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Executive Information Systems:
Their Impact on Executive
Decision Making
DOROTHY E. LEIDNER AND JOYCE J. ELAM
DOROTHY E . LECDNER is Assistant Professor of Information Systems in the Hankamer
School of Business at Baylor University, Waco, Texas. She received a Ph.D. in
infonnation systems in 1992 from the University of Texas at Austin where she also
received a B.A. and M.B.A. Her research interests include executive support systems,
infonnation technology for improving classrooms, and the behavioral impacts of
information technology.
JOYCE J. ELAM is the James L. Knight Eminent Scholar in Management Information
Systems in the Department of Decision Sciences and Information Systems, College
of Business Administration, Florida Intemationai University, Miami, Horida. Before
joining the faculty at Florida Intemationai University in 1990, she was an Assistant
Professor at the University of Pennsylvania’s Wharton School, an Associate Professor
in the College of Business Administration at the University of Texas at Austin, and a
Marvin Bower Fellow at the Harvard Business School. Dr. Elam earned both her Ph.D.
in operations research (1977) and her B.A. in mathematics (1970) from the University
of Texas. Her research deals with the competitive use of information technology, the
management of the infonnation services function, and the use of information technology
to support both individual and group decision making. She has served as associate
editor foi MIS Quarterly and is ctirrently on the editorial board for Information Systems
Research and the Journal of Strategic Information Systems.
ABSTRACT: An executive information system (EIS) is a computer-based information
system designed to provide senior managers access to infonnation relevant to their
management activities. With such trends as globalization and intense competition
increasing the importance of fast and accurate decision making, the use of these
systems by executives may become a particularly important component of their
decision-making behavior. Previous research on EIS has focused on descriptive
studies of how and why EIS are used. This research empirically examines the effects
of EIS use on aspects of the decision-making process by surveying 46 executive users of
EIS. The frequency and duration of EIS use are shown to increase problem identification
speed, decision-making speed, and the extent of analysis in decision making.
KEY WORDS AND PHRASES: decision making, executive information systems, executive
suppon systems, problem identification.
Acknowledgments: An earlier version of this paper was published in the Proceedings of the
Twenty-Sixth Hawaii International Cortference on System Sciences (IEEE Computer Society
Press, 1993).
Journal cfManagenuMInformation Systems/V/mva 1993-94, VoL 10. Na 3, pp. 139-155
Copyright © M.E Shaipe, Inc., 1994
140 DOROTHY E. LEIDNER AND JOYCE J. ELAM
1. Introduction
DECISION MAKING IS RECOGNIZED AS ONE OF THE MOST IMPORTANT roles of executives.
The availability of reliable information sources is a key component of executive
decision making. Sources of information may be oral, written, or computer-based. The
computer-based information sources remain the least studied in the context of executive
decision making because executives have tended to use other managers and their
own intuition as their primary information sources [25]. Recently though, the emergence
of computer-based systems that are directly tailored for use by executive
decision makers enables an examination of how executive use of computer-based
information systems affects executives’ decision-making processes.
Such systems, hereafter referred to as executive information systems (EIS), are
computer-based information systems designed to allow a senior manager access to
information relevant to his or her management activities. The idea of using computerbased
information systems to support management is not new. In fact, a new philosophy
of how computers could be used to support managerial decision making emerged
under the name DSS during the late 1970s. Today, DSS have become firmly established
in the mainstream of IS practice and applications have become common [12].
While DSS have been found to support upper management [19], this support is indirect
since intermediaries frequently are responsible for preparing the analysis requested
by executives. In addition, DSS tend to be narrow in scope, focusing on a particular
decision. For these reasons, the literature on the impacts of DSS [1, 18] may fail to
provide a complete picture of the effect of an EIS on executive decision making.
Initial research of EIS has consisted of descriptions of current implementations in
organizations [2, 3, 4, 21, 38, 41], and empirical examinations of the important
characteristics and purposes of EIS [5,6,50]. Yet research has not extended beyond
the descriptive phase to a theoretically based inquiry into the effect such systems can
have when used by senior managers. Obtaining responses from multiple users of EIS
across many organizations, this research surveys the users of the systems to examine
the following research question: what is the effect of EIS use on the decision-making
process of executives?
