TASK MANAGEMENT INFORMATION SYSTEM
JOURNAL
Electronic Commerce Research and Applications
Factors influencing the adoption of internet
banking: An integration of TAM and
TPB with perceived risk and perceived benefit
Members Group :
-
Nadia Mutiara (C1L012003)
-
Katherine Handayani U. (C1L012033)
-
Theresia Yulinda P (C1L012053)
--------------------------------------------------------------------------------------------------------------
Electronic Commerce Research and Applications
I.
BACKGROUND
because during the last decade of online banking is
the most beneficial thing the application of e - commerce and some previous
research projects have focused on factors that impact on the adoption of
information technology or the Internet , there is limited empirical work which
simultaneously captures the success factors ( positive factors ) and resistance
factors ( negative factor ) that helps customers to adopt online banking . it
explores and integrates the various advantages of online banks to form a
positive factor named perceived benefit . In addition , images of perceived
risk theory , five specific aspects of risk - financial , security / privacy ,
performance , social and time risk - are synthesized with perceived benefit and
integrated with the technology acceptance model ( TAM ) and theory of planned
behavior ( TPB ) model to propose a theoretical model to explain intention to
use online banking customers . such as online shopping , online auctions
,Internet stock trading and so on . However
, despite the fact that online banking provides many advantages , such as
faster transaction speed and lower handling costs However , despite the fact
that online banking offers many benefits , such as faster transaction speed and
lower handling costs still exists a large group of customers who refuse to
adopt such services because of uncertainty and security issues of these factors
ere said to be interesting because of the globalization of the world everyone
is always menginginkna more pratice and simple , without the need for
complicated when kebank queue example , the presence of certain online traksasi
masyarakyat much benefit as this . were becoming subject matter of security in
online transactions is sometimes considered to be safe , because it uses the
engine and sometimes mistakes can happen
HIPOTESIS
Since
TAM and TPB has been used in many
research to predict and understand user perceptions of the use of the
system and the possibility of adopting their system, the right tools to
understand the adoption of online banking. This study proposes to integrate the
five aspects listed above are considered risk with TAM and TPB to provide a
more omprehensive evaluation model and the adoption of online banking, includes
both negative (perceived risk) and positive factors (Perceived benefits)
simultaneously. This research can provide an improved understanding of risk
perception practitioner customers which can then be used to develop strategies
to reduce the risk and trust building mechanisms to encourage the adoption of
online trading. especially in the emerging area of e-payment.
RESEARCH
OBJECTIVE
1. To determine whether the
perceived risks and benefits are significantly
the impact
of behavioral intention to use online banking customers
adoption.
2. To clarify the factors more
influential in affecting
the decision to
use online banking.
3. To evaluate whether integration
of TAM with TPB giving
solid theoretical
basis to examine the adoption of online banking.
ADVANTAGES
OF RESEARCH
The new
online banking has come to be regarded as one of the
method is the most effective banking
Online banking has recently come to be regarded as one of the methods most
effective banking transactions . because it has a lot of advantages that
offline banking channels can not offer . Thus , online banking managers aim to
exploit this advantage to increase the rate of adoption of online banking
.Based to some extent with the reasons offered , there two main types of
perceived benefits , which can be categorized
advantages as direct and indirect .
Gain immediate and tangible benefits directly refer that customers will enjoy
using online banking . For example , customers can benefit from a wider range
of financial gain , faster transaction speeds , and increase transparency of
information . First , the broader financial gain including lower transaction
handling costs , higher deposits rate , the chance to win prizes and bonus
extra credit cards points . Second , faster transaction speeds obviously means
that time can be saved because online banking is not necessary paper documents
, processing which can lead to errors and delays , and who also need more
personnel . Automate online banking this process by mediating transactions
through the website and electronic data interchange , and also can reduce the
need for customers to communicate with the staff of the bank regarding the
transaction details because they can be obtained on the website . Third ,
during the transactions , online banking allows customers to monitor contract performance
at any time , or to confirm an automatic delivery .
In other words , more relevant
information is immediately
available and transparent to the customer
Ê Teori apa yang
digunakan:
ú Jelaskan isi teori (apa
& bagaimana?)
2.3.
Technology acceptance model
TAM
is an adaptation of the theory of reasoned action (TRA) by
Fishbein
and Ajzen (1975)and was mainly designed for modeling
user
acceptance of information technology (Davis et al., 1989). This
model
hypothesizes that system use is directly determined by
behavioral
intention to use, which is in turn influenced by users’
attitudes
toward using the system and the perceived usefulness
of
the system. Attitudes and perceived usefulness are also affected
by
perceived ease of use.
