deception detection ins small groups

7/28/2019 Deception Detection Ins Small Groups

1/27

http://sgr.sagepub.com/Small Group Research

/content/44/3/272

The online version of this article can be found at:

DOI: 10.1177/10464964134841782013 44: 272 originally published online 21 April 2013Small Group Research

Lina Zhou, Dongsong Zhang and Yu-wei SungThe Effects of Group Factors on Deception Detection Performance

Published by:

http://www.sagepublications.com

can be found at:Small Group ResearchAdditional services and information for

/cgi/alertsEmail Alerts:

/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

/content/44/3/272.refs.htmlCitations:

What is This?

- Apr 21, 2013OnlineFirst Version of Record

- May 14, 2013Version of Record>>

by guest on May 19, 2013pdf.highwire.orgDownloaded from
http://sgr.sagepub.com/http://sgr.sagepub.com/http://-/?-http://-/?-http://www.sagepublications.com/http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://www.sagepub.com/journalsReprints.navhttp://www.sagepub.com/journalsReprints.navhttp://www.sagepub.com/journalsPermissions.navhttp://-/?-http://-/?-http://-/?-http://online.sagepub.com/site/sphelp/vorhelp.xhtmlhttp://online.sagepub.com/site/sphelp/vorhelp.xhtmlhttp://online.sagepub.com/site/sphelp/vorhelp.xhtmlhttp://-/?-http://-/?-http://-/?-http://-/?-http://pdf.highwire.org/http://pdf.highwire.org/http://pdf.highwire.org/http://online.sagepub.com/site/sphelp/vorhelp.xhtmlhttp://-/?-http://-/?-http://-/?-http://www.sagepub.com/journalsPermissions.navhttp://www.sagepub.com/journalsReprints.navhttp://-/?-http://-/?-http://www.sagepublications.com/http://-/?-http://sgr.sagepub.com/


2/27

Small Group Research

44(3) 272297

The Author(s) 2013

Reprints and permissions:

sagepub.com/journalsPermissions.navDOI: 10.1177/1046496413484178

sgr.sagepub.com

Article

The Effects of GroupFactors on Deception

Detection Performance

Lina Zhou1, Dongsong Zhang1

and Yu-wei Sung1

Abstract

Deception has been an important problem in interactive groups, impedingeffective group communication and group work, yet deception detection insuch a context remains understudied. Extrapolated from the interpersonaldeception theory (IDT) and group composition research in cooperativecontexts, this research proposes that group factors, including diversityand familiarity, have influence on the performance of deception detection.

The measurement of group performance was not limited to success, asprevious deception studies did, but included efficiency as well because itis fundamental to the effectiveness of deception detection. An analysis ofdata collected from a real-world online community found that behavioralfamiliarity had a positive effect, and gender diversity had a negative effect,on group success in deception detection. In addition, behavioral familiarityhad a negative effect and functional diversity had a positive effect on thegroup efficiency of deception detection. The findings not only extend IDT

in several important ways but also suggest the need to distinguish betweennoncooperative and cooperative groups, an important theoretical implicationfor group composition research.

Keywords

deception detection, group familiarity, group diversity, online deception

1

UMBC, Baltimore, MD, USACorresponding Author:

Lina Zhou, Department of Information Systems, UMBC, 1000 Hilltop Circle, Baltimore, MD

21250, USA.

Email: [email protected]

SGR

443

10.1177/1046496413484178Small Group ResearchZhou etal.research-article

2013

http://pdf.highwire.org/http://pdf.highwire.org/http://pdf.highwire.org/http://pdf.highwire.org/


3/27

Zhou et al. 273

Despite the long-standing recognition of the importance of group work to

organizations (Guzzo & Shea, 1992), the benefits of working in a group

would be diminished if there were a deceptive member who consciously

attempted to direct the group to reach a false conclusion or impression. As theglobalized economy and society continues to grow, and the cost of group

communication and coordination continues decreasing (Byrne, 1993;

Donnellon, 1996; Jones & George, 1998), computer-supported group work

becomes increasingly common but not all group members willingly work

toward a unified group goal. A group may consist of a variety of members,

including some who intend to deceive others due to various motivations such

as self-interest and malicious intentions. For instance, a mole or double agent

pretends to be cooperative with one group, but is in fact loyal to anothergroup. As a result, the mole may provide false, or true but irrelevant, informa-

tion to the target group, aiming to either move the group off the right track or

gain the groups trust. A group involving such a deceptive member is not

limited to intelligence operations, but also includes negotiation, policy mak-

ing, economic decision making, and so forth. In a broader sense, online

phishing and spamming also exemplify deception in groups. These various

forms of deception pose increasing threats to group collaboration and online

communities. Therefore, group detection of deception is essential to creatinga trustworthy and secure environment for effective group collaboration and

communication.

Although many studies have examined deception detection (Bond &

DePaulo, 2006), research on group detection of deception is scarce. Park,

Levine, Harms, and Ferrara (2002) and Frank, Feeley, Paolantonia, and

Servoss (2004) compared deception detection accuracy between individual

and small groups in a noninteractive setting using videotapes of stimulus

communicators. The detection of deception in an interactive context, how-

ever, is distinctively different because it involves active and dynamic infor-

mation exchanges between senders and receivers. Interpersonal Deception

Theory (IDT; Buller & Burgoon, 1996) postulates that senders and receivers

continuously adjust themselves during an ongoing interaction in the form of

receivers suspicion display and senders reaction to those displays. In par-

ticular, receivers could be more suspicious when they are involved in less

interactive or interpersonal context, which may impair the detection accuracy

of some receivers (Burgoon, Buller, Ebesu, & Rockwell, 1994).

The detection of deception in dyadic interactions is also expected to differfrom group interactions for several reasons. First, additional participants in a

group decrease the perceived immediacy between any two participants,

which may cause group members to be more circumspect with incoming

messages and less tolerant of different opinions (Zhou & Zhang, 2006).

http://pdf.highwire.org/http://pdf.highwire.org/http://pdf.highwire.org/


4/27

274 Small Group Research 44(3)

Second, the likelihood of systematic processing of information is greater at a

group level than at an individual level, which would lead to a decrease in the

usage of the truth-bias heuristic (Park et al., 2002) and accordingly greater of

deception detection. Third, a deceiver likely experiences a higher level ofdeception arousal and detection apprehension when confronting more than

one group member because the deceiver has to divide his attention among all

other members instead of focusing exclusively on one member only, which

would in turn increase the nonstrategic leakage behavior (Zhou & Zhang,

2006). Lastly, deception detection can be viewed as an adversarial problem

solving task (Johnson, Grazioli, Jamal, & Berryman, 2001). Empirical evi-

dence has shown that groups are better at problem solving than individuals as

they behave more consistently with game-theoretic predictions in economicgames (Bornstein, Kugler, & Ziegelmeyer, 2004). Thompson, Peterson, and

Brodt (1996) also found that groups were better than individuals at achieving

Pareto-efficient outcomes in a multi-issue negotiation.

There are also alternative arguments for why detecting deception in group

interaction may be less accurate than that in dyadic interaction. A larger group

is expected to produce a greater amount of output (e.g., message exchanges),

making it more difficult for a receiver to monitor the interactive behavior of

another individual (e.g., deceiver). The greater amount of information maycause a receiver to selectively or heuristically process the information instead

of carefully searching for real cues to deception (Bauchner, Brandt, & Miller,

1977). In addition, a deceiver may become more conscious of exploiting

strategies and tactics (e.g., masking arousal cues) during the interaction with

multiple receivers than in a dyad (Marett & George, 2004).

