Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
Task Complexity and Deception Detection in a Collaborative Group Setting Gabriel A. Giordano Joey F. George MIS Department College of Business Florida State University
[email protected]
Abstract Deception and deception detection have become important research areas in business. However, little deception research has looked at the computer-mediated group settings that many organizational decisions are made in today. Further, past research has not looked at the influence of task complexity, a key part of group decision processes, on deception. This study looked at these unexplored areas by conducting an experiment to measure the effects of task complexity on group deception detection and group performance. Our findings revealed very low overall detection rates and showed no significant differences in the detection rates between groups performing tasks with different levels of complexity. We did find that even though all groups had the assets necessary to perform well, groups with the complex task performed significantly worse than groups with the less complex task, signaling that these groups were more affected by deception.
1. Introduction There is no doubt that organizational environments are changing. As environments are becoming more turbulent, managers are spending more time in meetings, and groups are more often making important decisions. Groups are important because they have the potential to outperform individuals due to individuals working together and sharing knowledge. However, as more people participate in groups, less seems to get accomplished. Past research has found that many groups either offer no benefit or have an overall negative effect on decision processes [9]. Although these detrimental effects have many different causes, some of these negative outcomes may be the result of group members’ deceptive behaviors, stemming from hidden personal agendas and motivations that differ from those of the group. There is a large amount of research on deception, however, most deception research has focused on noninteractive and non-group conditions that are not very
applicable to the real world [1]. Also, very little deception research has focused on the computer-mediated communication and human-computer interfaces that are central to group processes in modern businesses. Past deception research was not very applicable to business settings since the tasks studied did not mimic the time pressured, computer-mediated, and dynamic business settings where group decisions are often made. Further, much of the past deception research did not look at the influence of task complexity, an important part of group decision-making processes [10], on deception detection and group performance. Real world decision-making groups operate in a variety of settings and situations, and their processes differ significantly based on the complexity of the task that they face. We addressed these methodological deficiencies in the past deception research by conducting an experiment where groups performed an interactive task that simulated the dynamics involved in real organizational decisionmaking groups and by varying the complexity of the task that these groups faced. Our goal was to answer the questions: Does task complexity affect group deception detection accuracy in a computer-mediated and collaborative group setting, and does the coupling of deception and task complexity affect groups’ task performance in a collaborative group setting? The rest of this paper is organized as follows. First, we review the literature on the theoretical bases for our study, specifically interpersonal deception theory and social presence theory. We then look at how relational closeness, environmental complexity, and communication media are related to deception and its detection. The next sections of the paper describe our hypotheses, research method, findings, and limitations of the study. We end with our conclusion.
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
2. Literature Review A substantial amount of research on deception and cues to deception has been conducted in multiple academic fields [6], and there are now several theories that explain deception and deception detection processes. One key theory that has emerged and covers both deception and deception detection is interpersonal deception theory [1]. Interpersonal deception theory suggests that the relationship between deceivers and receivers is marked by multiple judgments and perceptions from both parties (see Figures 1, 2). According to interpersonal deception theory, when a deceiver sends a deceptive message, receivers either consciously or unconsciously search for cues to deception. The leakage of these cues takes place when deceivers fear detection and when they move effort away from maintaining normal behavior to the task of developing a deceptive strategy [8]. After leaked cues are received, individuals assess and manage the cues and exhibit some display of suspicion to the deceiver. This display may alert the deceiver, who will adapt communication in an attempt to send fewer deceptive cues. This idea of strategic deception is a key element of interpersonal deception theory [1].
