iBusiness, 2012, 4, 98-107
http://dx.doi.org/10.4236/ib.2012.42012 Published Online June 2012 (http://www.SciRP.org/journal/ib)
Does the Success of Information Systems Really Matters to
Firm Performance?
Rich C. Lee1,2
1Department of Information Management, National Sun Yat-sen University, Kaohsiung, Chinese Tapei; 2System Technology Group,
IBM, Chinese Tapei.
Received February 10th, 2012; revised April 1st, 2012; accepted April 11th, 2012
ABSTRACT
Many failed listed-enterprises had strong information capabilities and resources; however such advantage did not help
these enterprises survive during the economy difficult times. Previous research of DeLone and McLean (D & M) implied
the success of information systems will enhance the performance of enterprises. Based on this implication, many enter-
prises continuously invested resources on information systems as a strategy trying to gain advantage over competitors.
This paper argues the Net Be nefits in D & M model—resulted from the success of Information system—does not always
significantly improve the Enterprise Performance but rather has a limit on it. In fact, such an excess investment cannot
improve the Enterprise Performance but exhausts more valuable resources instead. The implication of this paper is to
encourage enterprises to revisit their v aluable service and reevaluate the Socio-Influences before investing more on in-
formation systems.
Keywords: Information System Success; Firm Performance; Service Science
1. Introduction
Information systems have become the backbone of en-
terprise operations for years. Many information systems
highly dependent enterprises address more on reliability
and sustainability. Enterprise competitiveness relies on
the success of information systems; consequently these
systems help enterprises generate more revenue. DeLone
and McLean (D & M) had argued that Information-Qual-
ity, System-Quality, and Service-Quality could stimulate
Intention-to-Use and User-Satisfaction. The stimulation
would further positively influence Net-Benefits [1]. Fig-
ure 1 illustrated the updated version of D & M Informa-
tion Systems Success model as follows:
Information systems success cannot be achieves with-
out adequate qualified resources. Resource-based view is
one of the fundamental theories to analyze the impact of
information technology on business performance. The
enterprise competitive advantages are determined by the
unique valuable resources [2] having the following char-
acteristics: 1) Valuable: the resource is used to conceive
or implement strategies that improve efficiency and ef-
fectiveness; 2) Rare: the resoursces is not easy to acquire
through a short period of time by competitors; 3) Imper-
fectly Imitable: the resource is not easy to replicate or to
imitate owing to its unique historical social inevitable
conditions; and 4) Non-Substitutable: The resource is
not easily replaceable. Figure 2 illustrated that Enterprise
Performance would be influenced by its valuable re sources
Figure 1. D & M Information systems success updated mod el.
Figure 2. Resource-based view information system perfor-
mance model.

