
would you analyze the data, if you would do an
experiment how would you analyze the data, etc.).
Discussion:
In 2-3 pages, describe what the hypothetical results
of the study would mean for real-world businesses
(i.e. what could other business leaders potentially
learn from your study, etc.).
40 points
20 points
10 points
30 points
2
Journal of Applied Psychology
2008, Vol. 93, No. 5, 1139 –1146
Copyright 2008 by the American Psychological Association
0021-9010/08/$12.00 DOI: 10.1037/0021-9010.93.5.1139
Exploring the Handshake in Employment Interviews
Greg L. Stewart and Susan L. Dustin
Murray R. Barrick
University of Iowa
Texas A&M University
Todd C. Darnold
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Creighton University
The authors examined how an applicant’s handshake influences hiring recommendations formed during
the employment interview. A sample of 98 undergraduate students provided personality measures and
participated in mock interviews during which the students received ratings of employment suitability.
Five trained raters independently evaluated the quality of the handshake for each participant. Quality of
handshake was related to interviewer hiring recommendations. Path analysis supported the handshake as
mediating the effect of applicant extraversion on interviewer hiring recommendations, even after
controlling for differences in candidate physical appearance and dress. Although women received lower
ratings for the handshake, they did not on average receive lower assessments of employment suitability.
Exploratory analysis suggested that the relationship between a firm handshake and interview ratings may
be stronger for women than for men.
Keywords: handshake, employment interviews, first impressions
the nonverbal act of shaking hands and employment interview
evaluations.
In this article, we empirically examine the role of the handshake
in employment interviews. We first seek to determine whether
quality of the handshake does indeed correspond with interviewer
assessments. We then explore the nature of what is being conveyed
through the handshake by examining relationships between the
handshake and personality. We also assess the effect of potential
gender differences in handshaking.
A firm handshake is often identified as an aspect of nonverbal
communication that has a critical influence on impressions formed
during employment interviews. Indeed, a recent search of the
Internet revealed nearly a million listings that detailed the importance of the handshake and gave advice about the proper way to
shake hands during an interview. In spite of seemingly widespread
acceptance of the important role the handshake plays in interview
success, empirical research examining the handshake in employment interviews is lacking.
Nonverbal cues other than the handshake, such as eye contact
during discussions and smiling, have been shown to have a critical
influence on interview assessments (DeGroot & Motowidlo,
1999). Although not studied in the interview context, the ubiquitous prevalence of the handshake at both the beginning and the end
of interviews suggests that nonverbal cues communicated through
the shaking of hands may convey important information about job
applicants. The handshake may specifically convey information
about an individual’s personality, as early research suggested a
traitlike relationship between the handshake and personality
(Chaplin, Phillips, Brown, Clanton, & Stein, 2000; Vanderbilt,
1957). In short, good handshakes are believed to communicate
sociability, friendliness, and dominance, whereas poor handshakes
may communicate introversion, shyness, and neuroticism (Chaplin
et al., 2000). Yet, research has not explored relationships between
Is Handshake Quality Related to Ratings in Employment
Interviews?
In the interview context, nonverbal behaviors are assumed to
convey useful information (Gifford, Ng, & Wilkinson, 1985;
Schlenker, 1980). The category of nonverbal cues can be broadly
defined as cues, other than the content of responses, or demographic differences like sex and race (Parsons & Liden, 1984).
Nonverbal behaviors commonly thought to be important during an
interview include eye contact, smiling, posture, interpersonal distance, and body orientation (Forbes & Jackson, 1980; Imada &
Hakel, 1977; Motowidlo & Burnett, 1995; Young & Beier, 1977).
These behaviors are assumed to influence interviewer reactions,
which in turn result in attributions of applicant characteristics such
as communication ability, intelligence, and self-confidence (DeGroot & Motowidlo, 1999; McGovern & Tinsley, 1978).
Given that a handshake typically occurs in the interview setting,
it is surprising that researchers have not looked at the role this form
of tactile nonverbal communication may play in the interview
setting. The handshake is a nonverbal touch behavior that can
convey an “immediacy” dimension in interviews (Imada & Hakel,
1977). Immediacy is an interaction between two individuals that
involves close physical proximity and/or perceptual availability
(Mehrabian, 1972). It has been theorized that greater immediacy
Greg L. Stewart and Susan L. Dustin, Department of Management and
Organizations, University of Iowa; Murray R. Barrick, Department of
Management, Texas A&M University; Todd C. Darnold, Marketing and
Management Department, Creighton University.
Correspondence concerning this article should be addressed to Greg
L. Stewart, Department of Management and Organizations, Tippie
College of Business, University of Iowa, Iowa City, IA 52242. E-mail:
greg-stewart@uiowa.edu
1139
RESEARCH REPORTS
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1140
leads to attributions of greater liking (Imada & Hakel, 1977;
Mehrabian, 1967). Because the act of shaking hands requires
physical contact, the handshake should influence immediacy evaluations. Physical touch is generally associated with warmth, closeness, caring, and intimacy (Edinger & Patterson, 1983). Of course,
awkward handshakes can also communicate negative information
(Edinger & Patterson, 1983; Schlenker, 1980). Desirable handshakes have been described as firm handshakes that include a
strong and complete grip, vigorous shaking for a lasting duration,
and eye contact while hands are clasped (Chaplin et al., 2000).
Given the high correspondence between other nonverbal cues and
interview assessments, we predicted that handshakes demonstrating these desirable characteristics would communicate positive
information about the individual being evaluated.
Hypothesis 1: Individuals with a firm handshake will receive
more positive evaluations during employment interviews.
What Does the Handshake Communicate?
Because shaking hands is often the first behavioral act that
occurs when people meet, information conveyed through the handshake is potentially critical. But what information does a handshake convey? What specific cues communicated through the
handshake might enhance an interviewer’s evaluation?
One possibility is that shaking hands during an interview creates
an impression about candidate personality traits that in turn influences assessments of suitability for employment. To explore this
effect, we examined existing research on the relationship between
traits and the handshake. Greeting behavior, such as the handshake, has mainly been investigated in anthropological and ethnographic studies (Astrom & Thorell, 1996; Schiffrin, 1974; Webster, 1984). Our search of the literature found only four empirical
studies related to handshaking, and none of them was conducted in
the interview context. Three studies were conducted in Sweden by
Astrom and associates (Astrom, 1994; Astrom & Thorell, 1996;
Astrom, Thorell, Holmlund, & d’Elia, 1993), who found moderate
relationships between the handshake and personality characteristics such as social extraversion. However, the generalizability of
these conclusions to an interview setting is limited, as participants
included psychiatric patients, therapists, and clergymen. Another
study by Chaplin et al. (2000) in a noninterview setting found a
firm handshake to be positively related to extraversion (r ⫽ .19)
and emotional expressiveness (r ⫽ .16) but to be negatively related
to shyness (r ⫽ ⫺.29) and neuroticism (r ⫽ ⫺.24). The findings
across studies suggest that the handshake is particularly informative for assessment of two personality traits: extraversion and
neuroticism (Chaplin et al., 2000).
Of the two personality traits identified as likely to be communicated through the handshake, extraversion, but not neuroticism,
appears to correspond with interview assessments. Tay, Ang, and
Van Dyne (2006) specifically found evidence of a relationship
with interview success for extraversion (r ⫽ .24) but not for
neuroticism (r ⫽ .06). Other studies (e.g., Caldwell & Burger,
1998; DeFruyt & Mervielde, 1999) have similarly identified extraversion as the personality trait most strongly related to employment interview outcomes. Moreover, meta-analytic evidence suggests that interviewer assessments of extraversion are related to
evaluations of work contribution ( ⫽ .33; Huffcutt, Conway,
Roth, & Stone, 2001). Thus, cues related to extraversion appear to
be particularly relevant for interpretation of personality information conveyed through shaking hands during employment interviews.
In the interview setting, a firm handshake may convey that the
applicant has a high level of extraversion and thus lead to a more
positive evaluation. In short, a firm handshake signifies persuasive
ability, sociability, and interpersonal skills (Astrom & Thorell,
1996; Chaplin et al., 2000), which are aspects of extraversion that
are particularly related to success in social interactions (Costa &
McCrae, 1992; Tay et al., 2006). We therefore hypothesized that
the handshake represents a behavioral manifestation of an individual’s extraversion.
