California University DeLone and McLean Model of Information Systems Success Summary Read the paper “Delone and Mclean model of information systems success

California University DeLone and McLean Model of Information Systems Success Summary Read the paper “Delone and Mclean model of information systems success” carefully and summarize it with your own words using the Paper Summary Template. Please using the Paper Summary Template. I updated the paper summary template example. Please using the example to finish this assignment. The DeLone and McLean Model of
Information Systems Success:
A Ten-Year Update
WILLIAM H. D E L O N E AND EPHRAIM R. McLEAN
is an Associate Professor of Information Systems and Chair of the
Information Technology Department at the Kogod School of Business at American
University in Washington, DC. Professor DeLone’s primary areas of research include
the assessment of information systems effectiveness and value, the implementation
and use of information technology in small and medium-sized businesses, and the
global management of information technology. He has been published in various
journals including Information Systems Research, MIS Quarterly. DATABASE, Journal of Global Information Management, and Journal of Information Technology
Management. Professor DeLone earned a B.S. in mathematics from Villanova University, an M.S. in industrial administration from Cai’negie Mellon University, and a
Ph.D. in Computers and Information Systems from the University of California, Los
Angeles.
WILLIAM DELONE
is a Regents’ Professor and George E. Smith Eminent Scholar’s
Chair in Information Systems in the Robinson College of Business at Georgia State
University, Atlanta. Prior to coming to Georgia State University in 1987, he was on
the faculty of the University of California, Los Angeles (UCLA) for 18 years. Dr.
McLean’s research focuses on the management of information sen/ices, the value of
IS investments, and career issues for IS professionals. He has published over 125
papers in such journals as Information Systems Research, Journal of Management
Information Systems, MIS Quarterly, Management Science, Communications of the
ACM. DATABASE. Han>aril Business Review, Sloan Management Review, and others;
and his coauthored book. Information Technology for Management, now in its third
edition, is currently the second largest selling IS textbook in the world. Dr. McLean
earned his B.M.E. in mechanical engineering from Cornell University and his S.M.
and Ph.D. degrees from the Sloan School of Management at the Massachusetts Institute of Technology (MIT). He is also the Executive Director of the Association for
Information Systems (AISj and in 1999 was made a Eellow of the AIS.
EPHRAIM R. MCLE.’N
Ten years ago, we presented the DeLone and McLean Information Systems (IS) Success Model as a framework and model for measuring the complexdependent variable in IS research. In this paper, we discuss many of the important IS
success research contributions of the last decade, focusing especially on research
efforts that apply, validate, challenge, and propose enhancements to our original model.
Based on our evaluation of those contributions, we propose minor i efinements to the
model and propose an updated DeLone and McLean IS Success Model. We discuss
the utility of the updated model for measuring e-commerce system success. Finally,
we make a series of recommendations regarding current and future measurement of
IS success.
ABSTRACT:
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10
DELONE AND MCLEAN
evaluation of information systems, impact of information
technology, infonnation quality, information systems success, service quality, systems quality, use of information systems, user satisfaction.
KEY WORDS AND PHRASES:
(IS) success or effectiveness is critical to our understanding of the value and efficacy of IS management actions and IS
investments. In 1992, we published a paper [8] in which we attempted to bring some
awareness and structure to the “dependent variable”—IS success—in IS research. We
proposed a taxonomy and an interactive model (hereafter referred to as the “D&M IS
Success Model”) as frameworks for conceptualizing and operationalizing IS success.
Since then, nearly 300 articles in refereed journals have referred to, and made use of,
this IS Success Model. The wide popularity of the model is strong evidence of the
need for a comprehensive framework in order to integrate IS researeh findings.
THE MEASUREMENT OF INFORMATION SYSTEMS
The D&M IS Success Model, though published in 1992, was based on theoretical
and empirical IS research conducted by a number of researchers in the 1970s and
1980s. The role of IS has changed and progressed during the last decade. Similarly,
academic inquiry into the measurement of IS effectiveness has progressed over the
same period. We reviewed more than 100 articles, including all the articles in Information Systems Research, Journal of Management Information Systems, and MIS
Quarterly since 1993 in order to inform this review of IS success measurement. The
purpose of this paper, therefore, is to update the D&M IS Success Model and evaluate
its usefulness in light of the dramatic changes in IS practice, especially the advent and
explosive growth of e-commerce.