Figure 1. The Deceptive Communication Event (From Carlson, George, Burgoon, Adkins, & White, 2003)
Figure 2. Deceptive Communication and Its Detection (From Carlson, George, Burgoon, Adkins, & White, 2003)
Social presence theory [12] is another theory that addresses the importance of communication cues that are relevant to the deception process. It recognizes that both the visual and non-verbal cues transmitted during communication convey a level of realness (or lack of) to individuals. A lack of realness can cause individuals to pay less attention to the social interactions of others, hindering their perception of cues and ultimately their detection of detection [2]. Another influence on deception processes is relational closeness. When individuals form relationships, they often expect the others to tell the truth, which is termed a truth bias [8]. Receivers with a truth bias are less successful at detecting deception [3]. Yet another influence on deception is environmental complexity. Complex cognitive tasks have been found to negatively affect the deception detection process [8, 16]. A complex task can result in a cognitive overload, which can cause individuals to subconsciously process information that is clear and easily accessible (such as task-based information) before processing ambiguous and partially hidden information (such as cues to deception), reducing their deception detection accuracy. Lewis et al. [7] recognize that individuals that are given too much information at one time often completely fail to process certain information, delay their processing of information until an overload abates, and lower the quality of their information processing. A last influence on deception is the communication medium. Zmud [18] proposes that technology can be an effective tool used by those who hope to deceive. This proposition is based on media richness theory [5], which recognizes differences in feedback, social cues, language
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
variety, and personal focus in different communication media. Face-to-face communication is considered the richest medium in terms of number of transmitted cues, while text-based media has one of the most limited cue capacities. Many cues to deception are related to the visual and vocal communication channels, and they include actions such as changes in pupil dilation and voice pitch [6]. Computer mediated communication limits the transmission of these types of cues, potentially making it much easier for a deceiver to disguise their deceptive motives.
3. Hypotheses We developed the following hypotheses based on our research question and the previously discussed literature: H1: Groups using computer-mediated communication and facing a complex task will be less accurate at detecting deception than groups facing a less complex task. A first influence on deception detection accuracy is the environment. The existence of multiple communicators in a group setting means that receivers must handle a larger number of communication cues. As outlined by interpersonal deception theory [1], receivers strategically handle a number of cues for each individual they are communicating with. These cues, when coupled with the complexities associated with group processes [10, 13, 14], significantly increase individuals’ cognitive loads. On top of the cognitive demands coming from a group setting, groups facing a more complex task need to participate more actively than groups performing an easier task. These demands increase the chance of an information overload and decrease the amount of cue deciphering individuals can handle [7]. Because of the decrease in cue deciphering ability, complex tasks, when coupled with the demands stemming from a group setting, should cause groups of individuals to be less successful at detecting the filtered cues to deception available in computer mediated communication. Visual and paralinguistic cues to deception are filtered in computer-mediated communication [11], leaving receivers with limited cues to deception. Previous research suggested that the more complicated and varied the task characteristics, the less likely a deceiver will be able to produce successful deceptive statements [3]. However, we feel that this effect will be minimized by a lean communication medium that does not allow the transmission of many cues to deception, making it unimportant to this study. H2: Groups with a deceiver using computer-mediated communication and facing a more complex task will
perform at a lower level than similar groups facing a less complex task. As previously mentioned, receivers in groups facing a more complex task will be less able to focus on the validity of deception information than receivers in groups facing a less complex task [7]. Thus, groups facing a complex task will be more influenced by deceivers since they do not realize the deceiver’s intention and because of the group’s inherent trust in the group’s members (i.e., the truth bias). This influence should cause these groups to perform worse at their tasks, even if they have the tools necessary to perform the task at a high level. This outcome should be especially visible when deceptive group members have a goal that is detrimental to the goal of the group. Without deception, individuals with the tools necessary to perform their task should perform at a similar level to groups with an easier task, as long as they are not overloaded with demands from that task or from another influence.
4. Research Method In order to test our hypotheses, we conducted a laboratory experiment. Since the focus of our research question was not to build new theory but rather to apply existing theory to a new setting, a laboratory experiment was the appropriate methodology. The lab experiment provided us with a high level of control, increasing the internal validity of our results. One of the limits of laboratory experiments is low external validity [17]. However, given the limitations of studying deception in a real business environment (i.e., site access, ethical concerns) and the limited level of experimenter control available in a field setting, we felt that a laboratory experiment was the best research method for testing our hypotheses.