Hypothesis 2: Extraversion will correlate positively with
handshake ratings.
Hypothesis 3: The handshake is a behavioral mediator of the
relationship between extraversion and hirability evaluations
in employment interviews.
Although extraversion is the only five factor model (FFM) trait
previously linked both to the handshake and to interview outcomes, we sought additional insight concerning traits. We thus
included the remaining FFM traits—neuroticism, agreeableness,
conscientiousness, and openness to experience—as exploratory
measures.
Meta-analytic evidence also suggests that interviewers may use
candidate appearance for spontaneous personality assessments at
the beginning of the interview (Hosoda, Stone-Romero, & Coats,
2003). To control for possible effects of the “what is beautiful is
good” stereotype (Eagly, Ashmore, Makhijani, & Longo, 1991),
we obtained measures of candidate physical attractiveness and
professional appearance. Prior research suggests that physically
attractive candidates obtain more positive interviewer evaluations
than do candidates who are less attractive (Forsythe, Drake, &
Cox, 1985; Motowidlo & Burnett, 1995). Professional appearance,
which includes appropriateness of hygiene, personal grooming,
and dress (Kinicki & Lockwood, 1985; Mack & Rainey, 1990), is
expected to have even larger effects during the interview, because
candidates are assumed to have more control over their own
cleanliness and dress and interviewers are influenced by expectations about customary social behavior or conduct during the interview (Posthuma, Morgeson, & Campion, 2002). To better isolate
the effect of shaking hands, we included both measures of candidate appearance as covariates.
Does a Weaker Handshake Place Women at a
Disadvantage in Employment Interviews?
Considerable research has investigated how demographic characteristics, including gender, impact interview outcomes. Given
equal qualifications, research suggests that women tend to be
evaluated less positively than do men in ratings of their credentials
on paper (Arvey, 1979; Barr & Hitt, 1986; Hitt & Barr, 1989;
Parsons & Liden, 1984). On the other hand, female applicants have
been found to be judged more favorably than male applicants on
some nonverbal interview behaviors, such as posture and eye
contact (Parsons & Liden, 1984). Nevertheless, many of these
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RESEARCH REPORTS
effects are modest and may largely reflect similarity between
applicant and interviewer (Arvey & Campion, 1982; Dipboye,
1982; Harris, 1989; Posthuma et al., 2002; Schmitt, 1976).
Goldberg and Cohen (2004) posited that, in relation to nonverbal cues, gender may impact recruiters’ assessments of applicants
differently than do verbal cues. For example, research suggests
that women are perceived as being more adept at conveying
nonverbal communication than are men (Buck, Miller, & Caul,
1974; Goldberg & Cohen, 2004; Graham, Unruh, & Jennings,
1991; LaFrance & Mayo, 1979). In contrast, men are typically seen
as being more rational in their presentation of ideas than are
women (Burke, 1996). Goldberg and Cohen (2004) found that
nonverbal skills were a stronger predictor than were verbal skills
of overall interview assessments. However, they found only marginal support for expected gender differences. This finding highlights the need for research that clarifies gender differences associated with nonverbal communication.
Potential gender differences are of particular concern when it
comes to the handshake. Chaplin et al. (2000) found handshaking
scores to be lower for women than for men. They suggested that
this may be so because women have less experience in handshaking, as the practice has historically been more common between
men than it has been between women or between women and men.
Thus, a positive relationship between the handshake and interview
outcomes might have a negative impact on women. If handshakes
for women are evaluated as less desirable, the result might be
lower interviewer assessments of suitability for hiring. This expected difference in handshaking resulted in our final hypothesis.
Hypothesis 4: Handshakes from women will be rated less
favorably than are handshakes from men, which will result in
lower interviewer assessments for women.
Method
Participants and Procedures
Participants in this study were 98 undergraduate students enrolled
in an elective, one-credit career preparations class at a large midwestern university. Their mean age was 21 years (SD ⫽ 2.7), and 69%
were juniors and seniors. Of the participants, 50 were women and
90% were Caucasian. As part of the class, students participated in a
mock interview. Participants were instructed to treat this experience
just as they would a “real” interview (e.g., by dressing appropriately
and researching the company prior to the interview). They were also
informed that past participants had occasionally obtained real interviews, which led to actual jobs, as a result of favorable mock interviews. Informal conversations with participants and interviewers following the mock interviews indicated that the participants did take the
opportunity seriously and put forth their best effort.
Human resources professionals from local organizations volunteered their time to conduct the mock interviews, which typically
lasted about 1 hr. A mock interview included a 30- to 45-min
interview and 15–20 min of feedback provided to the participant.
Interviewers were instructed to use the same interview format they
presently followed with actual candidates and to focus on the job
with the most frequent openings. Hence, the mock interview was
based on actual selection practices and corresponded to an interview the candidates could expect to engage in during their own job
1141
search. Because some interviewers conducted more than one interview, we assessed potential bias from nonindependence of measures. Following the procedures of Kenny and Judd (1986), we
conducted an analysis of variance, with interview ratings as the
dependent variable and interviewer as the independent factor, and
found no evidence of rater effects, F(26, 74) ⫽ 1.26, ns. The obtaining
of multiple ratings from interviewers thus appears not to have created
problems associated with nonindependence of measures.
Handshake firmness was assessed by five independent raters,
who scored each participant’s handshake at different times during
the mock interview process. The raters shook hands while greeting
each participant, either before or after the mock interview, so both
interviewees and interviewers were unaware that handshakes were
being evaluated. None of the handshake evaluators served as an
interviewer. Two raters greeted and shook hands when a participant arrived for the mock interview. Participants were then introduced to a third rater, who shook hands. After the mock interview,
a fourth rater greeted participants, shook hands, and introduced
them to the fifth rater, who shook hands. Within 5–10 s of shaking
hands, raters excused themselves from participants and completed
an evaluation form. To avoid priming interviewers to pay undue
attention to the handshake, we did not ask them to provide explicit
assessments of the handshake.
Rater Training
Following the procedures of Chaplin et al. (2000), we trained
raters in handshake evaluation. On contact with an individual’s
hand, raters were instructed to close their hand around the participant’s hand but to wait for the participant to initiate the strength
of the grip and the upward-and-downward shaking. Furthermore,
the raters were instructed to release their grip only when the
participant began to relax his or her grip or otherwise show signs
of terminating the handshake. Raters practiced their handshaking
technique on each other and on other individuals until they had
mastered the evaluation concepts and technique.
The training included information about the handshake dimensions. Definitions of the completeness of grip, strength, duration,
vigor, and eye contact were provided. Extreme examples of each
dimension were illustrated. Individuals were recruited to shake
hands with the raters and were instructed to shake hands the same
way with all five raters. The raters coded the practice handshakes
on all dimensions. We discussed discrepancies in the ratings to
create a common frame of reference among raters.
Measures
Handshake ratings. The raters assessed the five handshake
characteristics on 5-point rating scales (Chaplin et al., 2000).
Given that each student’s handshake was scored by five independent raters, we calculated estimates of interrater reliability for
completeness of grip (1 ⫽ very incomplete to 5 ⫽ full; intraclass
correlation [ICC(2)] ⫽ .77), strength (1 ⫽ weak to 5 ⫽ strong; ICC ⫽
.83), duration (1 ⫽ brief to 5 ⫽ long; ICC ⫽ .73), vigor (1 ⫽ low to
5 ⫽ high; ICC ⫽ .71), and eye contact while grasping hands (1 ⫽
none to 5 ⫽ direct; ICC ⫽ .68). Given high intercorrelation among
the handshake characteristics, we also created an overall handshake
score represented by the mean of the five items (ICC ⫽ .85).
Personality. Participants completed the Personal Characteristics Inventory (Mount, Barrick, & Wonderlic Consulting, 2002) in
RESEARCH REPORTS
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1142
a classroom context not directly related to the mock interview. The
inventory comprises 150 Likert-type items that measure conscientiousness, extraversion, agreeableness, emotional stability, and
openness to experience. Coefficient alpha estimates are .89, .90,
.91, .90, and .85, respectively.