Zhou and Zhang (2006) examined the moderating effect of group size on

deception behavior in dyadic and triadic interactions. Cohen, Gunia, Kim-

Jun, and Murnighan (2009) compared groups and individuals in terms of who

lies more. Zhou, Sung, and Zhang (2013) empirically investigated deception

in midsized online groups. However, none of these studies addressed decep-

tion detection performance. In view that group factors such as group size and

composition have important implications for group performance in coopera-

tive group work (Campion, Medsker, & Higgs, 1993; Gruenfeld, Mannix,

Williams, & Neale, 1996; Guzzo, 1986; Pelled, 1996; Valacich, Dennis, &

Nunamaker, 1992), studying group detection of deception would not only

extend existing deception research to the group environment, but also expand

group research from cooperative contexts to noncooperative contexts.This research investigates group detection of deception. The focus of this

study is on the influences of familiarity and diversity of group members on

the performance of deception detection. Deception detection performance

has been routinely measured by success in terms of accuracy of deception



5/27

Zhou et al. 275

judgments only. Although efficiency has been widely used to study group

performance in traditional group work research, it is largely overlooked in

deception research. The efficiency with which a group is able to collectively

make a detection judgment through task-related interactions is particularlyrelevant to deception detection because early detection of deception would

allow for reducing or even preventing possible losses caused by otherwise

successful deception. We empirically tested the hypothesized effects of group

familiarity and diversity on the accuracy and efficiency of group deception

detection with real-world data collected from an online community. The find-

ings of this study have significant research and practical implications for both

deception detection and group work.

Theoretical Background

Deception detection has been historically and widely recognized as a difficult

task (Bond & DePaulo, 2006; Burgoon et al., 1994; Caspi & Gorsky, 2006).

Extant research on deception detection has focused on either a noninteractive

context in which receivers passively form impressions of the believability of

senders, or an interactive context where a single receiver is engaged in inter-

personal exchange with the sender. However, group detection of deceptionthat involves a group of receivers largely remains a largely uncharted terri-

tory. In this study, we use IDT (Buller & Burgoon, 1996) as the overarching

theoretical framework for guiding the development of research hypotheses

because the theory and its series of assumptions and propositions offer a

number of implications for deceptive communication in a variety of commu-

nication contexts. More importantly, that theory focuses on interactive situa-

tions where receivers actively engage in communication with senders via

dynamic interpersonal information exchanges. Nevertheless, we extend IDT

by viewing deception as an intragroup as well as interpersonal interaction.

We believe that research on deception detection from a group communication

perspective would advance our knowledge and understanding of the nature of

deception and the deception detection process.

IDT (Buller & Burgoon, 1996) was developed to explain and predict

deception in interpersonal and interactive contexts that involve active

exchange of information between senders and receivers who encode and

decode information simultaneously. Interaction processes are assumed to be

moderated by individual differences (e.g., behavioral repertoires and skills),by relationship factors (e.g., familiarity between the sender and a receiver),

and by cognition (e.g., expectations and evaluation) related to behaviors. IDT

analyzes both deception and deception detection within a communication

framework with an emphasis on the dynamics of interpersonal information



6/27


exchanges. Similar to deception, deception detection is a cognitively com-

plex task in that receivers must add detection to their conversational goals

and tasks. During deception detection, receivers may hold their belief with

inadequate proof or certainty that a sender may be dishonest or untruthful.Moreover, receivers must be alert to the senders awareness of their suspicion

and to the success of their own detection efforts to guide their subsequent

behavioral adjustments.

IDT posits that some preinteraction factors influence the senders initial

detection apprehension and deception displays, as well as receivers initial

suspicion and continued detection accuracy. Particularly influential are indi-

vidual behavioral repertoires and skills that senders and receivers bring to

the interaction. Moving from interpersonal to intragroup deceptive interac-tion has implications for extending these influential factors. In addition, IDT

postulates the effects of context interactivity and relational familiarity

(including informational and behavioral familiarity) on deceptive interac-

tions. The degree of familiarity is posited to alter interaction patterns in an

iterative process of receivers suspicion display and senders reactions to

those displays. The more the two people know each other, the more they

could exhibit truth biases, selectivity, and low suspicion. In the group con-

text, familiarity can be extended from the relationship between individualsto that between the sender and the receiver group or between an individual

and a group (e.g., a single sender). Furthermore, behavioral familiarity and

relational familiarity might have different bearings on the outcome of decep-

tive group interaction.

In group research, group composition, development, interaction process,

and contextual influences have been examined in relation to group perfor-

mance (cf. Guzzo & Shea, 1992). Group composition such as group diversity

is one of the widely studied aspects. Jehn, Northcraft, and Neale (1999) pro-

posed a complex conceptualization of group diversity, which contains infor-

mational diversity, social category diversity, and so forth. Informational

diversity refers to differences in knowledge and perspectives that individual

group members bring to a group. Such differences are likely to arise as a func-

tion of differences in education, experience, and expertise among group mem-

bers. Social category diversity refers to explicit differences among group

members in social category membership, such as gender, race, and ethnicity

(Jackson, 1992; Pelled, 1996). Diverse groups have been found to outperform

homogenous groups in problem-solving and creativity tasks (Hoffman &Maier, 1961; Jackson, 1992; Jehn et al., 1999; Nemeth, 1986). However,

homogenous groups may avoid the process loss associated with poor commu-

nication and excessive conflicts that often plague diverse groups (Ancona,

1987; OReilly III, Caldwell, & Barnett, 1989; Steiner, 1972), thus becoming



7/27

Zhou et al. 277

more effective than diverse groups (Schutz, 1958). A review of 40 years of

research on group diversity did not yield consistent main effects of group

diversity on organizational performance (Williams & OReilly, 1998). A more

recent analysis (Mannix & Neale, 2005) concluded that surface-level socialcategory differences would be more likely to have negative impact on the abil-

ity of groups to function effectively. In contrast, differences in functional

background, education, or personality are often positively related to group

performance by promoting creativity or group problem solving. Their impact,

however, occurs only when the group process is carefully controlled.

Previous studies on group composition assumed that all group members

would consciously work toward achieving the same goals or accomplishing

some tasks via collaboration. They did not consider noncooperative behaviorwhere a deceiving member intentionally leads a group to a wrong conclusion

or bad outcome. It should also be noted that deception is qualitatively differ-

ent from competition, and conflicts naturally arise when multiple people

work together on the same task. Thus, the impacts of group composition on

deception detection remain unclear. Accordingly, the overarching research

question of this study is: How does group composition affect group detection

of online deception?

Hypotheses Development

Extrapolated from IDT, group factors may influence the process of deception

detection. According to group research, group performance may depend on

group characteristics, such as the extent to which group members know one

another and the extent to which they hold common or specialized knowledge

(Gruenfeld et al., 1996). Therefore, to address the overall research question,

this study aims to investigate the effect of group familiarity and group diver-

sity on the success and efficiency of group deception detection.

Group Familiarity

IDT postulates the effect of relational familiarity on deceptive interactions

(Buller & Burgoon, 1996). People with a higher level of familiarity (assum-

ing that the relationship is not negative) tend to exhibit less suspicion.

Familiarity is a complex understanding of others often based on previous

interactions, experiences, or learning from others (Luhmann, 1988). Groupfamiliarity or preacquaintance of group members is a group structure variable

(Smolensky, Carmody, & Halcomb, 1990). We focus on two types of group

familiarity that are likely related to deception detection: relational familiarity

and behavioral familiarity.



8/27


Relational familiarity refers to the degree to which group members are

acquainted with one another. It combines personal knowledge of information

senders background and habits through the firsthand experience with their

particular interaction styles (Burgoon et al., 1994). Trusting a familiar personmay be essential to maintaining intimacy. Relational familiarity should

enable receivers to utilize verbal and nonverbal information effectively to

make accurate judgments of truthfulness. However, when attempting to

detect whether a familiar person deceives, a large number of the targets nor-

mal behavior patterns will be brought to mind, which then results in selective

processing causing authentic deception cues to be missed and detection accu-

racy to be reduced (Millar & Millar, 1995). Specifically, as relationships

among group members become more intimate and stronger, a group memberwill be more likely to consider other members as being truthful (i.e., truth

bias; Stiff, Kim, & Ramesh, 1992). Such a truth bias will be intensified for

familiar others (Burgoon et al., 1994; Van Swol, Malhotra, & Braun, 2012),

resulting in decreased accuracy of deception detection (McCornack & Parks,

1986). In contrast, all-stranger groups were found to be more likely to cor-

rectly identify a suspect than familiar groups when information is fully shared

(Gruenfeld et al., 1996). Thus, we predict that relational familiarity has a

negative influence on group success.