5. Sample Our sample population consisted of undergraduate business students at a large southeastern university. Business students were used because they have a common level of familiarity with computerized communication technologies and because of their similarity to individuals in real business environments. Students from a variety of business programs were studied in order to minimize other possible commonalities that might limit the generalizability of this study’s findings [4]. A total of 60 undergraduate business students participated in the experiment. Subjects were asked to sign up for an experiment session with two other subjects. They were told that they would be participating in a fun, quick multiplayer strategy game, and that they would be given a monetary incentive
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
for participating. The subjects were not informed of the true nature of the study, which was accuracy in deception detection. They were only told that they would be participating in a group-based communication study.
• •
The 1st asset could search 1 game board gridsquare per turn and was very accurate. The 2nd asset could search 2 game board gridsquares per turn and was moderately accurate.
6. Experimental Procedure The task subjects performed was based on a multiplayer game named StrikeCOM, which was designed and built by the Center for the Management of Information (CMI) at the University of Arizona. StrikeCOM provided a simulation environment that was designed to foster group communication in a cooperative activity. The object of the game was for a team of players to methodically search a game board and find a fixed number of targets. The game included a built-in text messaging area that allowed for computer-mediated communication between the players (see Figures 3, 4). We selected StrikeCOM as the experimental group task because it allowed for spontaneous interactive communication between group members. This type of interaction was important because it closely resembled the interpersonal deception theory interactive process. The game also provided sufficient motivation for deception and it supported fully computer-mediated communication. The goal in the game was for players to find enemy bases in a grid board with their group members. Players were instructed to discuss their search results with their teammates and to work together to locate the targets. Each team's combined resources provided them with the potential to find all of the enemy targets. One participant was randomly selected to be a deceiver in each group. They were given special instructions on top of the instructions that the other participants received (see Attachment-2). The deceivers were told to pretend that they were from the area being targeted in the simulation and that they still had friends and family in the area; further, they knew that the enemy was hiding among civilians, and so their family could be killed in the strikes. They were then told that their goal in the game was to deceive their team members about the true locations of the enemy and to get them to destroy empty targets. In order to protect their family, they could not reveal their true motive at any time. They were given information as to where the targets were located, and they were told to get their teammates to avoid those squares by concealing information, misdirecting their searches, and lying and deceiving in any way they saw fit. Instructions were given to participants in written form in order to avoid experimenter-based biases (see Attachment-1 for the game-play instructions). The following is brief description of the game: Participants each played a role as a space, air, or intelligence commander. They each had 2 search assets (see Figure 3):
Figure 3. A StrikeCOM Search
Their search results were as follows (see Figure 4): • • •
Red X - signaled that they likely found an enemy base Green check - their asset had nothing to report (it was unlikely that there was a base there) Yellow diamond - suspicious activity was found in the square (an inconclusive finding)
Figure 4. StrikeCOM Search Results
There were three turns in the game. On each turn participants conducted a search using both of their assets. On their final turn they were told to work with their teammates to devise a common bombing plan in an attempt to destroy the enemy targets. They were able to
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
communicate with their teammates at any time using the text-messaging feature of the game. Half of the groups were randomly selected to have a more complex game setup than the others. The number of grid squares and the number of targets in the simulation were increased to make the game more difficult. This experimental manipulation was not revealed to the participants at any stage of the experiment. Both settings were piloted beforehand, and pilot participants perceived the complex settings as being significantly harder than the easy settings. The easy games had a 3-by-3 sized game board with 3 targets, and the complex games had a 4-by-4 sized game board with 4 targets. All participants played the game in separate rooms and communicated only through the text-messaging feature of StrikeCOM. Participants had code names in the game (Air, Space, and Intel), so they did not know who they were communicating with during the experiment, minimizing the potential influence of relational closeness [8].