Hiring recommendation. Interviewers completed a final hiring
recommendation at the end of the interview. The evaluation consisted of five questions that are used to assess perceived applicant
suitability (Cable & Judge, 1997; Higgins & Judge, 2004; Stevens
& Kristof, 1995). Questions were rated on a 5-point scale. Examples include “This student appears to be very qualified” (response
options ranged from strongly disagree to strongly agree) and
“How satisfied do you think you would be if you were to hire this
student for a full-time position?” (response options ranged from
strongly dissatisfied to very satisfied). Coefficient alpha for the
hiring recommendation was .90 in this sample.
Candidate appearance. Each candidate was videotaped while
sitting for 5 s (with no audio). Four raters, independent from the
interviewers and other raters in the study, evaluated applicant
physical attractiveness and professional appearance. Physical attractiveness was assessed according to the single-item measure
used by Cable and Judge (1997): “Please rate the overall level of
physical attractiveness of this candidate on a 5-point scale ranging
from 1 (very unattractive) to 5 (very attractive).” Ratings of
professional dress relied on a five-item scale adapted from Parsons
and Liden (1984) and Kinicki and Lockwood (1985). Items include
“The applicant was appropriately dressed,” with answers ranging
from 1 (strongly disagree) to 5 (strongly agree). Coefficient alpha
for the five-item professional dress scale was .80. Interrater agreement was shown by ICC values of .79 for physical attractiveness
and .89 for professional dress.
Results
Table 1 shows means, standard deviations, and intercorrelations
among the variables. Hypothesis 1 predicted a relationship be-
tween a firm handshake and interview ratings and was supported
(r ⫽ .29, p ⬍ .05). All five handshake dimensions also related
significantly to the interviewer evaluation (rs .24 –.31), although
none were significantly different from the effect found for the
overall handshake. We thus include only the overall average rating
for our tests of mediation. As expected, extraversion correlated
positively with interviewer ratings (r ⫽ .28, p ⬍ .05). Supporting
Hypothesis 2, extraversion correlated positively with handshake
quality (r ⫽ .23, p ⬍ .05). The two covariates, physical appearance
and professional dress, were not significantly correlated with the
interviewer’s hiring recommendation (rs ⫽ ⫺.01 and .15, respectively) but were correlated with the ratings of the handshake (r ⫽
.19 and .42, respectively). None of the other FFM traits were
related to either the handshake or the interviewer evaluations.
In a result similar to those of previous studies, women received
lower ratings for the overall handshake rating (M ⫽ 3.47 for
women vs. M ⫽ 3.70 for men). As shown in Table 2, item-level
analysis revealed that this effect was attributable to gender differences on handshake strength (M ⫽ 3.11 vs. M ⫽ 3.64) and grip
(M ⫽ 3.51 vs. M ⫽ 3.89).
We used path analysis (Bentler & Wu, 1995) to test Hypotheses
3 and 4. We tested and compared three models. Model 1 includes
both direct and indirect effects for extraversion and gender. Models 2 and 3 are nested within Model 1. To test whether part of the
effect of extraversion and gender on interviewer ratings is mediated by the handshake, Model 2 eliminates paths from these
variables to the handshake by fixing these parameters to zero.
Comparison of the fit of Model 2 to that obtained for Model 1
enabled us to test whether there were mediation effects. Model 3
removes the direct paths from extraversion and gender to interviewer ratings. Lack of difference in the fit of Models 1 and 3
would provide support for the more parsimonious complete mediation model (Model 3). Each model controlled for the effect of
agreeableness, conscientiousness, emotional stability, openness to
experience, physical attractiveness, and professional dress on in-
Table 1
Means, Standard Deviations, and Correlations
Variable
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Applicant gender
Conscientiousness
Extraversion
Agreeableness
Emotional stability
Openness to
experience
Overall handshake
Strength
Vigor
Grip
Duration
Eye contact
Physical attractiveness
Professional dress
Interviewer
assessment
M
SD
1
1.51 0.50
—
115.3 12.14 ⫺.10
115.0 13.31
.11
76.6
9.97 ⫺.22*
105.1 14.22
.16
2
3
(.89)
.36* (.90)
.37* .42*
.47* .58*
4
5
6
8
9
10
11
12
13
14
15
(.91)
.33* (.90)
69.5
3.58
3.37
3.33
3.70
3.57
3.93
4.43
6.79
9.51
.06
0.55
.20*
0.72
.34*
0.53
.14
0.75
.24*
0.48
.14
0.59 ⫺.06
0.61 ⫺.17
1.43
.03
.26*
.09
.12
.09
.02
.09
.11
.12
.12
.44* .23*
.23* .05
.26* .03
.20* .08
.19 ⫺.03
.18 ⫺.01
.18
.14
.14
.15
.17
.11
.28* (.85)
.12
.05
.19
.06
.08 ⫺.01
.11
.09
.11
.01
.01
.04
.06
.01
.05
.13
3.77
0.93 ⫺.06
.17
.28*
.12
Note. N ⫽ 98. Reliabilities are shown in the diagonal.
*
p ⬍ .05.
7
.13
.16
(.85)
.92* (.83)
.92* .88* (.71)
.91* .82* .77* (.77)
.92* .82* .82* .78* (.73)
.82* .61* .71* .66* .75* (.68)
.19
.09
.19
.11
.17
.33* (.79)
.42* .32* .44* .33* .39* .43* .38*
.29*
.24*
.25*
.24*
.26*
.31* ⫺.01
(.89)
.15 (.94)
RESEARCH REPORTS
Table 2
Variable Means by Participant Gender
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Men
Women
Variable
M
SD
M
SD
Conscientiousness
Extraversion
Agreeableness
Emotional stability
Openness to experience
Overall handshake
Strength
Vigor
Grip
Duration
Eye contact
Physical attractiveness
Professional dress
Interviewer assessment
3.80a
3.88a
3.72a
3.58a
3.50a
3.70a
3.64a
3.42a
3.89a
3.65a
3.90a
6.84a
4.33a
3.72a
0.41
0.45
0.49
0.48
0.50
0.55
0.66
0.55
0.70
0.51
0.65
1.71
0.51
0.93
3.88a
3.79a
3.94b
3.43a
3.45a
3.47b
3.11b
3.25a
3.51b
3.50a
3.96a
6.75a
4.53a
3.83a
0.40
0.44
0.49
0.46
0.45
0.53
0.68
0.49
0.75
0.44
0.53
1.13
0.68
0.93
Note. N ⫽ 48 men and 50 women. Means in a row that do not share a
subscript are significantly different.
terviewer ratings. Given an expected relationship between physical
appearance and professional dress, we allowed the error terms for
these variables to covary.
Table 3 shows results for each model.To estimate model fit, we
evaluated the chi-square statistic, root-mean-square error of approximation (RMSEA; Browne & Cudeck, 1993), goodness of fit
index (GFI; Jöreskog & Sörbom, 1993), and comparative fit index
(CFI; Bentler, 1990). Model 1, with both direct and indirect
effects, exhibited good fit, 2(16, N ⫽ 98) ⫽ 24.15, p ⫽ .09,
RMSEA ⫽ .07, GFI ⫽ .95, CFI ⫽ .95. Fit for Model 2 was not as
good, 2(18, N ⫽ 98) ⫽ 31.68, p ⫽ .02, RMSEA ⫽ .09, GFI ⫽
.94, CFI ⫽ .91, and a chi-square difference test suggested that it
was significantly worse, 2(2, N ⫽ 98) ⫽ 7.15, p ⬍ .05, than was
the fit for a model that included mediating effects (Model 1). This
result shows that at least some of the effect of extraversion and
gender on interviewer assessments was mediated by the handshake. Fit for Model 3 was marginal, 2(18, N ⫽ 98) ⫽ 31.28, p ⫽
.03, RMSEA ⫽ .09, GFI ⫽ .94, CFI ⫽ .91, and the chi-square
difference test suggested that fit for Model 3 was significantly
worse than was fit for Model 1, ⌬2(2) ⫽ 7.13, p ⬍ .05. This result
supports partial mediation, as the direct paths from gender and
extraversion (included in Model 1) retain some explanatory power.