Does the Success of Information Systems Really Matters to Firm Performance? 99
through Information Management Capabilities.
The Information Management Capabilities influence
the Enterprise Performance though three important or-
ganizational capab ilities: 1) customer, 2) process, and 3)
performance management capability [3]. While the en-
terprise performance can be measured by various results:
1) customer-focused, 2) financial, 3) human resource,
and 4) organization effectiveness. Figure 3 illustrated
that Enterprise Performance would be influenced by its
Information Management Capabilities through three me-
diators—organizatio nal capabilities.
However, Enterprise Performance is a perceived out-
come of effective Information Management Capabilities.
It does not always reflect to the real business situation.
Business situation is detersmined by many none techno-
logical factors such as economy growth, government
regulations, and competitors movement. It will be re-
flected on the outcome of business activities—Actual
Enterprise Performance—reported by enterprise finan-
cial statements. Several measurements are commonly
used in evaluating Actual Enterprise Performance [4-6]:
 Return on Assets (ROA): It is the ratio of Net In-
come from Income Statement divided by Total Assets
from Balance Sheet. ROA reflects the ability of a
company to utilize its assets to gain a net profit. Net
Income is the amount earned by a company after sub-
tracting out the expenses incurred, including depre-
ciation and taxes.
 Return on Equity (ROE): It is the ratio of Net In-
come from Income Statement divided by Equity—
Assets minus Liabilities—from Balance Sheet. ROE
reflects the efficiency of a company to spend the in-
vestment to gain a net profit.
 Tobin’s Q Rate: It is the ratio of market value of a
company’s assets divided by their replacement value.
In common financial practice, the ratio can be calcu-
Figure 3. Information management capabilities perform-
ance model.
lated by: (Equity Market Value + Liability Book Value)/
(Equity Book Value + Liability Book Value). The
company with high ratiocan spend more on exploring
new initiatives. This paper used Operating Profit
Margin (OPM) as Tobin’s Q fo r simplicity.
However, each enterprise plays the roles either sup-
plier or consumer, or both, in the value chain which is an
ecosystem with competition. Thus there should be a gap
between Perceived and Actual Enterprise Performance
due to both external and intern al Socio-Influences such a s
organizational, competitiveness, and economy situation
[7]. Such Socio-Influences may root from: 1) technology
leadership—gap needs to overcome, 2) component chal-
lenge—necessity within the ecosystem, 3) complement
challenge—alternatives being replaced, 4) vertical inte-
gration—repositioning in the ecosystem, and 5) tech-
nology maturity—the competitor could easily replicate
the technology [8]. Therefore, the Socio-Influences could
be the factors that depress the outcome from Perceived to
Actual Enterprise Performance; in other words, more
improvement or investment on information systems might
not significantly enhance Actual Enterprise Performance
at all.
This paper re-examined D & M information systems
success model, resource-based view capabilities of in-
formation management, and financial measurement of
Enterprise Performance, hypothesized that Socio Influ-
ences are the gap between Perceived and Actual Enter-
prise Performance. This paper also argued that when
information systems become mature to industry, cones-
quently, they are not valuable resou rces anymore; the Net
Benefits will become a constant to Enterprise Perform-
ance; it means more investment on information systems
will not produce significant Enterprise Performance any
more. In fact, even there is no information system in
present; the enterprise will still p ossess a minimal Enter-
prise Performance through manual processing.
2. Research Method
Figure 4 illustrated the proposed Enterprise Perform-
ance Gap model which consists three major parts: 1) In-
formation Management Capabilities; 2) Perceived En-
terprise Performance; and 3) Actual Enterprise Per-
formance. This paper posited the Socio Influences mod-
erating between actual and perceived Enterprise Per-
formance; three management capabilities namely per-
formance, customer, and process mediating between In-
formation Management Capabilities and perceived En-
terprise Performance; and Information Management Ca-
pabilitie s are p ositively influenced by D & M three quaili-
ties information, system, and service respectively.
2.1. Hypotheses
The hypotheses are described in regression formulae

Does the Success of Information Systems Really Matters to Firm Performance?
100
Figure 4. Enterprise Performance Gap model.
which are expected to be significant as follows: 01
Process ManagementCapacity
Information ManagementCapabilities




 (2-3)
 Information Management Capabilities (HA1, HA2,
and HA3 shown in Figure 4):
The resources and capabilities of information systems
interlink with their utilization, organizational perform-
ance and business value [9]. While the capabilities of
information systems are measured by their qualities con-
tributed, therefore, Information Management Capabili-
ties are positively influenced by three qualities namely
Information, System, and Service [3]. Formula 1 depicts
the linear regression as follows, and β1, β2, and β3 are
expected to be significant :
 Mediation of Perceived Enterprise Performance (HB4,
HB5, and HB6 shown in Figure 4):
Well-developed IT infrastructures give rise to superior
information management capability that plays a role in
facilitating development of important customer manage-
ment, process management, and performance manage-
ment capabilities, and furthe r more, reaches superior firm
performance [3]. Perceived Enterprise Performance is
positively influenced by Performance, Customer , and
Process Management Capacity respectively. Formula 3
depicts the linear regression as follows; β1, β2, and β3 are
expected to be significant respectively:
01
2
3
Information ManagementCapabilities
I
nformation QualitySystem Quality
Service Quality