2. Executive Infonnation Systems
WHILE EIS DIFFER CONSIDERABLY IN THE NUMBER AND SOPHISTICATION of features,
the most common feature of EIS is immediate access to a single database where all
current financial and operational data can be found [35]. In many cases, the information
made accessible was previously available but was difficult to access or use [42].
Features distinguishing EIS from such systems as management information systems
and decision support systems include a non-keyboard interface, status access to the
organizational database, drill-down analysis capabilities (the incremental examination
of data at different levels of detail), trend analysis capabilities (the examination of data
across desired time intervals), exception reporting, extensive graphics, the providing
of data from multiple sources, and the highlighting of the information an executive
EXECUTIVE INFORMATION SYSTEMS 141
feels is critical. In addition, whereas the traditional focus of MIS has been on the
storage and processing of large amounts of information, the focus of EIS is on the
retrieval of specific information about the daily operational status of the company’s
activities as well as specific information about competitors and the marketplace [16,
48]. EIS are also distinct from DSS: whereas the puipose of EIS is the monitoring and
scanning of the environment to give executives rapid exposure to changes in the
environment, the purpose of DSS is to support ad hoc decisions as well as some routine
analysis. And while the core of DSS is extensive modeling and analysis capabilities,
the core of ESS is status information about the organization’s performance [48].
Previous research has examined why EIS are used [50], has examined development
methods that lead to successful implementation [10,20,49], and has examined the
features executives find most useful [5,41]. Research is now needed that examines
the impact of EIS use.
There are several frameworks that could be used to study EIS, including EIS as a
decision-making or problem-solving tool, EIS as a scanning tool, EIS as an intemal
monitoring tool, and EIS as a communication tool [8]. This research chose to examine
EIS as a decision-making tool because decision making involves scanning, monitoring,
and communicating and is therefore a broad framework for early research into
the impacts of EIS use. Given that the postindustrial environment demands that
organizations make faster decisions, and have better information acquisition and
distribution [23], an information system designed to meet the needs of executives
should address their decision-making needs. Furthermore, Rockart and DeLong [41,
p. 256] suggest that a decision-making framework for researching EIS should provide
new insights into how EIS provide value.
There are many decision-making variables that may conceivably be affected by the
use of EIS. This study chooses to examine three decision-making process variables
that have received considerable attention in recent theory on the impact of advanced
information technology use on decision making in organizations and are well
grotinded in organizational research [22].
3. The Decision-Making Process
THE VARIABLES EXAMINED INCLUDE THE SPEED of problem identification, the speed
of the decision-making process, and the extent of analysis in decision making.
3.1. The Speed of the Decision-Making Process
Rapid decision making has become more important as competitive situations have
increased and information has become critical to organizational performance [11,22].
Changes in technology and faster communication makes the time span of important
changes critical [13]. The time frame of decision making has hardly been studied [33]
although speed is considered panicularly important in highly uncertain, dynamic, or
“high-velocity” environments. Eisenhardt [11] found that the most effective firms of
those studied made strategic decisions quickly. She identified confidence and anxiety
142 DOROTHY E. LEIDNER AND JOYCE J. ELAM
as key components determining the speed of the process. El Sawy [13] suggests that
“to compete effectively in [today's] time-compressed infonnation intensive environment,
fast response is increasingly becoming a critical strategic capability.”
Managers describe their environment as having increased competition and reduced
time to make decisions [17]. One executive states: “we as decision makers are
constantly being faced with situations where we are required to make more decisions
than ever before” with “faster reaction times” [45]. Because information technology
allows fast infonnation processing and analysis, the availability and use of EIS by
upper executives may contribute to the speed with which they identify problems and
make decisions. The speed of problem identification is defined as the length of time
between when a problem first arises and when it is first noticed. The speed of decision
making is defined as the time between when a decision maker recognizes the need to
make some decision to the time when he or she renders judgment [45].
