整合數位浮水印與資訊安全之著作權保護管理系統

行政院國家科學委員會專題研究計畫 成果報告

整合數位浮水印與資訊安全之著作權保護管理系統

計畫類別： 個別型計畫

計畫編號： NSC92-2416-H-009-012-

執行期間： 92 年 08 月 01 日至 93 年 07 月 31 日

執行單位： 國立交通大學資訊管理研究所

計畫主持人： 蔡銘箴

報告類型： 精簡報告

處理方式： 本計畫可公開查詢

中 華 民 國 93 年 8 月 12 日

行政院國家科學委員會專題研究計畫成果報告

整合數位浮水印與資訊安全之著作權保護管理系統

計畫編號：NSC92-2416-H009-012

執行期限：92 年 8 月 1 日至 93 年 7 月 31 日

主持人：蔡銘箴 國立交通大學資訊管理研究所

電子信箱: [email protected]

Abstract

The fast development and utilization of

the Internet has dramatically transformed

the business transaction into the digital

format. However, the techniques of the

duplication and modification of digital

data are comparatively effortless.

Therefore, the copyright protection and

authentication management system is in

great demand to meet the business

applications since the buyer and the sale

identity must be verified, the ownership of

the document must be maintained.

Traditionally, network security issues

are handled through the cryptography

which involves sophisticated encryption

and decryption schemes. However,

cryptography can substantially ensures the

attributes of the confidentiality,

authenticity, and integrity only if the

message is transmitted through a public

channel, such as an open communication

network. It does not protect against

unauthorized copying after the message

has been successfully transmitted.

Therefore, watermarks embedded in the

data can uniquely identify the ownership

or usability of the document. The

watermarking provides sufficient

copyright protection. The main problem

with using watermark technology is its

reversibility, any mechanism which can

read or detect the watermark can also

remove it by inverting the watermark

process. Other approaches like digital

signal processing can also significantly

affect the integrity of the document.

The goal of this research project is to

design a digital copyright protection

management system which will elaborate

the digital watermarking technique in

conjunction with the data security

schemes to compensate the reversibility of

detectable watermark. Such mechanism

will make the authentication and

copyright protection more reliable for the

open network communication like the

Internet. We believe the proposed project

can provide a useful ownership

identification and protection framework

and more robust, reliable than pure

readable watermarking design

architecture.

Keywords: Internet, electronic commerce,

digital watermarking, data

security, digital right

management

中文摘要

網際網路的蓬勃發展已使得越來

越多的商業交易以數位化的格式來進

行， 然而數位資訊重製及改造的技術相

當容易，衍生出對所有權保護及認證技

術需求的日益迫切，交易的雙方必須對

資訊來源的真實性及使用者的身份，提

供認證。

傳統上，網路安全能夠經由密碼學

加以保障，但是密碼學僅在訊息傳輸經

過公共管道時才擔保訊息的機密性、鑑

別性與真確性，而且密碼學並不能阻止

成功傳輸卻未經授權的訊息。而數位浮

水印則是一個即使經過傳輸後也能確保

多媒體資料版權的有效方式。浮水印嵌

入在資料內，能夠有效地鑑別文件的擁

有者或經授權的使用者。然而，任何可

以閱讀或察覺浮水印的機制，可以藉由

浮水印嵌入程序的作法來反向操作去移

除嵌入的數位浮水印，或甚至用濾鏡等

數位處理的方式來破壞數位浮水印。

本計畫之主要目的，為設計發展一個

數位著作權保護管理系統，以浮水印為基

礎並結合密碼學技術，以彌補浮水印可逆

性結構的不足，達到使數位多媒體物件之

著作權管理機制能夠在 Internet 等開放網

路環境架構下順暢運行的可靠性，以保護

智慧財產權著作人的所有權。並同時使用

可測性浮水印結構，用來克服浮水印可逆

性的問題；如此的機制，可提供信賴的智

財權保護機制，並且比其他可讀性的浮水

印結構更為強韌與可靠。

壹、

文獻探討

¾ 數 位 著 作 權 管 理 系 統 (Electronic

Copyright Management Systems,

ECMS)：

ECMS 可自動化管理並在開放網路

下發佈經交易的多媒體文件，並且考

慮到能夠連結網路環境、協同合作以

保護多媒體資料之智慧財產權的整體

服務。

目前有許多計畫正在發展 ECMS，

例如最新一代的 MPEG 標準 (MPEG-21)

將，在法律允許以及高可靠度的保證

下，建立合理範圍內的智慧財產多媒

體文件交易的規則或協定。

以下為建立有效 ECMS 系統的兩個

方法，這兩個方法皆需要散佈多媒體

文 件 前 之 所 有 權 認 證 工 具

(authoring tools) ：

預防盜版，例如IBM的Cryptolope

(

http://www-3.ibm.com/software/s

ecurity/cryptolope

)。

追 蹤 盜 版 ， 例 如 the ECfunded

Imprimatur

(http://www.imprimatur.net)。

¾ 以 密 碼 學 為 基 礎 的

ECMS

（Cryptography-based ECMSs）

在以密碼學為基礎的 ECMS 中，著

作者在著作權管理系統中將包裹的數

位物件譯為密文來整合應用。因為使

用者無法存取未經授權應用的文件，

故資料擁有者可以很容易的控制其資

料的用途，例如使用者可以在電腦螢

幕上呈現影像卻無法列印，或者可以

播放音樂但無法儲存等。

這個方法的主要缺點是難以建立

一個嵌入應用標準，而且當多媒體文

件最後傳送到終端使用者手中後（例

如顯示在個人電腦螢幕上或是播放出

來），它仍然可能未經允許而被擷取

或 複 製 。 例 如

Liquid Audio

(http://www.liquidaudio.com) 就是

一個應用於商業系統的例子。

¾ 以 浮 水 印 為 基 礎 的

ECMS

（Watermark-based ECMS）

作者 (Annie) Collecting Society (CS) 傳佈媒介 (McDarrel) 購買者 (Peter) 1 2 (3) 3 (3)