Results for the best fitting model—Model 1—are shown in
Figure 1. Extraversion had an indirect effect on interviewer ratings
through its relationship with the handshake ( ⫽ .19), as well as a
1143
direct effect ( ⫽ .31). Hypothesis 3 was supported, as the handshake operated as a mediator of the relationship between extraversion and interviewer assessments. Hypothesis 4 was not supported.
Women received lower ratings for the handshake ( ⫽ .18) but
somewhat higher interviewer ratings ( ⫽ ⫺.14). Weaker handshakes for women did not translate into lower interviewer assessments. In fact, the negative indirect relationship for women
through the handshake was compensated for by a positive but
nonsignificant direct relationship with the interviewer assessment.
This effect is labeled suppression by Cohen and Cohen (1983).
Negative relationships with some personality traits for women
created additional indirect paths that were negative. Taken together, these effects show that about one half of the positive effect
for women on interviewer ratings was nullified by indirect and
spurious effects through the handshake.
None of the covariates exhibited a significant relationship with
the interviewer assessment. However, the handshake influenced
interviewer ratings even after we had controlled for ratings of
physical attractiveness and professional dress, as well as for the
remaining FFM personality traits.
Discussion
To our knowledge, this is the first study that empirically supports the commonly held assumption that the handshake matters in
employment interviews. The high degree of interrater reliability
associated with the handshake evaluation provides strong support
for the notion that people present a consistent handshake when
greeting others. Furthermore, as hypothesized, individuals who
follow common prescriptions for shaking hands, such as having a
firm grip and looking the other person in the eye, receive higher
ratings of employment suitability from interviewers.
As this is the first empirical study to have examined the handshake in employment interviews, a number of issues that still need
clarification. One issue is the extent to which our findings generalize to other settings. Our data were collected in a mock interview
setting, in which interviewers evaluated the suitability of candidates for a wide variety of jobs. Future studies should assess
whether the same relationships exist in actual interviews, with real
job offers on the line, and whether the effect is stronger for some
jobs than for others. Most of the interviewers in our study chose to
interview for jobs with at least moderate social demands, and it
may be that the handshake is not as strongly related to evaluations
for jobs in which social interaction is not integral. We also chose
to isolate the effect of the handshake by not sharing preinterview
information, such as test scores and résumés. Such information has
been linked to interview assessments (Macan & Dipboye, 1990),
Table 3
Fit Indices for Path Models
Model
1. Hypothesized model (partial mediation)
2. Paths removed to handshake (no mediation)
3. Paths removed to interview assessment (full mediation)
2
df
RMSEA
GFI
CFI
⌬2
df
24.15
31.68
31.28
16
18
18
.07
.09
.09
.95
.94
.94
.95
.91
.91
7.15*
7.13*
2
2
Note. N ⫽ 98. For ⌬2, Models 2 and 3 were compared with Model 1. RMSEA ⫽ root-mean-square error of approximation; GFI ⫽ goodness of fit index;
CFI ⫽ comparative fit index.
*
p ⬍ .05.
RESEARCH REPORTS
1144
Conscientiousness
.11
Openness to
Experience
.03
Agreeableness
.01
.15
Emotional Stability
.19*
Handshake Rating
.31*
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Extraversion
.18*
Applicant Gender
.38*
Professional Dress
Interviewer
Assessment
.01
.03
Physical Appearance
.17
.14
-.14
Figure 1. Path model with direct and indirect effects. Values are standardized coefficients. Personality and
gender variables are allowed to intercorrelate. Error terms between ratings of professional dress and physical
appearance are allowed to covary. *p ⬍ .05.
and provision of additional details about interviewee characteristics and qualifications may result in interviewers being less influenced by nonverbal cues like the handshake. Future studies should
thus explore whether or not the inclusion of preinterview information alters the relationship between the handshake and interview
evaluations.
Perhaps the most important question for future investigation is
whether the handshake represents superficial bias or truly communicates important information about job candidates. A longstanding concern about employment interviews is the possibility
that interviewers make quick first impressions and then seek
information that verifies their early perceptions (Dougherty, Turban, & Callender, 1994; Macan & Dipboye, 1988). Is the relationship between the handshake and hiring recommendation evidence
of quick judgmental bias for interviewers, or are interviewers
actually obtaining valid information when they shake hands? In the
present study, we sought to minimize judgmental bias by obtaining
handshake ratings from a source other than the interviewers. The
link between interviewer evaluation of the candidate and handshake ratings provided by other individuals increases our confidence that a quality handshake conveys something meaningful
about the interviewee that is also reflected in the rating of employment suitability. Moreover, the link between handshake quality
and personality is consistent with the notion of information relevant to job performance (i.e., extraversion; Huffcutt et al., 2001)
being communicated through this nonverbal interaction. Nevertheless, additional research should clarify the extent to which the
handshake operates as either a biasing influence or an indicator of
valid information.
Of course, interviewee actions during the course of the interview
will result in additional information that should be taken into
account when the interviewer makes a hiring recommendation.
This fact illustrates how noteworthy it is to find a consistent effect
for the handshake even after 30 min of social interaction during the
interview. One explanation for the relationship is the possibility
that the handshake itself is recalled and factored into the final
evaluation. Another explanation is that individuals with a firm
handshake engage in other positive behaviors during the interview.
Once again, the link between extraversion and interview ratings
supports such an effect. More extraverted interviewees present a
firmer handshake, and they likely engage in other positive behaviors that reflect their ability to perform work successfully.
Indeed, Huffcutt et al. (2001) found interviewer assessments of
extraversion to be an important predictor of job performance. It
thus seems likely that interviewers subconsciously combine
information obtained during the handshake with other information obtained during the interview to arrive at an evaluation of
employment suitability that is a valuable predictor of future
performance.
Our findings also provide important insights concerning gender
and the handshake. The suppression effect identified in our data
illustrates that, even though women may be less adept at handshaking, they engage in other actions that overcome the effects of
a weak handshake. Our dimensional results show that the negative
effect for women is carried through strength and grip rather than
through eye contact. This finding, coupled with other research
demonstrating that women excel at coding and decoding other
nonverbal cues (e.g., facial expression and posture; Graham et al.,
1991), suggests that women have other strengths that can overcome the liability of a handshake that lacks a firm and complete
grip.
In terms of gender, our a priori prediction was simply that
women would receive lower ratings for quality of handshake. We
did not hypothesize that the influence of the handshake on evaluations provided at the end of the interview would differ for men
and for women. Yet, post hoc exploratory analyses also suggest
that women may benefit more from a firm handshake than do men.
Specifically, we probed how gender interacts with the handshake
by regressing interviewer ratings on handshake ratings, gender,
and the interaction between gender and handshake. A possible
trend we identified suggests that handshake firmness (a combined
measure of strength and grip) interacts with gender. Although this
relationship was below conventional standards for statistical significance (⌬R2 for interaction term ⫽ .02, p ⫽ .20), a plot of the
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
RESEARCH REPORTS
results suggests a stronger relationship with a firm handshake for
women than for men. Men and women with a weak handshake
(one standard deviation below the mean) received almost identical
ratings for employment suitability, but women with a firm handshake (one standard deviation above the mean) received substantially higher ratings than did men with a handshake of the same
firmness. Thus, even though women on average present a weaker
handshake, those women who do present a very firm handshake
receive higher ratings than do men with an equally firm handshake.
This effect was not found for the eye contact dimension or for the
overall handshake rating. The combined findings that there is a
potential interaction between firmness and gender and that the
same dimensions of strength and grip are, on average, lower for
women suggest that the value of a firm handshake may be greater
for women than for men. The fact that an interviewer is less likely
to receive a firm handshake from a woman than a man makes
handshake firmness more salient to the interviewer when he or she
evaluates women and thereby increases the potential benefit of a
strong and complete grip for women.
The results of this study therefore provide three specific contributions toward an understanding of the handshake in employment
interviews. First, we provide the first empirical link between the
handshake and interviewer assessments. Second, we show that a
firm handshake partially mediates the effect of extraversion, which
implies that the handshake is more than a biasing factor and can
indeed communicate meaningful information about job applicants.
Third, we demonstrate that women overcome the effects of weaker
handshakes, such that on average they do not receive lower interview performance ratings from interviewers, and that women may
actually benefit more than do men if they present a strong and
complete grip when they shake hands.