3.2. The Extent of Analysis
Fredrickson and Mitchell [15] identified six characteristics of strategic decision
making: process initiation, role of goals, means/ends relationship, explanation of
strategic action, comprehensiveness in decision making, and comprehensiveness in
integrating decisions (to form strategy). Analytic comprehensiveness as defined by
Fredrickson and Mitchell [15] is the extent of analysis in situation diagnosis, altemative
generation, altemative evaluation, and decision integration. This research is
interested in the extent of analytic techniques used in decision making. Analysis is
defined as the “reflective thought and deliberation given to a problem and the array
of proposed responses” [32]. Time spent on interrelating symptoms to get at the root
cause of problems and the effort spent to generate solutions are examples of the
analytic process [22].
Although sometimes viewed as antithetical to fast decision making, extensive
analysis may coexist with speed when an EIS is providing both real-time data and
analytic tools. Typical MIS do not provide sufficient inquiry and analysis capabilities
compared with their perceived importance to decision makers [31]. However, the
information database of an EIS provides a source of raw information that can be used
by analytical executives to perform their own analysis [41, p. 102].
4. Research Model and Hypotheses
THE PREVIOUS DISCUSSION OF RESEARCH RELATED TO DEQSION MAKING in organizations
suggests the research model shown in figure 1 conceming EIS use and an
executive’s decision making process.
The model states that the use of EIS by an executive will increase the speed of his
or her problem identification, increase the speed of his or her decision making, and
increase the extent of his or her analysis in decision making. Problem identification is
separated from decision making because many EIS discussed in the literature up to
now have been monitoring systems. If the sole purpose of the system is to improve an
EXECUTIVE INFORMATION SYSTEMS 143
EIS Use ^^ Decision Making Process
* Speed of Problem Identification
* Speed of Decision Making
* Extent of Analysis
Figure 1. Research Model
executive’s ability to monitor performance, it is probable that he or she may identify
problems faster because of the system, but may not necessarily make a decision
conceming the situation any faster. If, in fact, the EIS affected problem identification
but not the entire decision-niaking process, this would be difficult to discem solely by
a decision-making speed variable.
4.1. Hypotheses
The hypotheses examine the impact of EIS use on certain important characteristics of
an executive’s decision-making process. Consistent with DSS research, EIS use is
defined both in terms of the frequency of system use by the executive and the length
of time the executive has been using the EIS [30]. The hypotheses are worded using
both the frequency of EIS use and the length of time of EIS use. The hypotheses
conceming frequency of use assume that an executive who is actively using the system
somewhat regularly will perceive greater results from his or her usage than an
executive using the system only irregularly. The hypotheses conceming length of time
of use assumes that the longer the system has been in use, the more likely that results
have been observed. This is for two reasons: (1) use would presumably have been
discontinued if it had not been producing a desirable impact, and (2) it may take time
before impacts of system use are realized or noticed; frequency would not reveal such
a time effect
4.2.1. EIS and Problem Identification Speed
Intemal monitoring and environmental scanning are key activities of senior managers
[26]. Such activities provide “early waming indicators” which enable executives to
identify and react faster to problems and to competitive trends and product changes
[44]. One executive is quoted as saying, “what matters is how quickly I can get a
comprehensive overview and draw conclusions from the data. The EIS helps me
do that much faster” [41, p. 106]. By providing external data and by allowing
quicker access to operational information, EIS enables faster scanning [41, p. 82].
And timely access to extemal and internal infonnation may enable problems to be
identified faster. The more frequent the use, the more likely problems will be
identified faster. The longer the system has been in use, the more adept the executive
will be in interpreting the information and determining where problems exist. It is
therefore hypothesized that:
144 DOROTHY E. LETONER AND JOYCE J. ELAM
Hypothesis la: The more frequent the executive’s use of EIS, the faster the speed
of problem identification.
Hypothesis lb: The greater the length of the executive’s use of EIS. the faster the
speed of problem identification.