 
(1)
01
2
3
PerceivedEnterprise Performance
Performance ManagementCapacity
Customer Management Capacity
Process ManagementCapacity









(3)
 Mediation of Management Capabilities (HB1, HB2,
and HB3 shown in Figure 4):
Information management capability enables valuable
organizational capabilities through these three important
organizational capabilities which mediating the links
between information management capability and several
measures of firm performance [3]. Performance, Cus-
tomer, and Process Management Capacities are posi-
tively influenced by Information Management Capabili-
ties respectively. Formulas (2-1), (2-2), and (2-3) depict
the linear regression as follow s, and β1 and β2 are expected
to be significant:
01
Performance ManagementCapacity
Information ManagementCapabilities




(2-1)
01
Customer Management Capacity
Information ManagementCapabilities




(2-2)
 Moderation of Actual Enterprise Performance (HC1
and HC2 shown in Figure 4):
Information systems have impacts on social, economy,
organization, and the way of management, but also are
mutated under the influences from them [9]. Socio Influ-
ences are positively influenced by Perceived Enterprise
Performance; and Actual Enterprise Performance is po-
sitively influenced by Socio Influences as well. Formula
4 depicts the moderation regression as follows; β1, β2,
and β3 are expected to be significant respectively:
The hypotheses are described in regression formulae
which are expected not to be significant as follows (HBC
and HCD shown in Figure 4):

01 2
ActualEnterprisePerformancePerceivedEnterprise PerformanceSocio Influences
PerceivedEnterprise PerformanceSocio Influences
3
 

 

(4)

Does the Success of Information Systems Really Matters to Firm Performance? 101
When above mentioned Mediation effects (Manage-
ment Capabilities and Enterprise Performance) are posi-
tive, the linear regression depicted by Formula s 5 and 6
should not be significant (i.e. both β1 are not expected to
be significant respectively).
01
PerceivedEnterprise Performance
Information ManagementCapabilities




(5)
01
ActualEnterprise Performance
PerceivedEnterprise Performance





(6)
 S-Shape Correlation between Perceived and Actual
Enterprise Performance:
Formulas (7-1) and (7-2) demonstrates a theoretical
model in S-shape illustrated in Figure 5 about the ideal
correlation between actual and perceived Enterprise Per-
formance. The coefficient ρ represents how significant
Perceived Enterprise Performance shall influence on Ac-
tual Enterprise Performance; γ represents the minimal
Actual Enterprise Performance without information sys-
tems; and ε represents the phase that information systems
have not produced significant perceived Enterprise Per-
formance yet.
Let (7-1)
perceived
x Performance 

1
xx
actual
Performancee e

 (7-2)
2.2. Variables
Information Quality: The desirable characteristics of the
system outputs including: relevance, understandability,
accuracy, conciseness, completeness, understandability,
currency, timeliness, and usability [10].
System Quality: The desirable characteristics of an in-
formation system, such as: ease of use, system flexibility,
system reliability, and ease of learning, as well as system
features of intuitiveness, sophistication, flexibility, and
Figure 5. S-shape between Perceived and Actual Enterprise
Performances.
response times [10].
Service Quality: The quality of the support that system
users receive from the IS department and IT support
personnel, such as: responsiveness, accuracy, reliability,
technical competence, and empathy of the personnel staff
[10].
Information Manag ement Capabilities: The ability to
1) provide data and information to users with the appro-
priate levels of accuracy, timeliness, reliability, security,
and confidentiality; 2) provide universal connectivity and
access with adequate reach and range; and 3) tailor the
infrastructure to emerging business needs and directions
[3].
Performance Management Capacity: The ability to
design and manage an effective performance measure-
ment and analysis system, including selection of appro-
priate metrics, gathering of data from appropriate sources
of performance, analysis of data to support managerial
decision making, communication of performance to ap-
propriate stakeholders, and alignment of the performance
management system with current and future business
needs and directions [3].
Customer Management Capacity: The ability to deter-
mine the requirements, expectations, and preferences of
its customers and markets and is of significant importance
in the contemporary business environment marked by
hyper- com pe t i ti o n [3].
Process Management Capacity: The ability to attain
flexibility, speed, and cost economy through the design
and management of three major types of processes: 1)
product design and delivery processes, including new
product development and manufacturing; 2) non-product
and non-service business growth processes, including
innovation, research and development, supply chain ma-
nagement, supplier partnering, outsourcing, mergers and
acquisitions, global expansion, and project management;
and 3) support processes, such as finance and accounting,
facilities management, and human resources management.
[3].
Perceived Enterprise Performance: An equivalent of
D & M Net Benefits. The exten t to which IS a re con tr ibu -
ting to the success of individuals, groups, organizations,
industries, and nations, such as: improved decision-making,
improved productivity, increased sales, cost reductions,
improved profits, market efficiency, consumer welfare,
creation of jobs, and economic development [10].
Socio-Influences: Both external and internal influences.
The external influence is about social, political, econo-
mic and technological factors; the internal influence is
about organizational structure, strategies, and goals [7].
Actual Enterprise Performance: The financial indica-
tors derived from enterprise financial reports including
ROA, ROE, and Tobin’s Q Rate [4-6].