The D&M IS Success Model
THE PRIMARY PURPOSE OF THE ORIGINAL DeLone
and McLean paper [8] was to synthesize previous re.search involving IS success into a more coherent body of knowledge and to provide guidance to future researchers. Based on the communications
research of Shannon and Weaver [431 ^”tl the information “influence” theory of Mason [31 ], as well as empirical management information systems (MIS) research studies from 1981-87. a comprehensive, mulfidimensional model of IS success was
postulated. Shannon and Weaver defined the technical level of communications as
the accuracy and efficiency of the communication system that produces information.
The semantic level is the success of the information in conveying the intended meaning. The effectiveness level is the effect of the information on the receiver. In the
D&M IS Success Model, “systems quality” measures technical success; “information
quality” measures semantic success; and “use, user satisfaction, individual impacts,”
and “organizational impacts” measure effectiveness success. In spite of the passage
of time since the Shannon and Weaver framework in 1949 and Mason’s extensions in
1978, both appear as valid today as when we adopted them a decade ago.
THE DHLONE AND Md.KAN MODEL OF INFORMATION SYSTEM SUCCESS
11
Based on both process and causal considerations, these six dimensions of success
are proposed to be interrelated rather than independent. This has important implications for the measurement, analysis, and reporting of IS success in empirical studies.
A temporal, process model suggests that an IS is first created, containing various
features, which can be characterized as exhibiting various degrees of system and
information quality. Next, users and managers experience these features by using the
system and are either satisfied or dissatisfied with the system or its i riformation products. The use of the system and its information products then impacts or influences
the individual user in the conduct of his or her work, and these individual impacts
collectively result in organizational impacts. The resultant D&M IS Success Model is
reproduced in Figure I |8, p. 87].
In contrast to a process model, a causal or variance model studies the covariance of
the success dimensions to determine if there exists a causal relationship among them.
For example, higher system quality is expected to lead to higher user satisfaction and
use, leading to positive impacts on individual productivity, resulting in organizational
productivity improvements. The purpose of combining the success taxonomy with
the success model was to aid in the understanding of the possible causal interrelationships among the dimensions of success and to provide a more parsimonious exposition of the relationships. Unhappily, for some critics this combination has proved
troublesome, leading them to suggest a number of reformulations. These will be discussed later in this paper.
The primary conclusions of the original paper were:
1. The multidimensional and interdependent nature of IS success requires careful attention to the definition and measurement of each aspect of this dependent variable. It is important to measure the possible interactions among the
success dimensions in order to isolate the effect of various independent variables with one or more of these dependent success dimensions.
2. Selection of success dimensions and measures should be contingent on the
objectives and context of the empirical investigation; but, where possible, tested
and proven measures should be used.
3. Despite the multidimensional and contingent nature of IS success, an attempt
should be made to reduce significantly the number of different measures used
to measure IS success so that re.search results can be compiu^ed and findings
validated.
4. More field study research should investigate and incorporate organizational
impact measures.
5. Finally, “[tlhis success model clearly needs further development and validation before it could serve as a basis for the selection of appropriate IS measures” L8, p. 88].
Model Adoption
RESEARCH ADOPTION OF THE D & M IS SUCCESS MODEL has exceeded our expecta-
tions. A citation search in the summer of 2002 yielded 285 refereed papers in journals
12
DFI.ONE AND MrLEAN
System
Quality
Use
Individual
Impact
Information
Quality
-1
Organizational
Impact
User
Satisfaction
Figure J. D&M IS Success Model.
Reprinted by permission, W. DeLone and E. McLean, Information Systems Success: The
Quest for the Dependent Variable. Information Systems Research. 3(1), 1992, pp. 60-95.