7. Instrumentation & Level of Analysis The following variables were part of this study: Independent Variable: •
Task complexity
Dependent Variables: • •
Group deception detection success (average individual accuracy) Group task performance (average individual performance)
8. Preliminary Findings At the conclusion of the experiment, several findings were immediately evident. First, players often left the experimental sessions with a feeling that there was someone incompetent in the group (rather than someone being deceptive). Many subjects wrote that a confused group member had messed up their game. This led us to believe that deception detection rates would be very low. A second finding was that deceivers had no problem lying; all of the participants who were selected to be deceivers provided a detailed explanation of how they lied during the game. We had an initial concern that some of the chosen deceivers would not lie, but all of the deceivers were sufficiently motivated to lie in the game. A third finding was that subjects enjoyed the game; many subjects wrote comments mentioning that they had fun (but that they were confused as to why they did not perform well at the end of the game), signaling that they had put forth a good effort and did not rush through the game. All groups spent between twenty and thirty minutes on the game, also signaling that they did not rush through the game. A last preliminary finding was that not all groups carried out a common bombing plan. Some groups ended up with multiple plans, even though they were asked to try and devise a common bombing plan (35% of the groups did not have a common bombing plan). Group members may have been frustrated with the deceivers at the end of the games, even though many of them did not realize that the deceivers were being deceptive. This finding did not affect our analyses since we averaged individual game scores in order to obtain group scores.
9. Results Questionnaires were administered after the experiment. Non-deceptive participants were asked if they thought any group members had been deceptive during the game and why they felt individuals that they rated as deceptive were deceptive. Deceivers were asked to explain how they had been deceptive. On the questionnaire, non-deceptive participants were asked “Did you believe that any person was deceptive or dishonest during this exercise?” and they were told to decide if each group member had been “dishonest,” “honest,” or if they were “unsure.” Detection success was measured by counting one point for each correct rating and subtracting one point for each incorrect rating. No points were added or subtracted for an “unsure” rating. Group ratings were calculated by taking the average of individual group members’ ratings. Task performance was calculated by averaging group members’ total correct strikes divided by the number of targets in the game.
9.1. Deception Detection The following deception detection rates calculated at the conclusion of the experiment: • • •
were
10% of participants correctly identified the deceiver 20% of the participants were unsure about the deceiver 5% of the participants incorrectly identified another group member as a deceiver
As is evident from the results above, most participants thought that no group members had been deceptive. Many subjects realized that one group member had provided different information than the other players, but (as previously discussed) they felt that this was due to that group member’s incompetence.
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
More participants were unsure about the deceiver (20%) than were sure about deception (15%), possibly signifying that participants were hesitant to give a strong judgment and label individuals as being deceptive. Warning participants of possible deception may have changed this behavior significantly.
9.2. Hypothesis Tests We first hypothesized that groups using computermediated communication and facing a complex task would be less accurate at detecting deception than groups facing a less complex task. Even though mean deception detection accuracy was lower for the groups with the complex task (-.05) than for the groups with the less complex task (.2), this hypothesis was not supported when tested with a simple t-test (see Table 1): t = 1.14, p < .136.
Descriptive Statistics
Task Setting Easy Hard
Std. Error Mean
N
Mean
Std. Dev
10
.2000
.42164
.13333
10
-.0500
.55025
.17401
groups playing StrikeCOM on a 5x5 versus a 6x6 game board (with more difficult asset settings) with no deceivers (~.20 game score for the 5x5 vs. ~.16 game score for the 6x6 with more difficult asset settings), leading us to believe that deceivers had more influence on groups’ performance in our complex task manipulation. The validity of this finding can be increased by testing groups with and without deceivers in future studies.
Descriptive Statistics
Task Setting Easy
N
Mean
10
.3830
.20870
.06600
10
.2250
.15366
.04859
t
D.F.