Does the Success of Information Systems Really Matters to Firm Performance?
102
2.3. Sample
According to Semiconductor Equipment and Materials
International (SEMI), Taiwan has been poised to over-
take Japan in 2011 as the world’s largest semiconductor
materials market with growth rate of 36.2% from 2009.
The semiconductor production depends on the success of
manufacturing execution systems (MES) intensively. Fi-
nancial institute operations rely on service and risk man-
agement systems to comply with the regulations and to
maximize the revenue as well. The information man-
agement capabilities of these industries are considered
mature than other general business. This paper collected
two types of data, the empirical data from Taiwan public-
listed companies of these two sectors, and the actual En-
terprise Performance data from Taiwan Economy Jour-
nal (TEJ). TEJ has been widely used in technology and
finance related researches on Taiwan issues [11,12].
The survey questionnaires were collected from depart-
ment managers related to IS, finance, manufacturing,
customer-care, and auditing. It also asked the surveyed
managers to focus on their core information systems in-
cluding: 1) customer relationship management system; 2)
supply-chain or vendor management system; 3) manu-
facturing execution system for semiconductor, core-bank-
ing system for banks, trading system for securities, and
policy system for insurance respectively.
2.4. Encoding and Data Set
The sample frame covered the numbers of surveyed enter-
prises across sectors: 1) sixty-eight for semiconductor; 2)
thirty-fivefor finance; and 3) seventy for optoelectronic in-
dustries respectively. The response rate was 45% for the
first round, 35% for the second round, and 11% for the
third round, 91% in total.
In order to facilitate the analysis efficiently, this paper
used the same company security codes as Taiwan Stock
Market designated for the surveyed enterprises, and used
abbreviations for different sectors and departments as
well (see Appendix 1). The schema of data set covered:
1) respondent profile; 2) qualities; 3) capabilities; 4) socio-
influences; and 5) performances as hypotheses suggested
(see Appendix 2).
2.5. Procedure
Companies were contacted through a multi-staged proce-
dure. First, a contact list derived from TEJ was used to
control and monitor the progress of data collection (see
Appendix 3). Secondly, a survey web site was prepared
to facilitate the data collection process; the participants
can find resources about the survey. Thirdly, a series of
telephone calls were made to verify the contact informa-
tion about managers of each company on the list, and
briefly described the would be a survey kindly requiring
their responses. Four thly, a cover letter of explaining the
purpose the survey, how the expected research results
would benefit to their companies, and the survey (see
Appendix 4) was emailed to the listed managers. Fifthly,
a second-round telephone calls were made to thank to
those already-responded managers and to remind those
who had not responded yet. A gift was sent to each res-
ponded-manager through the first line sales or business
partners of the author’s company. Finally, all responded
data were imported to a database for further statistical
analysis and tests.
The factors of Actual Enterprise Performance were
derived from financial reports instead of from perception.
These factors were clustered into five groups as Likert-
type scales applying k-means algorithm which has been
widely used in financial profitability prediction [13,14].
Actual Enterprise Performance for each enterprise was
calculated as Formula 8. The weights of α, β and γ are 1
as default value but also are sector dependent. For example,
For those semiconductor enterprises, their operations rely
on equipment intensively, thus the weight for ROE shall
be assigned greater value than 1.For finance enterprises,
the weight for OPM can be assigned greater value than 1
to emphasize the importance on operation cost.