A.2.2. EIS and Decision-Making Speed
The need for fast decision making has resulted from increased competition and the
globalization of world markets. Causes of slow speed include the consideration of
many altematives, wide participation [52], political behavior [27], comprehensiveness
[14], and scheduling and feedback delays [33]. Bourgeois and Eisenhardt [7] found
that decision speed affects firm performance in high-velocity environments and is a
key characteristic differentiating the consequences of strategic decisions. Arguing that
the aspects of the decision process following problem or opportunity recognition might
be more effective when using advanced information technology, Huber [22] suggested
that the use of sophisticated information technologies would allow decision makers
or their assistants to analyze information quickly. Hence, the use of such technologies
would lead to more rapid and accurate identification of problems, would reduce the
time required to authorize proposed organizational actions, and would reduce the time
required to make decisions [22].
Eisenhardt [11] found that the most effective firms among the eight studied in the
high-velocity environment made strategic decisions quickly and had a shorter time
frame in which the decisions were made. The decisions were faster because real-time
information was used and multiple altematives were considered simultaneously. An
EIS can provide such real-time information. The provision of real-time, accurate, and
easily accessible information should allow executives to make decisions more quickly.
Those executives who use the EIS the most frequently should notice the greatest
increase in their decision-making speed; likewise, those who have used the system the
longest will be accustomed to quickly assessing their needed information. It is
therefore hypothesized that:
Hypothesis 2a: The more frequent the executive’s use of EIS, the faster the speed
of the decision-making process.
Hypothesis 2b: The greater the length of the executive’s use of EIS, the faster the
speed of the decision-making process.
4.2.3. EIS and the Extent of Analysis in Decision Making
Fredrickson and Mitchell [15] found that comprehensiveness is positively related
to performance in stable environments but Fredrickson [14] found that it is
negatively related to performance in unstable environments. One explanation of
these results is that unstable environments require fast decision making for effective
performance, but fast decision making is hindered by the comprehensive
approach. Others have found that being inordinately analytic leads to slow decision
EXECUTIVE IKFORMATION SYSTEMS 145
making or postponement of the decision until too late [39] and the stifling of creativity
and innovation [34,51].
Eisenhardt [11], however, found that the most effective firms used an analytic
decision process, with the negative effects reduced by other behaviors such as the
reliance on experienced cohorts. Her results suggest that in the unstable environments,
comprehensiveness must involve simultaneous consideration of multiple altematives
and real-time information in order to avoid the slow, inefficient decision process found
by Fredrickson [14]. Bourgeois and Eisenhardt [7] suggest that, as the speed of
environmental change accelerates, effective executives deal with their extremely
uncertain world by stmcturing it through a thorough, analytic process. An executive
interviewed by Rockart and DeLong [41, p. 97] felt that the EIS allowed more time
to be devoted to substantive analysis of operational problems, opportunities and
potential acquisitions.
EIS can aid executives in the analytic process by providing real-time information
and the means of understanding it through analytic capabilities. With its ability to
facilitate analysis of problems with drill-down and trend analysis, an EIS may
significantly increase the extent of a decision maker’s analysis. Those using the EIS
most frequently would be most likely to be doing their own analysis on the system,
and those using the system for the longest time are likely to be comfortable using the
EIS for analysis. It is therefore hypothesized that:
Hypothesis 3a: The morefreqtient the executive’s tise of EIS, the greater the extent
of analysis in decision making.
Hypothesis 3b: The morefrequent the executive’s use of EIS, the greater the extent
of analysis in decision making.
5. Methodology
THE STUDY USES A SURVEY INSTRUMENT TO GATHER DATA tO test the relationships
expressed in the hypotheses. The hypotheses will be tested for association rather than
causality. While there is a theoretical argument for causality, it could not be tested
directly using the survey methodology.
