Case Tools In System Analysis And Design Pdf

Computer Aided Software Engineering (CASE) consists of a set of tools that support automation of various software engineering processes. CASE is extensively used by software developers in industry in systems analysis and design due to gains it provides in productivity and improved documentation quality. Studies indicate that with the proliferation of information technology in today's organizations and the capital investments required that users must use and accept technology to improve productivity. In this study we examine the determinants of CASE tool acceptance and use. We simulate a systems development environment in a classroom and examine the determinants of CASE tool use. We use the Unified Theory of Acceptance and Use of Technology (UTAUT) model to identify and test core determinants of user intention to use these tools. Data was collected and analyzed from 85 students regarding their use of CASE technology in the classroom. Results show partial support for the UTAUT in that participants' performance expectancy and social influence affect behavioral intention to use CASE tools while effort expectancy did not. Results also show that facilitating conditions, computer anxiety and attitude toward using technology have an effect on intention to use CASE tools. Considering the substantial investment required to acquire CASE tools, we believe our findings on the determinates of CASE tool acceptance are important both to industry in the use of CASE and to universities in teaching with CASE.

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USER ACCEPTANCE OF CASE TOOLS IN SYSTEMS ANALYSIS

AND DESIGN: AN EMPIRICAL STUDY

Subhasish Dasgupta, Ph.D.

George Washington University

dasgupta@gwu.edu

Maliha Haddad, Ph.D.

George Washington University

Peter Weiss, Ph.D.

George Washington University

Enrico Bermudez

George Washington University

ABSTRACT

Computer Aided Software Engineering (CASE) consists of a set of tools that

support automation of various software engineering processes. CASE is

extensively used by software developers in industry in systems analysis and

design due to gains it provides in productivity and improved documentation

quality. Studies indicate that with the proliferation of information technology in

today's organizations and the capital investments required that users must use

and accept technology to improve productivity. In this study we examine the

determinants of CASE tool acceptance and use. We simulate a systems

development environment in a classroom and examine the determinants of

CASE tool use. We use the Unified Theory of Acceptance and Use of

Technology (UTAUT) model to identify and test core determinants of user

intention to use these tools. Data was collected and analyzed from 85 students

regarding their use of CASE technology in the classroom. Results show partial

support for the UTAUT in that participants' performance expectancy and social

influence affect behavioral intention to use CASE tools while effort expectancy

did not. Results also show that facilitating conditions, computer anxiety and

attitude toward using technology have an effect on intention to use CASE tools.

Considering the substantial investment required to acquire CASE tools, we

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believe our findings on the determinates of CASE tool acceptance are important

both to industry in the use of CASE and to universities in teaching with CASE.

Keywords: Computer Aided Software Engineering, CASE Tools, UTAUT model,

technology acceptance

I. INTRODUCTION

Computer Aided Software Engineering (CASE) consists of a set of tools

that support automation of various software engineering processes. Software

developers in some organizations use CASE tools in systems analysis and

design to produce the various models required by different development

methodologies. Research points to productivity gains from CASE analysis and

design tool usage [Tsuda, 1992] and reduction in personnel requirements and

system development costs [LeBlanc and Korn, 1992] in system development.

CASE tools have become integral part of organizations that are interested in

improving their process maturity level [Goldenson and White, 2000; Wittman,

1995]. Some recent examples where CASE tools have been widely used in

practice were reported by Berenbach [2006], Brambilla [2006], Lange, Chaudron,

and Muskens [2006], and Sriplakich, Blanc, and Gervais [2006].

Some universities have followed the lead of the software industry by

incorporating CASE tools in their course offerings, for examples, see Bothe,

Schutzler, Budimac and Zdravkova [2005] and Diethelm, Geiger and Zundorf

[2005]. A detailed description on contemporary usage levels of CASE tools in

U.S. colleges and universities is provided by Chinn, Lloyd, and Kyper [2005]. As

evident in a recent proposed model for information technology curriculum

[Ekstrom et al., 2006], the use of CASE tools in universities will continue at least

in the near future. It is believed that the use of a CASE tool improves student

understanding of systems development concepts and also provides them with a

skill that may be considered valuable in the job market. Industry greatly benefits

from having students gain experience in the use of CASE tools within their

university program. It enables the organization to more easily assimilate

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graduates into the system development process. In addition, the academic CASE

experience is valuable since many industries want and need students with CASE

competencies. It is also safe to assume that the experience of students with a

particular technology might influence their use of that technology later on the job.

The focus of this study is to evaluate the use of CASE tools in systems

analysis and design; to be more specific we examine factors that influence the

use of CASE in a systems development environment. We simulate a systems

development environment in a classroom and examine the determinants of

CASE tool use. We use the Unified Theory of Acceptance and Use of

Technology (UTAUT) model proposed by Venkatesh, Mo rris, Davis, and Davis

[2003] to identify and test core determinants of user intention to use these tools.

An examination and acceptance of CASE tools is important to both the industry

and academic departments that have either integrated or are contemplating

integration of such tools due to the substantial investment required to procure

such tools.

Our paper is organized as follows. We present our literature review next.

Here we report on research on CASE tools in the software industry, CASE tools

in education, and then provide an overview of the UTAUT. After the literature

review we present our research model followed by the research methodology.

Finally, we present the results and conclusion.

LITERATURE REVIEW

The literature review covers the three research areas of interest to our

study: research on CASE tools in the software industry, research on CASE tools

in education, and the model for technology acceptance and use. In the pages

that follow, we consider each of the research areas separately.

CASE tools in the Software Industry

CASE is considered to be a technology that includes a diverse range of

automated products to assist in system development tasks. CASE software has

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been defined as "meta-software", software used to develop software [Perrone

and Potter, 1992]. Many organizations in the late 80s turned to CASE tools as a

promising technology to increase productivity and quality of software

development. Some even thought that CASE would replace the programmer with

code generation capability that certain tools possess. Recently, there has been

recognition that CASE is not the panacea for all software development problems

and that it represents only one factor in improving the software development

effort and the quality of software [Blackburn, Scudder, and Van Wassenhove,

2000]. CASE is sometimes viewed as a supporting technology that aids in the

efficient development and maintenance of software. Currently, there are many

CASE products on the market that range from standalone tools that automate

specific life cycle tasks to integrated environments that automate most life cycle

tasks [see Lange et al., 2006, and Sriplakich et al., 2006].

When examining the use of CASE tools in the software industry, the

literature tends to focus on three primary areas. The first discusses how CASE

tools provide value to the industry. The second area examines the reasons for

use or non-use or reasons for failure of CASE tools in some organizations. The

third discusses how CASE can be used to aid in improving corporate Capability

Maturity Model (CMM) ratings [Wittman, 1995]. Research points to productivity

gains from CASE analysis and design tool [Tsuda, 1992]. CASE was also found

to provide value by enforcing development standards and constraints specified

by methodologies on software developers. Although such constraints frustrate

designers and may limit their freedom to modify methodologies to suit their

preferences, abilities, and circumstances, they benefit in ensuring that designs

conform to a development methodology and to notation standards [Scott, 2000].