圖 1 　交易的精簡模型

以浮水印為基礎的 ECMS 能夠牢固

且強韌地在已被購買的數位物件中嵌

入與智慧財產權相關的浮水印資訊，

這些隱藏的智財權資料可由著作權所

有者命名、或是由系統給定一個獨一

無二的識別代碼以辯認文件的真偽。

浮水印能夠在文件中隱藏資料傳佈者

(distributor) 或 經 授 權 購 買 者

(buyer)

之

識

別

資

訊

(identification)

或

指

紋

(fingerprinting)，它能夠檢驗文件

的法律地位，而且追蹤網路上侵犯智

財權的物件散佈路徑。

目前浮水印技術的主要限制是它

的可逆性，也就是說任何可以閱讀或

偵測出浮水印的人都可以移除它。所

以雖然目前看來尚有一段很長的路要

走，但只有致力發展非對稱性浮水印

的方法才能克服浮水印技術本質上的

限制。另一方面來說，因為智財權資

料已直接嵌入數位內容本身中，故以

浮水印為基礎的智財權管理系統不需

要使用者採用特定已加入浮水印的多

媒體內容。

貳、

研究成果

我們發展了一個以浮水印為基礎並結

合密碼學的 ECMS，以補浮水印可逆性結構

的不足，來達到高可靠度的著作權保護。

在開放網路環境下交易的多媒體文件牽涉

到許多參與者 (actors)：文件的著作者

(author) 或 著 作 者 群 、 編 輯 者

(editor) 、 傳 佈 媒 介 (media

distributor) 、購買者 (buyer) 等等；

這也牽涉到電子付款的議題，例如資訊安

全與顧客隱私等。為了簡化我們的描述，

我們限制了參與者的數量，並且不涉及付

款與隱私保護的問題。

貳.1 交易模型 Transaction Model

圖 1 為一個簡化的交易模型：

a. 著 作 者 (Document author or

authors)：數位影像作者或智慧版

權擁有者，須向 CS/CA 申請專利。

b. 第三方認證、存證單位(CS and CA，

Collecting Society) ： 公 正 第 三

方，處理平台上專利認證問題。包

括：發給各成員 PIN、著作品 CUN、

提供著作者申請 Public Key 等等，

並對於所申請之專利留下存證，以

供日後發生法律問題使用。

c. 數

位

影

像

電

子

商

店

（distributor）：經過平台 CA/CS

所認證過的數位影像商務業者，可

接受著作者的作品販賣請求以及對

購買者提供數位影像販賣的服務。

d. 購買者（Buyers）：數位影像最終

購買者。

e. 識別資料：

PIN(Personal Identifier

Number)：個人識別號碼，對於平台

中的各個成員，CA/CS 會發給獨一的

PIN 來識別該成員。

CUN(Creation Unique Number)：著

作品識別號碼，對於每一件通過專

利申請的產品，CA/CS 會發給獨一的

CUN 予以識別。

貳.2 系統建置 System Implement

系統建置

ECMS 的核心在於浮水印的嵌入與

抽取，我們將整個 ECMS 系統先縮小至

只有 Author、CS、buyer 三方，即將

販賣者與 CS 合併，這樣有助於整個系

統的建置（圖 2）。

因此在實際系統上，我們將做到

1. 可以讓 Author 註冊身份以獲得 PIN。

2. 讓 Author 能夠上傳影像，並獲得該影

像之 CUN。

3. 利用 Web 系統來嵌入浮水印到影像

裡，且讓 Author 可以下載及管理。

4. Buyer 可以瀏覽並購買喜歡的影像。

5. 系統可以讓 Author 驗證認為有問題的

影像，並判定是否有侵權問題。

這些功能可說是整個 ECMS 的核心，因此在

選擇 Middleware 上，我們選擇了兩種不同

平 台 ， 一 是 由 Microsoft 提 出 的

Web-Service，另一則是由 Sun 提出的 J2EE

Platform，這兩種平台各有優缺點，所使

用的技術也不相同，我們將其都實做出

來，並比較其差異。

一、J2EE Platform

Java 是由 Sun 所提出的一個軟體平

台，其依據平台應用領域分為三個版本，

標準版本（J2SE，Java 2 Standard Edition）

用於開發個人電腦上的應用軟體，企業版

本（J2EE，Java 2 Enterprise Edition）

用於開發企業級的商用程式，如資料庫應

用軟體、ERP 系統等，以及微型版本（J2ME，

Java 2 Micro Edition）則是針對消費性

裝置的應用開發。

而我們選擇 J2EE，目的是希望提出一

個低成本、高可用性、高可靠性以及高可

擴展性的網路應用程式平台，透過這個統

一的開發平台，J2EE 降低了開發多層次網

路應用程式時所需的費用及其複雜性，同

時也對現有的應用程式提供良好的支援，

完全支援 EJB(Enterprise JavaBean)，具

有良好的封裝與佈署能力，可與現有的加

解浮水印程式結合，達到我們的目的。圖 3

是我們設計的元件圖。

整個架構由幾個原件構成，User 端只

需要一般的瀏覽器，在伺服器端，又分成

兩個部分，在前端的呈現是由 JSP 與 Java

Servlet 作 為 溝 通 介 面 ， 另 一 則 是 EJB

（Enterprise JavaBean） Container 中的

EJB 元件，這個 EJB 元件是負責將現有的浮

水印程式包覆起來，並藉由 JSP 或 Servlet

的呼叫來執行，並可以將執行的結果回傳

到網頁上或是直接進入 Database，相同

地，因為系統會有 Author 來註冊身份，故

同樣需要與 Database 進行直接溝通，如此

一來，有幾項優點：

Author

CS

Buyer

圖2 系統建置模型

A. 不論是 JSP 或是 Servlet，其安全性都

較高。

B. EJB 的元件可以不斷地重複使用，例如

浮水印程式一旦被包覆起來，就可以

藉由傳送參數來重複執行，且若是要

修改原程式碼也非常方便。

C. 除錯將非常快速，一旦這些元件都各

自獨立後，除錯將非常快速，有錯誤

的環節將可以很快被偵測出來，並修

改完成。

¾ 建構環境

圖 5 系統首頁

„ Windows 2000 Server

„ Tomcat

5.0（Web Server）

„ JSP

2.0

„ Servlet

1.1

„ Enterprise

JavaBean

„ MySQL

4.0（DataBase）

¾ 系統流程

圖 4 是整個網站地圖，

一旦進入首頁就會看見 Author 與 Buyer 各

自專區（圖 5）

我們可以依照網站地圖按圖索驥，不過我

們的重點將注重於 author 與 CS 之間註冊

與加解密浮水印的過程，因此，針對 Author

會有較詳盡的說明。

圖 6 Author 登入畫面

圖 6 是 Author 登入畫面，已經註冊的

author 必須輸入獲得的 PIN 作為登入帳

號，若無 Author PIN，則需註冊並獲得一

份 Author PIN，才能進入 Author 專區（圖

7）。

圖 7 Author 功能專區

Author 專區的功能可說是整個 ECMS 的核

心功能

a. 註冊新影像：提供 Author 上傳並

註冊一份新的影像，這份影像將經

過 EJB 所包覆的浮水印程式如圖 8

這份影像將經過負責影像上傳的

Servlet 控制（圖 9-1），一旦

Servlet 接收到影像後，就會針對

影像呼叫 EJB 進行嵌入浮水印的動

作，並且嵌入之後會給予 Author

屬於該文件的 CUN（圖 9-2）。

b. 第二個部分是影像管理功能（圖

10），如圖所示，在此可以管理

Author 所註冊過的所有文件，並可

以瀏覽這些影像或是下載回去，

圖 9-2 Author 獲得該影像之 CUN 　

c. 第 三 個 部 分 是 檢 驗 影 像 的 合 法

性，這部分的主要目的是讓 Author

在發現某張影像未經授權時，可以

藉由此驗證區進行該影像的浮水

印驗證（圖 11-1）；這畫面是要讓

使用者輸入他在網路上發現可能

未經授權之影像，以及原影像的

CUN，系統可以將這兩張影像經由

抽取浮水印的程式，進行影像驗

證。

而依照驗證的結果，系統也將

給予說明是否有侵權的問題（圖

11-2）。

d. 第四部分則是讓 Author 修改密

碼，這只是一般的資料庫存取，在

此就不多做贅述。

圖 8-1 Author 註冊新影像

圖 9-1 Author 註冊新影像

圖 10 Author 影像管理介面

e. 最後是從 Buyer 區進入，就可以看

見 Author 欲販賣的影像，並可以

信用卡付帳購買（圖 12）。

二、Web-Service

¾ Web Service 架構

