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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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