Sig. (1tailed)
Mean Diff
Std. Error Diff
1.928
16.541
.035
.1580
.08196
Hard
t-test
Std. Error Mean
Std. Dev
Table 2. Group Task Performance
10. Limitation of Method Applied
t-test
t
D.F.
Sig. (1tailed)
Mean Diff
Std. Error Diff
1.140
16.859
.135
.2500
.21922
Table 1. Group Deception Detection
The lack of support for this hypothesis may stem from the fact that group members were dispersed and that they were not aware of potential deception. The combination of the task demands and the dispersed, computermediated communication likely made deception detection very difficult. Also, the lack of warning about potential deception may have led participants to believe that nobody was being deceptive in the game. We were also interested to see if deceivers negatively affected their groups’ performances more with the complex task than with the easy task. Group performance was calculated by dividing the number of hits from the bombing turn by the number of targets. A simple t-test (see Table 2) revealed that groups performed worse in the difficult game than in the easy game (Easy_Mean = .383, Hard_Mean = .225; t = 1.928, p < .036), possibly signifying that deceivers had been more influential in the difficult task manipulation. The groups in both manipulations should have been able to perform well without the presence of a deceiver. Recent studies conducted at the University of Arizona did not find large differences in group performance between
There were several limitations in this study. A first limitation was the fact that the experimental design only measured individuals’ deception detection success at one point in time. Detection successes would have likely changed over time as individuals got to know the task and the other group members better. Depending on how deceivers adjusted their strategic deception and how receivers changed their interpretation of cues, their rate of deception detection could have increased or decreased. This problem could be solved by conducting a study over multiple experimental sessions, which would allow group members to learn more about each other and the task over time. Another limitation was our dichotomization of the independent variable. Task difficulty was measured with two levels. This dichotomization likely oversimplified the real-world variable and may have limited our external validity. However, the simplification of the variable allowed for more easily measurable and interpretable results, increasing our internal validity and increasing our chance of finding statistically significant results. A last limitation was the possibility that the experimental directions may have influenced the number of squares participants’ targeted in the game. Individuals have a natural tendency to stay close to a given initial value when making decisions [14]. Participants in both experimental manipulations were told to target a minimum of 3 targets, which may have anchored their targeting decisions. Groups in the easy experimental
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
manipulation targeted an average of 3 targets, and groups in the complex experimental manipulation targeted an average of 3.6 targets. A possible solution to this problem would be to not specifying a minimum number of squares in the task instructions. Groups would then have no anchor value, but there would be more ambiguity as to how many targets are on the game board. Another possible solution would be to provide a different minimum number of targets for groups in the different experimental manipulations. A last solution would be to calculate group performance with the correct number of hits that a group has. However this could be problematic since it would allow groups to use a large number of strikes and would fail to account for the instruction to minimize the damage to innocent civilians in the game.
11. Conclusion This study empirically tested differences in deception detection and task performance between groups performing easy and complex tasks in a computermediated and collaborative group setting. We first predicted that groups facing a more complex task would be less likely to interpret cues to deception, and would therefore be less successful at detecting deception. We then predicted that groups facing the complex task would be more influenced by deceivers, and would therefore perform worse than groups with the easy task. Unfortunately, our findings revealed very low overall detection rates and showed that there were no significant differences in the detection rates between the groups. A first cause of the insignificant results may have been the limited number of cues to deception that were available in the computer-mediated communication. The text-only communication significantly reduced the number of available cues to deception. A second cause may have been that participants did not want to label other students as deceivers. A significant number of participants were unsure about the deceivers and didn’t label those persons as deceivers. This could have been due to the fact that the students were not aware of the potential for deception in a study such as this. Another cause of the insignificant findings may have been that the task was simply too distracting. Many participants mentioned that they enjoyed the game, possible signifying that they were highly distracted by the game, regardless of the level of complexity. A last reason for these findings might have been the levels of task complexity that we manipulated. The less complex game distracted participants enough so that they did not recognize the deception that had taken place. It may have been beneficial to test an even easier game or an even more complex game setup (i.e. a 6x6 game board) that may have reduced deception detection accuracy significantly.