Hypothesis 1 (H1): Relational familiarity impairs group success in decep-

tion detection.

If group members are familiar with one another, they will feel more com-

fortable working together (Gruenfeld et al., 1996) and are more likely to

engage in social interaction, such as using informal speech (Castell, Zornoza

Abad, Prieto Alonso, & Silla, 2000). Although such kinds of informal inter-

actions are important for fostering social cohesion and establishing a sound

social climate; They divert group members attention from accomplishing the

task at hand and from discussing a task (Kreijns, Kirschner, & Jochems,

2003). Additionally, familiar group members may feel more comfortable

with expressing disagreement and reconciling conflicts than strangers

(Gruenfeld et al., 1996), but such groups need to take time to resolve cogni-

tive conflicts (e.g., opposing ideas). Furthermore, familiar members would

be more likely to pool their unique information while groups of strangers

would be more likely to aggregate individual choices and adopt the majoritypreference in group decision making (Gruenfeld et al., 1996; Kerr & Tindale,

2004). The information pooling strategy is expected to be less efficient than

the aggregation strategy. Therefore, we hypothesize that a familiar group

would be less efficient than an unfamiliar group in deception detection.



9/27

Zhou et al. 279

Hypothesis 2 (H2): Relational familiarity impairs group efficiency of

deception detection.

Behavioral familiarity refers to a priori knowledge about or exposure todeception behavior, previous training on cues to deception, or experience

with deceptive behavior in general (Burgoon et al., 1994). Following the

principle of Constructivist Learning Theory (Glaserfeld, 1989), experiencing

deceptive behavior first hand would allow people to identify and test new

deception behavior, thus giving them reliable knowledge. Consistent with

constructivists, experiential learning emphasizes the importance of learning

through concrete experience, observation and reflection, and the formation of

abstract concepts (Kolb & Fry, 1975). Both professionals (e.g., police andcustom officers) and laypersons believe that professionals are better at iden-

tifying truth and lies than laypersons (Garrido, Masip, & Herrero, 2004). This

can be attributed to the fact that professionals develop their expertise through

experiential learning and appropriate training. Knowledge about deceptive

behavior can also be obtained through specialized training of deception

assessment criteria. For instance, deception detection training has been

shown to improve peoples accuracy of discriminating between honest and

deceptive transcripts (K. Colwell et al., 2009; L. H. Colwell et al., 2012). Asimilar training program was able to improve the hit rate of deception

detection (i.e., correct identification of deception), leading to significant

increase of participants overall accuracy of discriminating between genuine

and falsified emotional displays (Porter, Juodis, ten Brinke, Klein, & Wilson,

2010). Therefore, we expect a positive effect of behavioral familiarity on the

outcome of deception detection.

Hypothesis 3 (H3): Behavioral familiarity improves group success in

deception detection.

Behavioral familiarity, by definition, increases with experience or task-

related training. Research has repeatedly shown that professionals are more

confident in their veracity judgments (cf. Vrij, Granhag, & Porter, 2010).

Experienced professional lie detectors have more confidence in their credi-

bility-assessment abilities than their less experienced counterparts (Porter,

Woodworth, & Birt, 2000). A higher level of confidence often results in

quicker decisions based on limited information (Levine & McCornack, 1992;Lord, Ross, & Lepper, 1979). For instance, high confidence may make inves-

tigators detect lies via demeanor alone and not search for physical evidence

(L. H. Colwell, Miller, Lyons, & Miller, 2006). In addition, research has pro-

vided evidence showing that the average organizational tenure of a team is



10/27


positively related to the teams efficiency (Bell, Villado, Lukasik, Belau, &

Briggs, 2011). Therefore, we propose the following hypothesis:

Hypothesis 4 (H4): Behavioral familiarity improves group efficiency indeception detection.

Group Diversity

Group diversity is typically referred to as differences among individuals on

any attribute(s) that may lead to the perception that another person is different

from self (Levine & McCornack, 1992; Triandis, Kurowski, & Gelfand,

1994; Williams & OReilly, 1998). Pelled (1996) conceptualized group diver-sity in terms of job-related attributes, which include experiences, skills, or

perspectives pertinent to cognitive tasks. Other attributes that are less related

to task performance include age, gender, race, and so forth. Such two ends of

a group diversity scale can also be referred to as demographic diversity and

functional diversity (van Knippenberg & Schippers, 2006).

In the demographic diversity dimension, we chose to focus on the gender

diversity in this study. Gender diversity refers to the degree of heterogeneity

of a group with respect to genders of group members (Pelled, Eisenhardt, &Xin, 1999). Demographic attributes such as gender form the context of gen-

eral social relationships and are not directly associated with team objectives

(Sessa & Jackson, 1995). Nonetheless, research has suggested that within a

group, diversity with respect to members demographic backgrounds can

have a powerful negative effect on a groups performance (Bell et al., 2011;

Hare, 1976; Pelled et al., 1999). In addition, dominance is related to decep-

tive interaction because it can be an integral part of deception strategy direct-

ing deception behavior (Bernstein, 1981; Burgoon, Johnson, & Koch, 1998;

Cody & OHair, 1983; Zhou, Burgoon, Zhang, & Nunamaker, 2004), which

in turn may influence group detection of deception. According to social dom-

inance theory (Sidanius, Levin, Liu, & Pratto, 2000; Sidanius, Pratto, &

Bobo, 1994), men have significantly higher social dominance orientation

than women and such a difference is largely invariant across cultural, demo-

graphic, and situational contexts such as age, social class, religion, education

level, political ideology, ethnicity, racism, region of national origin, and gen-

der role. Scattered evidence has shown that men are inferior to women in

detecting deception (McCornack & Parks, 1990; Tilley, George, & Marett,2005). The dominant role of male members in group interaction would fur-

ther exacerbate the influence of their bias in the groups detection judgment.

As a result, higher gender diversity is more likely to lead to social categoriza-

tions and accordingly low group cohesion, as well as social dominance



11/27

Zhou et al. 281

and accordingly high bias, which will negatively affect the identification of

cues to deception and the ultimate success of deception detection.

Hypothesis 5 (H5): Gender diversity impairs group success in deceptiondetection.

Gender differences in social dominance orientation has been confirmed by

numerous studies (cf. Schmitt & Wirth, 2009). It is found that men are asser-

tive and opinionated, while women tend to act friendly, agree with others, and

be process oriented (Myaskovsky, Unikel, & Dew, 2005; Wegge, Roth,

Neubach, Schmidt, & Kanfer, 2008). Dominance occurs when an interper-

sonal behavior is initiated by one individual and accepted by another, result-ing in reduced verbal communication of the other (Youngquist, 2009).

Dominance is particularly salient in close relationships (Dunbar & Burgoon,

2005), which is the case for a deception detection group where individual

members collectively identify deception and make a detection decision. On a

related note, a decision-making group is more subject to the influence of

dominance behavior than a brain-storming group because of the higher inter-

dependence of members in the former group (Guzzo & Shea, 1992).

Therefore, in a more diversified deception detection group, male membersare more likely to dominate female members, with the latter likely being

more submissive and agreeable than the former. Ultimately, the domi-

nancesubmission distinction in a gender-diverse group should help improve

group efficiency in deception detection. Furthermore, gender-diversified

groups are less likely to incur open debates (Palmer, 2001), which further

contributes to the improvement of their efficiency. Therefore, we propose the

following hypothesis.

Hypothesis 6 (H6): Gender diversity improves group efficiency of decep-

tion detection.

