Sources of Influence on Beliefs about IT Use: an Empirical Study of Knowedge Workers

An Article by William Lewis,Ritu Agarwal, V. Sambamurthy

This research examined the simultaneous of individual belief, institutional, and social context of influences on beliefs about usefulness and ease of use in the context of a contemporary technology targeted at autonomous knowledge workers.


Lewis et al. 2003 stated that research on individual beliefs as the main factor for the acceptance of an information system in an organization is acceptable. However, A research that focus only on the individual beliefs without understanding why the individual has the beliefs is no longer interesting. This is because we know that the beliefs is formed by a process from collecting, processing to synthesizing. Therefore, a factor that to be as antecedents of the beliefs need to be examined. Although there have been previous empricial studies that have examined the factors, but unfortunately the studies only focus upon a specific and limited set of antecedents.


The primary purpose of Lewis et al. 2003 research, therefore, was to present empirical evidence that institutional forces, social forces, and individual characteristics exhibit significant and differential impacts on two key individual beliefs about the use of information technologies such as beliefs related to usefulness and ease of use from Davis (1989) and Davis et al. 1989.


Lewis' et al.2003 findings suggest that beliefs about technology use can be influenced by top management commitment to new technology and the individual factors of personal innovativeness and self efficacy. Surprisingly, social influences from multiple sources exhibited no significant effects.


This research used field study research by inviting 1,121 academic faculty members that use internet technology in his/her activities to fulfill a paper form which was delivered via campus e-mail. Of These, 229 respondents have participated in this research, 181 of which were completed questionnaires and used for data analysis.


The data was analyzed using structural equation modeling by using partial least square. PLS, a latent structural equations modeling technique, was utilized to test the posited research hypotheses. PLS uses a component based approach to estimation that places minimal demands on sample size and residual distributions (Chin 1998). It also permits simultaneous analysis of both the measurement model and the
structural model.


The analysis data consist of descriptive statistics, factor analysis, inter-construct correlations, and t-test.


The implication of this research are:
1. Provide additional evidence regarding salient predictors of key beliefs in technology acceptance.


2. Help sift out and provide initial insights into the relative effects of these predictors on the target beliefs. Lewis et al. 2003 posited and confirmed that the effects of all factors are not invariant across beliefs. Institutional influences were most salient for instrumental outcomes, and individual factors, in contrast, were significant antecedents of both usefulness and ease of use. Finally, the
non-significance of social influences in this study is an interesting finding. It is possible that social influences manifest effects through beliefs not specifically examined in this work, such as image. Indeed, Venkatesh and Davis (2000) found a
significant relationship between subjective norm and image beliefs.



3. From a pragmatic perspective, it is evident that the institutional context for technology use is a critical predictor of individual behavior toward information technologies, via its effects on the mediating construct of beliefs. Our findings suggest that managers need to focus careful attention on exhibiting commitment to a new technology for contingent adoption decisions. Unless individuals perceive the power elite within the organization as strongly behind the use of a new technology
through the messages conveyed as well as overt and specific resource provisioning actions, they are unlikely to develop positive beliefs about the usefulness of that technology. Managerial commitment and support serves the key role of providing structures for the signification and legitimization of technology use. As observed by others (e.g., Compeau and Higgins 1995), it is important for technology implementers to assist individuals in developing positive perceptions about their ability to use the new technology. Finally, as suggested by Agarwal and Prasad
(1998), individuals who are personally more innovative in the use of information technology could be utilized as important change agents because they are likely to exhibit positive beliefs about technology use.

Composite Reliability

Composite reliability
composite reliability is a measure of the overall reliability of a collection of heterogeneous but similar items
individual item reliability (test the reliability of the items using Croinbach Alpha )vs. composite reliability (of the construct, the latent variable)
The factor loadings are simply the correlation of each indicator with the composite
(construct factor), and the factor correlations are oblained by correlating the composites.
calculate composite relaibility for the latent variables, LISREL does not output the "composite reliability" directly. You have to calculate it by hand.
SEM approach for reliability analysis, the reliability estimate from the SEM approach tends to be higher than Cronbach’s α. Structural equation model for estimating
the reliability for the composite consisting of congeneric measures.
Composite reliability--- a measure of scale reliability, Composite reliability assesses the internal consistency of a measure, 2 means square, see Fornell & Larcker (1981)