Although our first hypothesis was not supported, we did have an interesting finding with our second hypothesis. Groups in the complex experimental manipulation performed significantly worse in the game than did the groups in the easy manipulation. Since groups in both manipulations had the assets necessary to perform well in the game, this finding may signal that the receivers were more vulnerable and that the deceivers were more influential in the complex task experimental manipulation. Receivers may have been more distracted by the game with the complex task manipulation, causing them to be more influenced by the deceivers’ motives than receivers with the less complex task manipulation. The findings and limitations of this study reveal the next steps that need to be taken in this line of deception research. Two questions that need to be answered are: “Do group detection rates differ when group members are in close proximity to each other in a collaborative group setting?” and “Do deception rates differ when individuals are aware of potential deception in a collaborative group setting?” The answers to these questions will reveal more about how groups handle more cues to deception and how they handle knowledge of potential deception. It would also be beneficial to test groups’ performance ratings with and without deceivers in the same study. This would solidify our finding that groups performed worse in the complex task, even though they had the assets necessary to perform similar to the groups facing the easy task. Once these steps are taken and the answers to these questions are better understood, organizations will be more able to minimize negative outcomes originating from deceptive behavior, improving the efficiency of organizational groups. Eventually, they will be able to warn individuals about potential deception, train individuals to better detect deception, and build systems to automatically detect deception.
12. Attachment 1 – Game Instructions The following instructions were part of a quick reference guide given to participants while they played the game: 1.
2.
3. 4.
Communicate with your teammates at any time by clicking in the text box at the upper right and hitting the ‘submit’ button. All messages will be displayed in the box below. Each Tab (right above the map) represents one of your search tools (assets). Click on the grid and hit the ‘submit’ button (below the map) to use the asset. Once you submit both assets, your turn is over. You then wait for your teammates to use their assets. You will then see the search results from your assets displayed on the screen. See the previous page for explanations of the results.
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
5.
Click on the (next) turn tab once you are ready to go again. You can review past results by clicking on the previous turn tabs.
On your final turn (the ‘Strike Turn’) you will try to bomb the enemy targets. Try to devise a common strike plan with your teammates, and click on the grid squares you would like to bomb. You can bomb any number of squares, but you may bomb innocent civilians if you hit non-target grid squares. Try to only hit the targets squares to maximize your score in the game (and spare innocent civilians). You must strike a minimum of 3 squares and you must hit ‘submit’ once your bombing plan is finalized.
13. Attachment 2 - Deceiver Instructions The following instructions were given to the participants that were randomly selected to be a deceiver: You will be playing a multiplayer strategy game in the experiment today. Your team will search for enemy targets on a game board and try to destroy the targets on your final turn. Your role will be to pretend that you are from the area being targeted in the game, and that you still have family living in the area. You know that the enemy being targeted is hiding among civilians, and your family could be killed in strikes. Your individual goal is to deceive your team members about the location of the enemy and to get them to destroy empty targets. In order to protect your family, you cannot reveal your true motive to your team members. You will now be given information as to where the civilians are located. Remember, try to get your teammates to avoid these squares by concealing information, misdirecting their searches, and lying and deceiving in any way you see fit in order to protect your family. Please do not tell anybody else about your role in this experiment, even after the conclusion of the experiment today. Thank you. Here are the targets you don’t want your group to strike:
Portions of this research were supported by funding from the U. S. Air Force Office of Scientific Research under the U. S. Department of Defense University Research Initiative (Grant #F49620-01-10394). The views, opinions, and/or findings in this report are those of the authors and should not be construed as an official Department of Defense position, policy, or decision.
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& M.S. Poole (eds), Communication & group decision making. (second edition), Thousand Oaks, CA: Sage Publications, Inc., 215-241.
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