Simons, Pelled, and Smith (1999) argue that job relatedness leads to more

effective team performance. Creating groups in a way that maximizes mem-

ber differences may contribute to the performance of group problem solving

and decision making (Guzzo, 1986; Guzzo & Shea, 1992) and organizational

performance (Martin, Reinhard, & Ajay, 2000). This is especially the case

when tasks assigned to a group are diverse, because a wide range of compe-tencies are needed (cf. Campion et al., 1993). From an information/decision

making perspective (Williams & OReilly, 1998), diversity in more job-

related dimensions such as functional background is more likely to have posi-

tive effects on group performance (Bell et al., 2011; Jehn et al., 1999; Pelled



12/27


et al., 1999). Functionally diverse groups are likely to possess a broader range

of task-relevant knowledge, skills, and abilities, and consist of members with

different opinions and alternative perspectives (Cox, 1993; Gruenfeld et al.,

1996; Milliken & Martins, 1996). Such groups have a larger pool of resourcesthat may be helpful in dealing with nonroutine problems (Williams &

OReilly, 1998). Thus, we predict that groups with greater diversity will out-

perform less diverse groups in deception detection when members possess

complementary knowledge.

Hypothesis 7 (H7): Functional diversity improves group success in decep-

tion detection.

Members of functionally diverse groups appear to communicate more for-

mally but less frequently with each other than members of less diverse groups

(Milliken & Martins, 1996). Accordingly, group members with functional

diversity tend to focus on solving a task rather than participating in irrelevant

interactions due to the lack of a common language. Thus, functional diversity

is expected to improve group efficiency.

Hypothesis 8 (H8): Functional diversity improves group efficiency ofdeception detection.

Method

Data Collection

To test the hypotheses, we chose an online version of the Mafia game as the

group task scenario. The goal of the game is for a group to identify one mem-

ber who plays the role of mafia. Each game consists of the mafia, a policeman,and multiple villagers. The game roles can be split into two sides. One is the

player in the role of mafia who has to simultaneously deceive and evade from

detection in order to win a game. The other side includes players who play the

roles of policeman and villager. They work collectively toward the goal of

detecting the deceiver (i.e., mafia). All the roles in a game are randomly

assigned to players (i.e., group members) by a third-party game coordinator.

No one was aware of others roles in the same game. The group interaction

takes place via online chat rooms. To minimize possible effects of group sizeon the proposed relationships, we chose a subset of games with the group size

ranging from six to eight members, resulting in a total of 1,242 games.

The game proceeds through a series of runs. In each run, which lasts for a

fixed period of time, a group discusses the problem of identifying the mafia



13/27

Zhou et al. 283

member, and then each member votes one of other members as the possible

mafia player based on his or her judgment. The player who receives the

majority of the votes would be eliminated from the game. At the end of each

run, the mafia player, if he/she does not receive the majority votes and geteliminated, will eliminate another group member of his/her own choice via a

private chat room, and the policeman will investigate the true identity of one

suspect. If one of the two game termination conditions is met, namely the

mafia player is voted off the game and all players except the mafia player

have been eliminated, the game ends. Otherwise, the game moves into the

next run and the same process will be repeated.

We collected data from a popular Chinese gaming website. For each game,

we collected the following data such as game setup (e.g., the number of vil-lagers), the number of runs, and the final outcome of the game. In addition,

we also collected information about each individual players game history,

including the roles played, the number of games, and the outcomes of each

game that he/she had played. We selected the mafia game for two main rea-

sons. First, mafia players are motivated to deceive and other players are moti-

vated to detect deception in order to compete for game rewards (e.g., virtual

money and score ratings) and to improve their own game skills. This plus

being situated in the natural setting of an online community lead to signifi-cant improvement of the ecological validity of the collected data over the

data collected from laboratory experiments. Second, like many other games,

a mafia game typically lasts multiple runs, allowing us to assess the effi-

ciency of deception detection.

Independent Variables

The group was used as the unit of analysis in this study. In other words, all the

independent variables were operationalized with respect to groups nonmafia

members. Relational familiarity was measured by the average number of

games in which each of the nonmafia members of the current group had

played with the mafia member prior to the current game. Behavioral

Familiarity was measured as the average number of games in which a non-

mafia member had played as each nonmafia member prior to the current

game.Functional diversity was conceptualized in terms of a players detec-

tion skill, which is measured by the percentage of games that the player has

won as a nonmafia prior to the current game. To measure functional diversity,we adopted coefficient of variation (Allison, 1978; Jehn & Bezrukova, 2004),

which is defined as the standard deviation of detection skill divided by its

mean among nonmafia group members. Gender diversity measures how a



14/27


group of nonmafia members are distributed across genders with Blaus index

(Blau, 1977). In view that Blaus maximum is limited by the number of pos-

sible categories (0.5 in case of gender; cf. Harrison & Klein, 2007), we stan-

dardized the index of gender by its theoretical maximum. As a result, thevalue of gender diversity ranges between 0 (when a group is homogeneous)

and 1 (when a group is equally divided).

Dependent Variables

Success of deception detection measures whether a group successfully

detected the mafia player in a game. If the answer wasyes, the value of suc-

cess would be 1; otherwise, it would be 0.Efficiency was defined as the per-centage of runs in a game where a group of players converged on whom the

mafia player might be. The larger the value, the more efficient a group is. It

is noted that each run follows a majority rule.

Data Analysis and Results

The data analysis started with descriptive analysis of individual-level mea-

surements because all the variables were defined as the group aggregates ofindividual-level values. In addition to means and standard deviations, the

skewness, kurtosis, and histogram of each variable (Tabachnick & Fidell,

1989) were also examined. The results show that the distributions of the

values used to derive behavioral familiarity and relational familiarity were

either skewed or peaked. To correct the potential problems of heterosceda-

siticy and nonlinearity, those values were log-transformed before they were

used to compute values for the two familiarity variables. In addition, to

correct a similar problem for functional diversity, its values were trans-

formed with the square root function. Further, the analyses of gender diver-

sity were based on 35 games instead of the entire data set. This was because

gender information was optional for user registration and most users chose

not to disclose their gender. As a result, subsequent regression analyses

were performed on gender diversity and the other three independent vari-

ables separately. The correlations and descriptive statistics are reported in

Table 1. The results show that both behavioral familiarity and functional

diversity are strongly correlated with efficiency (p < .001), and both behav-

ioral familiarity and gender diversity are correlated with success of decep-tion detection (p < .05).

To test the hypotheses of behavioral familiarity, relational familiarity, and

functional diversity in relation to efficiency, a multiple linear regression



15/27

Zhou et al. 285

analysis was performed with an enter method. The method was preferred

when no a priori hypotheses had been made to determine the order of entry

for predictor variables (Tabachnick & Fidell, 1989). To control possible

effects of group size, we first performed a hierarchical regression analysis.

The results show that group size has a significant effect on efficiency of

deception detection (adj.R2 = .011,B =.046, p < .001). After incorporating

the three independent variables, the model fit was improved for the predic-

tion of efficiency (adj. R2 = .052, p < .001), and the likelihood ratio testshowed that the improvement of model fit was significant (p < .001).

Specifically, behavioral familiarity (B = .027, SE= .01,p = .006) and func-

tional diversity (B = .229, SE= .085,p = .007), but not relational familiarity

(B = .013, SE= .013, p = .324), were found to have significant effects on

efficiency. Although the impact of behavioral familiarity on detection effi-

ciency was significant, the impact was negative instead of positive as hypoth-

esized. Collinearity diagnostics was also performed on the linear regression

model. The results confirmed that the model did not have a high degree ofmulticollinearity because none of the condition indexes of the standardized

variables approached 30, making it unnecessary to examine variance propor-

tions. Thus, hypothesis 8 was supported, but hypotheses 2 and 4 were not

supported.

Table 1. Correlations and Descriptive Statistics (N = 1,242).

Variablesa 1 2 3 4 5b 6 7c

1. Group size 2. Relational

familiarity.004

3. Behavioralfamiliarity

.044 .167***

4. Functionaldiversity

.063* .058* .733***

5. Genderdiversityb

.428* .127 .123 .137

6. Efficiency .109*** .003 .195*** .199*** .114 7. Successc .039 .001 .073* .032 .391* .247***

M 6.91 .633 4.39 .421 0.55 .51 .48

SD .811 .715 1.42 .164 0.33 .34 .50

Note. aVariables: The analyses of independent variables were performed on their transformedvalues. bDemographic diversity: The statistics was generated based on a small subset of thedata (N = 35). cSuccess:1 = success, 0 = failure.*p < .05. ***p < .001.