From a practical perspective, our findings suggest that the effect
of the handshake in employment interviews should not be ignored.
Interviewers can obtain important information about interviewee
traits through the nonverbal cue of the handshake. Indeed, given
that Huffcutt et al. (2001) found a stronger correlation with job
performance for a rating of extraversion from interviewers ( ⫽
.33) than is typical for a correlation with self-report measures ( ⫽
.15; Barrick, Mount, & Judge, 2001), obtaining trait evaluations
through behavioral indicators such as the handshake may be a
valuable approach that can increase the validity of selection decisions. Of course, the likelihood of accurate assessment of traits
through behavioral acts such as the handshake is likely to attenuate
if job applicants receive training to provide firmer handshakes.
When it comes to handshake training, a practical implication of the
results is that women, as compared with men, have a greater
chance of improving their interview evaluations by learning to
shake hands with a firm and complete grip.
In the end, our findings add to a long-running historical analysis
of the handshake. The handshake is thought to have originated in
medieval Europe as a way for kings and knights to show that they
did not intend to harm each other and possessed no concealed
weapons (Hall & Hall, 1983). The results presented in this study
show that this age-old social custom has an important place in
modern business interactions. Although the handshake may appear
to be a business formality, it can indeed communicate critical
information and influence interviewer assessments.
1145
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Received December 19, 2006
Revision received November 14, 2007
Accepted February 12, 2008 䡲
Interdisciplinary Description of Complex Systems 13(1), 154-166, 2015
BUSINESS SAMPLE SURVEY MEASUREMENT
ON STATISTICAL THINKING AND METHODS
ADOPTION: THE CASE OF CROATIAN
SMALL ENTERPRISES
Berislav Žmuk*
Department of Statistics, Faculty of Economics and Business – Zagreb, University of Zagreb
Zagreb, Croatia
DOI: 10.7906/indecs.13.1.14
Regular article
Received: 19 September 2014.
Accepted: 12 January 2015.
ABSTRACT
The objective of this research is to investigate attitudes of management in Croatian small enterprises
that use statistical methods towards statistical thinking in order to gain an insight into related issues.
The research was conducted in 2013 using a web survey with a random sample of 631 Croatian small
enterprises, but this paper focuses only on those enterprises that use statistical methods. In order to get
detailed information, a complex stratified sample survey design was used. In the analysis, chi-square
tests of independence were used. In the statistical tests of proportion, the nonresponse adjustment
factors as weights and weighted proportions were used. It has been shown that the vast majority of
Croatian small enterprises (65,93 %) do not even use statistical methods in their business. On the
other hand, the enterprises which use statistical methods have recognized the value and capabilities of
statistical methods use. The research has shown that the vast majority of enterprises do not use
statistical methods due to administrative reasons. In spite of using statistical methods as a supporting
tool in the decision-making process in very important and key business cases, Croatian small
enterprises admitted the lack of statistical methods use in their business. Also, investments into the
statistical methods use are very scarce. This has led to employees’ low statistical methods use
knowledge level. The statistical methods use led to better business results in more than 90 % of small
enterprises. It has been shown that statistical methods use effects on business results have on average
a 6-12 months lag. This research leads to the conclusion that more efforts should be put into
development of statistical thinking in these enterprises and familiarizing them with statistical methods
use, with the aim of increasing their use and improving business results.
KEY WORDS
statistical thinking, business survey, complex sample survey design, weighted stratified proportion
estimator, chi-square tests of independence
CLASSIFICATION
JEL:
C12, C42, M21
*Corresponding author, : bzmuk@efzg.hr; +385 1 2383372;
*Faculty of Economics and Business – Zagreb, Trg J.F.Kennedyja 6, HR – 10000 Zagreb, Croatia
*
Business sample survey measurement on statistical thinking and methods adoption: the case …
INTRODUCTION
Statistical thinking is a philosophy of learning and action based on the following principles:
all work occurs in a system of interconnected processes; variation exists in all processes, and
understanding and reducing variation are keys to success [1]. The development of statistical
thinking, which began in the 1990s, is the next step in the evolution of the statistics discipline [2].
According to Moore [3; p.134] statistical thinking has become the most important part of
inquiry. Box [4] agrees with that and emphasizes the need to develop a statistical way of
thinking and to change the approach to problems. According to him, these developments and
changes are needed because of the intensive computer use in dealing with different business
problems. Despite the increased need for statistical knowledge, which resulted in an increased
number of statistical subjects and students who are interested in statistics [5-7], statistics has
been viewed by students as difficult and unpleasant to learn, and by instructors as frustrating
and unrewarding to teach [8; p.4]. An inadequate level of computers use due to insufficient
institutions’ funds and a general aversion of students to statistics and statistical thinking are
seen by Cobanovic [9] as the main barriers to the further development and improvement of
education in the application of statistical methods.
Presented situation in statistics education reflects on statistics use and statistical thinking in
enterprises. According to Dransfield, Fisher and Vogel [10] the statistical methods use has an
important role in the measurement of enterprises’ organizational effectiveness, which is of
fundamental importance for achieving business excellence. Montgomery [11] agrees that the
statistical methods use in enterprises has become necessary. He also notes that, since the 1980s,
a significant progress has been made not only in the use of statistical methods in enterprises,
but also in their development. But research from different countries, such as Canada [12],
Croatia [13-17], the Czech Republic [18], Finland [19], Germany [20], Great Britain [19, 21, 22],
Kosovo [23], Malaysia [24], Portugal [19], South Africa [25-27], Spain [20], Sweden [28-31]
and the United States [32], has shown that statistical methods and statistical thinking are used
only in a small number of enterprises and that in most cases enterprises use only the simplest
statistical methods and approaches.
It is indicative that enterprises slowly accept and implement statistical methods in their
business. According to Makrymichalos et al. [33] the main reasons and barriers to further
development and increase of the statistical methods usage level in enterprises are: different
opinions on statistical methods of enterprises and their employees; inadequate education at
universities in the field of statistical methods use; managers’ fear of the statistical methods
use. Deleryd [34] has formed four groups of reasons for seldom statistical methods
application: problems in management; conservative personal employees’ attitudes; practical
problems; methodological problems. Antony et al. [18] consider that the reasons for scarce
statistical methods use are the following: a lack of quality management’s awareness of
general statistics methods principles; a lack of awareness about the statistical methods use
importance and the resulting benefits from their implementation; academic institutions
provide inadequate education on the statistical methods use in enterprises; managers’
insistence on one-factor approach application to the process optimization; inadequate skills
and competence in the advanced statistical methods application field; a negative attitude
towards quality improvement and process optimization strategies; a lack of funds to run a
pilot study. Another empirical research has shown that the main reasons for a lack of
statistical methods use include low managers’ commitment and poor statistical background of
employees in enterprises [35]. Antony [36] concludes that there is a huge difference between
the current level of statistical methods use knowledge, and the level required for successful
solving of enterprises’ problems.
155
B. Žmuk
On the other hand, enterprises do not use statistical methods because: an enterprise is already
considered as successful; a lack of awareness about the benefits of applying these methods;
the lack of resources; time constraints; and managers’ decisions [21]. More advanced
statistical methods are not being used because enterprises are not aware of their existence and
potential, or they are not ready and trained for their use [25].
Small enterprises are the base of each economy. The better business results small enterprises
in a country have, the better the country’s economy and prosperity is. Previous research has
shown that statistical methods use and statistical thinking in general can improve enterprises’
business results. Because of that, it is very interesting to research what the position of
statistical thinking in modern Croatian small enterprise is. The main research hypothesis is:
Statistical thinking is widespread in Croatian small enterprises. The research hypothesis
implies that the majority of Croatian small enterprises have developed statistical thinking and
that statistical methods are used in them.
The aim of the research is to test if statistical thinking is widely accepted by Croatian small
enterprises. In order to make a conclusion about the named research hypothesis, an original
scientific web survey in Croatian small enterprises has been conducted. The article is
organized as follows. Section 2 introduces the statistical population, the survey design and
analysis methods. Section 3 presents the analysis taking the complex survey design into
account. Section 4 presents the conclusions of the study.