Web Services 的觀念其實就可以

想像 Internet 上充滿了各種型式的服

務 （ www 網 頁 也 可 算 是 其 中 一 種 服

務），只要是 Internet 使用者，便可

以在 Local 端使用世界各地發表的

Web Service 。 更 清 楚 的 來 說 ， Web

Service 就像是 Internet 上的元件服

務，不論使用何種系統平台、何種程

式語言所撰寫出的應用程式，都可以

將它們引用到自己的應用程式之中。

網路服務就是存在於網際網路上

面的一種應用程式，不同於本機端開

發的應用程式，每個功能模組都可以

自行定義及開發，透過網路服務的機

制，任何人可以在網際網路上面尋找

自己想要的應用程式模組，將其納入

所要開發的應用程式中，也可以將自

行開發完成的應用程式模組，經過註

冊後提供給網際網路上使用者使用。

¾ Web-Service 基本架構

所示為網路服務的基本架構概念

圖，從此圖中可以更清楚的了解網路

服務的運作機制是由三個單元組成

（圖 13）

，分別是服務提供者（Service

Provider）、服務需求者（Service

Requester）及服務註冊機構（Service

Registry），而三者之間的關係分別

存在有發行（Publish）、尋找（Find）

及聯結（Bind）。

分散式的問題藉由整個系統採用 Web

service 建置而獲得解決。Web service 的

環境，可以依賴.Net Framework 本身對於

Web service 架構的支援輕易達成。電子商

務技術及介面問題，則仰賴 ASP.NET 和 Web

service 配合資料庫所開發的網頁介面來

滿足系統需求。第三點，舊有程式與系統

整合，是整個架構在建置上比較迫切的問

題，如何讓需求者透過網頁介面來使用這

圖 12 Buyer 瀏覽欲購買之影像

圖 11-1 Author 影像驗證畫

圖 13 Web-Service 基本架構圖

圖 11-2 Author

影像驗證結

些舊有的系統或程式，不但是架構是否能

快速建置的癥結，也是日後系統能否迅速

推展的要素之一。因此，使用 wrap 的方式

把舊系統包進 web service 之中（圖 14），

如此一來，服務需求者可以藉由網頁上的

介面，透過 web service 所提供的 method，

來利用這些系統及服務。雖然架構不同，

但是畫面和功能都是相同的，因此就不做

介面介紹了。

參、研究討論

藉由這個雛形系統的建立，我們看到

了數位影像發展的未來，且也體會到

整個商務數位影像發展仍有不少難關

需要克服：

1. 兩種不同的平台所建立的網站功能

大致相同，速度上是 Web Service

佔了優勢，原因為我們使用的作業

系統是 Microsoft Windows，已針對

這方面進行最佳化，J2EE Platform

終究是多了一層，連包覆浮水印程

式都要再多一層 EJB，不若 VB.NET

直接使用。且在開發速度上也是 Web

Service 較快。但在安全性上就是

J2EE 佔了優勢，因 Web Service 在

安全協定上尚有許多未規範之處，

使得系統建立雖快，卻有許多安全

上的疑慮，不若 JSP 般穩定且安全。

2. 目前浮水印的技術仍然侷限於 RAW

格式的檔案，並非所有的格式都可

以嵌入浮水印，且影像的大小也有

所限制，例如不能小於 64×64。

3. 在我們的雛形系統中，我們只先使

用了一層浮水印，但是在整個 ECMS

的架構下，加入三層的浮水印，如

此是否會影響影像的品質，仍需要

進一步研究。

肆、結論

在使用浮水印技術以落實著作權

法律規範應用到真實世界之前，還需

要再做更深一層的研究，除了使系統

更加強韌之外，我們還需要有更深入

的 協 定 級 分 析 (protocol-level

analysis) 才能澄清浮水印技術能達

成什麼目標、或不能夠達成什麼目標。

雖然目前浮水印技術上的彈性尚

不足以應付其實務需求，但是以數位

浮水印落實著作權的保護仍是一件非

常可行的辦法，且此技術可以用在不

同的多媒體視訊、音訊等媒體上，並

達成資訊隱藏的效果，具有不影響到

原有的訊號、強固性高等優點，由本

文的 ECMS 雛形系統就能清楚的展示浮

水印技術的潛力。

此 外 ， 我 們 所 研 究 的 中 介 軟 體

上，Web-Service、J2EE 都已具備了相

當優異的平台架構，可以結合現有的

應用軟體，大大地節省了開發軟體的

時間，以及有更加的整合性，整合多

個不同的單位，加上跨平台概念的實

行，未來 PDA、行動裝置等都可以使

用，可想見數位影像的電子商務將是

明日之星。

伍、結果與討論

本研究計劃已獲得相當豐富的研究成

果，由於前一年相關計畫的前導，再加上

這一年孜孜不倦的努力，在本年內，已有

數篇英文會議論文的發表。

第 一 篇 英 文 會 議 論 文 是 發 表 在 ICE

B2003，於民國 92 年 12 月 9 日至 12 月

13 日在日本東京市舉行，論文題目是“The

Analysis of Critical Factors of E-Learning

System for E-Business”，內容請見附件一。

第二篇英文會議論文是發表在 ICDCS

的 MNSA workshop，於民國 93 年 3 月

23 日至 3 月 26 日在日本東京市舉行，論

文 題 目 是 “DCT and DWT-based Image

Watermarking by Using Subsampling”，內容

請見附件二。

陸、計劃成果自評

本研究計劃研究成果，已獲得相當具

體及深入的學術成果，並提供電子商務的

實際應用與數位浮水印，資訊安全之著作

權保護管理系統智財權之可延續性的研

究；在此同時，將繼續做更深入的探討外，

也努力參與相關學術研討及論文發表，以

達更專精的學術研究為目的。

柒、參考文獻

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87.

♦ Cappellini, V.; Bartolini, F.; Caldelli,

R.; De Rosa, A.; Piva, A.; Barni, M.;

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<

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附件一

The Analysis of Critical Factors of E-Learning System for E-Business

Tzu-Hsin Yang and Min-Jen Tsai

Institute of Information Management

National Chiao Tung University

Hsinchu, Taiwan

[email protected] and [email protected]

Abstract

Many factors such as barriers, reasons, vendor consideration, success factors and challenges play important roles in implementing electronic-learning systems for e-business. In this paper, a questionnaire is used to collect respondents’ attitudes toward those factors, and the result is analyzed.

The result of chi-square test indicates that the respondents who have e-learning systems in their organizations are mostly from industries, and for those who have not tend to emphasize more on “cost and unawareness” which scored under 0.4 (i.e. low internal consistency) in reliability. However, the variance of the respondents’ attitudes toward the remaining six factors is not large.

Keywords: e-Learning, e-Business, barriers,

reasons for implementation, vendor