2.4.
Theory of planned behavior
The
TPB underlying the effort of TRA has been proven successful in predicting and
explaining human behavior across various
information
technologies (Ajzen, 2002, 1991). According to TPB,
a
person’s actual behavior in performing certain actions is directly
influenced
by his or her behavioral intention and, in turn, is jointly
determined
by his or her attitude, subjective norms and perceived
behavioral
controls toward performing the behavior. Behavioral
intention
is a measure of the strength of one’s willingness to exert
effort
while performing certain behaviors.
ú Berikan argumen mengapa
teori tsb relevan utk riset ini?
Technology
acceptance model relevan because can help to
The appeal of this model lies in
that it is both specific
and parsimonious and displays a
high level prediction power of
technology use. These determinants
are also easy for system developers to understand and can be specifically
considered during system requirement analysis and other system development
stages.These factors are common in technology-usage settings and can
be applied widely to solve the
acceptance problem , and Theory of planned behavior relevan because can help to
Furthermore, a favorable or unfavorable attitude directly influences the
strength of the behavior and beliefs
regarding the likely outcome.
In sum, grounded on the effort of
TRA, TPB is proposed to eliminate the limitations of the original model in dealing
with behavior over which people have
incomplete volitional control
Ê Berbagai Konsep:
ú Apa, bagaimana dan
mengapa?
What
? The use of concept is upon two primary research streams, information
technology (IT) adoption theory and perceived risk theory, to develop this
study’s research model and associated hypotheses.
How
? Recently, a growing body of research has focused on integrating them to
examine IT usage and e-service acceptance because the two models are
complementary, and the results have showed that the integration model had
better exploratory power than the individual use of TAM and TPB
Why
? Since the focus of this study is online banking service adoption, which is an
instance of acceptance of innovative technology intertwined with social systems
and personal characteristics, the integration of TAM and TPB for our research
framework should be comprehensive in order to examine the consumers’ intentions
towards, and acceptance of, online banking.
Ê Telaah Riset Sebelumnya
ú Jelaskan urut tahun
(apa yang sudah diteliti, dimana, bagaimana dan hasilnya apa)
We
drew upon two primary research streams, information technology (IT) adoption
theory and perceived risk theory, to develop this study’s research model and
associated hypotheses. Over the past decade, TAM and TPB have been widely
applied to examine IT usage and e-service acceptance (Davis, 1993; Hsu, 2004;
Hsu and et al., 2006). However, neither TAM nor TPB have been found to provide
consistently superior explanations or behavioral predictions (Chen et al.,
2007). Recently, a growing body of research has focused on integrating them to
examine IT usage and e-service acceptance because the two models are
complementary, and the results have showed that the integration model had
better exploratory power than the individual use of TAM and TPB (Bosnjak et
al., 2006; Chen et al., 2007; Wu and Chen, 2005). Since the focus of this study
is online banking service adoption, which is an instance of acceptance of
innovative technology intertwined with social systems and personal
characteristics, the integration of TAM and TPB for our research framework
should be comprehensive in order to examine the consumers’ intentions towards,
and acceptance of, online banking. There are 12 constructs in our model, which
includes perceived ease of use, perceived benefit, performance risk, financial
risk, time risk, social risk and security risk as independent variables,
perceived usefulness, attitude, subjective norm
ú Apa kelemahan riset
tersebut?
Deficiency
Theory of Technology
Acceptance Model
Each
of these can be
described
as follows :
1
. Theory TAM does not accommodate the role of everyone around them in
influencing the attitudes and behavior of individuals . Whereas in the various
studies psychology , individual behavior is influenced by the behavior of
others around him . Psychological concepts such as conformity and social
influence ( Latane , 1981) proceed from the assumption that a person's behavior
is strongly influenced by the behavior and the presence of other people .
2
. Existence of individual differences in behavior ( individual differences) .
In Psychology proved decisive nature of individual behavior. The presence of
psychological tests ( cognitive and personality ) and its application in life
is evidence of individual differences . The differences may stem from
differences in cognitive abilities , personality traits and values espoused
governance .
3
. Theory TAM does not consider the role of the ability of each person to
realize his wish .deficiency theory of planned behavior adalalah The basic
assumption of the TPB is a lot of behavior is not under complete control of
everything that needs to be individualized ditambakan perceived behavioral
control concept .