3
A
ctualEnterprise Performance
OPMROE ROA   (8)
2.6. Statistical Analysis
 Multi-Trait/Multi-Method Analysis: To analyze the
Convergent and Discriminant Validity, estimate the
degree of method specificity, and scrutinize the ge-
neralizability of results of empirical studies and assess-
ment procedures across methods [15].
 Multivariate Analysis of Variance (MANOVA): To
check whether there are differences among semicon-
ductor, optoelectronic and finance sectors on Qualities,
Management Capabilities, Enterprise Performances,
and Socio Influences respectively.
 Linear Regressions: To see if there were significant
correlations against hypotheses under 95% confidence
level. Also to validate the mediating and moderating
effects of Management Capabilities and Socio Influ-
ences respectively.
 Ordinary Least Squares (OLS) Regression Fitting:
To check the correlation between Perceived and Actual
Enterprise Performances fits S-shaped curve. The cost
prediction in business behaves as an S-shaped curve
requiring better regression than the usual linear regress-
ion [16]. The behavior of Enterprise Performances is
very similar to that concept and expected to be S-shap-
ed as well.

Does the Success of Information Systems Really Matters to Firm Performance? 103
 Clustering Analysis: Using k-means to check there
were significant differences for: 1) Perceived and
Actual Enterprise Performances; and 2) Socio-Influ-
ences within the sector and across sectors as well
[17].
3. Discussion
This paper revalidated the robustness of models of D&M
Information Systems Success, Resource-based View In-
formation System Performance, and Information Mana-
gement Capabilities Performance as well. It also presents
a composite model—Enterprise Performance Gap Model
—to articulate the depression effect of Perceived Enter-
prise Performance by Socio Influences reflected through
financial measurements.
The process of information technology adoption and
its use over time—before and after adoption—is critical
to measure the benefits of the adoption [18]. Enterprises
with similar business model entered the market at dif-
ferent timing. This influences the maturity of information
technology adoption. Latter entrants will certainly adopt
newer technologies than their predecessors. Evidence
showed that in hig h-tech industries the newer technolog y
adoption is faster than ever due to globalization [19]. The
predecessors will also enhance or update their informa-
tion systems to maintain the co mpetitivene ss. The assu mp-
tion behind this is that the continuous investment on
information systems will be expected to gain positive
competitiveness in the market. As a matter of fact, the
reason why information systems improve productivity is
through internal process optimization—management ca-
pabilities; otherwise it will be just another example of
productivity-paradox [20]. The investment decision-mak-
ing process of information systems keenly varies by
different IT governance patterns [21]. This makes more
obscure that the investment should be based on the
outcome of Actual Enterprise Performance. When business
model becomes mature, the difference of Management
Capabilities between competitors is not significant any-
more, thus Socio Influences will dominate the behavior
of business performance.
For emerging technologies such as optoelectronic in-