consideration, success factors, challenge

factors.

1. Introduction

Problems may be encountered when

implementing e-Learning systems for

e-business; however if barriers are known in

advance, problems are easier to be solved. In

addition, reasons for implementation from

different stakeholders setup directions to be

followed for e-Learning systems. If the

expectation of an e-Learning system is known,

corporations can be more confident setting up

corresponding strategies (see Figure 1) and

†

This work was partially supported by the National

Science Council in Taiwan, Republic of China, under

Grant NSC 91-2623-7-009-016, NSC91-2416-H009-012

and NSC92-2416-H009-012.

implementation can be started. Furthermore,

suitable vendors can supply satisfactory

e-Learning solutions to corporations. Suitable

vendors which provide contents, technologies

and services help shorten the implementation

time, and guarantee a successful e-Learning

system for e-business. Success and challenge

factors are collected from related articles which

suggest actions to be taken for a better

implementation.

In sum, it is recommended to analyze the situation of the corporation as well as plan the expectations for e-Learning systems for e-business. Suitable vendors shall be chosen, and lastly success and challenge factors serve as references for their e-Learning systems.

2. Purpose

B2B e-Learning systems facilitate

enterprises’ (i.e. business-to-business) learning

mechanisms via the Internet. Some research

reports the factors of their implementations.

However, the relationships among the

responses toward these factors and whether

respondents have e-Learning systems in their

organizations are seldom observed. Why do

corporations need to understand all these

critical factors clearly? Because by doing so,

corporations save time and avoid spending

money on unnecessary places. If corporations

know exactly what different stakeholders feel

toward these items, the results will be valuable.

This research investigates implementation

factors, and provides practical advices. It

analyzes the collected data which is from the

survey of “critical factors of an e-Learning

system for e-business”, and tests such as

chi-square test, factor analysis and t-test are

used to verify whether there are significant

differences in respondents who have e-Learning

systems in their organizations, and those who

have not. Lastly, the differences and new

findings are emphasized.

Figure 1 Strategy and Stakeholders

3. Methodology

The research methodology consists of “select critical factors”, “questionnaire design”, “chi-square test”, “factor analysis”, “t-test” and “conclusions” (see Figure 2). The critical factors which collected from the related literatures (see Table 1) are categorized into barriers for e-Learning, reasons for implementation, vendor consideration, success and challenge. A questionnaire which includes nine demographic questions and thirty-eight questions of critical factors is thus designed.

Chi-square test, factor analysis and t-test are conducted to examine if there are significant differences among the responses toward these factors and whether the respondents have e-Learning systems in their organizations. Lastly, the results will be well examined and feedback to the survey for advanced research.

3.1 Designing Questionnaire

Three different types of questionnaires that are web-based, e-mail and hardcopy are provided. The majority of the respondents prefer the web-based questionnaire. The questionnaire consists of six sections. Section 1 identifies the demographic information of the respondents. Questions include gender, age, career, department, position and education. Section 2 focuses on the attitudes of respondents toward the identified “four barriers”. Section 3 emphasizes their attitudes toward “reasons for implementation”.