Some found benefits in using CASE in specific, focused areas such code

production or testing [Sharma and Rai, 2000].

Although CASE provides valuable benefits, there are also problems that

impede the adoption of CASE tools within organizations. One problem is that the

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current method-centered CASE tools are not attractive enough to the users. To

better contribute to the software development practice, CASE tools must be more

user oriented, and support creative problem-solving aspects of software

development as well as rigorous modeling [Jarzabek and Huang, 1998]. Another

reason for the infrequent use of CASE has been attributed to the lack of support

for work groups [Vessey and Sravanapudi, 1995] and the perception of high

complexity to train and use [Livari, 1996]. This perception of complexity is

relevant as it makes it difficult to appreciate the advantages of CASE. Therefore,

any intervention that can change these perceptions can be expected to be

significant in the adoption of CASE tools and may be a factor in influencing the

intention to use. CASE training should also pay more attention to the

significance of perceived complexity in CASE adoption. It is important to devise

strategies in which the perceived complexity is under scrutiny all the time.

Lending and Chervany [1998] provided an interesting insight on the use of

CASE tools by developers in industry by identifying reasons for non-use. The

study addressed use of CASE tools, features most used, enjoyment using them

and their perceived usefulness. The study concluded that developers who follow

a formal methodology tend to use CASE tools more. Motivation is another factor

that influences use. One conclusion of the study was that if a tool is enjoyable to

use, it is more of a motivation to use the tool than the advanced features it may

have. This motivation would more likely help organizations to achieve the

benefits of using CASE tools. Cost is another problem impeding the adoption of

CASE. Many organizations have found the costs of adopting CASE tools tend to

exceed their original estimates [Huff, 1992]. These unanticipated costs may lead

management to terminate a promising CASE tool project or may increase

resistance to future CASE tool acquisitions. However, if management believes

that CASE technologies will be used and accepted by users to improve

productivity, the investment may be justified. Other factors that encourage

adoption include the existence of a product champion, strong top management

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support, and the perception that CASE technology has advantages [Premkumar

and Potter, 1995].

In trying to understand the reason that CASE tools seem to be

dearly bought but sparsely used, Huang [1998] pointed out that based on

practical experience, CASE tools are not user-oriented, and that CASE users

have not been given enough consideration in the production of CASE tools.

Today's CASE tools are motivated by new paradigms and techniques in the area

of software engineering; therefore, makers of CASE tools are often driven by

techniques, rather than by real users' needs and expectations. Some suggested

strategies for building CASE tools include modeling users' behaviors and

incorporating knowledge about users, domains, methodologies and techniques in

the next generation CASE tools. According to LeBlanc and Korn [1992] CASE

tools for application development will reduce personnel requirements and system

development costs. However, the less than expected perfo rmance of CASE may

be explained by weak or non- existent selection procedures.

The third area on using CASE tools in industry discusses how

CASE can be used to aid in improving corporate Capability Maturity Model

(CMM) level. The CMM, a product of Carnegie Mellon's Software Engineering

Institute is a framework that describes the key elements of an effective software

process to achieve the production of higher quality software. The CMM

framework which characterizes organizations by their maturity, consists of five

maturity levels starting with level 1 (Initial), where few formal processes exist, to

level 5 (Optimized), where quantitative feedback is used for continuous process

improvement. Of interest to this research is CMM level 3 (Defined) and CMM

level 4 (Managed). CMM level 3 is characterized by an organizational

commitment (focus) to software process development and software engineering

methods and tools that are applied consistently to projects. Level 4 involves the

collection of detailed measures of software process and product quality. CASE

tools enforce rigid methods and standards that are important components of a

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process improvement program [Goldenson and White, 2000; Mathiassen and

Sorensen, 1994; Wittman, 1995]. Organizations focused on improving their

maturity level have implemented CASE tools that range from debuggers and

code producers to error tracking and reporting database applications. CASE

tools fit well within the Level 3 process of identification and utilization of software

engineering technologies and methods. They are also integral to their move to a

level 4 software process. By identifying and establishing measurements of the

relevant data being recorded they can begin to optimize their software processes

[Wittman, 1995]. Managing the introduction of CASE in such organizations

requires knowledge of how the specific characteristics of CASE technology

influence CASE introduction, how the organizational environment influence

CASE introduction and defining the role of organizational experiments in CASE

introduction [Mathiassen and Sorensen, 1994]. As previously stated, a significant

proportion of software-mature organizations use CASE as a standard practice

[Goldenson and White, 2000].

To summarize, research indicates that the selection of appropriate tools

and the effective use of CASE technology are critical for systems development

success in organizations. CASE is still viewed by some as a technology that

plays an important role within organizations that are involved in the development,

deployment and maintenance of large software-intensive systems. The demand

for software engineers with formal training in the use of CASE tools warrants

educators to incorporate CASE tools into the curriculum at the undergraduate

and graduate levels to prepare their students for careers in the software industry.

Research on CASE Tools in Education

Adoption and use of CASE tools in the software industry enhanced the

interest of educators in introducing them in the classroom. Integrating CASE

tools into the curriculum continues to be of interest to educators and the industry

due to the potential benefits in enhancing student learning experiences and

preparing them for work in the IS field [Granger and Little, 1996]. Students who

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learn how to use CASE tools within their university program can easily transfer

their skills to software environments within organizations that use CASE tools.

Professional organizations such as ACM, AIS, and AITP have developed model

information systems curricula which include courses where CASE tools can be

used. Some of the courses described include generic systems analysis

(structured and OO), system design (structured and OO), database systems,

project management, and integrative capstone courses focused on system

design projects that integrate concepts learned in other technical courses.

One of the early studies discussed the experience of integrating CASE

into Computer Science undergraduate education at the University of British

Columbia [Jeffrey, 1999]. The author reported on the objectives of the initiative,

the tools that were used, the implementation and difficulties encountered.

Overall, the initiative was challenging but rewarding to both instructors and

students who had a positive learning experience. Some of the reasons stated for

the integration of CASE tools were to provide students with new skills for careers

in the industry, to make them aware of CASE tools as a new technology and to

prepare them for research in the area. Other studies examine the appropriate

time at which CASE tools should be introduced, the benefits of enforcing

standardization and the relevance of CASE tools in learning software

development methods in a university setting [Eriksen and Stage, 1998]. At

Bentley College, by providing CASE tools to students who study systems

analysis and design, students were able to handle projects of greater difficulty

after switching to modeling and analysis using CASE tools. It was also easier for

students to learn design concepts [Derringer, 1995]. Granger and Pick [1991]

reported reduced time in completing a design and coding project by students

using a CASE tool compared to those who did not.

Mynatt and Leventhal [1990] assessed the impact of the use of CASE in

an undergraduate software engineering course on student attitudes and found

that CASE can be a significant enhancement. The attitudes of students using

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CASE tools were positive and the quality of their work appeared to be better

compared to those in which CASE was not used. The following are some of the

commonly cited benefits mentioned for the use of CASE in the classroom [Mynatt

and Leventhal, 1989]:

• Enforcement of the standards and the rules of the methodology taught in

the class. This can be a great benefit to students who are learning to

ensure that their designs conform to a development methodology and to

a notation.