(sum of standardized loading) 2 / [(sum of standardized loading) 2 + sum of indicator measurement error (the sum of the variance due to random measurement
error for each loading-- 1 minus the square of each loading ]

Let A be the standardized loadings for the indicators for a particular latent variable. Let B be the corresponding error terms, where error is 1 minus the reliability of the indicator; the reliability of the indicator is the square of the indicator's standardized loading.
The reliability of a measure is that part containing no purely random error
(Carmines & Zeller, 1979). In SEM terms, the reliability of an indicator is defined
as the variance in that indicator that is not accounted for by measurement error. It is
commonly represented by the squared standardized multiple correlation coefficient, which
ranges from 0 to 1 (Bollen, 1989; Jöreskog & Sörbom, 1993a). However, because
these coefficients are standardized, they are not useful for comparing reliability
across subpopulations.

composite reliability = [SUM(A)] 2 /[(SUM(A)] 2 + SUM(B).
Example, Suppose I have a construct with three indicators, i1, i2 and i3. When I run this construct in AMOS I get as standardized regression weights: 0.7, 0.8 and 0.9. For computing the composite reliability, I just make:
CR = (sum of standardized loading) 2 / (sum of standardized loading) 2 + sum of indicator measurement error)
CR = (0.7 + 0.8 + 0.9)2 / ((0.7 + 0.8 + 0.9)2 + (1-0.49 + 1-0.64 + 1-0.81)
CR = (5.76)/(5.76 + 1.06)
CR = 0.844
Average variance extracted (AVE), see Fornell & Larcker (1981),
The variance extracted estimate, which measures the amount of variance captured by a construct in relation to the variance due to random measurement error

sum of squared standardized loading / sum of squared standardized loading + sum of indicator measurement error--sum of the variance due to random measurement error in each loading=1 minus the square of each loading )

variance extracted = [(SUM(A 2)]/[(SUM(A 2) + SUM(ei))].
Example,
AVE = (sum of squared standardized loading) / (sum of squared standardized loading + sum of indicator measurement error)
AVE = (0.49 + 0.64 + 0.81)/((0.49 + 0.64 + 0.81) + (1-0.49 + 1-0.64 + 1-0.81) AVE = (1.94)/(1.94+1.06)
AVE = 0.647

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Resources from Kardi Tekno's Space

This resources consist of many materials from statistics to programming and network tutorial. They are like these following links below:

k-Means clustering
K Nearest Neighbor
Market Basket Analysis
Similarity and Distance
Normalization of Performance Index
Adaptive Learning from Histogram
Discriminant analysis
Reinforcement Learning
Monte Carlo Simulation
Bootstrap Sampling
Recursive Average
Kernel Regression
Difference equations
Summation Tricks
Ginger Bread Man and Chaos
Mean and Average
Mean, median, mode
Variance and Standard deviation
Time Average & Time Variance
Data Revival from Statistics
Sierpinski gasket
Regression Model
Generalized Mean
Graph Theory
Growth Model
Digital Root
Continued Fraction
PI
Convert Decimal to rational
Euler Number
Power rules
Logarithm Rules
Bayes Theorem
Independent Events
Conditional Probability
Kernel basis function

Visual Basic (VB) tutorial
Micrsoft Excel Tutorial
Microsoft Excel Macro
Tower of Hanoi
Newton Raphson
Excel Iteration
Finding Eigen Value
Root of Polynomial
Ordinary Differential Equation
Soving System Equation
Generalized Inverse
Runge-Kutta
Euler Integration
Prime Factor
ArcGIS tutorial
Learning from data
Data Analysis from Questionnaire
System dynamic
Break Even Point
Sensitivity and What If Analysis
Financial Analysis
Multicriteria decision making
Analytic Hierarchy Process (AHP)
LAN Connections Switch.

http://people.revoledu.com/kardi/resources/index.html

Do you know what is different between covariance based and variance based method

In the research we have a flexibility to choose the tools that we will use for the purpose of research achievement. If an analysis tool is not suitable because they do not meet the criteria required, then the alternative may be used. As an example in the application of tools based covariance analysis of data such as Amos and Lisrel. This analysis tool requires that the data used in the analysis must be in normal distribution. If this is not fulfilled, whether we will stop until this stage? Certainly not, Why? because there are other tools available that is based varians analysis tools such as PLS. Do you know what the differences between them? The difference is that Amos and Lisrel objective is to try to reproduce more covariance matrix, while the PLS objective is to try to maximize the variance can be explained.