Section 4 focuses on “vendor consideration”. Section 5 weights their viewpoints toward “success factors”, and lastly section 6 examines the attitudes toward “challenge factors”. These factors are measured using Likert-type scale which ranges from 5 to 1 with the following equivalence, “5”: “strongly agree”; “4”: “agree”; “3”: “neutral”; “2”: “disagree”; “1”: “strongly disagree”.

Figure 2 Research Methodology

Table 1 Factors Selected from Related

Literatures

Factors / Findings

Source

c Barriers

Š Budgetary considerations.

Š Immaturity of learning object

technologies.

Š Lack of awareness.

SRI [18]

Consulting

Business

Intelligenc

e

CEO

HR

Custom

Employ

Sales

Supplie

Vendor

Save

Financi

Competenc

Motivate

Integration with

Servic

Knowledg

Productivit

IT Dept

Researc

h

Select

Critica

Factor

Question-naire

Concl

u-sion

Chi-

Square

Š Cost versus value.

Š Quality of learning content.

Š Internal resistance to using

technology instead of

face-to-face learning.

Forum

Corp. [9]

d Reasons

Š Stay nimble and innovative.

Š Increase customer satisfaction.

Š Stomp the competition.

Š Cut costs.

Š Satisfy the IT department.

Š Improve my skills.

Š Earn more money.

Lance

Dublin and

Jay Cross

[14]

e Vendor Consideration

Š Content, Technology and

Service.

Digital

Think [4]

Š Experience.

Š Cost.

Rosenberg

[15]

f Success

Š Executive stakeholders.

Š Be the learner.

Š Marketing is your friend.

Š Virtual project teams.

Š Measure everything.

Cisco [2]

Š Include peer interaction.

Š Provide mentoring.

Š Offer performance feedback.

David

Price

Patrick von

Schlag [3]

g Challenge

Š The first seven items as

described in Section 3.2 –

Challenge Factors.

Digital

Think [4]

Š Perceived difficulty of using

such a system.

Forum

Corp. [9]

3.2 List of Factors under Investigation

In this survey, five main items are observed, and each of them contains sub-items The are listed below:

Factors of Four Barriers [9] [18]

B1 Cost too high

B2 Technology Immaturity B3 Solution Immaturity B4 Unawareness

Factors of Reasons for Implementation [14]

R1 Increase Competence R2 Stay Innovative R3 Support 24 x 7 Training R4 Reduce Training Time R5 New Training Technology R6 Reduce Training Cost R7 Increase Revenue

R8 Decrease Time Spending on Selling R9 Flexible Learning

R10 Win-Win Situation R11 Customer On-Line Learning R12 Enhance Customer Satisfaction

Factors of Vendor Consideration [4] [15]

V1 Content V2 Technology Integration V3 Service Quality V4 Implementation Experience V5 Implementation Cost V6 Reputation

Success Factors [2] [3]

S1 Organizational Support S2 Virtual Project Teams S3 Measure everything S4 Include Independent Learners S5 Include Peer Interaction S6 Provide Mentoring S7 Offer Performance Feedback S8 Marketing

Challenge Factors [4] [9]

C1 Correct Target Setup C2 LMS Configuration C3 Tutors and SMEs Integration C4 Content Creation C5 Multiple Modes of Learning C6 Back-End Systems Integration C7 Web Infrastructure

C8 Online Access Capability Training

3.3 Conceptual Model

A qualitative phase of this research is conducted to identify possible factors leading to the implementation of an e-Learning system for e-Business [1]. Related literatures on e-Learning systems for e-Business are also reviewed in order to select the factors of interest. Figure 3 depicts the conceptual model of the six factors naming, “Barriers”, “reasons”, “vendor consideration”, “success” and “challenge” and “implementation”.

4. Analysis Methods

Information on the attitudes toward critical factors of e-Learning systems for e-business is gathered through survey. Four types of analysis algorithms are used for different factors. Percentage analysis is used for demographic information, and chi-square test examines the relationships among different demographic data as well as whether the respondents have e-Learning systems in their organizations. Factor analysis extracts new factors from those five critical items. New factors are verified using Cronbach’s alpha test to measure the reliabilities. T-test examines the differences among the extracted factors and whether the respondents have e-Learning systems in their organizations.

Excel 2002 and SPSS10.0 are used to compute those results. Detailed explanation and diagrams are provided and discussed in the following sections. Chi-square test contains the row and column variables of the test. Factor analysis and Cronbach’s alpha test are explained in Section 4.2 and 4.3. T-test contains one diagram of the test and grouping variables.

Figure 3 Proposed Models of Factors of Implementing E-Learning Systems for e-Business

4.1 Chi-Square Test

Figure 4 depicts the variables of chi-square test. The relationships among gender, working field, department, role, experience and whether the respondents have e-Learning systems in their organizations are carefully examined.

Gender consists of female and male. Field contains students and the respondents from industries. Department is divided into two groups: Non-IT and IT departments. Role consists of the respondents’ experiences on implementation of e-Learning systems. Lastly, experience includes those who have or have not experiences of using e-Learning systems.

4.2 Factor Analysis

According to Foster [12], factor analysis is a technique or a family of techniques which aim to simplify complex sets of data by analyzing the correlations between them. A component or a factor explains the variance in the inter-correlation matrix, and the amount of variance explained is called the eigenvalue.

A factor loading is the correlation of a variable with a factor. If a loading is higher or equal to 0.3, it is frequently taken as meaningful when interpreting a factor. In this paper, principal components analysis is recommended as the method for reducing the number of variables. In order to obtain an orthogonal simple structure rotation, varimax method is used.

4.3 Cronbach’s Alpha Test

According to Foster [12], reliability refers to the consistency of the results on different items in a test.

Cronbach’s alpha is one of the standard ways to express the reliability of a test. The value can be obtained by using SPSS10.0. A reliability coefficient of 0.8 or higher is considered as “acceptable” in most social science applications. The value should not be lower than 0.7. However, tests of personality often have much lower values, partly because personality is a broader construct.

4.4 T-Test

Figure 5 depicts the test and grouping variables of t-test. The differences among “Cost and Unawareness” and “Immaturity” in barriers factor, “Training Effectiveness” and “New Revenues” under reasons for implementation, “Vendor Consideration”, “Success”, “Challenge” and whether the respondents have e-Learning systems in their organizations are carefully examined.