• Enhancing team communication on team projects. Communication both

between the developer and the customer and among developers

working on a project results in improved quality of deliverables.

• Enhancement of the quality of student projects through the

standardization and automatic checks provided.

• Improvement in the quality of documentation and the capability of CASE

tools to create a repository of documentation for the project.

• CASE allows the students to focus on the more important concepts and

on creativity since it handles the tedious clerical and mundane manual

tasks.

• Providing hands-on experience with a leading edge technology that is

used in the software industry.

Recent articles address the competitive global environment and the need

to incorporate CASE tools into the curriculum since it is considered one of the

new technologies [Rollier, 2002]. Although there is an increased interest in the

software industry in the object oriented methodologies, only one article

addressed the need to include a CASE tool as a component of an OO curriculum

to mirror the OO technologies currently being used for OO development [Douglas

and Hardgrave, 2000].

Considering the perceived benefits of CASE tools and its adoption in the

classroom, many CASE vendors are providing educational versions of their

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software at affordable prices to students. Some publishers are bundling CASE

products with textbooks so that they can be used by students in their courses.

Some vendors such as Microsoft, are forming partnerships with universities

where their software is provided free to students registered in IS courses. Many

universities are providing full versions of CASE software in their computer labs.

Examples of commonly used CASE tools at universities are Visible Analyst,

System Architect, Visio and Rational Rose. Considering the investments in

resources and time required to integrate CASE into the curriculum, it is

necessary to determine its acceptance and use by students.

Technology Acceptance and UTAUT Model

Studies indicate that with the proliferation of information technology in

today's organizations and the capital investments required, organizations believe

that technologies must be used and accepted by users to improve productivity

[Igbaria et al., 1997]. This led to the development of several theoretical

technology acceptance models which were rooted in information systems,

psychology and sociology to explain user intention and acceptance of new

technology. A well known and popular model, the Technology Acceptance Model

(TAM), was used in several studies to determine user acceptance [Davis, 1989].

"The goal of TAM is to provide an explanation of the determinants of computer

acceptance that is general, capable of explaining user behavior across a broad

range of end-user computing technologies and user populations…" [Davis 1989,

p. 985]. In the original TAM, Davis proposed that perceived ease of use and

perceived usefulness are two factors that influence an individual's intention to a

technology, and this in turn affects actual use. Davis also mentioned that there

are some external variables that can affect perceived ease of use and perceived

usefulness. The TAM has been validated by a number of studies. Adams,

Nelson, and Todd [1992] validated TAM by using it to examine users' acceptance

of voice mail and electronic mail. Szajna [1996] revalidated the model using a

longitudinal study of email use by graduate students. TAM has also been used

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to evaluate user acceptance of other technologies, such as, electronic commerce

[Keat and Mohan, 2004] and electronic learning [Ong et al., 2004].

Over the years researchers have extended the original TAM by adding

external variables that influence ease of use and perceived usefulness.

Venkatesh and Davis [2000] added independent variables such as subjective

norm, image, job relevance, job quality, and result demonstrability to the TAM

and called the revised model TAM2. Other variables that have been identified as

external variables in the model include intrinsic motivation [Venkatesh, 1999],

computer self-efficacy, objective usability, and experience [Venkatesh and Davis,

1996], and perceived system quality, individual's work load and prior

performance [Lucas and Spitler, 1999]. Although TAM has been validated,

tested and even extended in the past decade and half, other technology

acceptance models have also been proposed, including the Theory of Planned

Behavior [Azjen, 1991]. The extensive use of TAM, as well as these other

models, led to the creation of new integrated or unified model of technology

acceptance that we use in our study.

One of the latest technology acceptance models, the Unified Theory of

Acceptance and Use of Technology (UTAUT) synthesized elements across eight

well known technology acceptance models to achieve a unified view of user

acceptance [Venkatesh et al., 2003]. The eight models are: the theory of

reasoned action (TRA), the technology acceptance model (TAM), the

motivational model (MM), the theory of planned behavior (TPB), the combined

TAM and TPB , the model of PC utilization (MPTU), the innovation diffusion

theory (IDT) and the social cognitive theory (SCT). The resulting unified UTAUT

model consists of four core components or determinants of intention and usage

(these are described later). The model is claimed to be a useful tool for

managers to assess the likelihood of acceptance of a new technology within an

organization. It also helps in understanding factors that drive acceptance of a

new technology, so that appropriate features can be designed to facilitate

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acceptance of a new technology by users.

II. THE RESEARCH MODEL

In the previous section we identified research in the use of CASE tools in

industry and universities, and reviewed research on the acceptance of

technology. We draw on our literature review to present a research model to

investigate the user acceptance of CASE tools in a systems development

environment. We use the Unified Theory of Acceptance and Use of Technology

(UTAUT) proposed by Venkatesh et al. [2003] to understand the use of CASE

tools. The UTAUT identified seven factors that influence use of information

technology. They are: performance expectancy, effort expectancy, attitude

toward using technology, social influence, facilitating conditions, self-efficacy,

and anxiety. Definitions for each of these factors influencing information

technology use are provided in Table 1. Please note that we are using the terms

information technology and system interchangeably.

Table 1. Definitions of Determinants of Acceptance and Use of Technology*

Determinant Definition

Performance

Expectancy The degree to which an individual believes that using the system will

help him or her to attain gains in job performance.

Effort Expectancy The degree of ease associated with the use of the system.

Social Influence The degree to which an individual perceives that important others

believe he or she should use the new system.

Facilitating Conditions The degree to which an individual believes that an organizational and

technical infrastructure exists to support use of the system.

Self-Efficacy Judgment of one's ability to use a technology to accomplish a particular

job or task.

Attitude Attitude toward using technology is defined as an individual's overall

affective reaction to using the system.

Anxiety Evoking anxious or emotional reactions when it comes to performing a

behavior.

*These are the original definitions provided by Venkatesh et al. [2003].

The dependent variables in the original UTAUT model proposed by

Venkatesh et al. [2003] were intention to use the system and actual use. In their

research model, Venkatesh et al. [2003] expected only four factors to have a

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significant effect on user acceptance and usage behavior. The factors in this

category were: performance expectancy, effort expectancy, social influence and

facilitating conditions. The other three, attitude toward using technology, self-

efficacy, and anxiety were theorized not to be direct determinants of the intention

to use the system. A number of moderators were also identified, they included,

gender, age, voluntariness, and experience.

In our research, we focused our attention only on the behavioral intention

to use since the primary concern of this study is to examine factors that influence

students' intentions in their future use of CASE tools and not necessarily the

testing of the entire UTAUT model itself. Such decision is supported by the

original results found by Venkatesh et al. [2003] in which usage intention is the

only significant determinant for actual use over a longitudinal study; another

example of a study where behavioral intention is the final dependent variable, not

the behavior, is one conducted by Bock, Zmud, Kim, and Lee [2005].