dustry, the behavior of Enterprise Performance attributed
to technology investment agrees to S-shaped curve: slow
initial, accelerated, then diminishing improvement [22].
Another similar technology diffusion growth which fol-
lowed S-curve is mobile telephony [23]. For those less
information systems dependent companies than high-tech
industries, the S-shape will be flatter (coefficient ρ in
Formula 7-2 is smaller relatively) because the investment
on information systems is not so sensitive as those tech-
nology-dependent companies. This coincides to what the
paper has posited.
4. Conclusion
This paper re-examined D & M information systems suc-
cess model, resource-based view capabilities of infor-
mation management, and financial measurement of enter-
prise performance, posited that Socio Influences are the
gap between Perceived and Actual Enterprise Perfor-
mance. It also validated that the information technology
diffusion does have a limit unless newer valuable
services are brought out to their customers. These newer
valuable services will alleviate the impact from Socio
Influences. Otherwise the Perceived Enterprise Perfor-
mance contributed by information systems will be de-
pressed; the Actual Enterprise Performance will behave
as a near-constant just as the right-upper part of an
S-shaped curve. The S-shaped curve can be used to gain
insight about the relative payoff of investment in com-
peting technologies, as well as to provide more insights
about when and why some technologies overtake others
in the race for dominance as explained earlier. The
implication of this finding is to encourage enterprises to
revisit their valuable services, to gain better market
position, and to reevaluate the Socio Influences before
investing more on information systems.
REFERENCES
[1] W. H. Delone and E. R. McLean, “The DeLone and
McLean Model of Information Systems Success: A Ten-
Year Update,” Journal of Management Information Sys-
tems, Vol. 19, No. 4, 2003, pp. 9-30.
[2] T. P. Liang and J. You, “Resource-Based View in Infor-
mation Systems Research: A Meta-Analysis,” Proceed-
ings of Pacific Asia Conference on Information Systems,
Hyderabad, 10-12 July 2009, p. 72.
[3] S. Mithas, N. Ramasubbu and V. Sambamurthy, “How
Information Management Capability Influences Firm Per-
formance,” Management Information Systems Quarterly,
Vol. 35, No. 1, 2011, pp. 237-256.
[4] N. A. Morgan, D. W. Vorhies and C. H. Mason, “Market
Orientation, Marketing Capabilities, and Firm Perform-
ance,” Strategic Management Journal, Vol. 30, No. 8,
2009, pp. 909-920.
[5] E. Brynjolfsson, L. Hitt and H. Kim, “Strength in Numbers:
How Does Data-Driven Decisionmaking Affect Firm Per-
formance?” 2011. http://ssrn.com/abstract=1819486
[6] L. Bebchuk, A. Cohen and A. Ferrell, “What Matters in
Corporate Governance?” Review of Financial Studies, Vol.
22, No. 2, 2009, pp. 783-827. doi:10.1093/rfs/hhn099
[7] R. Stockdale and C. Standing, “An Interpretive Approach
to Evaluating Information Systems: A Content, Context,
Process Framework,” European Journal of Operational
Research, Vol. 173, No. 3, 2006, pp. 1090-1102.
doi:10.1016/j.ejor.2005.07.006
[8] R. Adner and R. Kapoor, “Value Creation in Innovation
Ecosystems: How the Structure of Technological Inter-
dependence Affects Firm Performance in New Technol-