16/27


The analysis of gender diversity followed a similar procedure. Since group

size was not found to influence efficiency of deception detection (R2 = .016,

B = .062, p = .47), a linear regression analysis was performed. Neither did

gender diversity yield any significant effect on efficiency (adj. R2 = .017,B = .117, SE= .177,p = .513). Thus, hypothesis 6 was not supported.

To test the hypotheses related to the impacts of behavioral familiarity,

relational familiarity, and functional diversity on group detection success, a

multiple binary logistic model was applied. Group size was first included as

a control variable and then dropped from the model because of its insignifi-

cant effect (Cox & SnellR2 = .001,B = .095, SE= .07,p = .174). It is shown

from the analysis of three independent variables (Cox & SnellR2 = .006) that

behavior familiarity had positive influence on group success (B = .155, SE=.06,p = .01), but relational familiarity (B = .042, SE= .081,p = .609) and

functional diversity (B = .578, SE= .513, p = .26) did not have significant

effects. Thus, hypothesis 3 was supported and hypotheses 1 and 7 were not

supported.

A similar binary logistic regression analysis was performed on gender

diversity separately. Again, group size was removed from the model because

of its insignificant effect (Cox & Snell; R2 = .047,B = .656, SE= .518,p =

.205). The data analysis yielded a significant effect of gender diversity ongroup success in deception detection (Cox & Snell; R2 = .148, B = 2.65,

SE= 1.22,p = .03). Thus, hypothesis 5 was supported.

Discussion

The objective of this research was to investigate the effects of group familiar-

ity and diversity on the performance of group detection of deception. In this

study, we used two diversity and two familiarity variables as predictors of

group performance in deception detection, which was in turn measured by

both success and efficiency. The empirical results show that behavioral famil-

iarity has a positive effect, and gender diversity has a negative effect, on

group success in deception detection. The results also show that behavioral

familiarity has a negative effect and functional diversity has a positive effect

on the group efficiency of deception detection.

Alternative ExplanationsThe finding on behavioral familiarity in relation to group success supports

our hypothesis. Interestingly, it is opposite to some of the previous findings if

we correlate behavioral familiarity with professional training in deception

detection on the basis of experiential learning (Kolb & Fry, 1975), namely



17/27

Zhou et al. 287

concrete experience and explicit feedback on deception detection provided

by the game. Previous empirical results showed that professionals were not

necessarily superior to laypersons in detecting deception (Vrij, 2004). That is

because professionals are more suspicious of their interviewees, which maylead to high lie bias in deception detection (DeTurck & Miller, 1990; Stiff &

Miller, 1986). In addition, professional detectors with training and expertise

may be over confident in their detection abilities (Vrij, 2004). The current

study differs from previous studies in three key aspects: (a) this study focuses

on detection performance of groups instead of individuals. We speculate that

it would be more effective for the entire group than for only a subset of group

members to receive training for the task of deception detection; (b) the task

environment selected in the current study affords immediate feedback to adetection decision, which allows players to adjust and improve their perfor-

mance over time; and (c) the group task was carried out via online communi-

cation instead of face-to-face communication. The higher reprocessability

the former communication medium in comparison to that of the latter medium

(Dennis & Valacich, 1999) may allow deceptive cues to be identified more

effectively.

The negative effect of behavioral familiarity on efficiency of deception

detection is opposite to our prediction. It suggests that the stereotypical cuesused by experienced detectors require significant effort to encode, particu-

larly in an online communication environment that lacks rich context for

interpreting behaviors. Moreover, extracting concrete deception cues from

online communication requires analytical thinking, which demands more

effort than intuitive or heuristic information processing. Furthermore, disen-

tangling the discourse of online interaction is particularly challenging in mul-

tiparty communication. The volume and pace of synchronous communication

further magnify the challenge.

The lack of effect of relational familiarity also differs from previous

research findings. We provide the following alternative explanations. First,

interpersonal relationships can be developed both online and offline. In this

research, we derived the relational familiarity among group members solely

based on their online interaction during games, which excluded the rela-

tionship developed through other communication channels. On a related

note, the data that we collected were left censored, meaning that all the

participants were assumed to be strangers prior to the data collection. This

assumption may not hold for some participants. Second, the developmentof relationships in groups may require different types of interactive pro-

cesses than that in interpersonal contexts. One problem associated with

group interaction is lurking ((Nonnecke & Preece, 2000), which could hin-

der the development of mutual relationships. Third, the type of workgroups



18/27


might influence the effect of relational familiarity. Groups in the Mafia

game are short-lived and narrow in their activities according to McGraths

categorization of group tasks (McGrath, 1984). Most of the time players

randomly join a game and are randomly assigned game roles. After a gameis over, a player may join another game with other players. Thus, a group

member may not have strong motivations to develop relationships with

other members.

Gender diversity was found to be a drag on group success in deception

detection. In other words, a predominantly male or female group would be

more effective than one that is balanced in gender distribution. This is consis-

tent with both our prediction and previous findings from other cognitive tasks

(e.g., Pelled et al., 1999). Contrary to our expectation, gender diversity hadno influence on group efficiency of deception detection. One possible expla-

nation is that gender dominance in an anonymous online group may not be as

salient as that in a face-to-face group.

The lack of effect of functional diversity on the success of deception

detection was not expected. This may have occurred because the game task is

narrowly focused, which does not require a wide range of competence and/or

solutions for nonroutine problems. In addition, the operationalization of

functional diversity based on detection skill also needs revisiting.

Research and Practical Implications

This is the first study that investigates the effects of group factors on group

performance in deception detection. This study also introduces efficiency as

a new measure of deception detection performance. In addition, most of the

previous research on deception detection examined the problem from a per-

spective of observers, who played a passive role in and/or were disengaged

from deceptive interaction. This study investigates deception detection from

a viewpoint of receivers who directly interact with the deceiver. Further,

research on group diversity itself has been dominated by U.S.-centric studies

(Jonsen, Maznevski, & Schneider, 2011). Our research helps expand the field

by examining diversity issues in a non-U.S. setting.

This research extends IDT in multiple aspects. First, we extend the theory

from the context of interpersonal interaction to intragroup interaction by posit-

ing and validating the effects of group factors on the interactive process of

deception detection and postinteraction outcomes and by explaining thereceiver-related preinteraction features in the context of group communication.

In particular, we approach deception as interaction between the sender and a

group of receivers and among receivers themselves, and as their dynamic

behavioral adaptations. Second, we distinguish the effects of behavioral



19/27

Zhou et al. 289

familiarity and relational familiarity on the process and outcome of deception

detection. Third, the lack of impact of relational familiarity on deception detec-

tion performance revealed in this study highlights the moderating role of com-

munication context on deceptive interaction. It also suggests that the relationalfamiliarity developed online has different implications for deception detection

from that developed via face-to-face interaction. Fourth, we introduce diversity

as a new category of factors in intragroup deceptive exchanges. Depending on

its conceptualization, the diversity of group composition may either impair or

improve some performance measures of deception detection.

This study provides some empirical evidence and concrete suggestions on

how to compose a group for effective and efficient deception detection. The

current findings show that functional diversity is desirable for deceptiondetection because it improves group efficiency in deception detection with-

out hurting its chance of success. One of its managerial implications is that in

organizing a team for the task of deception detection, it is not necessary to

seek only those who have demonstrated a track record of successful decep-

tion detection; instead, a mixed group consisting of members with varying

levels of deception detection skills would achieve a higher level of efficiency

while attaining a similar level of success in the task.

The findings on behavioral familiarity are encouraging for online decep-tion researchers and practitioners in deception detection training. As more

effective cues to online deception are identified and more users receive train-

ing on these cues, we can expect better performance in online deception

detection. Several studies have suggested that legal decision makers have a

lack of training in credibility assessment, have major misconceptions about

deceptive behavior, and hold false stereotypes about deceivers (Porter & ten

Brinke, 2009; Strmwall & Granhag, 2003; Vrij, Akehurst, & Knight, 2006).

Thus, deception detection training and practice will not only benefit layper-

sons but professionals as well.