5. Demographic Information

The survey was conducted from May 13th to May 27th, 2003. There is a total number of 142 respondents, including 56 females (39.44%) and 86 males (60.56%) respectively (Figure 6), agreed to participate in this research. Most of them were from Hsin-Chu Industrial Science Park and National Chiao Tung University.

Figure 4 Variables of Chi-Square Test

Figure 5 Test and Grouping Variables of T-Test From figure 7, it clearly illustrates that 29.58% of the respondents were students, and 23.24% of the respondents came from the information technology industries, 16.20% were from electrical and electronics, and 15.4% were from military, government and academic. After the analysis of the departments’ bar chart as shown in Figure 8, it is found that 21.13% of the respondents were from the departments of information technology, 11.27% were from management, 10.56% were from technical support, 8.45% were from research & design. 53.52% of the respondents have no e-Learning systems in their organizations (Figure 9). The respondents who have no experiences of implementing e-Learning systems accumulate 72.54% whereas the ones who have account for 27.46% (Figure 10). Lastly, Figure 11 illustrates their experiences of using e-Learning systems.

Male, 60.56% Female,

39.44%

16.20% 23.24% 1.41% 15.49% 8.45% 1.41% 29.58% 4.23%

Electric and Electronics Information Technology Finance Military, Government and

Education Manufacturing and Business

Self-Employment Student Others Figure 7 Industry 11.27% 4.23% 21.13% 8.45% 10.56% 0.70% 3.52% 0.70% 29.58% 9.86% Management Human Resource Information T echnology Research and Design T echnical Support Finance Sales Customer Service Student Others Figure 8 Department Yes 46.48% No 53.52%

Figure 9 E-Learning Systems Implemented

in Organizations

Yes 27.46%

No 72.54%

Figure 10 Joining the Implementations of E-Learning Systems 33.10% 32.39% 23.94% 5.63% 4.93% Never

Less than 1 Year

1 to 2 Years

2 to 3 Years

More than 3 Years

Figure 11 Experiences of Using E-Learning Systems

6. Chi-Square Test on Demographic Items

The chi-square test was conducted to test

whether there were significant differences

among different demographic data as well as

whether the respondents have e-Learning

systems in their organizations.

c Gender

The chi-square value is 1.087 (df=1, n=142)

and the p-value is .297 (p>0.05) which means

that there is no significant difference. Thus we

concluded that whether the respondents have

e-Learning systems in their organizations do

not have significant difference in gender.

d Working Field

The relationship between the respondents’

fields and whether they have e-Learning

systems in their organizations is shown in Table

2 (Note: c WO/EL = Without Organizational

E-Learning Systems; d W / EL = with

Organizational E-Learning Systems). The

chi-square value is 5.78 (df=1, n=142) and the

p-value is .016 (p<0.05) which means that there

is a significant difference. When comparing the

percentages of the two working field groups in

Table 2, the percentage of the respondents who

are students and have e-Learning systems in

their organizations (19.7%) are smaller than

those who are from industries (80.3%). It is

obvious that the majority of the respondents

who have e-Learning systems in their

organizations are from industries rather than

students. Figure 11 depicts the line chart of

field * organizational e-Learning systems.

Table 2 Field * Organizational

E-Learning Systems Cross Tabulation

Field

WO/ EL

W/ EL

Total

Students 29

(38.2%)

13

(19.7%)

42

(29.6%)

From

Industries

47

(61.8%)

53

(80.3%)

100

(70.4%)

Total

76

(100.0%)

66

(100.0%)

142

(100.0%)

Chi-Square

Value

X

=5.78 df=1 n=142

With E-Learning Systems

Student From Industries

Figure 12 Field * Organizational E-Learning

Systems

e Department

The chi-square value is 2.642 (df=1, n=142)

and the p-value is .104 (p>0.05) which means

that there is no significant difference. Thus we

conclude that whether the respondents have

e-Learning systems in their organizations do

not have significant difference in non-IT or IT

departments.

f Role in Implementation of E-Learning

System

The relationship between the respondents’

roles in implementations of e-Learning systems

and whether they have e-Learning systems in

their organizations is shown in Table 3. The

chi-square value is 20.033 (df=1, n=142) and

the p-value is .000 (p<0.001) which means that

there is a significant difference. In order to find

out which role group has more respondents, the

percentages of the two role groups in Table 3

are compared. When comparing the

respondents who have no e-Learning systems in

their organizations, it is clear that the

respondents who have no experiences of

implementing e-Learning systems accumulate

greater percentage (88.2%) than those who

have (11.8%). However, if comparing the

respondents who have e-Learning systems in

their organizations, the percentages of

respondents who have no experiences of

implementing e-Learning systems (54.5%) and

who have (45.5%) are very close. Therefore,

we conclude that most of the respondents who

have no e-Learning systems in their

organizations also have no experiences of

implementing e-Learning systems. Figure 13

depicts the line chart of role * organizational

e-Learning systems.

g Experiences on Using e-Learning Systems

The relationship among the respondents’

experiences on using e-Learning systems and

whether they have e-Learning systems in their

organizations is shown in Table 4. The

chi-square value is 24.506 (df=1, n=142) and

the p-value is .000 (p<0.001) which means that

there is a significant difference. In order to

figure out which experience group has more

respondents among those who have e-Learning

systems in their organizations, the percentages

of the two experience groups are compared. It

is obvious that the respondents with

experiences show greater percentage (87.9%)

than those who do not (12.1%). Thus we

conclude that the majority of the respondents

who have e-Learning systems in their

organizations also have experiences of using

e-Learning systems. Figure 14 depicts the line

chart of experience * organizational e-Learning

systems.

After the analysis of the chi-square test, we

conclude that only working field, role and

experience have significant differences between

the respondents who have no e-Learning

systems in their organizations and those who

have. The respondents who have e-Learning

systems in their organizations are mostly from

industries and have experiences of using

e-Learning systems. However, the majority of

the respondents who have no e-Learning

systems in their organizations also have no

experiences of implementing e-Learning

systems.