Copyright 1992, The Institute of Management Sciences (now INFORMS), 901 Elkridge
Landing Road, Suite 400, Linthicum, MD 21090 USA.
and proceedings that have referenced the D&M Model during the period 1993 to
mid-2002. Many of the.se articles positioned the measurement or the development of
theirdependentvariable(s) within the context ofthe D&M IS Success framework. By
using the model as a common framework for reporting and comparing research work
involving IS success or effectiveness, we believe one of the primary purposes of the
original article has been achieved.
Although many of the cited articles tended to justify their empirical measurement
of IS success by citing the D&M IS Success Model, some of them failed to heed our
cautions. Some researchers have used the model to support their chosen success variable rather than to inform the development of a more comprehensive success construct. They overlooked the main conclusion of the article—that IS success is a
multidimensional and interdependent construct—and that it is therefore necessary to
study the interrelationships among, or to control for, those dimensions. “Researchers
should systematically combine individual measures from the IS success categories to
create a comprehensive measurement instrument” [8, pp. 87-88]. Although these
authors did not choose to measure (or control for) the various dimensions of IS success, a number of other researchers have used multidimensional measures of IS success in their empirical studies and have analyzed the interrelationships among them.
Some of these studies are summarized in the next section.
Model Validation
UNLtKE A PROCESS MODEL, which merely states that B follows A, a causal model
postulates that A (.au.^ej B; that is, increasing A will cause B to increase (or decrease).
THE DELONE AND MCLEAN MODEL OF INHORMATION SYSTEM SUCCESS
13
In the 1992 article we proposed such interrelationships among the dimensions in our
model; but we did not test them empirically. Since 1992, a number of studies have
undertaken empirical investigations of the multidimensional relationships among the
measures of IS success.
Empirical testing and validation of the D&M IS Success Model was the primary
purpose of two research studies [38,41 ]. Seddon and Kiew [41] surveyed 104 users
of a recently implemented, university accounting system and found significant relationships between “system quality” with “user satisfaction” and “individual impact,”
between “infonnation quality” with “user satisfaction” and “individual impact,” and
between “user satisfaction” and “individual impact.” Rai et al. [381 performed a
goodness-of-fit test on the entire D&M IS Success Model based on survey responses
from 274 users of a university student IS. The study found that some goodness-of-fit
indicators were significant but others were not. However, all of the path coefficients
among success dimensions of the D&M IS Success Model were found to be significant.
Other empirical studies explicitly tested the associations among tbe measures identified in the D&M IS Success Model [10, 12, 14, 16, 22. 50]. Yet other empirical
studies have implicitly tested the model by investigating multiple success dimensions
and their interrelationships [II, 17, 48, 52, 54, 57, 58, 60|. Figure 2 displays the
D&M IS Success Model and the relationships confirmed (or not confirmed) in the 16
empirical studies cited above. These .studies were selected based on the fact that they
used multidimensional success constructs and they measured the association among
the success constructs. In the following paragraphs, these empirical results are summarized by the success links that were tested. Links with the strongest empirical
support are discussed first.
System Use—^Individual Impacts
Seven of the 16 studies in Eigure 2 [12, 14, 16, 48, 52, 54. 60] tested the association
between ‘”system use” and “individual impacts” and the association was found to be
significant in each of the studies. System use was typically voluntary and was measured
as frequency of use, time of use, number of accesses, usage pattern, and dependency.
Individual impacts were measured in terms of job performance and decision-making
performance.
System Quality—Individual Impacts
All five studies [10, 12, 41, 50. 57] that tested the direct association between “system
quality” and “individual impacts” found those associations to be statistically significant. System quality was measured in terms of ease-of-use, functionality, reliability,
flexibility, data quality, portability, integration, and importance. Individual impacts
were measured as quality of work environment and job perfonnance.
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THE DELONB AND MCLEAN MODEL OF INFORMATION SYSTEM SUCCESS
15
Information Quality—Individual Impacts
The four studies [10, 41, 50, 57] that tested the relationship between “information
quality” and “individual impacts” found the association to be significant. Information
quality was measured in terms of accuracy, timeliness, completeness, relevance, and
consistency. Individual impact was measured in terms decision-making performance,
job effectiveness, and quality of work.