Conventional Business Model in New Format

Technology has driven human's life for a change.
The digital revolution is upon us. We see it every day at home and work, in businessess, schools, and hospital, on road, and even the wars. One of its major aspects is the digital economy.
The digital economy refers to an economy that is based on digital technologies, including digital communication networks (the internet, intranets, extranet, and VAN's), computers, software, and other related information technologies. The digital economy is sometimes called the internet economy, the new economy, or the web economy. In this economy, digital networking and communications infrastructures provide a global platform over which people and organizations interact, communicate, collaborate, and search information.
The digital economy also refers to the convergence of computing and communications technologies on internet and other networks and the resulting flow of information and technology that is stimulating economic commerce and vast organizational changes.

28th years old before people conducted a business in EC, people conducted their business with their partner by face to face either directly or not. In payment for a transaction that has been made among the people, someone must have physically met each other. The people need the others to promote their product and soon. The people pay a fee for a transaction, the peole must pay a fee for a subscription. The people must pay a fee for advertising and pay a fee for a business partner, and soon. All of which conducted physically. We can say that situation as a conventional business model.

But in digital economy all of which has been changed. By using digital technologies, the model business is similar but to be different in business format or structure. The structuture of the digital economy model consist of two elements:
1. Revenue models
2. Value proposition

A revenue model outlines how the organization or the EC project will generate revenue. A company use its revenue model to describe how it will generate revenue and its business model to describe the process it will use to do. There are five common revenue model.
a. Transaction Fees model-Commissions paid on volume of transactions.
b. Subscription model- Fixed amounts are charged, usually monthly.
c. Advertisement model-payment from advertisers.
d. Affiliate model-Commissions for referring customers. for example; Wordtracker's affiliation program on the picture below



This firm offers a program affiliation for anyone is interested. The member of wordtracker's affiliation is given a fee pay per click for referring customer from member's site to wordtracker.

e. Sales model-revenue from sales of goods or services.

Value propositions also included in business model. A Value propositions refers to the benefits, including the intangible, nonquantitative ones, that a company can derive from using the model. A value proposition defines how a company's product or service fulfills the need of customers. The value proposition is an important part of the marketing plan of any product or service.

Reading: Turban, King, Viehland, and Lee,(2006). Electronic commerce-A Managerial Perspective, Pearson International Edition.

Do you know why people motivated?

When the motivation word is not there in managers' vocabularies, they motivated their employees what people called it by "carrot and stick". This word relevant to relationship between a human and a donkey. Donkeys will do something that people want after they has given a carrot, but is not reversely. By doing it, the donkey does not do anything, stick is used as a punishment. This situation is like a machine, so we can say a human is machine.
In human being, this situation can not stand for a long time. The human has feeling, perception, and heart. Therefore, researcher begin to identifify, examine, and make a conclusion about motivation. Finally, we have known that there are two classification of motivation theories.
1. Content theory - this theory aims to examine a question what motivate the people. this revolve around the identification of inward reward.The answer is need. All of people must have a need to survive in their life, such as physiology and psychology need.
We can refer to some scientist like Maslow, Alderfer, and Herzberg.

2. Process theory - this theory aims to examine a question why people behave as they do. Adam's equity theory is talking about this, incorporating such factors as perception and learning.

What is different between Type and Traits?

As we has known in psychologics, there are two classifications of personality that widely accepted by reseacrher. They are such as:
1. Traits and,
2. Type
Do you know what are they different?

Traits refer to any characteristics oh human being that is on a continuum ranging in a characteristic. You can say, that someone has a characteristic that can be anywhere on the continuum ranging in a characteristic whether it to be high, in the middle, or low.
Type refer to classify people into disticnt category. You can say that someone is said that as a introvert or extrovert. There is a distinct or discontinuous.