Table 3 Role * Organizational

E-Learning Systems Cross Tabulation

Role

WO/ EL

W/ EL

Total

Not Join

67

(88.2%)

36

(54.5%)

103

(72.5%)

Join 9

(11.8%)

30

(45.5%)

39

(27.5%)

Total

76

(100.0%)

66

(100.0%)

142

(100.0%)

Chi-Square

Value

X

=20.033 df=1 n=142

With E-Learning Systems

Not Join the Implmentation Join the Implementation

Figure 13 Role * Organizational E-Learning

Systems

Table 4 Experiences * Organizational

E-Learning Systems Cross Tabulation

Experience

WO/ EL

W/ EL

Total

Have no

Experienc

e

39

(51.3%)

8

(12.1%)

47

(33.1%)

Have

Experience

37

(48.7%)

58

(87.9%)

95

(66.9%)

Total

76

(100.0%)

66

(100.0%

)

142

(100.0%

)

Chi-Square

Value

X

=24.506 df=1 n=142

With E-Learning Systems

Have no Experience Have Experience Figure 14 Experiences *

Organizational E-Learning Systems

The following sections explain the results of factor analysis and Cronbach’s alpha test, which are carefully calculated using SPSS version 10.0. It uses the extraction method of principal components and varimax rotation. Additional information regarding the results is also described, such as factor loadings, eigenvalues, percentages of variance and Cronbach’s alpha values.

Every factor is labeled a new name which reflects the characteristics of the items it contains. Items are ordered according to their factor loadings (from highest to lowest) and grouped according to factors. However, if the difference between the item’s highest and second highest factor loadings is less than 0.15, the item is eliminated.

7.1 Analysis of Four Barriers

The factors analysis result of barriers indicates that there are two factors with eigenvalues greater than 1.0. A two-factor solution is suggested after examining the results (see Table 5).

Component one is labeled “Cost and Unawareness” and accounted for 33.372% of the variance. It includes “cost too high” and “unawareness”. The reliability (internal consistency) is 0.3702. Component two is labeled “Immaturity” and accounted for 30.031% of the variance. It includes “technology immaturity” and “solution immaturity”. The reliability is 0.3848.

Table 5 Factor Analysis of Barriers

Component & Factor

Loading

Item

1: Cost and

Unawareness

2:

Immaturity

B1 Cost

too

High

.811 -5.373E-02

B4 Unawareness

.682

.141

B2 Technology

Immaturity

-9.541E-02

.900

B3 Solution

Immaturity

.451

.606

Eigenvalue 1.335

1.201

% of Variance

33.372%

30.031%

Cronbach’s Alpha

0.3702

0.3848

Note. Boldface indicates highest factor

loadings.

Table 6 Factor Analysis of Reasons

Component &

Factor Loading

Item

1: Training

Effectiveness

2: New

Revenue

R3 Provide

24

x 7 Full

time

Training

.834 6.830E-02

R5 New

Training

Technology

.830

.164

R9 Flexible

Learning

.793

.289

R11 Customer

On-Line

Learning

.782

.132

R4 Reduce

Training

Time

.757

.228

R1 Increase

Competence

.738

.385

R6 Reduce

Training

Cost

.547

.378

R10Á

Win-Win

Situation

.538

.431

R8 Decrease

Time

Spending

on Selling

-7.382E-02

.886

R7 Increase

Revenue

.248

.672

R2Á Stay

Innovative

.464

.550

R12Á

Enhance

Customer

Satisfaction

.428

.507

Eigenvalue 4.796

2.457

% of Variance

39.964%

20.477%

Cronbach’s Alpha

0.9033

0.5678

Note. Boldface indicates highest factor

loadings.

Á indicates the difference

between two factor loadings is less

than 0.15.

7.2 Analysis of Reasons for Implementation

The factor analysis result of reasons indicates that there are two factors with eigenvalues greater than 1.0. A two-factor solution is suggested after examining the results (see Table 6).

Component one is labeled “Training Effectiveness” and accounted for 39.964% of the variance. It includes all the sub-items about training. The reliability is 0.9033. Component two is labeled “New Revenues” and accounted for 20.477% of the variance. It includes “decrease time spending on selling” and “increase revenue”. The reliability is 0.5678.

7.3 Analysis of Vendor Consideration

The factor analysis result of vendor consideration indicates that there is one factor with eigenvalue greater than 1.0. A one-factor solution is suggested after examining the results (see Table 7).

Component one is labeled “Vendor Consideration” and accounted for 62.289% of the variance. It contains all the items in vendor consideration. The reliability is 0.8658.

7.4 Analysis of Success Factors

The factor analysis result of success indicates

that there is one factor with eigenvalue greater than

1.0. A one-factor solution is suggested after

examining the results (see Table 8).

Component one is labeled “Success” and

accounted for 65.314% of the variance. It contains

all the items in success. The reliability is 0.9227.

7.5 Analysis of Challenge Factors

The factor analysis result of challenge indicates that there is one factor with eigenvalue greater than 1.0. A one-factor solution is suggested after examining the results (see Table 9).

Component one is labeled “Challenge” and accounted for 66.420% of the variance. It contains all the items in challenge. The reliability is 0.9274.

Table 7 Factor Analysis of Vendor Consideration

Component 1: Vendor

Consideration

Factor

Loading

V3

Service Quality

.911

V2

Technology Integration

.892

V4

Implementation

Experience

.875

V1

Content

.840

V5

Implementation Cost

.723

V6

Reputation

.344

Eigenvalue 3.737

% of Variance

62.289%

Cronbach’s Alpha

0.8658

Note. Boldface indicates highest factor

loadings.

Table 8 Factor Analysis of Success Factors

Component 1: Success Factors

Factor

Loading

S6

Provide Mentoring

.863

S5

Include Peer Interaction

.837

S1

Organizational Support

.829

S7

Offer Performance

Feedback

.820

S3

Measure Everything

.816

S4

Include Independent

Learners

.810

S2

Virtual Project Teams

.793

S8

Marketing

.685

Eigenvalue 5.225

% of Variance

65.314%

Cronbach’s Alpha

0.9227

Note. Boldface indicates highest factor

loadings.

