An Exploration of Information-seeking Behavior in Emergency Management * Qing Gu Information Systems Department New Jersey Institute of Technology Newark, NJ, USA
David Mendonca Information Systems Department New Jersey Institute of Technology Newark, NJ, USA
Dezhi Wu Information Systems Department New Jersey Institute of Technology Newark, NJ, USA
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Abstract - Groups in risky, time-constrained situations may be confronted with problems that cannot be solved by following predefined procedures. This study explores the impact of various factors on group informationseeking behavior in such situations. A simulated experiment in emergency scenarios was conducted with both expert and novice groups with or without decision support tools. The results suggest that, while patterns of information-seeking were similar between experts and novices, experts conducted a more efficient search than novices. Efforts of information-seeking made by group members who play different roles are different between supported and unsupported groups, but both groups look for similar information no matter whether they are provided with decision support or not. The paper concludes with a set of observations on group information-seeking behavior, and discusses the possible impact of information-seeking differences on decision making performance. Keywords: Information-seeking, management, Decision making.
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Emergency
Introduction
Information needs arising out of risky, timeconstrained situations are associated with the goals of the task the groups need to meet, the context of the work or task, and the interaction of decision makers during the decision making processes. These information needs will change since a number of influencing variables play their roles at different stages in the whole process [12]. In emergency situations, groups may not follow the predefined course of actions when unpredicted events occur. As Turoff [15] points out, “There is no way to exactly predict who is going to be doing what, when, why, and/or how at the command and control level in a crisis environment.” People need to seek and assimilate available information, find out the relevant information, and take courses of action based on the information at hand to solve the uncertain problems within a time limit. In the course of seeking information, groups may interact *
0-7803-7952-7/03/$17.00 2003 IEEE.
with manual or computer-based information systems [17]. Some empirical studies [8][16] investigate human information-seeking behaviors by conducting field studies, controlled experiments and so on. Various factors affecting information-seeking behaviors, such as demographics, context, tasks (degree of urgency and complexity), goals, and technology, have been identified [12]. In the current literature, little work has been done on information-seeking behavior specifically under emergency scenarios. Therefore, it is unclear what types of factors may affect decision makers’ information-seeking behavior in a risky, time-constrained environment. This paper explores how factors of expertise and decision support impact information-seeking behavior of groups of decision makers in a simulated emergency scenario. A brief review of these factors is presented. Two factors—individual expertise and decision support technology—are selected, and their impact on the information-seeking behavior in emergency scenarios is explored. Differences and similarities in informationseeking behavior between expert and novice groups, and between supported and unsupported groups, are identified. The paper concludes with a discussion of how information-seeking behavior may impact the types of decisions made by groups.
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Related Work
Information-seeking behaviors are driven by cognitive needs which arise from the context of the information needs. The context consists of the person, the role the person plays, and the environment in which information-seeking processes happens [8]. Previous studies on human information-seeking behavior have experienced the shift from a system-centered approach to a person-centered approach [17]. As discussed by Dervin [4], situation is an important element influencing human information-seeking processes, and the sense-making process seeks to bridge the gap between the contextual situation and the desired situation (e.g., uncertainty). Some models are proposed trying to generalize the common
characteristics of information-seeking behavior for all professional groups. While these models may not represent human information-seeking behavior under all conditions, they provide some clues and approaches for further studies of information-seeking behavior in different situations. These characteristics of and components affecting information-seeking can be generalized into four categories: individual-related, technology-related, goalrelated, and context-related factors. •
Individual-related factors. They include individual demographics of the information seekers, such as gender, ethnicity, age, and education, as well as their experiences with risks [6][8]. Education has been found to be an important indicator of a person’s ability to seek and process information. A model of seeking and processing information about risk proposes that an individual’s past experiences with the similar hazards and the preventive actions s/he takes will affect her/his reactions to a hazard.
•
Technology-related factors. Advances in electronic information systems and communications are applied in information-seeking and processing. These technologies bring change to the patterns and efficiency of traditional information-seeking processes [12].
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Goal-related factors. Research on different groups of professionals shows that “the roles and related tasks undertaken by professionals in the course of daily practice prompt particular information needs, which in turn give rise to an information-seeking process.” [8] Two factors that influence information needs are frequency (recurring needs or new) and complexity (easily resolved or difficult). Empirical study shows that an individual’s information needs vary according to task complexity [2].
•
Context-related factors. The contextual environment in which the tasks take place also constrains information-seeking processes. Organizational and social structures influence the receipt of informationseeking benefits, and task interdependence is the strongest predictor of receipt of information benefits [3]. When an individual has to accomplish tasks with inputs from others and distribute outputs, his/her information-seeking process is accompanied by the interaction and communication with information sources in the threshold. While facing different alternatives of information sources, an individual’s selection will be influenced by external factors such as the timeliness of the information, the accessibility of the sources of desired information, the cost (as measured by available time, physical distance, etc.) of the information, the quality of obtained information and the efficiency for seeking it [8][12].