The negative effect of behavioral familiarity on efficiency calls for methods

to improve the efficiency of extracting cues to deception. One possible solution

is to develop techniques for automatic extraction of cues from the discourse of

online communication. In view of the preliminary success in applying natural

language processing techniques to extracting cues from online text messages

(e.g., Zhou, Burgoon, Nunamaker, & Twitchell, 2004), augmenting human

judgment with these techniques may improve detection judgments.

Limitations and Future Directions

This research only focuses on a few group factors related to group composi-

tion. Many other group factors such as value diversity and interaction pro-

cess and contextual factors (e.g., type of groups) may also affect group



20/27


performance in deception detection but were not included due to the scope

of this study. In addition, there are alternative ways to operationalize the

variables included in the current study. For instance, functional diversity can

be measured based on job titles, and demographic diversity could be mea-sured based on age.

We assumed that group members develop relationships over time, but we

did not account for the valance of relationships. According to IDT (Buller &

Burgoon, 1996), When relationships are positively valanced, familiar others

(such as friends) show a greater truth bias than strangers. However, when

relationships are built on mistrust or are negatively toned, the truth bias

should be attenuated or even become a lie bias (p. 214). Thus, future research

should look into the intersection between relationship familiarity and rela-tionship valence.

We measured group gender diversity as a whole without looking into the

nature of the diversity. For instance, highly heterogeneous groups could con-

tain a high proportion of females or males. The dominant gender of a group

could play a moderating role in the effect of gender diversity on group perfor-

mance in deception detection.

The real-world data enhance the external validity of the findings of this

study, compared with laboratory environments. The former data collectionmethod presents, however, presents several challenges: real-world data are

noisy. For instance, missing values are common, which was the case for

gender diversity in our study. As a result, we had to perform two separate

regression analyses due to two disparate sample sizes. Second, real-world

data can lead to low explanatory power, despite the fact that explanatory

variables can be very important and practical in explaining or predicting a

response variable. Finally, there were many other factors that were out of our

control. For instance, we did not have access to information about game

players age and other relevant experience.

Concluding Remarks

Given the increasing deceptive behavior in group work and online communi-

ties and social networks, the objective of this research is to investigate the

influence of group familiarity and diversity on group performance in decep-

tion detection. To the best of our knowledge, this is the first study that inter-

twines deception research with group research. The current work extendsdeception theories by providing insights into impacts of group composition on

the performance of deception detection. This study also expands traditional

group research from cooperative environments to a noncooperative environ-

ment. The findings suggest that group functional diversity is desirable for



21/27

Zhou et al. 291

group detection of online deception, but gender diversity is not preferred. In

addition, behavioral familiarity has mixed effects, depending on the measure

of detection performance, but relational familiarity has no effect. These find-

ings represent one step toward creating a trustworthy and secure online envi-ronment for effective group communication. This study can be extended in a

number of directions such as the exploration of new group factors, the interac-

tion of different group factors, and the effect of different types of groups.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research,

authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publica-

tion of this article.

References

Allison, P. D. (1978). Measures of inequality. American Sociological Review, 43,

865-880. doi:10.2307/2094626

Ancona, D. G. (1987). Groups in organizations: Extending laboratory models. InC. Hendrick (Ed.), Review of personality and social psychology (Vol. 9,

pp. 207-230). Newbury Park, CA: Sage.

Bauchner, J. E., Brandt, D. R., & Miller, G. (1977). The truth/deception attribu-

tion: Effects of varying levels of information availability. In B. D. Ruben

(Ed.), Communication yearbook(Vol. 1, pp. 229-243). New Brunswick, NJ:

Transaction Books.

Bell, S. T., Villado, A. J., Lukasik, M A., Belau, L., & Briggs, A. L. (2011).

Getting specific about demographic diversity variable and team perfor-

mance relationships: A meta-analysis. Journal of Management, 37, 709-743.doi:10.1177/0149206310365001

Bernstein, I. S. (1981). Dominance: The baby and the bathwater. The Behavioral and

Brain Sciences, 4, 419-457. doi:10.1017/S0140525X00009614

Blau, P. M. (1977).Inequality and heterogeneity. New York, NY: Free Press.

Bond, C. F., Jr., & DePaulo, B. M. (2006). Accuracy of deception judg-

ments. Personality & Social Psychology Review, 10, 214-234. doi:10.1207/

s15327957pspr1003_2

Bornstein, G., Kugler, T., & Ziegelmeyer, A. (2004). Individual and group deci-

sions in the centipede game: Are groups more rational players? Journal ofExperimental Social Psychology, 40, 599-605. doi:10.1016/j.jesp.2003.11.003

Buller, D. B., & Burgoon, J. K. (1996). Interpersonal deception theory. Communication

Theory, 6, 203-242. doi:10.1111/j.1468-2885.1996.tb00127.x



22/27


Burgoon, J. K., Buller, D. B., Ebesu, A. S., & Rockwell, P. (1994). Interpersonal

deception: V. Accuracy in deception detection. Communication Monographs, 61,

303-325. doi:10.1080/03637759409376340

Burgoon, J. K., Johnson, M. L., & Koch, P. T. (1998). The nature and measure-ment of interpersonal dominance. Communication Monographs, 65, 308-335.

doi:10.1080/03637759809376456

Byrne, J. A. (1993, December). The horizontal corporation.BusinessWeek, 20, 76-81.

Campion, M. A., Medsker, G. J., & Higgs, C. A. (1993). Relations between work

group characteristics and effectiveness: Implications for designing effective work

groups. Personnel Psychology, 46, 823-847. doi:10.1111/j.1744-6570.1993.

tb01571.x

Caspi, A., & Gorsky, P. (2006). Online deception: Prevalence, motivation, and emo-

tion. CyberPsychology & Behavior, 9, 54-59. doi:10.1089/cpb.2006.9.54Castell, V. O., Zornoza Abad, A. M., Prieto Alonso, F., & Silla, J. M. P. (2000).

The influence of familiarity among group members, group atmosphere and

assertiveness on uninhibited behavior through three different communica-

tion media. Computers in Human Behavior, 16, 141-159. doi:10.1016/S0747-

5632(00)00012-1

Cody, M. J., & OHair, H. D. (1983). Nonverbal communication and deception:

Differences in deception cues due to gender and communicator dominance.

Communication Monographs, 50, 175-192. doi:10.1080/03637758309390163

Cohen, T. R., Gunia, B. C., Kim-Jun, S. Y., & Murnighan, J. K. (2009). Do groups liemore than individuals? Honesty and deception as a function of strategic self-inter-

est. Journal of Experimental Social Psychology, 45, 1321-1324. doi:10.1016/j.

jesp.2009.08.007

Colwell, L. H., Colwell, K., Hiscock-Anisman, C. K., Hartwig, M., Cole, L., &

Werdin, K. (2012). Teaching professionals to detect deception: The efficacy of a

brief training workshop.Journal of Forensic Psychology Practice, 12, 68-80. doi:

10.1080/15228932.2012.629592

Colwell, K., Hiscock-Anisman, C., Memon, A., Colwell, L. H., Taylor, L., & Woods,

D. (2009). Training in assessment criteria indicative of deception to improvecredibility judgments. Journal of Forensic Psychology Practice, 9, 199-207.

doi:10.1080/15228930902810078

Colwell, L. H., Miller, H. A., Lyons, P. M., & Miller, R. S. (2006). The training

of law enforcement officers in detecting deception: A survey of current prac-

tices and suggestions for improving accuracy. Police Quarterly, 9, 275-290.

doi:10.1177/1098611104273293

Cox, T. (1993). Cultural diversity in organizations: Theory, research, and practice.

San Francisco, CA: Berrett-Koehler.

Dennis, A. R., & Valacich, J. S. (1999).Rethinking media richness: Towards a the-ory of media synchronicity. Paper presented at the 32nd Hawaii International

Conference on Systems Sciences, Maui, HI.



23/27

Zhou et al. 293

DeTurck, M. A., & Miller, G. R. (1990). Training observers to detect deception:

Effects of self-monitoring and rehearsal. Human Communication Research, 16,

603-620. doi:10.1111/j.1468-2958.1990.tb00224.x

Donnellon, A. (1996). Team talk: The power of language in team dynamics.Cambridge, MA: Harvard Business School Press.