DATA COLLECTION AND RESEARCH METHODS
STATISTICAL POPULATION
The survey target population [37] includes active Croatian small enterprises that use
statistical methods. A small enterprise is an enterprise which meets two out of three of the
following criteri: the amount of total assets is lower than HRK 32 500 000,00; the amount of
revenue is lower than HRK 65 000 000,00; and the average number of employees during the
financial year is lower than 50 [38]. In order to be considered a Croatian enterprise an
enterprise has to be registerd in the Court Register of the Republic of Croatia. The research
included only enterprises with the legal form of a corporation [39].
The Croatian Company Directory of the Croatian Chamber of Economy was used as a
sampling frame [40]. The sampling frame contained the updated list of all registered small
enterprises. On 1 October 2012, there were 87 805 small enterprises in Croatia with the legal
form of a corporation. The problem was that the list included inactive enterprises as well as
enterprises that on avarage had no employees during a year. Consequently 30 449 enterprises
were excluded from the analysis and thus the target population was reduced to 57 356 small
enterprises. In spite of that representative sample, because of complete sampling frame and
known nonzero probabilities of selection, can be obtained [41].
SURVEY DESIGN
In order to get information about enterprises’ attitude towards statistical thinking, a web
survey was made. Another problem was that all enterprises did not provide a valid e-mail
address and because of that they were not able to participate in the survey. As a result, the
sampling population consisted of 24 618 Croatian small enterprises. It is assumed that there is
no statistically significant difference between enterprises that have and those that have not
provided a valid e-mail address.
It is known that each enterprise’s main activity area has its own special features. In order to
examine if the statistical thinking position is the same in different activity areas, a complex
156
Business sample survey measurement on statistical thinking and methods adoption: the case …
survey design was used and four strata were constructed based on the enterprises’ main
activities. In that way, an additional in-depth analysis was enabled. In order to split
enterprises into four strata The National Classification of Economic Activities (NACE) was
used [42]. NACE recognizes overall 21 main activity areas and indicates them with letters
from A to U. The first stratum includes industrial enterprises from the activity areas B, C, D,
E, and F. The second stratum consists of trade enterprises (the activity area G). The third
stratum contains enterprises which are considered as service-oriented enterprises (the activity
areas H, I, J, K, L, M, N, O, P, Q, S). The rest of enterprises are placed into the Other
stratum. The last stratum includes enterprises the main activity of which, according to NACE,
belongs into the activity areas A, R, T, or U.
Most enterprises were in the Services stratum (10 091), in the Trade (7 186) and in the
Industrial (6 769) strata. The fewest enterprises were in the Other stratum (572). In order to
achieve a margin of error equal to plus or minus 7%, with a confidence level of 95 % 196
eligible enterprises needed to be surveyed. Because the aim of the research is to investigate
the position of statistical thinking in Croatian small enterprises it is necessary to observe only
those enterprises that use statistical methods. Consequently, only such enterprises are eligible.
Taking into account the assumed share of small enterprises that use statistical methods and
the expected response rate, it was concluded that 21 000 small enterprises should be
contacted. In order to sample enterprises from each strata, proportionate allocation and the
simple random sampling method were used.
Within this survey, Croatian small enterprises are considered to be sampling and analysis
units while enterprises’ employees, primarily managers who represents the enterprise in
which they work, are seen as reporting units. Their responses are considered enterprises’
responses. They had to fill in the questionnaire that consisted of 6 different groups of
questions. Each group had a different number of questions and covered a different area about
the position of statistical thinking in an enterprise. The length and clarity of questions were
maximally optimized to avoid misunderstandings. It was estimated that an enterprise needed
at least 5 minutes and at most 15 minutes to complete the questionnaire. The computer
programme used for the web survey prevented enterprises from skipping some questions or
giving inappropriate answers. In that way the item nonresponse problem was avoided.
SURVEY RESPONSE RATE
The survey started in October 2012 and lasted 15 weeks. During that period two reminders
were sent to the enterprises. At the end, employees from 631 Croatian small enterprises took
part in the survey and filled in the questionnaire for their enterprise but only 215 or 34,07 %
of them use statistical methods in their business and are considered as eligible for the
research. Because there were no partially completed questionnaires, the response rate was
calculated as the total number of eligible enterprises responses over the total number of
contacted enterprises and it is equal to 1,02 %. This response rate is known as Response Rate
1 or the minimum response rate [43]. The response rates in strata vary from 0,86 % in the
Trade stratum and 0,88 % in the Industrial stratum to 1,22 % in the Services stratum and
1,23 % in the Other stratum.
ANALYSIS METHODS
Different statistical methods were used in the analysis. Descriptive statistics methods were
used as a base for inference. Selected statistical tests such as chi-square tests of independence
and tests of proportion were used in the inferential analysis. In the analysis, only nonresponse
adjustments weights were taken into account. The response adjustments weights were
calculated as reciprocal to the response rate for each stratum separately.
157
B. Žmuk
EMPIRICAL RESULTS AND DISCUSSION
ATTITUDES TOWARDS REASONS FOR STATISTICAL METHODS USE IN
SMALL ENTERPRISES
First of all, the enterprises that use statistical methods were additionally asked about the
reasons of their application. According to literature review, there are six most influential
reasons for statistical methods use. The enterprises could for each reason separately confirm
that the reason had an impact on their decision to use statistical methods or they could
disagree with that. Naturally, the enterprises could also answer that they did not know or they
were not familiar if a certain reason was one of the reasons for the statistical methods use.
Such answers were considered in the further analysis. Because of that, the total number of
answers for each selected reason is different. Table 1 shows enterprises’ estimated weighted
population proportions for each reason. The proportions were calculated as:
H
pst Wh psh ,
(1)
h1
where the weight Wh = Nh/N is the proportion of units in stratum h, Nh is the known number
of units in the population in stratum h, N is the known total number of units in the population,
and psh (1 / nsh ) ykh is the sample proportion in stratum h, y k is the total number of
units with observed characteristic k in stratum h, nsh is the number of units in the sample in
the stratum h. Because only the nonresponse adjustments weights are used in the analysis, all
sampled units in the stratum are given the same weights. In that way the “self-weighting” was
conducted and there was no need for introducing more complexity in calculating psh.
The first three reasons in Table 1 may be considered as true reasons for statistical methods
use because they are primarily oriented towards improving processes and business results. On
the other hand, the last three reasons are considered as wrong reasons for statistical methods
use because statistical methods are used only for administrative purposes. The majority of
enterprises, 95 % of 215 enterprises that use statistical methods, use them because they
facilitate the business decision-making process. Also, 90 % of enterprises agree that the
statistical methods use helps them improve business results. Only 28 % of enterprises use
statistical methods because their application has been a prerequisite for business certification
and because their competition also uses statistical methods (30 %). These results indicate that
the enterprises have recognized the true purpose of statistical methods as an additional tool
for better management.
In order to make inference about all small Croatian companies that use statistical methods,
the hypothesis test of estimated proportion was conducted. It was assumed for each reason for
statistical methods application that more than half of the enterprises accept the reason as a
reason of statistical methods application in their enterprise. So the test hypotheses are:
H0: p 0,5 and H1: p > 0,5. The population sampling variance of the stratified estimator was
calculated as:
H
Vst Wh2
h 1
1 fh
psh 1 psh ,
nsh 1
(2)
where fh = nsh/Nh. The results of the conducted statistical tests are shown in Table 2.
According to those results it can be concluded that at the significance level = 0,05 the null
hypothesis can be rejected for the following reasons: statistical methods facilitate the business
decision-making process; the statistical methods usage improves business results; the
statistical methods application leads to achieving cost efficiency. In that way, it is confirmed
158
Business sample survey measurement on statistical thinking and methods adoption: the case …
that most of the enterprises have right motives for using statistical methods and that they do
not use statistical methods only because of administrative reasons.
The 2 tests of independence between accepting the reason as a reason for statistical methods
application in an enterprise, and enterprises’ activity has also been conducted. In the analysis
the Other stratum was omitted because of an insignificant number of such enterprises. The
test results are given in Table 3. It can be concluded that at the significance level = 0,05
there is no statistically significant dependence between accepting the reason as a reason for
statistical methods application in an enterprise and enterprises’ activity for all reasons except
the reason that the statistical methods application leads to achieving cost efficiency. The reason
Table 1. Attitudes towards reasons for statistical methods use in small enterprises, weighted
proportions, n = 215. Source: conducted survey.