5.1. The Survey Instrument
In order to collect data on the variables described in the research model, a questionnaire
was developed. The stirvey collected information on the use of the system and on the
user’s perceptions of the impact of the EIS on decision making. A pilot version of the
survey was completed by several executives from a Texas bank. Suggestions were
incorporated into a second version that was then piloted by two more executives. One
additional suggestion was made and incorporated into the final version. Such apiloting
process helps establish content validity [46]. Bias in response from misinterpretation
of the instrument should therefore be reduced.
146 DOROTHY E. LEIDNER AND JOYCE J. ELAM
5.2. Measurements
In order to build upon previous research, a review of instruments used in other studies
examining information technology and/or decision-making processes was undertaken.
Because the speed of problem and decision making has been discussed in theory but
has not yet been examined empirically (although it has received attention in DSS and
GDSS experiments where direct measurement is possible), the items to measure the
speed variables are derived from Huber’s interpretation of these variables [22]. Items
to measure extent of analysis were drawn from Miller and Freisen [32] and Fredrickson
and Mitchell [15]. The items require that the user compare the current decision-making
process to the process before the EIS was used. While relying on executives for the
comparison may lead to two types of error—distortion and memory failure—there is
no reason why there should be systematic bias in the responses [33]. Questions are
expressed in terms of how the EIS has helped the decision-making process, rather than
why the EIS is used. This research is interested in determining some results of EIS use
rather than in determining the reasons behind EIS use.
5.2.1. Problem Identification Speed
The speed of problem identification is the time elapsed between the first appearance
of signs of a problem and their detection. It is examined separately from the speed of
decision making because the impact of an EIS may be more significant at this phase
than at the other phases because of the daily monitoring of real-time information. Items
measuring problem identification speed are:
To what extent has EIS helped you do the following:
• Identify potential problems faster
• Sense key factors impacting my area of responsibility
• Notice potential problems before they become serious crises
The respondents answered each question by circling the appropriate number on the
scale:
To no extent To a little extent To some extent To a large extent To a great extent
1 2 3 4 5
The items are to be averaged together for the composite score for the variable problem
identification speed. This same five-point scale was used for decision-making speed
and extent of analysis.
5.2.2. Decision Making Speed
Speed of decision making is the span of time beginning with problem identification
and ending with choice. Ideas were borrowed from Huber [22], who helped define and
clarify the speed of decision making. The items measuring decision-making speed are:
EXECUTIVE INFORMATION SYSTEMS 147
To what extent has EIS helped you:
• Make decisions quicker
• Shorten the time frame for making decisions
• Spend less time in meetings
5.2.3. The Extent of Analysis in Decision Making
An analytic decision process is characterized by systematic methods of identifying
problems, diagnosing problems, generating altematives, and evaluating altematives
using computational techniques. Ideas from Fredrickson and Mitchell [15] are useful.
They defined analytic comprehensiveness as the comprehensiveness in situation
diagnosis, altemative generation, and altemative evaluation. As measures, they examined
the breadth of participants’ expertise, breadth of outside infonnation sources
used, breadth of problem causes and solutions considered, breadth of analysis techniques
used, and breadth of factors considered important in the three phases. This
research is interested in the analytic component rather than the comprehensiveness
componenL Miller and Friesen [32] suggest that analysis is characterized by the time
spent on interrelating symptoms to get at the root cause of problems and the effort
spent generating and analyzing solutions. Based on these ideas, the items measuring
the extent of analysis in decision making are:
To what extent has EIS helped you:
• Spend significantly more time analyzing data before making a decision
• Examine more altematives in decision making
• Use more sources of information in decision making
• Engage in more in-depth analysis
While analysis and speed are often viewed as contradictory, it is possible that an EIS
enables both: not as much time need be spent gathering information so the increased
time spent analyzing may be offset by the capabilities of the EIS.
5.2.4. EIS Use
EIS use is measured according to frequency of use by the individual respondent and
according to the length of time it has been used by the individual respondent.
To measure an individual’s frequency of EIS use, the user was asked:
With what frequency do you personally use the EIS?
The respondent answered according to the following scale:
Infrequently Monthly 1-4 times per week Daily
1 2 3 4
The length of use was determined by asking, “When did you first begin using the EIS?”