Dunbar, N. E., & Burgoon, J. K. (2005). Perceptions of power and interactional

dominance in interpersonal relationships. Journal of Social and Personal

Relationships, 22, 207-233. doi:10.1177/0265407505050944

Frank, M. G., Feeley, T. H., Paolantonio, N., & Servoss, T. J. (2004). Individual and

small group accuracy in judging truthful and deceptive communication. Group

Decision and Negotiation, 13, 45-59. doi:10.1023/B:GRUP.0000011945.85141.af

Garrido, E., Masip, J., & Herrero, C. (2004). Police officers credibility judgments:

Accuracy and estimated ability.International Journal of Psychology, 39, 254-275.doi:10.1080/00207590344000411

Glaserfeld, E. (1989). Constructivism in education. Oxford, UK: Pergamon Press.

Gruenfeld, D., Mannix, E., Williams, K., & Neale, M. (1996). Group composition

and decision making: How member familiarity and information distribution

affect process and performance. Organizational Behavior and Human Decision

Process, 67, 1-15. doi:10.1006/obhd.1996.0061

Guzzo, R. A. (1986). Group decision making and group effectiveness in organiza-

tions. In P. S. Goodman (Ed.),Designing effective work groups (pp. 34-71). San

Francisco, CA: Jossey-Bass.Guzzo, R. A., & Shea, G. P. (1992). Group performance and intergroup relations in

organizations. In M. D. Dunnette & L. M. Hough (Eds.),Handbook of industrial

and organizational psychology (Vol. 3, pp. 269-313). Palo Alto, CA: Consulting

Psychologists Press.

Hare, A. P. (Ed.). (1976).Handbook of small group research (2nd ed.). New York,

NY: Free Press.

Harrison, D. A., & Klein, K. J. (2007). Whats the difference? Diversity constructs

as separation, variety, or disparity in organizations? Academy of Management

Review, 32, 1199-1228. doi:10.5465/AMR.2007.26586096Hoffman, L. R., & Maier, N. R. F. (1961). Quality and acceptance of problem solutions

by members of homogeneous and heterogeneous group. Journal of Abnormal

and Social Psychology, 62, 401-407. doi:10.1037/h0044025

Jackson, S. (1992). Team composition in organizations. In W. S. W. Wood &

J. Simpson (Eds.), Group process and productivity(pp. 1-12). London, UK: SAGE.

Jehn, K. A., & Bezrukova, K. (2004). A field study of group diversity, workgroup

context, and performance. Journal of Organizational Behavior, 25, 703-729.

doi:10.1002/job.257

Jehn, K. A., Northcraft, G. B., & Neale, M. A. (1999). Why differences make a dif-ference: A field study of diversity, conflict, and performance in workgroups.

Administrative Science Quarterly, 44, 741-763. doi:10.2307/2667054

Johnson, P. E., Grazioli, S., Jamal, K., & Berryman, R. G. (2001). Detecting decep-

tion: Adversarial problem solving in a low base-rate world. Cognitive Science,

25, 355-392. doi:10.1016/S0364-0213(01)00040-4



24/27


Jones, G. R., & George, J. M. (1998). The experience and evolution of trust:

Implications for cooperation and teamwork. The Academy of Management

Review, 23, 531-546. doi:10.5465/AMR.1998.926625

Jonsen, K., Maznevski, M. L., & Schneider, S. C. (2011). Special review article: Diversityand its not so diverse literature: An international perspective.International Journal

of Cross Cultural Management, 11, 35-62. doi:10.1177/1470595811398798

Kerr, N. L., & Tindale, R. S. (2004). Group performance and decision mak-

ing. Annual Review of Psychology, 55, 623-655. doi:10.1146/annurev.

psych.55.090902.142009

Kolb, D. A., & Fry, R. (1975). Toward an applied theory of experiential learning.

In C. Cooper (Ed.), Theories of group process (pp. 33-58). London, UK: John

Wiley.

Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for socialinteraction in computer-supported collaborative learning environments: A review

of the research. Computers in Human Behavior, 19, 335-353. doi:10.1016/S0747-

5632(02)00057-2

Levine, T. R., & McCornack, S. A. (1992). Linking love and lies: A formal test of

the McCornack and Parks model of deception detection. Journal of Social and

Personal Relationships, 9, 143-154. doi:10.1177/0265407592091008

Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude

polarization: The effects of prior theories on subsequently considered evidence.

Journal of Personality and Social Psychology, 37, 2098-2109. doi:10.1037/0022-3514.37.11.2098

Luhmann, N. (1988). Familiarity, confidence, trust: Problems and alternative. In

D. Gambetta (Ed.), Trust: Making and breaking cooperative relations(pp. 94-108).

Oxford, UK: Blackwell.

Mannix, E., & Neale, M. A. (2005). What differences make a difference?Psychological

Science in the Public Interest, 6, 31-55. doi:10.1111/j.1529-1006.2005.00022.x

Marett, L. K., & George, J. F. (2004). Deception in the case of one sender and

multiple receivers. Group Decision and Negotiation, 13, 29-44. doi:10.1023/

B:GRUP.0000011943.73672.9bMartin, K., Reinhard, A., & Ajay, M. (2000). Top management-team diversity and

firm performance: Examining the role of cognitions. Organization Science, 11,

21-34. doi:10.1287/orsc.11.1.21.12569

McCornack, S. A., & Parks, M. (1986). Deception detection and the other side of

trust. In M. L. McLaughlin (Ed.), Communication yearbook(Vol. 9, pp. 377-389).

Beverly Hills, CA: SAGE.

McCornack, S. A., & Parks, M. R. (1990). What women know that men dont: Sex

differences in determining the truth behind deceptive messages.Journal of Social

and Personal Relationships, 7, 107-118. doi:10.1177/0265407590071006McGrath, J. E. (1984). Groups: Interaction and performance. Englewood Cliffs, NJ:

Prentice-Hall, Inc.

Millar, M., & Millar, K. (1995). Detection of deception in familiar and unfamiliar

persons: The effects of information restriction. Journal of Nonverbal Behavior,

19, 69-84. doi:10.1007/BF02173167



25/27

Zhou et al. 295

Milliken, F. J., & Martins, L. L. (1996). Search for common threads: Understanding the

multiple effects of diversity in organizational groups.Academy of Management

Journal, 21, 402-433. doi:10.5465/AMR.1996.9605060217

Myaskovsky, L., Unikel, E., & Dew, M. (2005). Effects of gender diversity on perfor-mance and interpersonal behavior in small work groups. Sex Roles, 52, 645-657.

doi:10.1007/s11199-005-3732-8

Nemeth, C. J. (1986). Differential contributions of majority and minority influence.

Psychological Review, 93, 23-32. doi:10.1037/0033-295X.93.1.23

Nonnecke, B., & Preece, J. (2000).Lurker demographics: Counting the silent. Paper

presented at the Proceedings of the SIGCHI conference on Human Factors in

Computing Systems, The Hague, The Netherlands.

OReilly, C. A., III., Caldwell, D. F., & Barnett, W. P. (1989). Work group demogra-

phy, social integration, and turnover.Administrative Science Quarterly, 34, 21-37.doi:10.2307/2392984

Palmer, S. L. (2001). Executive group decision-making and demographic diversity:

Exploring the roles of generating issues and ideas and evaluating outcomes.

Dissertation Abstracts International, 62, 1628.