Furthermore, behavioral intention has already been widely accepted to as a

predictor of actual behavior in the IS field and reference disciplines [Ajzen, 1991;

Sheppard et al., 1988; Taylor and Todd, 1995]. Figure 1 provides a graphical

representation of our research model. Here we have identified the four

determinants of behavioral intention to use based on the original UTAUT model.

Since we were evaluating the use of CASE tools in the classroom where most

students are more or less in the same age group, we did not include age as a

moderating variable. We also dropped voluntariness of use from the UTAUT

model because use of CASE tools was voluntary for students.

Our hypotheses are based on the original UTAUT model [Venkatesh et al.,

2003]. We have modified the hypotheses based on the variables that we have in

our model. The following are our proposed hypotheses:

H1: The influence of performance expectancy on behavioral intention will be

moderated by gender.

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H2: The influence of effort expectancy on behavioral intention will be moderated

by gender and experience.

H3: The influence of social influence on behavioral intention will be moderated

by gender and experience.

Performance

Expectancy

Effort

Expectancy

Social

Influence

Facilitating

Conditions

Behavioral

Intention to Use

Gender

Experience

Figure 1. The Research Model

In addition to the hypotheses above, the UTAUT model also proposed that

some of the factors including facilitating conditions, computer self-efficacy,

computer anxiety and attitude towards using technology will not have a

significant influence on behavioral intention to use the technology. Therefore, we

state the next four hypotheses as follows:

H4: Facilitating conditions will not have a significant influence on behavioral

.intention.

H5: Computer self-efficacy will not have a significant influence on behavioral

.intention.

H6:… Computer anxiety will not have a significant influence on behavioral

.intention.

H7: Attitude toward using technology will not have a significant influence on

.behavioral intention.

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In this section we have proposed our research model and hypotheses.

The UTAUT provides the theoretical underpinnings of our study. We utilize the

UTAUT model to examine the role of the factors identified in the model on the

acceptance of CASE tool technology in a systems development environment. In

the following section we describe our research methodology including the

sample, data collection and analysis.

III. METHODOLOGY

SAMPLE

The sample for our study consisted of students in the undergraduate and

graduate information systems program at a large mid-Atlantic university in the

United States. These were students who were taking courses in systems

development or database design. CASE tools provide valuable diagramming

abilities that are especially useful in representing information systems

components during analysis and design. Use of the CASE tool was voluntary for

undergraduate and graduate students in this study. Undergraduate students had

a separate laboratory assigned for the course which would meet once every

week in addition to a lecture class. Here students were given preliminary

instruction on the use of the CASE tool. The students then solved problems and

exercises using the tool. At the graduate level, students were given privileges to

download CASE tool applications from the departmental server and use it on

their own. Students, both undergraduate and graduate, had a project

requirement in the course. The project was a real-life case from an organization

where the students, as systems analysts, had to analyze an existing system that

had problems and design a new and improved information system. The project

accounted for at least 25 per cent of a student's grade fo r the course. It was in

this section of the course requirement where CASE tools were primarily used in

the course. This project requirement created a systems development

environment similar to those found in real organizations.

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Sample Characteristics

Our sample consisted of 85 students. Descriptive statistics for our sample

are provided in Table 2 through Table 5. Nearly 60 percent of students were

male (see Table 2) and almost half of all students had one to five years of work

experience (Table 3). Three-quarters of the students were in a graduate class

(Table 4) while most students used Microsoft Visio as the CASE tool (Table 5).

In the next section we report on how we collected data.

Table 2. Sample Characteristics – Gender

Gender Frequency Percent

Female 35 41.67

Male 49 58.33

Total 84* 100.00

*Missing values = 1

Table 3. Work Experience

Number of Years Frequency Percent

Less than 1 year 20 24.39

1 to 5 years 38 46.34

5 to 10 years 10 12.20

10 years or more 14 17.07

Total 82* 100.00

*Missing values = 3 Table 4. Course Level

Course Level Frequency Percent

Undergraduate 23 27.06

Graduate 62 72.94

Total 85 100.00

Table 5. CASE Tool Used

CASE Tool Frequency Percent

Visible Analyst 13 15.29

Microsoft Visio 72 84.71

Total 85 100.00

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Table 6. Questionnaire Items

Performance Expectancy Items

1. I find the CASE tool useful in my course.

2. Using the CASE tool enables me to accomplish tasks more quickly.

3. Using the CASE tool increases my productivity.

4. If I use the CASE tool, I increase my chances of getting a good grade

Effort Expectancy Items

5. My interaction with the CASE tool is clear and understandable.

6. It would be easy for me to become skillful at using the CASE tool.

7. I find the CASE tool easy to use.

8. Learning to operate the CASE tool is easy for me.

Attitude Items

9. Using the CASE tool is a good idea.

10. The CASE tool makes work more interesting.

11. Working with the CASE tool is fun.

12. I like working with the CASE tool.

Social Influence Items

13. People who influence my behavior think that I should use the CASE tool.

14. People who are important to me think that I should use the CASE tool.

15. The senior management and faculty of the university have been helpful in the use of the

CASE tool.

16. In general, the university has supported use of the CASE tool.

Facilitating Conditions Items

17. I have the resources necessary to use the CASE tool.

18. I have the knowledge necessary to use the CASE tool.

19. The CASE tool is not compatible with other systems I use.

20. A specific person (or group) is available for assistance with CASE tool difficulties.

Anxiety Items

21. I feel apprehensive about using the CASE tool.

22. It scares me to think that I could lose a lot of information using the CASE tool by hitting

the wrong key.

23. I hesitate to use the CASE tool for fear of making mistakes I cannot correct.

24. The CASE tool is somewhat intimidating to me.

Behavioral Intentions Items

25. I intend to use the CASE tool in the next two semesters.

26. I predict I would use the CASE tool in the next two semesters.

27. I plan to use the CASE tool in the next two semesters.

Self-Efficacy Items

I could complete a job or task using the CASE tool if...

28. There is no one around to tell me what to do as I go.

29. I could call someone for help if I got stuck.

30. I had a lot of time to complete the job for which the software was provided.

31. I had just the built-in help facility for assistance.

Data Collection

Data was collected using a questionnaire to test the research model in our

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study. Since the UTAUT model is used to study acceptance/use of CASE

technology in the classroom, we used the questionnaire developed by Venkatesh

et al. [2003] in their original study; the original questionnaire was modified to

identify the CASE tool as the subject of the questionnaire. In addition to

questions for gathering demographic information (including years of experience),

the questionnaire included items shown in Table 6. Data was collectedfor the

independent variables performance expectancy, effort expectancy, social

influence, facilitating conditions, self-efficacy, attitude and anxiety, and for the

dependent variable behavioral intention to use CASE tools. Most constructs had

three or four items in the questionnaire, with each item measured using a Likert

scale. Completion of the questionnaire was voluntary.

Analysis

Statistical analysis consisted of three steps. First, we averaged the

associated survey items from each respondent to arrive at an aggregated score

for each factor., Second, the multicollinearity of independent variables were

checked by identifying correlations equal to or greather than 0.70. Finally, we

ran separate regression models to test each of the hypotheses. The details of

the results are provided in the results section.