The influence of these factors may happen at different stages of the decision making process, depending on the emergent factors occurring during the course of interactions and the feedback of the previous informationseeking round [8][12]. Failure to satisfy information needs leads to the adjustment of information-seeking strategies. Besides information-seeking behavior models, the information-seeking behavior in various fields has been investigated. A recent study [13] examines the effects of a searcher’s knowledge and experience on informationseeking on the WWW. Measurements of a researcher’s behavior used in this study include “result” (target discovered or not), “time,” “number of pages” and “kinds of pages.” There are substantial differences in the searching behaviors of experts and novices. Analysis of the behavioral processes of experts and novices reveals that experts’ behavioral processes are well organized by sets of basic units while novices’ behavior processes are characterized by depth-first and breadth-first search. Though some factors influencing informationseeking behaviors have been explored, relatively little is known about how and to what extent these factors affect information-seeking behavior, particularly in emergency situations. In general, an emergency response organization refers to a group of people who play different roles working together to manage the response to an emergency [1]. People who are seeking information or making decisions usually interact dynamically, interdependently, and adaptively [14]. In emergency scenarios, the four categories of factors mentioned previously may impact information-seeking behavior in different ways.
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Research Questions
In this study, the context of the scenarios is characterized by risk and time-constraint; the goals the group decision makers need to meet are provided to them. Individual-related and technology-related factors are investigated: expertise reflects the work experience and domain knowledge of group decision makers; decision support indicates whether or not the groups had access to a computer-based decision tool. The following questions motivate this exploratory research: • • •
Is there any difference in information-seeking behavior between expert and novice groups in the emergency scenarios? Is there any difference in information-seeking behavior between supported and unsupported groups in the emergency scenarios? Is there any relationship between the differences of information-seeking behavior and those of group decision making in the emergency scenarios?
4 4.1
Table 1. Number of observations in each condition
Methods Experimental environment
The data are drawn from a series of experiments on decision making in simulated emergency response scenarios [9][10][11]. A total of 28 groups were involved in the experiment. Each group member took on one of five roles: Coordinator (CO), Police Department (PD), Fire Department (FD), Medical Officer (MO), and Chemical Advisor (CA). They were asked to work on two separate emergency response cases, drawn from actual incidents. Case One concerns a cargo ship fire with an oil spill; Case Two concerns a collision between two ships with a resulting chemical emission. Their task was to allocate the resources in the cases to meet the goals of the emergency response. Each case has two phases. In phase two, a computer-based tool provided recommendations on courses of action to pursue. In the sessions, each non-CO role could view only the resources at the sites belonging to that role. The CO had accessibility to all sites in each case. Individuals therefore had incomplete information locally but complete information globally. Figure 1 shows the interface for Case 2. The map at the left side displays the locations of resources and the incident location.
Case 1
Support No support Support No support
Case 2
4.3
Novice 2 4 2 4
Expert 3 5 3 5
Measurement
Frequency of search is regarded as a good indicator of human information-seeking behavior. There is a strong relationship between accessibility and frequency of use [5]. More specifically, the information-seeking behavior of soccer fans on the WWW is examined [7]. The primary measure of information search in that research is the number of hits to soccer websites. A similar measurement is adopted in this study. Two functions of “number of clicks on the resource sites” are created here to measure information-seeking behavior. These two measures for the “number of clicks” are proportion of number of clicks per site (M1) and percent of number of clicks per participant (M2). The definitions of the two measures are explained further in the following. M1: Proportion of number of clicks per site:
P(i ) = ( Nowner (i ) + Nco(i )) N
(1)
where i = {A, B, C, …, Q} in Case One; i = {A, B, C, …, N, P, Q, R, S} in Case Two. Nowner(i) is the number of clicks on site i made by its owners, and Nco(i) is the number of clicks on site i made by CO. N is the total number of clicks on all sites. For instance, for Case Two the five roles and the sites they can view are listed in Table 2. AR represents Alternative Resources, which can be viewed by all five participants. Table 2. Case 2: Roles and their corresponding sites
Figure 1. Interface of the DSS tool 4.2
Participants
Novice participants were college students enrolled in business or engineering programs, while expert participants were students at the U.S. National Fire Academy. Groups were randomly assigned to use the computer support tool or not in each case. The three independent variables are expertise (expert vs. novice), availability of support (supported vs. unsupported), and case (case one vs. case two). The number of subject observations under each condition is shown in Table 1.
Role CA FD MO PD CO AR
Site C, J B, E A, F, M, R K, H, D, G, I All N, P, S, L, Q
According to the definition, P(A)=(NMO(A) + NCO(A)) / N.
M2: Percent of number of clicks per participant: Pct ( j ) = N ( j ) N
(2)
where j= {CA, FD, MO, PD, CO}. N(j) is the number of clicks made by participant j on the sites belonging to j, and N is the total number of clicks made by all participants. 4.4
Data Analysis and Results
Data used to analyze information-seeking behavior are stored in computer logs that record the group, participant, stream, time, and event happened at that time point. All records are time synchronized for analysis. A sample record from the log file is shown in Table 3. It shows that participant MO in group A session NFA1 clicked site D at 2:03:23AM (tape time 384789). “Stream P” means this is an event belonging to “Process.” Table 3. A sample record from the log file Session Group Participant Stream Time Tape_T Event
Figure 2. Number of clicks per site by expertise for Case 2 Phase 2
NFA1 A MO P 2:03:23AM 384789 "D"
Only the data collected from phase two are used because phase 1 only gives a planning time limit while phase 2 has both planning and executing time constraint. Of interest in this study is information-seeking behavior in situations requiring executing and planning at the same time. Result 1: Frequency of visits on each site suggests that novice groups look for information more frequently than expert groups, as shown in Figure 2. Chi square goodness of fit tests on the proportion of number of clicks per site and the percent of number of clicks per participant between expert and novice groups, which are shown in Table 4, demonstrate that (i) proportion of number of clicks per site (M1) between expert and novice groups is not significantly different in either Case1 or Case2; and (ii) percent of number of clicks per participant (M2) between expert and novice groups is significantly different in both cases (p