The respondents answered the question by providing a month and year.
148 DOROTHY E. LEIDNER AND JOYCE J. ELAM
5.3. The Hierarchical Level of the Respondent
Research on EIS has typically used the vice president level and above in the term
executive or senior manager. Mittman and Moore [36] defined executives as vice
presidents and above. Sixty-four percent of their respondents were vice presidents.
Isenberg [24] defined senior manager as general manager and above. Both consultants
and developers of EIS have included vice presidents as “senior manager” or “executive”
below the president [29,44]. Others have called top management the president
and one level below president, while middle is two levels below president [53]. The
term “executive” in this research refers to a manager who is responsible for a
contribution that materially affects the firm’s ability to perform and obtain results [40].
This is operationalized as a manager reporting no more than two levels below the
president or CEO.
5.4. Selection of Companies
Through an extensive review of business, trade, and academic journals, and through
contact with the major suppliers of EIS equipment and consultants for EIS development,
the researchers identified approximately 100 companies in the United States
with an EIS. A contact person was identified in each company. The contact person
was typically from the information systems department and had an important role in
designing, developing, and/or maintaining the EIS.
The contact person was given the option of distributing the survey to selected users
or providing us with the names of selected users whom we could contact. In all cases,
the contact person chose to distribute the survey him or herself. The reason for this
was that the contact person was typically at a lower hierarchical level than the users
and did not want to be responsible for giving out their names.
This obviously introduces a possible source of response bias, namely, that the
contact person would distribute the survey only to frequent users of the system. The
contact person was requested to give the surveys to both frequent and infrequent users.
Often the contact person was not even aware of who was a frequent user and who was
not, so that the distribution was in effect random.
Another source of bias is that individuals who do not like the system and who do
not use it but who have access to it are not included in the responses (i.e., there is an
undercoverage problem). This bias was unavoidable because the survey and research
are designed to examine the impact of EIS use as opposed to EIS availability for use.
Therefore, responses from nonusers, even if the system is available for their use, would
make no sense (the individual would leave the survey blank because it would not
apply).
A final source of bias is that of nonresponse. This occurs when the contact person
gives the survey to an individual who never completes it. This bias was difficult for
the researchers to control since the researchers did not have the names of the
individuals to whom the contact person distributed the survey. The degree of nonresponse
as indicated by the nonresponse rate of individuals from participating
EXECUTIVE INFORMATION SYSTEMS 149
companies was 50 percent The proportion including those companies with an EIS
that did not elect to participate is estimated at 76 percent, again assuming that ten
surveys would have been sent to each of the ten companies not choosing to participate.
5.5. Analytical Techniques
Because the major purpose of analysis is to assess the strength of associations among
variables, correlation or regression is the appropriate method [47]. Bivariate correlation
measures the strength and direction of association between two variables. Spearman’s
correlation coefficient was used because the Spearman coefficient is a nonparametric test
that therefore does not make numerous assumptions about the parameters—^in other words,
it is a “distribution free” test that does not assume underlying continuity in the variables
under study [43]. This results in conclusions that require fewer qualifications. Nonparametric
tests are particularly useful for small sample sizes [43].
6. Analysis and Results
FORTY-SIX RESPONSES WERE RECEIVED FROM SENIOR MANAGERS. Of the total 34
contacts who agreed during the phone conversation to distribute surveys, responses
were received from 23. The organizational response rate is thus 59 percent. The
response rate for total surveys sent is 32 percent (sometimes not every survey sent to
the organization was retumed). The respondents were from 23 companies, representing
financial services, electronics manufacturing, public utility, telecommunications,
oil and gas, food products, and consulting industries.
6.1. Validity
Content validity—the representativeness of the measures [46]—^was assessed by
subjecting the survey to pilot testing by five executives and scmtiny by four professors
in the field of decision making and information systems. The pilot testing suggested
that the questions and instructions were clear.