Table 9 Factor Analysis of Challenge Factors

Factors Loading

C3 Tutors and SMEs

Integration

.849

C2 LMS

Configuration

.837

C6 Back-End

Systems

Integration

.821

C4 Content

Creation

.816

C7 Web

Infrastructure

.814

C8 Online Access Capability

Training

.812

C1 Correct Target Setup

.774

C5 Multiple Modes of

Learning

.796

Eigenvalue 5.314

% of Variance

66.420%

Cronbach’s Alpha

0.9274

Note. Boldface indicates highest factor loadings.

8. T-Test of Seven Extracted Factors

T-test is conducted to examine whether there are significant differences between the above seven factors and whether the respondents have e-Learning systems in their organizations. The seven factors are “Cost and Unawareness” and “Immaturity” under barriers, “Training Effectiveness” and “New Revenues” under reasons for implementation, “Vendor Consideration”, “Success” and “Challenge”.

8.1 Barriers

The t-test result of “Cost and Unawareness” (t=-2.147; p<0.05) from t-test shows significant differences between the respondents who have no e-Learning systems in their organizations and those who have. However, the result of “Immaturity” (t=-.773; p>0.05) from t-test does not have significant difference. The mean value of “Cost and Unawareness” from the respondents who have no e-Learning systems in their organizations is 6.8158; while from those who have is 6.2879. It is obvious that the respondents who have no e-Learning systems in their organizations emphasize more on “Cost and Unawareness” than those who have (see Table 10).

Table 10 Group Statistics of Cost and

Unawareness

Group Mean

t-valu

e

p-valu

e

c Without

6.8158 -2.147

0.034

Organizational

E-Learning

Systems

d With

Organizational

E-Learning

Systems

6.2879

8.2 Reasons for Implementation

The t-test results of both “Training Effectiveness” (t=-.162; p>0.05) and “New Revenues” (t=.987; p>0.05) do not show significant differences between the respondents who have no e-Learning systems in their organizations and those who have.

8.3 Vendor Consideration

The t-test result of “Vendor Consideration” (t=-1.009; p>0.05) does not have significant differences between the respondents who have no e-Learning systems in their organizations and those who have.

8.4 Success

The t-test result of “Success” (t=-.683; p>0.05) does not have significant differences between the respondents who have no e-Learning systems in their organizations and those who have.

8.5 Challenges

The t-test result of “Challenge” (t=-.964; p>0.05) from t-test does not have significant differences between the respondents who have no e-Learning systems in their organizations and those who have.

At the end, we conclude that only “Cost and Unawareness” have significant differences between the respondents who have no e-Learning systems in their organizations and those who have. Furthermore, the respondents who have no e-Learning systems in their organizations obviously consider it more important than those who have. On the contrast, regardless the respondents who have e-Learning systems in their organizations or not, they do not significantly differ in the attitudes toward other remaining factors.

9. Conclusion

9.1 New Findings

The following represents the new findings of this investigation. They are gathered from the results of chi-square test, factor analysis and t-test.

c Results of Chi-Square Test

The results of chi-square test indicate that the majority of the respondents who have e-Learning systems in their organizations are mainly from industries. Most of the respondents who have not e-Learning systems in their organizations also have not experiences of implementing e-Learning systems.

d Results of Factor Analysis

From the results of factor analysis, only the two factors in barriers have reliabilities lower than 0.4 which indicates low internal consistencies. However, the other five factors have reliabilities higher than 0.7 which represents high internal consistencies.

e Results of T-Test

When examining the results of t-test, the respondents who have not e-Learning systems in their organizations emphasize more on “Cost and Unawareness” than those who have. However, whether the respondents have e-Learning systems in their organizations, they do not have significant different attitudes toward the other six factors which are “Immaturity”, “Training Effectiveness”, “New Revenues”, “Vendor Consideration”, “Success” and “Challenge”.

9.2 Contributions

The following represents the seven contributions of this investigation. They are approaches and considerations, advantages and disadvantages of e-Learning systems for e-Business, elementary concepts and understanding, useful information, examples of benefits, where corporations stand and references from other e-Learning stakeholders

c Basic Approaches and Considerations

There are some basic approaches and considerations proposed to help the corporations who are just getting started with the implementations of e-Learning systems for e-Business. d Advantages and Disadvantages of E-Learning Systems for

E-Business

It advises the decision makers what the advantages and disadvantages are. They shall balance from the situations they choose, and avoid the failures from other people’s experiences. Different stakeholders shall know their own responsibilities and jobs.

e Elementary Concepts and Understanding

The elementary concepts and understanding about the implementations of e-Learning systems are introduced. It also gives a good e-Learning guide and roadmap. No matter the reader is a beginner or an expert, this paper can enrich his / her e-Learning knowledge.

f Useful Information

All the analytical results in the study provide useful information on how the respondents rate on all the critical factors proposed. The information leads corporations to have a successful e-Learning system for e-Business.

g Benefits

If corporations know respondents’ attitudes toward the barriers, barriers are easier to be solved. By knowing the reasons for implementation, corporations can propose a sound e-Learning project. The results of vendor consideration can aid to choose an appropriate one. The rates of success and challenge factors undoubtedly give strong evidences for a better e-Learning system.

h Where Corporations Stand

With a clear understanding of these results, corporations know where they stand. Furthermore, they can setup corresponding strategies and objectives which lead them to a smooth implementation of e-Learning system for e-Business. i References from other Stakeholders

The vendors of e-Learning solutions can figure out what end-users emphasize the most when choosing suitable vendors. Different stakeholders shall consider all the perspectives. By doing so, they can understand what others feel toward a better implementation of e-Learning systems for e-Business.

9.3 Limitations

There are five points of limitations must be acknowledged. All of them are listed and explained in the following. They are time, manpower, demographic, response rate, validity, flexibility and reliability limitations.

c Time and Manpower Limitation

Due to the limited time and manpower, there are still spaces for further investigation.

d Demographic Limitation

Most of the respondents came from Hsin Chu Science Park and National Chiao Tung University, so the results are limited to these areas.

e Response Rate and Validity

As people tend to dislike questionnaires, thus a low sample size is gathered. Furthermore, the conditions under which the questionnaires are finished cannot be controlled.