Does the Success of Information Systems Really Matters to Firm Performance?
104
ogy Generations,” Strategic Management Journal, Vol.
31, No. 3, 2010, pp. 306-333. doi:10.1002/smj.821
[9] A. Borek, M. Helfert, M. Ge and A. K. Parlikad, “An
Information Oriented Framework for Relating IS/IT Re-
sources and Business Value,” Proceedings of the Interna-
tional Conference on Enterprise Information Systems,
Beijing, 8-11 June 2011, pp. 358-367.
[10] S. Petter, W. DeLone and E. McLean, “Measuring Infor-
mation Systems Success: Models, Dimensions, Measures,
and Interrelationships,” European Journal of Information
Systems, Vol. 17, No. 3, 2008, pp. 236-263.
doi:10.1057/ejis.2008.15
[11] W. Y. Wang and C. Chang, “Intellectual Capital and Per-
formance in Causal Models: Evidence from the Informa-
tion Technology Industry in Taiwan,” Journal of Intellec-
tual Capital, Vol. 6, No. 2, 2005, pp. 222-236.
doi:10.1108/14691930510592816
[12] S. M. L. Wong, S. Opper and R. Hu, “Shareholding Str-
ucture, Depoliticization and Firm Performance,” Econo-
mics of Transition, Vol. 12, No. 1, 2004, pp. 29-66.
doi:10.1111/j.0967-0750.2004.00171.x
[13] S. A. Ionescu, C. S. Murgoci, C. M. Gheorghe and E.
Ionescu, “Making Predictions of the Profitability on the Fi-
nancial Markets Using Discriminant Analysis,” Recent Ad-
vances in Artificial Inteligence, Knowledge Engineering
and Data Bases, 2009, p. 495.
[14] S. A. Ionescu, C. S. Murgoci, C. M. Gheorghe and E.
Ionescu, “Towards Profitability on the Financial Mar kets:
A Discriminant Analysis Approach,” WSEAS Transac-
tions on Business and Economics, Vol. 6, No. 3, 2009, pp.
99-111.
[15] M. Eid, C. Geiser and F. W. Nussbeck, “Multitrait-Multi-
method Analysis in Psychotherapy Research: New Meth-
odological Approaches,” Psychotherapy Research, Vol.
19, No. 4-5, 2009, pp. 390-396.
doi:10.1080/10503300802621198
[16] O. A. Isaac, A. A. Adedeji and I. R. Ismail, “Polynomial
Regression Model of Making Cost Prediction in Mixed
Cost Analysis,” Mathematical Theory and Modeling, Vol.
2, No. 2, 2012, pp. 14-23.
[17] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko,
R. Silverman and A. Y. Wu, “An Efficient K-Means
Clustering Algorithm: Analysis and Implementation,”
IEEE Transactions on Pattern Analysis and Machine In-
telligence, Vol. 24, No. 7, 2002, pp. 881-892.
[18] E. Karahanna, D. W. Straub and N. L. Chervany, “Infor-
mation Technology Adoption across Time: A Cross-Sec-
tional Comparison of Pre -Adoption and Post-Adop tion Be-
liefs,” Management Information Systems Quarterly, Vol.
23, No. 2, 1999, pp. 183-213. doi:10.2307/249751
[19] D. Comin and B. Hobija, “An Exploration of Technology
Diffusion,” The American Economic Review, Vol. 100,
No. 5, 2010, pp. 2031-2059.
[20] V. Grover, J. Teng, A. H. Segars and K. Fiedler, “The
Influence of Information Technology Diffusion and Busi-
ness Process Change on Perceived Productivity: The IS
Executive’s Perspective,” Information & Management, Vol.
34, No. 3, 1998, pp. 141-159.
doi:10.1016/S0378-7206(98)00054-8
[21] Y. Xue, H. Liang and W. R. Boulton, “Information Tech-
nology Governance in Information Technology Invest-
ment Decision Processes: The Impact of Investment Cha-
racteristics, External Environment, and Internal Context,”
Management Information Systems Quarterly, Vol. 32, No.
1, 2008, p. 67.
[22] M. A. Schilling and M. Esmundo, “Technology S-Curves
in Renewable Energy Alternatives: Analysis and Implica-
tions for Industry and Government,” Energy Policy, Vol.
37, No. 5, 2009, pp. 1767-1781.
doi:10.1016/j.enpol.2009.01.004
[23] F. S. Wu and W. L. Chu, “Diffusion Models of Mobile
Telephony,” Journal of Business Research, Vol. 63, No. 5,
2010, pp. 497-501.