IV. RESULTS

Identification of multicollinearity among the independent variables can be

seen in the correlation results in Table 7. We found high correlations between

the following pairs of independent variables: performance expectancy and effort

expectancy, performance expectancy and attitude, and effort expectancy and

attitude. Correlations between these variables were >0.70 with p-values <

0.0001. However, the computed variable inflation factors (VIFs) –which indicate

the levels of multicollinearity with 1.00 indicating little or no multicollinearity--

ranged from 1.36 to 3.43, which are still acceptable. Hair, Anderson, Tatham,

and Black [1995] suggests 10.0 as the cut-off score for acceptable VIF. To

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reduce the effect of multicollinearity, we ran separate regression analyses for

each of the hypotheses. The results are provided in the Table 8.

Table 7. Correlation Matrix

Variable Perf.

Expectancy Effort

Expectancy Attitude Social

Influence Facilitat-

ing Self-

Efficacy Anxiety

Performance

Expectancy 1.000 0.723* 0.742* 0.518* 0.285** -0.322** 0.489*

Effort

Expectancy 1.000 0.737* 0.510* 0.466* -0.398** 0.544*

Attitude 1.000 0.598* 0.376** -0.237*** 0.542*

Social

Influence 1.000 0.471* 0.050 0.596*

Facilitating 1.000 0.070 0.247***

Self-

Efficacy 1.000 -0.118

Anxiety 1.000

*p < 0.0001, **p < 0.01, ***p < 0.05

We used regression analysis for testing hypotheses 1, 2 and 3. We

included the moderating variables, gender and experience, along with the

independent variable in the hypotheses. In hypotheses 1 and 3, gender and

experience were eliminated from the model. These results show that the

independent variables, performance expectancy and social influence have a

significant effect on behavioral intention to use the CASE tool. However, we did

not find support for hypothesis 2 – effort expectancy did not influence intention to

use the system.

If we examine the results obtained from the first three hypotheses we find

that the degree to which the individual believes that CASE tools will help in

attaining the desired goals of the system development will have a positive impact

on the use of the tool. This is a logical conclusion since students will use CASE

tools more if they believe that use of the tool will help attain the goals of their

project. The support for hypothesis 3 (social influence) means that the extent to

which individual perceives that important others believe he or she should use the

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new system has a positive effect on intention to use the system. This may due to

the fact that the project environment is a group oriented, and peer pressures can

be great in this group environment. The lack of support for hypothesis 2 (effort

expectancy) can be attributed to the ease of use of the CASE tool. We believe

that the degree of ease associated with use of the CASE tool did not affect

intention to use the system because the tool itself was intuitive and relatively

easy to use. Individuals did not think that the ease of use will be a factor that will

influence their use of the system.

Table 8. Results of Regression Analysis

Hypothesis Dependent

Variable R2 F-value

Independent

Variable Coefficient

H1 Behavioral

Intention 0.048 4.09* Performance

Expectancy 0.190*

H2 Behavioral

Intention N.S. N.S. Effort

Expectancy N.S.

H3 Behavioral

Intention 0.166 16.35*** Social

Influence 0.350***

H4 Behavioral

Intention 0.068 6.02* Facilitating 0.288*

H5 Behavioral

Intention 0.013 1.17 Self-

Efficacy 0.091

H6 Behavioral

Intention 0.059 5.17* Anxiety 0.197*

H7 Behavioral

Intention 0.072 6.37* Attitude 0.234*

*p-value < 0.05, **p < 0.01, ***p < 0.001; N.S. = Not significant

We also did not find support for hypotheses 4, 6 and 7. The independent

variables facilitating conditions, anxiety, and attitude have significant effects on

the behavioral intention to use a CASE tool. However, we found support for

hypothesis 5 which states that computer self-efficacy has no effect on intention to

use. According to our hypothesis 4, facilitating conditions, which refer to the

perception that infrastructure exists to support use of CASE tools, should have

no impact in the intention to use these tools. This may be a finding that is unique

to the technology of CASE tools. It seems organizational and technical

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infrastructure does influence use. Moreover, this result may be due to the

environment in which this study was conducted. Any software that is available in

a classroom environment is generally supported, since it is part of the students'

learning environment. It is unlikely that students would adopt a technology for a

class unless it is has a high performance expectancy, effort expectancy and

social influence. Two other independent variables, attitude and anxiety, were

found to have a significant impact on behavioral intention to use CASE tools.

This contradicts the findings of UTAUT but may be characteristic of CASE tool

use. These results taken together with the lack of support for effort expectancy

provides an interesting result – an individual's attitude towards using technology

and the anxiety they feel towards it influence their intention to use CASE tools,

but the effort expectancy or ease of use of the technology does not affect their

intention to use. One may even argue that ease of use could have an effect on

attitude and anxiety. Our finding that self-efficacy has no effect on intention to

use CASE supported the original UTAUT.

V. CONCLUSION

In this study we found support for part of the UTAUT model. Our results

show that performance expectancy and social influence affect behavioral

intention to use CASE tools in the classroom. This confirmed the findings of the

UTAUT model. Effort expectancy did not have an effect on intention to use the

case tool. This was contrary to the UTAUT model. Based on the UTAUT model

we did not expect facilitating conditions, computer self-efficacy, computer anxiety

and attitude toward using technology to have an effect on intention to use CASE

tools. But, our results show that all these factors other than computer self-

efficacy influence intention to use. This is different from the findings of the

UTAUT model.

It is important for us to examine the implications of this study for

information systems education. It is possible that the factors that influence

information technology acceptance are very different in an organizational or

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corporate environment compared to an educational setting. UTAUT research in

organizations has shown that performance expectancy, effort expectancy, and

social influence affect intention to use systems. In our study, which was

conducted in an educational setting showed performance expectancy, social

influence, facilitating conditions, anxiety and attitude influence intention to use to

CASE tools in systems analysis and design. The differences between the two

results could be attributed to differences in the environ ment in which the studies

were conducted (organizational versus educational) and even on the type of

systems considered (functional systems versus CASE tools in our study). This

has implications for the way CASE tools are taught in the classroom. Students

use CASE tools if they believe that CASE tools will impact their performance in

the classroom (performance expectancy). So, our courses should be designed in

such a way that students believe that use of CASE tools will positively influence

their performance. The ease of use of the system does not impact students'

intention to use (effort expectancy). Moreover, if instructors tell students to use a

CASE tool, students are inclined to use it since students believe that important

others such as their instructors think that they should use the tool (social

influence). Instructors should also provide adequate support for the use of CASE

tools (facilitating conditions), and reduce the anxiety students feel while learning

a new tool. In short, all these factors have to be taken into account while

designing systems analysis and design courses that use CASE tools.

This study also has its limitations. Use of student subjects in any research

raises some validity issues, and our study is no exception. But we believe that

our sample could be representative of systems development teams in

organizations. Around 38 per cent of our subjects had one to five years of work

experience and nearly a quarter of all students had over five years experience.

Moreover, a number of studies on technology acceptance have successfully

used student populations [Szajna, 1996]. Another thing to note is that we

investigated only the voluntary use of technology. It will be interesting to

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compare determinants of technology use when use is voluntary with factors that

influence use when use is mandatory.