The reason for
stat. methods use
Yes
No
0,9502
0,0498
0,8955
0,1045
0,8718
0,1282
0,4360
0,5640
0,3048
0,6952
0,2763
0,7237
Reason
Statistical methods facilitate the business decision-making process
Statistical methods usage improves business results
Statistical methods application leads to cost efficiency
The management demands statistical methods usage
Statistical methods have been used by competitors
Statistical methods use has been a prerequisite for business certification
No. of
answers
202
194
183
195
149
183
Table 2. Hypothesis tests of proportions of reasons for statistical methods application in
small enterprises, n = 215. Source: conducted survey.
Reason
Statistical methods facilitate the
business decision-making process
Statistical methods usage improves
business results
Statistical methods application leads to
achieving cost efficiency
The management demands statistical
methods usage
Statistical methods have been used by
competitors
Statistical methods usage has been a
prerequisite for business certification
Sample
size
Estimated
proportion
Standard
error
z-value
p-value
202
0,9502
0,0157
28,70
0,0000
194
0,8955
0,0221
17,93
0,0000
183
0,8718
0,0233
15,95
0,0000
195
0,4360
0,0364
–1,76
0,9608
149
0,3048
0,0398
–4,90
1,0000
183
0,2763
0,0340
–6,59
1,0000
2
Table 3. tests of accepting the mentioned reason as the main reason for statistical methods
application in an enterprise and enterprises’ activity, without the Other stratum, n = 209.
Source: conducted survey.
Reason
Statistical methods facilitate the business decision-making
process
Statistical methods usage improves business results
Statistical methods application leads to achieving cost
efficiency
The management demands statistical methods usage
Statistical methods have been used by competition
Statistical methods usage has been a prerequisite for business
certification
Sample
size
df
Chi-square
test statistic
pvalue
196
2
0.512
0.7741
188
2
0.824
0.6625
178
2
9.227
0.0099
189
144
2
2
1.062
4.783
0.5881
0.0915
177
2
3.093
0.2130
159
B. Žmuk
reason for such a conclusion is the proportion of Services enterprises that agree that the
statistical methods application leads to achieving cost efficiency, which is considerably lower
than the proportion of Industrial and Trade enterprises.
STATISTICAL METHODS POSITION IN ENTERPRISES’ KEY BUSINESS CASES
An additional proof that the enterprises have recognized the true purpose of statistical
methods use is the level of their involvement in enterprises’ key business cases. So, 178
enterprises or 83 % of enterprises in the sample that use statistical methods use them as a
decision-making support in very important and key business cases. Out of that number, 46
enterprises always use statistical methods in such cases, and 132 enterprises do that
occasionally. Only 30 enterprises or 14 % of enterprises in the sample that use statistical
methods do not use them as a decision-making support in very important and key business
cases. The rest of enterprises were not familiar with the fact whether statistical methods were
used in their key business cases.
REASONS FOR LACK OF STATISTICAL METHODS APPLICATION
Croatian small enterprises that use statistical methods have recognized the importance and the
role of statistical methods. But according to survey results, only 80 enterprises or 37 % of
enterprises that use statistical methods, are satisfied with the level of statistical methods use
in their business. On the other hand, 112 enterprises or 52 % of enterprises that use statistical
methods think that they should use more statistical methods. The employees in the rest of
enterprises (11 %) could not evaluate the level of statistical methods use in their enterprise.
Such enterprises are omitted from the analysis in conducting the hypothesis test of
proportions on upper limit. It is assumed that the majority of Croatian small enterprises are
not satisfied with the level of statistical methods use in their business. According to that, the
test hypotheses are H0: p 0,5 and H1: p > 0,5. The test results suggest that, at the
significance level of = 0,05, the null hypothesis may be rejected (n = 192; pˆ 0.5982 ;
se 0.0358 ; z 2.75 ; p 0.0030 ). Consequently, it might be concluded that statistical
methods are not used enough in more than 50% of enterprises. The same conclusion can be
brought for Industrial ( n 41 ; pˆ 0.6585 ; se 0.0781 ; z 2.03 ; p 0.0212 ), and Trade
enterprises ( n 48 ; pˆ 0.6667 ; se 0.0722 ; z 2.31 ; p 0.0105 ). On the other hand,
this conclusion cannot be made for Services enterprises at the significance level of 0.05
( n 97 ; pˆ 0.5052 ; se 0.0508 ; z 0.10 ; p 0.4596 ).
In total, six reasons for a lack of statistical methods use are identified. Enterprises that use
statistical methods could agree or disagree with each of these reasons according to their impact
on the level of statistical methods use in their enterprise. According to the results in Table 4
Table 4. Reasons for a lack of statistical methods application in small enterprises, weighted
proportions, n = 215. Source: conducted survey.
Reason
The existing overloaded of employees with other jobs and assignments
Statistical methods are not well known in the enterprise
The lack of qualified personnel
Lack of financial resources
The nature of the product / service
The additional statistical methods application would not have
animpact on business results
160
The reason for lack
No. of
of stat. methods use
answers
Yes
No
0,7563
0,2437
200
0,7366
0,2634
198
0,5827
0,4173
193
0,5583
0,4417
193
0,5185
0,4815
191
0,2799
0,7201
180
Business sample survey measurement on statistical thinking and methods adoption: the case …
the most important reason for a lack of statistical methods applications is that the employees
are already overloaded with the existing jobs and assignments. The second important reason
is that statistical methods are not well known in the enterprises. In order to solve these two
reasons and the third one, the lack of qualified personnel, an enterprise should employ one or
more statisticians. In that way, a highly qualified person would perform statistical analyses
and other employees would in that case have more time for other tasks. Of course, an
enterprise should employ a new person only if it has enough financial resources for it.
STATISTICAL METHODS USE KNOWLEDGE IN SMALL ENTERPRISES
Before any investments in additional education, the enterprise should be familiar with
employees’ knowledge about statistical methods application. Croatian small enterprises that
uses statistical methods were asked to give an average grade for their employees’ knowledge
about statistical methods. Out of 182 enterprises, only 8 enterprises or 4 % gave the highest
grade, grade A, to their employees for statistical methods use knowledge. Most enterprises,
70 enterprises or 38 %, gave grade C to their employees. The lowest grade, grade E, was
given to employees in 19 or in 10,44 % of enterprises.
It is assumed that grades C, D and E present a non-satisfactory statistical knowledge level. In
order to determine if the majority of enterprises have a non-satisfactory statistical knowledge
level a statistical hypothesis test for proportion on upper limit was conducted. Consequently,
the hypotheses are H0: p 0,5 and H1: p > 0,5. At the significance level 0.05 the null
hypothesis can be rejected (n = 182; p̂ = 0,7784; se = 0,0308; z = 9,05; p = 0,0000), which
means that in over than 50% of enterprises employees do not have a satisfactory level of
statistical methods application knowledge. The same conclusion can be made for Industrial (n = 41;
p̂ = 0,7805; se = 0,0781; z = 3,59; p = 0,0002), Trade (n = 45; p̂ = 0,8444; se = 0,0745; z = 4,62;
p = 0,0000) and Services (n = 92; p̂ = 0,7174; se = 0,0521; z = 4,17; p = 0,0000) enterprises.
It is obvious that the enterprises should invest more into employees’ education in the field of
statistical methods use. But the question if they and how much they invest in their employees’
education in this field remains. The conducted hypothesis test of proportion on upper limit, with
hypotheses H0: p 0,5 and H1: p > 0,5, at the significance level 0.05 has shown that the
majority of Croatian small enterprises do not invest in statistical methods education (n = 191;
p̂ = 0,6105; se = 0,0357; z = 3,09; p = 0,0010). It is remarkable that only 18 enterprises out
of 191 (9,42 %) invest more than HRK 10 000,00 into statistical education yearly.
No significant progress in statistical methods knowledge in Croatian small enterprises is
expected because 161 or 75 % of the sample enterprises that use statistical methods are not
familiar with statistical education possibilities available on the Croatian market. It leads to the
conclusion that first a lot of effort should be invested into informing enterprises about
statistical education possibilities. After that it would be possible to suggest the most
appropriate topics for their business. Also it has been shown that 33 or 15 % of enterprises in
the sample are not satisfied with statistical methods education offer on the market. On the
other hand, 21 or only 10 % of enterprises in the sample are satisfied with statistical methods
education offer available on the market.