Construct validity—the meaningf ulness of the measures—^was assessed by common
factor analysis [28]. Reliability—the stability of the measures [46]—^was assessed by
Cronbach’s alpha. Content validity, construct validity, and reliability ensure instmment
validity.
6.2. Construct Validity
Construct validity addresses the question of whether the measures are true constmcts
describing the event or merely artifacts of the methodology [46]. Eigenvalues greater
than 1 and scree plots were used in determining the number of factors. For an item to
be considered in the composition of a variable, it had to have a loading of at least 0.5
on the factor, with no loading exceeding 0.3 on another factor, had to conform to a
prior assignments, and had to add to the variable’s reliability.
150 DOROTHY E. LEIDNER AND JOYCE J. ELAM
In general, factor analysis supported the proposed scales. Minor exceptions were
that for problem identification speed, one item was not retained for failing to have
high enough loadings, for decision-making speed, one item was not retained for failing
to have high enough loadings, and one item was not included in the extent of analysis
variable for failing to load properly.
6.3. Reliability
The mean of the items in each scale was used to combine the items into the scale.
Cronbach’s alpha was used to assess the interitem reliability of the final multi-item
scales. While a reliability score of 0.6 is usually considered acceptable [37], all of the
variable’s reliability scores exceeded 0.8. Thus, although the items were largely
derived indirectly from previous theory, the high alphas indicate that the variables are
reliable. The factor loadings and reliability are shown in Table 1.
6.4. Variable Descriptive Statistics
Table 2 presents the mean, standard deviation, minimum, maximum, and number of
responses for each variable.
6.5. Statistical Analysis Performed
Table 3 presents the correlations of the variables with each other. As would be
expected, the decision-making variables are highly correlated with one another. This
would be expected given that variables were purposely chosen that were thought to
characterize decision making. However, in interpreting the conelations, one mustkeep
in mind that two variables may be conelated with each other only because they share
conelation with a common other variable.
It was necessary to ensure that it is viable to treat frequency of use and length of use
as independent variables; in the case where a strong relationship exists, there would
be no reason to have hypotheses for both variables. The conelation of length of use
with frequency of use is 0.17 with a p value of 0.26. Thus, the two variables can be
treated as independent variables; there does not appear to be a strong relationship
between the two.
6.6. Hypotheses Testing
Hypothesis la predicted that the more frequent the use of EIS, the faster executives
would notice problems. This hypothesis was supported. Problem identification speed
was correlated with the frequency of EIS use (r = 0.4, p = 0.006). Hypothesis lb
predicted that the longer the EIS had been used, the faster the problem identification
would occur. This was also supported: length of time of EIS use was correlated with
problem identification speed (r = 0.31,p = 0.04).
Hypothesis 2a predicted that frequent use of EIS would increase the speed of the
EXECUTIVE INFORMATION SYSTIEMS 151
Table 1 Factor Loadings and Reliability
Factor Items Cronbach’s alpha
Factor 1: Problem identification speed
Sense key factors impacting my area of
responsibility
Notice potential problems before they
become serious crises
Factor 2: Decision-making speed
Make decisions quicker
Shorten the time frame for making
decisions
Factor 3: Extent of analysis in ciedsion
making
Spenci significantly more time analyzing
data before making a decision
Examine more alternatives in decision
making
Use more sources of information in
decision making
0.89
0.92
0.87
0.55
0.84
0.56
0.67
0.55
0.94
0.62
Table 2 Descriptive Statistics
Variable N Mean Std.Dev. Median Min Max
Problem identification
speed
Decision making speed
Extent of analysis
Frequency of EIS use
Length of time of EIS
use
46
46
43
46
45
2.5
2.3
2.2
3.3
32.2
1
1
0.91
0.87
25.6
2.5
2.3
2.3
3
26
1
1
11
4
4.3
4.5
4.3
5
99
Table 3 Spearman Correlation Coefficients
Variable Frequency Length of Problem Decision- Extent of
of EIS use EIS use identifica- making analysis
tion speed speed
Frequency of EIS use
Length of Eis use
Problem identification
speed
Decision-making speed
Extent of analysis
0.17
0.26
0.4
0.006
0.37
0.01
0.48
0.0008
0.31
0.04
0.37
0.01
0.34
0.02
0.77
0.0001
0.58
0.0004
0.69
0.0001
152 DOROTHY E. LEIDNER AND JOYCE J. ELAM
decision-making process. This hypothesis was supponed. Frequency of EIS use was
correlated with decision-making speed (r = 0.37, p = 0.01). Hypothesis 2b predicted
that the longer the EIS had been in use, the faster the decision-making process would
be. This hypothesis was likewise supponed. The length of time of EIS use was
correlated with decision making speed (r = 0.37, p = 0.0004).