Other Links
With one exception, all the other links or associations in the D&M IS Success Model
were empirically validated. The one empirical study that found the associations not
significant was a survey of Dutch managers [II], where the association between system use and organizational revenues and profitability was not statistically significant.
In conclusion, 36 of the 38 success factor associations that were empirically tested
in the 16 studies summarized in Eigure 2 were found to be significant. Taken as a
whole, these empirical studies give strong support for the proposed associations among
the IS success dimensions and help to confirm the causal structture in the model.
Model Issues
that have tested and validated the D&M IS Success Model, several articles have been published that challenge, critique, or extend
tbe model itself. On balance, these articles have contributed to a better understanding
of success and its dimensions. These articles and the issues tbey raise to the D&M IS
Success Model are summarized below.
IN ADDITION TO THE MANY PAPERS
Process Versus Causal Models
The D&M IS success taxonomy and its six success categories are based on a process
model of IS |43|. fnaddition, we argue that the six dimensions are interrelated, resulting in a success model that indicates that causality flows in the same direction as the
information process. However, citing an earlier paper by Newman and Robey i35|,
Seddon argues that “the boxes and arrows in variance- and process-model diagrams
represent quite different concepts and cannot be combined meaningfully in one
model. . . . Unfortunately, combining variance and process models is exactly what
[DeLone and McLean have] attempted to do” [40]. Seddon further argues that DeLone
and McLean have “attempted to combine both process and causal explanations of IS
success in their model. After working with this model for some years, it has become
apparent that the inclusion of both variance and process interpretations in their model
leads to so many potentially confusing meanings” [40, p. 240J. Seddon goes on to
propose a respecified variance model of IS success.
16
DELONE A N D MCLEAN
We agree with Seddon’s premise that the combination of process and variance
interpretations of IS success in one model can be confusing. However, we believe
that Seddon’s reformulation of the D&M Model into two partial variance models
[40, p. 245] unduly complicates the success model, defeating the intent of the original model.
The creation of the D&M IS Success Model was driven by a process understanding
of IS and their impacts. This process model has just three components: the creation of
a system, the use of the system, and the consequences of this system use. Each of
these steps is a necessary, hut not sufficient, condition for the resultant outcome(s).
For instance, without system use, there can be no consequences or benefits. However,
with system use, even extensive use. which is inappropriate or ill-informed, there
may also be no benefits. Thus, to understand fully the dimensions of IS success, a
variance model is also needed. Thus, as Seddon himself pointed out [40, 42], the
application of our model to empirical research also requires a contextual variance
specification of the model. Here too there are three components: the first is production, the second is use, and the third is net benefits. The only argument is whether
these two necessary dimensions can be combined into one model. Along with Seddon,
we believe that they can; only our formulations are different.
System Use as a Success Measure
Seddon |40) further argues for the removal of “system use” as a success variable in
the causal success model, claiming that use is a behavior, appropriate for inclusion in
a process model but not in a eausal model. He argues that use must precede impacts
and benefits, but it does not cause them. We disagree. We believe that system usage is
an appropriate measure of success in most cases.
The problem to date has been a too simplistic definition of this complex variable.
Simply saying that more use will yield more benefits, without considering the nature
of this use, is clearly insufficient. Researchers must also consider the nature, extent,
quality, and appropriateness of the system use. The nature of system use could be
addressed by determining whether the full functionality of a system is being used for
the intended purposes. Young and Benamati [59]. for example, suggest that full functional use of an e-commerce system should include informational use, transactional
use, and customer service use. With regard to the extent of use, Lassila and Brancheau
[27] identify various states of systems utilization based on the use or nonuse of basic
and advanced system capabilities. Simply measuring the amount of time a system is
used does not properly capture the relationship between usage and the realization of
expected results. On the other hand, it can be argued that declining usage may be an
important indication that the anticipated benefits are not being realized.
The rejection of system use as a success variable when system usage is mandatory
is also flawed for the reasons cited above. Even …
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