IMPACT OF STATISTICAL METHODS USE ON BUSINESS RESULTS
The sample includes 56 enterprises that used statistical methods from their founding. There
are 113 enterprises that started to use statistical methods at a certain point in time after the
enterprise’s founding. In 76 or 67 % of such enterprises business situation was better than in
the time before statistical methods use. Detailed results grouped based on the main enterprise
activity are shown in Table 5.
161
B. Žmuk
The results in Table 5 show whether the business situation in enterprises is better, the same or
worse after introducing statistical methods application. But these results do not reveal either
strength or the level of business results improvement. According to the results in Table 6 in
45 % of enterprises the impact of statistical methods application on the enterprises’ business
situation is significant. That means that there is a huge gain in business results due to
statistical methods use. Significantly less gain or no significant business results improvement
is present in 46 % of enterprises. Only 9 % of enterprises stated that the statistical methods
use had not led to any business results improvement.
In the process of statistical methods use in enterprises the time lag for positive impacts on
business results to be evident appeared to be very important. So, Croatian small enterprises
were also asked about the average time needed for the impact of statistical methods use on
business results to be noticable. Detailed results are shown in Table 7.
In most enterprises the results of statistical methods applications are noticeable on average
within 6 to 12 months. Overall, in the vast majority of enterprises in the sample (79 %), the
statistical methods use shows its impacts on the business results in less than one year. In the
rest of the observed enterprises, the statistical methods use impact is reflected on the business
results in the period which is longer than one year. In those enterprises it is questionable if the
statistical methods are applied in a proper way and if they are applied efficiently.
Table 5. Situation in small enterprises after introducing statistical methods use, n = 113.
Source: conducted survey.
Activity
Industrial
Trade
Services
Other
Total
Better
No.
19
24
31
2
76
%
79
67
62
67
67
Situation in the enterprise
The same
Worse
No.
%
No.
%
4
17
1
4
12
33
0
0
19
38
0
0
1
33
0
0
36
32
1
1
Total
No.
24
36
50
3
113
%
100
100
100
100
100
Table 6. Statistical methods application impact on enterprises’ business results improvement,
n = 180. Source: conducted survey.
Activity
Industrial
Trade
Services
Other
Total
Significant
No.
%
24
56
21
45
34
40
2
50
81
45
Level of impact
Non significant
No.
%
14
33
25
53
42
49
2
50
83
46
No impact
No.
%
5
12
1
2
10
12
0
0
16
9
Total
No.
43
47
86
4
180
%
100
100
100
100
100
Table 7. Average time needed for the impact on business results due to statistical methods
use, n = 149. Source: conducted survey.
Activity
Industrial
Trade
Services
Other
Total
162
Average time needed to have an impact on business results
Less than
1–3
3-6
6 – 12
1-3
More than
1 month
months
months
months
years
3 years
%
%
%
%
%
%
5
30
14
35
16
0
0
22
17
39
17
5
15
12
24
27
19
3
0
0
50
0
50
0
8
19
20
32
19
3
Total
No.
37
41
67
4
149
%
100
100
100
100
100
Business sample survey measurement on statistical thinking and methods adoption: the case …
CONCLUSIONS
The article presents the statistical thinking position in Croatian small enterprises. It has been
shown that the vast majority of Croatian small enterprises (65,93 %) do not even use
statistical methods in their business. This data do not support the main research hypothesis
that statistical thinking is widespread in Croatian small enterprises. On the other hand, it has
been shown that enterprises that use statistical methods have recognized the value and
capabilities of statistical methods use. So, the right reasons for statistical methods use have
prevailed over the administrative reasons. This goes in favor of the hypothesis that statistical
thinking is developed in Croatian small enterprises. The fact that 83 % of enterprises that use
statistical methods use them as a decision-making support in very important and key business
cases indicated that statistical methods use has an important place in Croatian small
enterprises business. However, half of the enterprises (52 %) admit that there is a lack of
statistical methods use in their business. It has been shown that the two main reasons for
scarce statistical methods use in small enterprises are the existing overload of employees with
other jobs and assignments and the employees’ insufficient statistical methods use
knowledge. Another problem is that enterprises do not invest or invest almost nothing into
employees’ statistical methods use knowledge. The main reason for that could be the fact that
three quarters of the enterprises are not familiar with statistical education possibilities in
Croatia. The survey shows that in only one enterprise the situation in the enterprise has
become worse than before introducing statistical methods. In only 9 % of small enterprises
statistical methods use have not led to any business results improvement. In most enterprises
the statistical methods use impact on business results is noticeable on average in the period
from 6 to 12 months from the time their introduction.
The conducted research has shown that statistical thinking is not widespread in Croatian
small enterprises. Despite this, it can be concluded that statistical thinking has a very
important position and role in enterprises that use statistical methods in their business.
Naturally there is a lot of space for improvement. More efforts should be invested into
introducing statistical methods use features and benefits to enterprises that do not use them.
The future research should investigate the reasons why some enterprises do not use statistical
methods. In order to get a better insight into statistical methods use in enterprises, the future
research should be able to answer questions about the commonly used statistical methods in
enterprises, as well as frequency and quality of their use. The answers to these questions will
provide the ground for further improvement and better affirmation of statistical methods in
enterprises. All these efforts should lead to even better business results. The main limitation
of this research is that it only observes Croatian small enterprises. Future research should also
observe medium-sized and large enterprises.
ACKNOWLEDGEMENT
This work has been fully supported by the Croatian Science Foundation within the project
STRENGTHS (project no. 9402).
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ANKETNO ISTRAŽIVANJE O PRIHVAĆENOSTI
STATISTIČKOG NAČINA RAZMIŠLJANJA I METODA
U HRVATSKIM MALIM PODUZEĆIMA
B. Žmuk
Katedra za statistiku, Ekonomski fakultet – Zagreb, Sveučilište u Zagrebu
Zagreb, Hrvatska
SAŽETAK
Predmet istraživanja rada jest ispitati stav menadžera u hrvatskim malim poduzećima koja primjenjuju
statističke metode prema statističkom načinu razmišljanja kako bi se bolje upoznala problematika povezana s
tim područjem. Istraživanje je provedeno 2013. godine primjenom web ankete na slučajnom uzorku od 631
malog hrvatskog poduzeća. kako bi se dobile što detaljnije informacije korišten je složen istraživački dizajn
uzorka. U analizi su korišteni hi-kvadrat testovi neovisnosti i statistički testovi proporcija u kojima su proporcije
bile vagane temeljem faktora neodgovora. Pokazalo se da većina hrvatskih malih poduzeća (65,93%) ne koristi
statističke metode u svojem poslovanju. S druge strane, poduzeća koja primjenjuju statističke metode
prepoznala su njihovu važnost i mogućnosti. Istraživanjem je utvrđeno da većina poduzeća ne primjenjuje
statističke metode zbog administratorskih razloga. Premda koriste statističke metode kao podršku u procesu
donošenja poslovnih odluka u veoma važnim i ključnim poslovnim slučajevima, hrvatska mala poduzeća
priznaju da je razina primjene statističkih metoda u njihovom poslovanju na nezadovoljavajućoj razini. Također,
investicije u primjenu statističkih metoda su veoma oskudna. Navedeno je dovelo do spoznaje o niskoj razini
znanja zaposlenika iz područja primjene statističkih metoda. Primjena statističkih metoda dovela je do boljih
poslovnih rezultata u više od 90 % malih poduzeća. Rezultati primjene statističkih metoda se prepoznaju u
rezultatima poslovanja u prosjeku s odmakom od 6 do 12 mjeseci. Istraživanjem je zaključeno da bi se trebalo
više ulagati u razvoj statističkog načina razmišljanja u poduzećima s ciljem povećanja primjene statističkih
metoda te unapređenja rezultata poslovanja.
KLJUČNE RIJEČI
statističko razmišljanje, istraživanje u poduzećima, složeni istraživački dizajn uzorka, vagani stratificirani
procjenitelj proporcije, hi-kvadrat testovi neovisnosti
166
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