Does the Success of Information Systems Really Matters to Firm Performance? 105
Appendices
Appendix 1. Abbreviations for Data.
Field Name Representation
Semiconductor Code Optoelectronic Code Finance Code
Design SD Bank FB
Foundry SF Insurance FI
Package Test SP Securities FS
Sector
Others SO
OE
Others FO
Company TWSE Company Code
Function Code Function Code Function Code
Customer P A C Department
Finance
Process Audit
F Information
I Others O
Appendix 2. Data Set Schema.
Quality Management Capabilities
Sector Company Department Information SystemServicePerformance Customer Process
Survey Survey
Socio-Influences Actual Enterprise Performance
Perceived Enterprise Performance External InternalROE ROA Tobin’s Q
Encoded
Survey Derived from TEJ & Clustered by k-means
Appendix 3. Survey Progress Control Table (Sample).
Manager Time of Calls Response of Calls
Company Position NameTELEmail1st2nd3rd1st 2nd Gift Operator
Customer C Y/N
Finance F
Process P
Information I
Audit A
TWSE Company Code
Others O
Appendix 4. Questionnaire.
Background
emiconductorS Finance
Sector Design Foundry Package-Test Other Bank Securities Insurance Other
Company
Managerial Position Customer Finance Audit IS Process Others
Evaluated System Customer Relationship Vendor Process Others
Qualities
Worse-Good 1 2 3 4 5
)1 On what degree that information system could provide adequate information to manage customers and
vendors?
Information
)2 On what degree that information system could provide adequate information to manage
revenue-generating processes?

Does the Success of Information Systems Really Matters to Firm Performance?
106
Continued
)3 On what degree that information system could provide adequate information to facilitate internal
processes?
Worse-Good 1 2 3 4 5
)1 On what degree that information system could provide rich functionalities to manage customers and
vendors?
)2 On what degree that information system could provide rich functionalities to facilitate internal processes?
System
)3 On what degree that information system could provide rich fun ctionalities to ma nag e reve nue -ge ner ati ng
processes?
Worse-Good 1 2 3 4 5
)1 How stable information system could support servicing to customers and vendors?
)2 How stable information system could support servicing to core internal processes?
Service
)3 How stable informatio n system could sup port servicing to revenue-generating processes?
Management Capabilities
Worse-Good1 2 3 4 5
)1 On what degree that information system could improve product/service process eff iciency?
)2 On what degree that information system could improve personnel productivity?
Performance
)3 On what degree that information system could improve decision-making quality?
Worse-Good1 2 3 4 5
)1 On what degree that information system coul d improve customer intim acy?
)2 On what degree that information system could improve vendor intimacy?
Customer
)3 On what degree that information system could improve product/service sales?
Worse-Good1 2 3 4 5
)1 On what degree that information system could provide effective communication among departments?
)2 On what degree that information system could provide useful data for internal processes?
Process
)3 On what degree that information system could support core revenue-generating processes?
Worse-Good1 2 3 4 5
)1 On what degree that information system could improve company’s performance?
)2 On what degree that information system could improve company’s customers/vendors intimacy?
Perceived
Performance
)3 On what degree that information system could improve company’s process efficiency?
Worse-Good1 2 3 4 5
)1 On what degree that information system could improve company’s performance due to
inter-organizationalconflict?
)2 On what degree information system could improve themarket position to beat competition?
Socio-Influences
)3 On what degree information system could improve revenue-generating to defy the recession?
)4 On whatdegree information system could improve the competitive strength?
)5 On what degree information system could improve the relationship among suppliers?

Does the Success of Information Systems Really Matters to Firm Performance?
107
Appendix 5. Actual Enterprise Performance (Semiconductor 2011 Q3 Sample).
Company Code Operation Profit Margin Return on Assets Return on Equity Actual Performance Performance Category
2408 –98.12% –9.33% –122.12% –0.48174 1
3474 –72.21% –5.08% –16.75% –0.18316 2
2425 –10.84% –4.74% –6.50% –0.06938
Data were omitted for short.
2351 6.20% 0.66% 1.35% 0.01768
5305 5.52% 0.79% 1.34% 0.01801
6243 37.44% 0.39% 0.47% 0.01900
2303 19.79% 0.75% 0.94% 0.02407
Data were omitted for short.
8110 15.47% 1.24% 2.71% 0.03732
3035 43.63% –1.02% –1.26% 0.03828
3
2363 35.15% 1.30% 1.35% 0.03951
8271 7.78% 2.33% 3.49% 0.03985 4
4919 34.63% 2.18% 4.06% 0.06742
3189 32.58% 2.72% 3.50% 0.06769
Data were omitted for short.
6286 37.86% 4.94% 7.71% 0.11298
2388 27.82% –15.35% –22.57% 0.21281
5