Deficiency
Theory Of Planned Behavior adalalah
The basic assumption of the TPB is a lot of behavior is not under complete
control of everything that needs to be individualized ditambakan perceived
behavioral control concept .
Ê Kerangka Pemikiran
Teoritis dan Perumusan Hipotes
ú Gambar/Deskripsikan
kerangka pemikiran teoritis yang digunakan
There
are 12 constructs in our model, which includes perceived ease of use, perceived
benefit, performancen risk, financial
risk, time risk, social risk and security risk as independent variables,
perceived usefulness, attitude, subjective norm, perceived behavioral control
as intervening variables, and intention to use as the dependent variable. We
will test the strength of the hypothesized relationships embedded in the
theoretical model and the robustness of the model in predicting customers’
intention to adopt online banking in the Taiwan business environment. The
theoretical model is graphically presented in Figgure.
ú Apakah rumusan
hipotesisnya (jika ada)
Nothing
------------------------------------------------------------------------------------------
1.
Research
Model
A.
Hypothesis
Development
·
Hypothesis about TAM
and TPB
As TAM and TPB are used as the base
models, we need to test the following TAM and TPB hypotheses in the context of
online banking adoption. Hypotheses 1, 2, 5, 6, and 7 are proposed based on TAM
as discussed in Section 2.2 while hypotheses 3 and 4 based on TPB as described
in Section 2.3.
H1: Perceived usefulness positively
influences the intention to use online banking.
H2: Attitude positively influences
the intention to use online banking.
H3: Subjective norm has positively
influences the intention to use online banking.
H4: Perceived behavior control
positively influences the intention to use online banking.
H5: Perceived usefulness positively
influences attitudes towards the use of online banking.
H6: Perceived ease of use
positively influences attitudes towards the use of online banking.
H7: Perceived ease of use
positively influences the perceived usefulness of the use of online banking.
·
Hypothesis Regarding
Performance Risk
The performance risk refers to
losses incurred by deficiency or malfunction of online banking websites.
Therefore, it follows that:
H8a: Performance risk negatively influences
the perceived usefulness of using online banking.
H8b: Performance risk negatively
influences attitudes towards the use of online banking.
·
Hypothesis Regarding
Financial Risk
Financial risk refers to the
potential for monetary loss due to transaction errors or bank account misuse.
Accordingly, the following hypotheses are proposed:
H9a: Financial risk negatively
influences attitudes towards the use of online banking.
H9b: Financial risk negatively
influences intentions towards the use of online banking.
·
Hypothesis Regarding
Social Risk
Based on these studies, it is
reasonable to expect that social risk could negatively
influence customers’ attitude to
use online banking. Thus, it follows that:
H10a: Social risk negatively
influences attitudes towards the use of online banking.
And consumers are likely to believe
that referents would approve less of their purchasing the
H10b: Social risk negatively
influences the subjective norm regarding the use of online banking.
·
Hypothesis About Time /
Convenience Risk
Hypothesized that:
H11: Time risk negatively
influences attitudes towards the use of internet banking.
·
Hypothesis About
Security / Privacy Risks
Security risk is a significant
impediment to the adoption of online banking. It is therefore hypothesized
that:
H12a: Security/privacy risk
negatively influences attitude towards the use of online banking.
H12b: Security/privacy risk
negatively influences intentions to use online banking.
·
Hypothesis about
Perceived Benefit
Therefore, it is reasonable to
infer that perceived benefits positively influence user attitude and intention
to adopt online banking, and we hypothesized
that:
H13: Perceived benefit has a
positive effect on attitude to use online banking.
H14: Perceived benefit has a
positive effect on intention to use online banking.
2.
Reaserch
Method
a.
Data
Colletive
This online survey, which yielded
446 responses, was conducted for one month, with incomplete responses and
missing values deleted, resulting in a sample size of 368 users for an overall
response rate of 83%. Sample demographics are depicted in Table 2. Fifty-eight
percent of the respondents were male and 42% were female. The majority of
respondents (61%) were over 30 years old. About 69% of the respondents did not
have experience using online banking. Finally, the education levels of
respondents were 62.5% college and 13.6% high school.
b.
Measurement
development
This part of questionnaire was used
to collect basic information about respondents’ characteristics including
gender, age, education, occupation, and experience using online banking.
3.
Result
In
analyzing the collected data, we followed the two-step procedure suggested by
Anderson and Gerbing (1988). First, we examined the measurement model to
measure convergent and discriminant validity. Then, we examined the structural
model to investigate the strength and direction of the relationships among the theoretical
constructs.
a.