This is a preliminary study of the use of case tools in a systems

development environment. We recommend that similar research is necessary in

an organizational setting to validate the findings of this study. We also found

only partial support for the UTAUT model in the acceptance of CASE. This

deserves further investigation. It may be important to check whether our findings

are unique only to a systems development environment. We recommend that

additional factors be identified that can influence user acceptance of CASE

technology in systems analysis and design. We believe our research is a step in

the right direction and we recommend additional studies in the field.

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ABOUT THE AUTHORS

Dr. Subhasish Dasgupta is Associate Professor of Information Systems in the

School of Business, George Washington University. Dr. Dasgupta received his

Ph.D. from Baruch College, The City University of New York (CUNY), and MBA

and BS from the University of Calcutta, India. He has published his research in

refereed journals such as, Decision Support Systems, European Journal of

Information Systems., Journal of Global Information Management, Electronic

Markets journal, Simulation and Gaming journal and Electronic Markets. Dr.

Dasgupta has published two edited books, Internet and intranet technologies in

organizations, and Encyclopedia of Virtual Communities and Technologies. He

has also presented his research in major regional, national and international

conferences.

Dr. Maliha Haddad is an Assistant Professor of Information Systems and

Technology Management in the School of Business at George Washington

University. She received a Doctor of Science degree in Engineering

Management and Systems Engineering from George Washington University , a

Masters in Computer and Information Systems from GA Tech and a Bachelor in

Mathematics from GA State University. Dr. Haddad industrial experience spans

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a variety of roles involving all aspects of information systems development as a

consultant, principal information engineer, project manager in the US and

overseas. Dr. Haddad has published articles on software engineering and

process improvement. Her most recent efforts are in the areas of hidden costs

incurred in software acquisition projects, applying knowledge management

concepts to acquisition processes and use of CASE tools within the IS

curriculum.

Dr. Peter Weiss is an Assistant Professor of Information Systems and

Technology Management in the School of Business at George Washington

University. He received a Doctor of Science degree in Computer Science from

George Washington University , a Master of Science in Administration degree in

Business Management from George Washington University, a Masters in

Electrical Engineering from the Johns Hopkins University, and a Bachelors

degree in Engineering from Case Western Reserve University. Before joining the

faculty of George Washington University , Dr. Weiss had industrial experience in

system development, project management and IT consulting to both commercial

and government clients.

Enrico Bermudez is a Doctoral Candidate in Information Systems in the School

of Business, George Washington University. His professional experience

includes Biomedical Information Systems Officer for the U.S. Army, Chief

Information Officer of a government hospital in California, USA and information

management officer for a large regional medical center in Hawaii, USA. He

received his Bachelor of Science in Computer Science from The Pennsylvania

State University. He has also co-authored a conference paper and presented his

research during the International Conference on Informatics Education and

Research 2005.

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... Venkatesh, et al. [5] also found that effort expectancy had an effect on behavioral intention. Instead, Dasgupta, et al. [6] finds that effort expectancy does not affect behavioral intention. ...

... In the basic concept of user acceptance model that has been developed, behavioral intention becomes the intermediate variables of user reaction relationships over the use of information technology with actual use (use behavior). The role of behavioral intention as predictor use behavior has been widely accepted in various user acceptance models [5] and [6] 3. HYPOTHESIS DEVELOPMENT Kang [7] Research of mobile application using UTAUT model shows that performance expectancy does not have a significant influence towards behavioural intention when it comes to measuring the use of mobile apps. Martins, et al. [8] conducted a research using UTAUT model to measure the user adoption of internet banking; their finding shows a significant relationship between performance expectancy and behavioural intention. ...

... In line with our results, previous research did reveal non-significant effects of ease of use. [59][60][61][62] Dasgupta et al. 62 assume that the perception of a particularly intuitive and simple handling system might be the cause that the degree of perceived ease of use did not affect behavioural intention. Arman and Hartati 60 argue that perceived ease of use is not a barrier to the acceptance of innovations for consumers with a high level of experience with electronic devices. ...

Considering the low market penetration of smart-lighting technology, this study investigates the drivers and consequences of consumer resistance to these innovative lighting products. The paper builds on research on passive and active consumer resistance and on the technology acceptance theory to analyse individuals' inclination to adopt or reject smart-lighting products. Moreover, the paper evaluates the moderating effects of gender and age. In addition to the influence of passive and active innovation resistance and based on a representative survey of German consumers (N¼653), the empirical results identify performance expectancy, social pressure and compatibility and health concerns as major determinants of consumer behaviour. Furthermore, the results vary among consumers of different ages and genders. A follow-up study (N¼115) investigates consumers' health concerns more deeply. This study provides practical implications and avenues for future research.

... Regardless of whether a modeling environment or an application is being developed, user acceptance is the decisive factor for success [14,89]. In order to actually support the user and generate added value, features on the one hand and a customized user interface on the other must be realized. ...

Philip Zweihoff
Bernhard Steffen

The use of low- and no-code modeling tools is today an established way in practice to give non-programmers an opportunity to master their digital challenges independently, using the means of model-driven software development. However, the existing tools are limited to a very small number of different domains such as mobile app development, which can be attributed to the enormous demands that a user has on such a tool today. These demands exceed the mere use of a modeling environment as such and require cross-cutting concerns such as: easy access, direct usability and simultaneous collaboration, which result in additional effort in the realization of such tools. Our solution is based on the idea to support and simplify the creation of new domain-specific holistic tools by generating it entirely based on a declarative specification with a domain-specific meta-tool. The meta-tool Pyro demonstrated and analyzed here focuses on graph-based graphical languages to fully generate a complete, directly executable tool starting from a meta-model in order to meet all cross-cutting requirements.

... A study revealed that 85 students that used the CASE tools improved their achievements in the classroom (Performance Expectancy). The tools also affected their behavioural intentions (Social Influence) to employ CASE tools in the place of effort expectancy [7]. This shows that students' performance can be enhanced by using technological tools in learning. ...

Received: 09 April, 2020 Accepted: 07 June, 2020 Online: 25 June, 2020 21st century learning focuses on the flow of information, media, and technology. In Malaysia, many university students face problems in English writing. Thus, students should be exposed to the technology training in innovative ways to produce students with a dynamic in this ever-changing world. Recently, the transformation and the evolution of mobile have created a huge impact on mobile users, as it is the current trend. Due to this matter, university students are now experiencing innovative learning development through mobile application and this can certainly improve their learning performance. The purpose of the study is to examine the application of mobile learning technologies through mind mapping applications for augmenting writing performance at Malaysian universities. The study was based on three different research theories -Flower and Hayes Writing Process Model, Radiant Thinking Theory, and Unified Theory of Acceptance and Use of Technology (UTAUT). The results of the study show that the students had positive responses towards English writing skills background, mobile technologies application background and mind mapping applications background. The proposed conceptual framework, Mobile-assisted Mind Mapping Technique Model (MMMTM) supports the need for Malaysian university students to augment their writing performance. It is hoped that this study will benefit the policymakers, tertiary educators and university students in teaching and learning specifically in writing courses.