Park, E. S., Levine, T. R., Harms, C. M., & Ferrara, M. H. (2002). Group and indi-

vidual accuracy in deception detection. Communication Research Reports, 19,

99-106. doi:10.1080/08824090209384837

Pelled, L. H. (1996). Demographic diversity, conflict, and work group outcomes:

An intervening process theory. Organization Science, 7, 615-631. doi:10.1287/orsc.7.6.615

Pelled, L. H., Eisenhardt, K. M., & Xin, K. R. (1999). Exploring the black box: An

analysis of work group diversity, conflict, and performance. Administrative

Science Quarterly, 44, 1-28. doi:10.2307/2667029

Porter, S., & ten Brinke, L. (2009). Dangerous decisions: A theoretical framework

for understanding how judges assess credibility in the courtroom. Legal and

Criminological Psychology, 14, 119-134. doi:10.1348/135532508X281520

Porter, S., Juodis, M., ten Brinke, L. M., Klein, R., & Wilson, K. (2010). Evaluation

of the effectiveness of a brief deception detection training program. Journal ofForensic Psychiatry & Psychology, 21, 66-76. doi:10.1080/14789940903174246

Porter, S., Woodworth, M., & Birt, A. R. (2000). Truth, lies, and videotape: An inves-

tigation of the ability of federal parole officers to detect deception. Law and

Human Behavior, 24, 643-658. doi:10.1023/a:1005500219657

Schmitt, M. T., & Wirth, J. H. (2009). Evidence that gender differences in social

dominance orientation result from gendered self-stereotyping and group-inter-

ested responses to patriarchy. Psychology of Women Quarterly, 33, 429-436.

doi:10.1111/j.1471-6402.2009.01520.x

Schutz, W. C. (1958).FIRO: A three dimensional theory of interpersonal behavior.New York, NY: Rinehart.

Sessa, V., & Jackson, S. (1995). Diversity in decision-making teams: All differences

are not created equal. In M. Chemers, S. Oskamp & M. Costanzo (Eds.),Diversity

in organizations: New perspectives for a changing workplace (pp. 133-157).

Thousand Oaks, CA: SAGE. doi:10.4135/9781452243405.n6



26/27


Sidanius, J., Levin, S., Liu, J., & Pratto, F. (2000). Social dominance orientation,

anti-egalitarianism and the political psychology of gender: An extension and

cross-cultural replication. European Journal of Social Psychology, 30, 41-67.

doi:10.1002/(sici)1099-0992(200001/02)30:13.0.co;2-oSidanius, J., Pratto, F., & Bobo, L. (1994). Social dominance orientation and the

political psychology of gender: A case of invariance?Journal of Personality and

Social Psychology, 67, 998-1011. doi:10.1037/0022-3514.67.6.998

Simons, T., Pelled, L. H., & Smith, K. A. (1999). Making use of difference: Diversity,

debate, and decision comprehensiveness in top management teams.Academy of

Management Journal, 42, 662-673. doi:10.2307/256987

Smolensky, M. W., Carmody, M. A., & Halcomb, C. G. (1990). The influence of

task type, group structure and extraversion on uninhibited speech in com-

puter-mediated communication. Computers in Human Behavior, 6, 261-272.doi:10.1016/0747-5632(90)90022-9

Steiner, I. D. (1972). Group process and productivity. New York, NY: Academic

Press.

Stiff, J. B., & Miller, G. R. (1986). Come to think of it . . .: Interrogative probes,

deceptive communication, and deception detection. Human Communication

Research, 12, 339-357. doi:10.1111/j.1468-2958.1986.tb00081.x

Stiff, J. B., Kim, H., & Ramesh, C. (1992). Truth biases and aroused sus-

picion in relational deception. Communication Research, 19, 326-345.

doi:10.1177/009365092019003002Strmwall, L. A., & Granhag, P. A. (2003). Affecting the perception of verbal cues to

deception.Applied Cognitive Psychology, 17, 35-49. doi:10.1002/acp.851

Tabachnick, B. G., & Fidell, L. S. (1989). Using multivariate statistics (2nd ed.). New

York, NY: Harper & Row.

Thompson, L., Peterson, E., & Brodt, S. E. (1996). Team negotiation: An examina-

tion of integrative and distributive bargaining.Journal of Personality and Social

Psychology, 70, 66-78. doi:10.1037/0022-3514.70.1.66

Tilley, P., George, J. F., & Marett, K. (2005). Gender differences in deception and its

detection under varying electronic media conditions. Paper presented at the 38thAnnual Hawaii International Conference on System Sciences, Hilton Waikoloa

Village, HI.

Triandis, H. C., Kurowski, L. L., & Gelfand, M. J. (1994). Workplace diversity. In

T. H. C. M. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organi-

zational psychology(pp. 770-827). Palo Alto, CA: Consulting Psychologists Press.

Valacich, J. S., Dennis, A. R., & Nunamaker, J. F. (1992). Group size and anonymity

effects on computer mediated idea generation. Small Group Research, 23, 49-73.

doi:10.1177/1046496492231004

van Knippenberg, D., & Schippers, M. C. (2006). Work group diversity.Annual Reviewof Psychology, 58, 515-541. doi:10.1146/annurev.psych.58.110405.085546

Van Swol, L. M., Malhotra, D., & Braun, M. T. (2012). Deception and its detection:

Effects of monetary incentives and personal relationship history. Communication

Research, 39, 217-238. doi:10.1177/0093650210396868

Vrij, A. (2004). Why professionals fail to catch liars and how they can improve.Legal

and Criminological Psychology, 9, 159-181. doi:10.1348/1355325041719356



27/27

Zhou et al. 297

Vrij, A., Akehurst, L., & Knight, S. (2006). Police officers, social workers, teachers

and the general publics beliefs about deception in children, adolescents and adults.

Legal and Criminological Psychology,11, 297-312. doi:10.1348/135532505x60816

Vrij, A., Granhag, P. A., & Porter, S. (2010). Pitfalls and opportunities in nonverbaland verbal lie detection.Psychological Science in the Public Interest, 11, 89-121.

doi:10.1177/ 1529100610390861

Wegge, J., Roth, C., Neubach, B., Schmidt, K., & Kanfer, R. (2008). Age and gender

diversity as determinants of performance and health in a public organization:

The role of task complexity and group size.Journal of Applied Psychology, 93,

1301-1313. doi:10.1037/a0012680

Williams, K. Y., & OReilly, C. A. (1998). Demography and diversity in organi-

zations: A review of 40 years of research. In B. M. Staw & R. Sutton (Eds.),

Research in organizational behavior(Vol. 20, pp. 77-140). Greenwich, CT: JAIPress.

Youngquist, J. (2009). The effect of interruptions and dyad gender combination on

perceptions of interpersonal dominance. Communication Studies, 60, 147-163.

doi:10.1080/ 10510970902834874

Zhou, L., & Zhang, D. (2006). A comparison of deception behavior in dyadic and tri-

adic group decision making in synchronous computer-mediated communication.

Small Group Research, 37, 140-164. doi:10.1177/1046496405285125

Zhou, L., Burgoon, J. K., Nunamaker, J. F., & Twitchell, D. (2004). Automating

linguistics-based cues for detecting deception in text-based asynchronous com-puter-mediated communications. Group Decision & Negotiation, 13, 81-106.

doi:10.1023/ B: GRUP.0000011944.62889.6f

Zhou, L., Burgoon, J. K., Zhang, D., & Nunamaker, J. F. (2004). Language dominance

in interpersonal deception in computer-mediated communication. Computers in

Human Behavior, 20, 381-402. doi:10.1016/S0747-5632(03)00051-7

Zhou, L., Sung, Y., & Zhang, D. (2013). Deception performance in online group negoti-

ation and decision making: The effects of deception experience and deception skill.

Group Decision and Negotiation, 22, 153-172. doi:10.1007/s10726-012-9303-9

Author Biographies

Lina Zhou is an associate professor of Information Systems, University of Maryland,

Baltimore County, USA. Her current research interests include deception detection,

ontology, social network analysis, and mobile web.

Dongsong Zhang is an associate professor in the Department of Information Systems

at the University of Maryland, Baltimore County, USA. He received his PhD in

Management Information Systems from the University of Arizona. His current

research interests include context-aware mobile computing, computer-mediated col-laboration and communication, knowledge management, and social computing.

Yu-wei Sung is a PhD candidate of Information Systems at the University of

Maryland Baltimore County. He received his Masters degree in Management

Information Systems from National Sun Yet-Sen University, Taiwan. His research

interests include deception detection in online groups, web services, and mobile

i

deception detection ins small groups

Documents