Analysis
of the measurement model
We evaluated the measurement scales
using the three criteria suggested by Fornell and Larcker (1981).
(1) All indicator factor loading
(k) should be significant and exceed 0.5.
(2) Construct reliabilities should
exceed 0.8.
(3) Average variance extracted
(AVE) by each construct should exceed the variance due to measurement error for
the construct (e.g. AVE should exceed 0.5).
b.
Multicollinearity
A threshold VIF that is less than
or equal to 10 (i.e. tolerance >0.1) is commonly suggested (Asher, 1983;
Hair and et al., 1998). The VIFs for PU, PEOU and attitude were 9.23, 7.22, and
9.21, respectively, in predicting intention, providing further evidence against
multicollinearity.
c.
Analysis
of the structural model
The results of structuralequation
modeling obtained for the proposed conceptual model revealed a ratio of
chi-square to the degree of freedom (v2/df) of 2.04 (p < 0.001),
goodness-of-fit index (GFI) of 0.91, adjusted goodness-of-fit index (AGFI) of
0.85 comparative fit index (CFI) of 0.95, normed fit index (NFI) of 0.95,
relative fit index (RFI) of 0.94, and root mean square error of approximation
(RMSEA) of 0.05 (see Fig. 2). Generally, fit statistics greater than or equal
to 0.9 for GFI, NFI, RFI, and CFI indicate a good model fit (Bagozzi et al.,
1991; Hair and et al., 1998). Furthermore, RMSEA values ranging from 0.05 to
0.08 are acceptable (Hair and et al., 1998); therefore, the RMSEA suggested
that our model fit was acceptable. Other fit indices, except AGFI, indicated
that our proposed model obtained an adequate model fit. Low AGFI statistics may
have resulted from the small sample size used.
d.
Hypothesis
Testing
Intention to use Internet banking in
this study was jointly predicted by PU (b = 0.21, Standardized path
coefficient, p < 0.05), perceived benefit (b = 0.32, p < 0.05), attitude
(b = 0.25, p < 0.01), perceived behavior control (b = 0.12, p < 0.05),
financial risk (b = 0.26, p < 0.05), security risk (b = 0.35, p < 0.05) and
subjective norm (b = 0.13, p < 0.05) and these variables together explained
80% of the variance of intention to use (R2 = 0.80, coefficient of
determination). As a result, Hypotheses 1, 2, 3, 4, 9a, and 12b were all
supported. Attitude was predicted by PU (b = 0.29, p < 0.01), PEOU (b =
0.35, p < 0.05), financial risk (b = 0.21, p < 0.01), time risk (b =
0.13, p < 0.05), performance risk (b = 0.11, p < 0.05), and security risk
(b = 0.29, p < 0.01). Together these variables explained 76% of the total
variance. These findings validated Hypotheses 5, 6, 8b, 9a, 11, and 12a
respectively. Social risk (b = 0.78, p < 0.001) significantly influenced
subjective norm while explaining 61% of the total variance in subjective norm.
Accordingly, Hypothesis 10b was supported. Time risk (b = 0.13, p < 0.05)
significantly influenced attitude. Consequently, Hypothesis 11 was supported.
Both performance risk (b = 0.17, p < 0.001) and PEOU (b = 0.81, p <
0.001) significantly influenced PU and jointly explained 68% of the total
variance in PU. As a result, Hypotheses 7 and 8a were supported. Social risk (b
= 0.02, p > 0.05) did not significantly affect attitude. Hence, Hypotheses
10a was not supported.
----------------------------------------------------------------------------------------------
Definition and Measurement of Variables
Definition:
Implies
variable size or characteristic possessed by members of a different group to
those of the other groups. Another understanding that the variable is something
that is used as a characteristic, trait or measure owned or acquired by a unit
of study about something specific concepts.
Variable
measurement is the process of determining the amount or intensity of
information about people, events, ideas, and or a particular object and its
relationship with the business problem or opportunity.
In other words,
using the measurement process is to assign a number or a table of the
characteristics or attributes of an object, or any kind of phenomena or events
using certain rules which indicate the amount and or quality of the factors
studied.
Population
The population in this study is the overall TAM and TPB.
Sample
The sampling process is done by sorting the population based forces first,
then after it was taken respondents
random. With these methods it is certain that the sample will illustrate
significantly the actual population.