Hypothesis 3a predicted that the more frequent the use of EIS, the greater the extent
of analysis before making a final decision would be. This hypotheses was supported.
Frequency of EIS use was correlated with extent of analysis (r = 0.48, p = 0.0008).
Hypothesis 3b predicted that the longer the EIS had been in use, the greater would be
the analysis before decisions. This hypothesis was supported. Length of time of EIS
use was conelated with the extent of analysis (r = 0.34, p = 0.02).
Table 4 summarizes the results of hypothesis testing.
7. Implications and Research Directions
THE PURPOSE OF THIS STUDY WAS TO EMPIRICALLY EXAMINE the relationship of
executive information systems’ use to the executive decision-making process. The
research used results of a survey of 46 senior manager EIS users across 23 organizations
to test hypotheses conceming the relationship ofthe frequency and length of time
of EIS use with aspects of decision making. Six hypotheses were tested. All were
supported.
Frequency of use and length of time of use are both significantly associated with an
executive’s decision-making process. Although Carlsson and Widmeyer [8] have
suggested that the decision-making framework is not a good framework to research
EIS, these results suggest that, in fact, EIS impact certain facets of the decision-making
process and that future research should consider more decision-making variables.
While the extent of analysis in decision making and the speed of decision making
are often viewed as incompatible, they were both related to EIS use. EIS use was
positively and significantly associated with problem identification speed (HI) and
decision-making speed (H2), as well as with the extent of analysis in decision making
(H3). The more frequent the use and the longer the use, the faster the reported problem
identification speed and decision-making speed, and the greater the extent of analysis.
Chen [9] found that the length of time an information system is in use does not affect
a user’s overall satisfaction with the system; this study indicates that there is a time
effect involved in perceiving impacts from EIS use. The longer the user had been using
the EIS, the greater the impact the user perceived and attributed to the EIS. And the
more frequent the use, the greater the perceived impact. Thus, having a fiexible EIS
that will continue to be used over time becomes critical to an executive’s perception
of results from system use. However, maintaining an EIS is no trivial task and requires
extensive support.
This study examined the effect of EIS use on executive decision making at the
individual level of analysis, and determined that EIS use is related to problem
identification speed, decision-making speed, and the extent of analysis in decision
making. Future research can include other decision-making variables, can examine
EXECUTIVE INFORMATION SYSTEMS 153
Table 4 Support for the Hypotheses
Hypothesis Support
H1 a: The more frequent an executive’s use of EIS, the faster the Yes
problem identification speed
H1 b: The longer the executive’s use of EIS, tiie faster the problem Yes
identification speed
iH2a: The more frequent an executive’s use of EIS, the faster the Yes
decision-making speed
H2b: The longer the executive’s use of EiS, the faster the decision- Yes
making speed
H3a: Tiie more frequent an executive’s use of EiS, the greater tiie Yes
extent of analysis in decision making
H3b: The longer the executive’s use of EIS, the greater the extent Yes
of anaiysis in decision making
effects at the organizational level of analysis, can compare executives using an EIS to
those not using an EIS, and can determine whether the impacts observed in this study
actually lead to better or more effective decision making. Once system usage is
widespread enough to categorize systems into several basic types (such as monitoring,
communication, analysis, or scanning), research can examine the various effects of
the different types of EIS.
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