... Sebagian besar studi empiris juga menemukan hasil yang serupa(AbuShanab et al, 2010;Dasgupta et al, 2007;Foon & Fah, 2011;Jairak et al, 2009;Sedana & Wijaya, 2010;Wu et al, 2007;Giannakos & Vlamos, 2013; Khechine et al, 2014;Lallmohamed et al, 2013;Lewis et al, 2013;Oechslein et al, 2014;Raman & Don 2013).Facilitating conditions didefinisikan sebagai tingkat sejauh mana seseorang meyakini bahwa infrastruktur organisasi dan teknis yang ada mendukung penggunaan sistem (Venkatesh et al, 2003). Venkatesh et al menemukan bahwa facilitating conditions tidak berpengaruh tehadap behavioral intention, namun mempengaruhi use behavior. ...

Pemanfaatan teknologi informasi merupakan hal yang penting bagi organisasi, karena dapat meningkatkan efektifitas dan efisiensi kinerja organisasi. Namun penerapan teknologi informasi tidak selalu berhasil. Salah satu faktor penentu keberhasilan penerapan teknologi informasi adalah sikap pengguna yang memanfaatkan teknologi tersebut. UTAUT merupakan kombinasi delapan model user acceptance of technology yang telah dikembangkan sebelumnya. Studi empiris yang mengadopsi model UTAUT telah banyak dilakukan. UTAUT juga digunakan oleh beberapa peneliti untuk melihat niat dan perilaku pengguna teknologi informasi di bidang pendidikan. Karena itu penelitian ini juga dikembangkan dengan mengadopsi model UTAUT untuk melihat perilaku pengguna SIAK (Sistem Informasi Akademik) pada program studi-program studi keperawatan di lingkungan Universitas Katolik Musi Charitas. Empat konstruk dari UTAUT digunakan sebagai determinan yang mempengaruhi niat pengguna (behavioral intention), yaitu: performance expectancy, effort expectancy, social influence dan facilitating conditions. Populasi penelitian ini adalah mahasiswa aktif prodi D3 Keperawatan, S1 Keperawatan dan prodi Profesi Ners yang menggunakan SIAK. Data dikumpulkan melalui kuesioner yang disebarkan kepada 297 responden. Data diolah dan dianalisis dengan teknik regresi linier berganda. Uji validitas dan reliabilitas menunjukan bahwa kuesioner yang digunakan sebagai instrumen penelitian valid dan reliabel. Hasil uji asumsi klasik menunjukan bahwa data terdistribusi normal dan bebas multikolinieritas, namun belum memenuhi asumsi heteroskedastisitas.Hasil uji hitopesis menunjukan bahwa performance expectancy, effort expectancy, sosial influence, dan facilitating condition berpengaruh positif signifikan terhadap behavioral intention. Kata kunci: UTAUT, performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention

... Therefore, Venkatesh et al. (2003) argued, for the UTAUT model, FC are not influential on the intention to use a technology. In researching Computer Aided Software Engineering, Dasgupta, Haddad, Weiss, and Bermudez (2007) found that FC had no effect on intentions. Other prior studies, in their research contexts, have verified the same result (Amoroso & Hunsinger, 2009;Liu & Tsai, 2011). ...

p class="3">The use of e-textbooks has become popular in certain countries, yet there is debate in the literature about whether it is advantageous to adopt e-textbooks and if they positively influence students' learning and performance. Prior studies on the acceptance of e-textbooks were mainly based on one theoretical perspective, and did not differentiate samples between experienced and inexperienced users. From a social- and task-related view, this study aims to identify the critical factors that stimulate acceptance intentions of e-textbooks among tertiary students, particularly between experienced and inexperienced users. Based on 912 questionnaires, this study found that performance expectancy, perceived enjoyment, and perceived task-technology fit are the factors affecting students' behavioral intention for acceptance in both sampling groups. However, social impact only has significant influence on acceptance intention of inexperienced users. Also, gender has a moderating effect on the relationship of performance expectancy and behavioral intention of inexperienced users only. This study provides useful implications for marketing e-textbooks, and fills the literature gap.</p

... Model UTAUT tidak dapat menilai kesuksesan sistem informasi [4]. Model kesuksesan sistem informasi Delone dan McLean sangat baik untuk menilai kesuksesan system informasi.Atas temuan-temuan tersebut maka disusunlah model penelitian seperti yang ditunjukkan pada Gambar 3. [7], Performance Expectancy berpengaruh terhadap Behavioral Intention [5], Temuan yang sama juga didapat dari penelitian lain [2], [5], [8], [9], [10]. [5]. ...

Erick Andika
Djajasukma Djajasukma
Herry Heryanto

Evaluasi dilakukan untuk mengukur proses pembelajaran yang telah dilaksanakan. Pelaksanaan ujian, pemeriksaan hasil ujian, serta pengumuman nilai ujian pada SMK Pasim Plus telah menggunakan sistem informasi ujian online. Penelitian dilakukan kepada 466 populasi siswa dengan pengambilan jumlah sampel berdasarkan rumus Slovin yaitu sebanyak 215 siswa. Model penelitian yang digunakan adalah D&M IS Success Model untuk mengukur kesuksesan sistem informasi, serta model UTAUT (Unified Theory of Acceptance and Use of Technology) untuk mengukur niat dan penggunaan sistem informasi. Teknik pengumpulan data dilakukan menggunakan observasi dan kuesioner tertutup menggunakan skala Likert dimana kuesioner tersebut telah diuji validitas dan realibilitas. Data diolah dengan teknik SEM (Structural Equation Modelling) menggunakan SPSS 22 dan AMOS 22. Variabel yang diolah dalam penelitian ini antara lain Performance Expectancy, Effort Expectancy, Sosial Influence, Facilitating Conditions, Behavioral Intention, User Satisfaction, dan Net Benefits. Pengujian dilakukan menggunakan teknik Kolmogrov-Smirnov Goodness of Fit Test, pengujian normalitas data, pengujian validasi konstruk, hingga pengujian keseluruhan model. Hasil penelitian yang diperoleh diuraikan dan dibahas pada makalah ini.Kata kunci: UTAUT, D&M, SEM, kesuksesan, penerimaan

... Venkatesh et al menemukan bahwa effort expectancy memiliki pengaruh terhadap behavioral intention. Hal senadaJURNAL SISTEM DAN TEKNOLOGI INFORMASI KOMUNIKASI 15Sebagian besar studi empiris juga menemukan hasil yang serupa(AbuShanab et al, 2010;Dasgupta et al, 2007;Foon & Fah, 2011;Jairak et al, 2009;Sedana & Wijaya, 2010;Wu et al, 2007;Giannakos & Vlamos, 2013; Khechine et al, 2014;Lallmohamed et al, 2013;Lewis et al, 2013;Oechslein et al, 2014;Raman & Don 2013).Facilitating conditions didefinisikan sebagai tingkat sejauh mana seseorang meyakini bahwa infrastruktur organisasi dan teknis yang ada mendukung penggunaan sistem (Venkatesh et al, 2003). Venkatesh et al menemukan bahwa facilitating conditions tidak berpengaruh tehadap behavioral intention, namun mempengaruhi use behavior. ...