-
Sampling
is a part of the population to be observed; viewed as an estimate of the
population, but not the population itself. Regarded as a representative sample
of the results are representative of the overall population of the symptoms
observed. The size and diversity of the sample into deciding whether or not the
samples were taken. There are two ways of sampling, ie at random (random)/probability
and random (non-random) / non-probability. This online survey, which yielded
446 responses, was conducted for one month, with incomplete responses and
missing values deleted, resulting in a sample size of 368 users for an overall
response rate of 83%. Sample demographics are depicted in Table 2. Fifty-eight
percent of the respondents were male and 42% were female. The majority of
respondents (61%) were over 30 years old. About 69% of the respondents did not
have experience using online banking. Finally, the education levels of
respondents were 62.5% college and 13.6% high school
-
Instrument Research
Is a tool used in research to collect the data. in this case study uses
multiple electronics that can connect online internet banking.
-
Test Quality Data
The study measured the variables using a questionnaire instrument should be
tested against the quality of the data obtained. This test aims to determine
whether the instruments used valid and reliable because the accuracy of data
that is processed will determine the quality of research results.
Hasil Riset
Overall clearly the strength of our research models has a
square-PPA 80% for intention to use online banking and 76% PPA-square for the attitude
towards online banking, suggests that the extended TAM model with TPB. able to
explain the relatively high proportion of the variation in intention to adopt
online banking.
Implikasi
a.
Implikasi Manajerial
Hasil penelitian ini menjelaskan beberapa isu
penting terkait dengan niat pelanggan terhadap perbankan online yang belum
telah ditangani oleh penelitian sebelumnya. Pertama, meskipun keduanya
dianggap manfaat dan risiko memiliki pengaruh signifikan pada niat, studi ini
mengungkapkan bahwa yang terakhir adalah faktor yang lebih berpengaruh,
menyiratkan bahwa mengendalikan risiko perbankan online adalah lebih penting
daripada memberikan manfaat.
b.
Implikasi akademis
Penelitian ini bertujuan untuk mengembangkan teori
baru dengan mendasarkan variabel baru dalam integrasi dari dua sekolah model
struktur nomological (TRA) dan menerapkan mereka ke dalam konteks
baru. Penting untuk dicatat bahwa kedua variabel baru - manfaat yang
dirasakan dan risiko - yang kompatibel dengan variabel TAM dan TPB yang telah
ditempatkan dalam kerangka TRA (Davis, 1989). di mana resiko keamanan memiliki efek negatif terbesar (b = 0,35),
sedangkan manfaat yang dirasakan memiliki
efek positif kuat (b = 0,32). Hasil ini menunjukkan bahwa faktor risiko memberikan sebuah efek
yang lebih kuat pada keputusan pelanggan.
Keterbatasan riset
The first risk research in security systems ,This underlines the
fact that concerns about fraud and identity theft are foremost in the minds of
Internet users .
The second risk is the financial risk also has a significant negative effect . that
the difficulty in using the online system becomes less of a concern because
they are more user-friendly . In addition , because the online system more
general and current standards ,the public has become increasingly competent in
their use .
Third , the results indicate that the risk of performance has a significant
negative effect on perceived benefits damage site can increase the willingness
consumers to conduct online transactions .
The findings revealed that all four social risk attitudes influence is not
significant . This indicates that customers are not pedulitentang social
pressure from friends / family / work groups with regard to their online
banking .
Fifth , the study found that the risk of time / convenience of having a negative
effect on attitudes toward intention to adopt online banking . This means that
online banking users may be concerned about delays in receiving payments online
and may be concerned with the length of time involved in waiting for a website
or learn how to operate it .
Ê Saran untuk riset selanjutnya
Suggestions for further research
Advice for first risk is the risk that a security system is providing strong
encryption and authentication to prevent fraud and identity theft should be a
priority in this field.
Suggestions for a second risk is the risk of the financial sector in the system is this
may explain why many customers resisted adopting online banking.
Suggestions for third risk is the risk of field performance in such systems is to
minimize the risk of damage to the site can increase the willingness of
consumers to conduct online transactions.
Suggestions for a fourth risk is the risk of the social field in the system are
mengopmilalkan social norms can significantly determine the intention to use in
the context of mandatory-use internet online social network.
Suggestions for the fifth risk is the risk in the field of time /
convenience in the system is to
reduce the possibility of late payments and the waiting time is an important
issue for service providers online banking.
Ê Jika topik artikel ini
akan anda jadikan topik skripsi, judul apa yang sesuai?
“ Model Behavior The use of IT (information technology ') In
System Online-Based Information
Integration Methods Tam and TPB’’
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Berikut ini adalah Tugas Meringkas PPT (presentasi power point) Management Information System.
TASK MANAGEMENT INFORMATION SYSTEM
Chapter 12
ENHANCING DECISION MAKING
-
Nadia Mutiara (C1L012003)
-
Katherine Handayani U. (C1L012033)
-
Theresia Yulinda P (C1L012053)
ENHANCING
DECISION MAKING
This chapter
focuses on the information
systems that support decision-making in a firm and discusses the value of
improved decision-making in an organization. Such as What are the different types of decisions and how does the
decision-making process work? How do
information systems support the activities of managers and management decision
making? How do business intelligence and business
analytics support decision making?How do different decision-making
constituencies in an organization use business intelligence? What is the role of information systems in helping people working in a
group make decisions more efficiently? Business analytics software to analyze patterns in sales data, create
pricing profiles and buyer profiles for different regions, locales, even times
of day, Demonstrates the use of business intelligence
and analysis systems to improve sales and profits, Illustrates
how information systems improve decision making , it means that to take the
decision making the company must be have good management information system to Improving
hundreds of thousands of “small” decisions adds up to large annual value for
the business and
type of decision making such as :
Unstructured:
Decision maker must provide judgment, evaluation, and insight to solve
problem
Structured: Repetitive
and routine; involve definite procedure for handling so they do not have to be
treated each time as new
Semistructured:
Only part of problem has clear-cut answer provided by accepted procedure
---------------------------------------------------------------------------
There is 3
level of
managerial :
–
Make many unstructured
decisions
–
E.g. Should we enter a new market?
–
Make more structured
decisions but these may include unstructured components
–
Why is order fulfillment
report showing decline in Minneapolis?
•
Operational managers, rank
and file employees
–
Make more structured
decisions
–
E.g. Does customer meet criteria for credit?
---------------------------------------------------------------------------
After we know the
type of decision making ,
this 4 stages of the decision making
process :
Discovering,
identifying, and understanding the problems occurring in the organization
Identifying
and exploring solutions to the problem
Choosing
among solution alternatives
Making chosen
alternative work and continuing to monitor how well solution is working
---------------------------------------------------------------------------
Planninng , organizing,
coordinating, deciding, and controlling
More
contemporary behavioral models :
Actual behavior of managers appears to be less systematic, more informal,
less reflective, more reactive, and less well organized than in classical model
---------------------------------------------------------------------------
---------------------------------------------------------------------------
Three main reasons why investments in information technology do not
always produce positive results :
High-quality
decisions require high-quality information
Managers have
selective attention and have variety of biases that reject information that
does not conform to prior conceptions
·
Organizational
inertia and politics
Strong forces
within organizations resist making decisions calling for major change
---------------------------------------------------------------------------
The concept of
business intelligence and analytics. The text gives the example of Hallmark
Cards, which uses SAS analytics software to
analyze buying patterns and determine the most effective marketing plan
for different types of customers. For example, which customers would respond
best to direct mail or email, and to what types of messages. It is important to
understand that business intelligence and business analytics are products
defined by hardware and software vendors. This is also one of the fastest
growing segments in the U.S. software environment. Ask students why this might
be so.
---------------------------------------------------------------------------
Six elements in the business intelligence
environment
·
Data from the business
environment
·
Business intelligence
infrastructure
·
Business analytics toolset
·
Managerial users and methods
·
Delivery platform – MIS,
DSS, ESS
---------------------------------------------------------------------------
Business
intelligence and analytics capabilities
Goal is to deliver accurate real-time information to decision-makers
Main functionalities
of BI systems
•
Ad hoc query/search/report creation
•
Drill dowN Forecasts, scenarios, models
Examples of
BI applications
•
Use patterns in data to predict future behavior
•
E.g. Credit card companies use predictive analytics to
determine customers at risk for leaving
•
Help users see patterns and relationships that would
be difficult to see in text lists
Geographic information
systems (GIS)
•
Ties location-related data to maps
---------------------------------------------------------------------------
Management
strategies for developing BI and BA capabilities
1.
One-stop integrated solution
–
Hardware firms sell software that run optimally on
their hardware
–
Makes firm dependent on single vendor – switching
costs
2.
Multiple best-of-breed solution
–
Greater flexibility and independence
–
Potential difficulties in integration
–
Must deal with multiple vendors
Operational
and middle managers
–
Monitor day to day business
performance
–
Make fairly structured
decisions
“Super user”
and business analysts
–
Use more sophisticated
analysis
–
Create customized reports