Kristoforus Jawa Bendi

Ahmad Sobri Shuib
Muhammad Nidzam Yaakob
Muhammad Fariddudin

PENGARUH SIKAP, KECEKAPAN DIRI DAN KERESAHAN TERHADAP JANGKAAN PENERIMAAN LAMAN JARINGAN SOSIAL DALAM KALANGAN PELAJAR INSTITUT PENDIDIKAN GURU Ahmad Sobri Shuib, PhD sobri@ipda.edu.my Muhammad Nidzam Yaakob nidzam@ipda.edu.my Muhammad Fariddudin Wajdy Anthony fariduddin@ipda.edu.my Abstrak Laman Jaringan Sosial menyediakan satu lagi platform untuk pembelajaran pelajaran. Pelajar perlu mengambil peluang untuk menggunakan pelbagai jaringan laman sosial secara percuma yang mudah di dapati dan mudah di akses sebagai kelengkapan proses pembelajaran milenia. Tujuan kajian ini adalah mengenalpasti jangkaan penerimaan penggunaan rangkaian sosial dalam kalangan siswa guru Institut Pendidikan Guru Kampus Darulaman berasaskan pendekatan Theory of Acceptance and Use of Technology (UTAUT). Bilangan sampel kajian ialah 90 guru pelatih semester 8 Program Ijazah Sarjana Muda Perguruan. Dapatan kajian menunjukkan faktor sikap, kecekapan diri dan keresahan mempunyai pengaruh terhadap jangkaan penerimaan laman Jaringan sosial dari segi faktor Jangkaan Prestasi, Jangkaan Usaha, Pengaruh Sosial, Hasrat Perlakuan dan Kemudahan. Penggunaan laman sosial dalam proses pembelajaran menjadikan konsep pembelajaran lebih terbuka dan menyediakan pelajar satu pengalaman pembelajaran yang menarik dan bermakna. Kata kunci: Siswa Guru, Laman Jaringan Sosial , UTAUT

Nurus Sa'idah

Surabaya is a pioneer of E-Health innovation in Indonesia by providing online registration system to facilitate to take a quenue number anytime anywhere. The users online registration at RSUD dr.M. Soewandhie Surabaya is the largest, but dissatisfaction figure of online registration services reached 31,4%. Therefore, the purpose of this research is to analyze use behavior online registration at RSUD dr.M. Soewandhie based on Unified Theory of Acceptance and Use of Technology (UTAUT). This research was an analytic observational with quantitative approach and cross sectional design. Sample were collected by systematic random sampling and consist of 50 users and 50 nonusers. The result showed that individual characteristic which has p value below 0,05 was experience, knowledge and IT skills. Performance expectancy (p = 0,044) significantly influence behavioral intention , whereas effort expectancy (p = 0,982) and social influence (p = 0,124) do not. Facilitating condition (p = 0,812) and behavioral intention (p=0,189) had no influence with use behavior because p value was above 0,05. In conclusion, performance expectancy has a significant influence with behavioral intention, therefore experience, knowledge and IT skills had influence with use behavior online registration in RSUD dr.M. Soewandhie Surabaya. Keyword: e-Health, online registration, Unified Theory of Acceptance and Use of Technology (UTAUT), use behavior

Brian Berenbach

One of the problem areas in requirements engineering has been the integration of functional and non-functional requirements and use cases. Current practice is to partition functional and non-functional requirements such that they are often defined by different teams. Functional requirements are defined by writing text-based use cases or, less frequently, creating a business model, then walking through the use cases, and extracting (often in a haphazard fashion) detailed requirements. I believe that it is possible by taking advantage of the extensibility of modern CASE tools to create a single, homogeneous UML use case model that seamlessly and harmoniously connects use cases, functional requirements and non-functional requirements. Using such a model the facilities of UML are leveraged and traceable requirements sets can be extracted.

Gee-Woo Bock
Robert W. Zmud
Young-Gul Kim
Jae-Nam Lee

Individuals' knowledge does not transform easily into organizational knowledge even with the implementation of knowledge repositories. Rather, individuals tend to hoard knowledge for various reasons. The aim of this study is to develop an integrative understanding of the factors supporting or inhibiting individuals' knowledge-sharing intentions. We employ as our theoretical framework the theory of reasoned action (TRA), and augment it with extrinsic motivators, social-psychological forces and organizational climate factors that are believed to influence individuals' knowledge-sharing intentions. Through a field survey of 154 managers from 27 Korean organizations, we confirm our hypothesis that attitudes toward and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. Additionally, we find that anticipated reciprocal relationships affect individuals' attitudes toward knowledge sharing while both sense of self-worth and organizational climate affect subjective norms. Contrary to common belief, we find anticipated extrinsic rewards exert a negative effect on individuals' knowledge-sharing attitudes.

Robert Wittman

Illustrates the initial integration of CASE technology into the JTLS software lifecycle. The Software Engineering Institute's (SEI) software process maturity model is used to measure the utility of CASE integration. Current CASE integration focuses on several areas within the JTLS software process. The software development areas include some of the lower components of software production, testing and configuration management. Areas for future integration include the upper phases of software production. These areas include: requirements analysis, software design, software development scheduling and quality assurance. The main purpose of this paper is to provide simulation managers with a reference point for CASE integration into their projects

Many software organizations face serious problems in their attempts to make expectations and realities meet in introduction of CASE technology. One promising and substantial approach to understand CASE introduction better and to provide guidelines for how to manage it more effectively has been developed by Watts S. Humphrey, Bill Curtis, and others. By relating CASE introduction to software process maturity they have provided a rich framework, both for analyzing and understanding the complexity of the implementation process, and for designing and managing specific implementation efforts.This paper reviews software process maturity as a framework for CASE introduction. The relevance of the framework is discussed and three critical questions are explored: 1) How do the specific characteristics of CASE technology influence CASE introduction? 2) How does the organizational environment influence CASE introduction? 3) What is the role of organizational experiments in CASE introduction? The aim of the paper is by way of this discussion to explicate the strengths and limits of software process maturity as a framework for CASE introduction, and to identify the most important supplementary issues.

Computer-Aided Software Engineering (CASE) technologies are tools that provide automated assistance for software development [3]. The goal of introducing CASE tools is the reduction of the time and cost of software development and the enhancement of the quality of the systems developed [3], [20]. This paper explores the use of CASE tools. We ask several questions. Are CASE tools being used? If yes, what features within the tool are being used? Next, we explore two potential reasons for the expected low use. Do CASE tools change the job of the systems developer in an unattractive way? And are the people who are expected to use CASE tools motivated to use them?233 systems developers were surveyed to answer these questions. We found that CASE tools are being used but not in many companies. Within the companies that have adopted CASE tools, few people are actually using the tools. The systems developers who use CASE tools are using formal methodologies more often than systems developers who do not use CASE tools. Systems developers allocate their time differently depending on whether they are using a CASE tool or not. Those who use the tools are using few of the functions within the tools. Finally we found that people were basically neutral on whether they enjoyed using the tool and whether the tool was useful.