Reintermediating the Value Chain: Cybermediaries and...

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This study evaluates to what extent reintermediation of the virtual value chain is occurring in

the South African Travel and Tourism Industry. The aim of the research is to ascertain what,

if any, e-commerce reintermediation models are currently being employed in the industry.

Several reintermediation strategies and business models are evaluated and a model of the

structure of South African Travel and Tourism Industry is developed.

The study showed that reintermediation is currently occurring in the Travel and Tourism

Industry in South Africa to a relatively significant extent. There was, however, insufficient

statistical evidence to quantitatively conclude that reintermediation is a more successful e-

commerce strategy than disintermediation. The research also identified what the industry

perceives to be the key success factors and core competencies that characterise a successful

cybermediary. These were compared to the current status of the industry in terms of actual

website sophistication and online marketing and promotion strategies. Further statistical

analysis also revealed additional key success factors.

It was found that there was generally good agreement between what the industry perceives to

be the key success factors and core competencies for success, and what is actually being

implemented in terms of website technology and e-commerce strategies. When compared to

the academic literature, however, it emerged that there is some disparity between what the

questionnaire respondents perceive to be important and what contemporary theory regards as

important in terms of core competencies and key success factors.

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2.3.1 Research Question 1 ...................................................................................................................... 7




4.2.1 Designing the Semi-Structured Interview Questions ................................................................... 21

4.3.1 Designing the Questionnaire ....................................................................................................... 22

4.3.2 Implementing the Questionnaire Online...................................................................................... 22

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4.3.3 Testing the Questionnaire............................................................................................................ 22

4.3.4 Distributing the Questionnaire .................................................................................................... 23

4.4.1 Decoding the Responses and Collating the Data......................................................................... 23

4.4.2 Overall Analysis of Responses..................................................................................................... 24

4.5.1 Data Analysis Tools Used............................................................................................................ 28

4.5.2 Research Question 1:................................................................................................................... 29

4.5.3 Research Question 2 .................................................................................................................... 30



5.1.1 Broad Questions .......................................................................................................................... 35

5.1.2 Focussed Questions ..................................................................................................................... 39

5.2.1 Location of the Respondent Organisations.................................................................................. 42

5.2.2 Organisation Size......................................................................................................................... 42

5.2.3 Annual Revenues for 1999/2000 .................................................................................................. 43

5.2.4 After-Tax Profits for 1999/2000 .................................................................................................. 44

5.2.5 Industry Sectors Represented....................................................................................................... 45

5.3.1 Classification of Business Models ............................................................................................... 47

5.3.2 Determining the Extent of Reintermediation ............................................................................... 47

5.4.1 Types of Reintermediation Business Models................................................................................ 51

5.4.2 Choosing Appropriate Measures of Success................................................................................ 53

5.4.3 Relative Success of Reintermediation Business Models............................................................... 54

5.4.4 Is Reintermediation a More Successful E-Commerce Strategy than Disintermediation?

(Hypotheses I and II) ................................................................................................................... 55

5.5.1 Relating the Business Models to the Origin of Hits ..................................................................... 57

5.6.1 Analysis of Core Competencies, Website Key Success Factors & Value Added Services ........... 60

5.6.2 Analysis of Respondent's Websites............................................................................................... 65

5.6.3 Analysis of Respondents' Marketing and Promotion Strategies .................................................. 69

5.6.4 Analysis of Correlations Between Key Questions........................................................................ 74

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Overseas Visitors to South Africa

0

200

400

600

800

1000

1200

1400

1600

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Nu

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isit

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(00

0)

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Questions 1 and 2

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Question 2

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COPYRI

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why why

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http://www.freesite.co.za/business/uctmba/questionnaire/

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Item Data

Total E-mails Sent 1196

"Bounced"1 e-mails 153

Successful E-mails 1043

Total Responses 105

% Responses 10.1%

Responses with Questionnaire 51

% Questionnaire Responses 4.9%

4.4.2.1 Reasons for Low Questionnaire Response Rate

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ReasonNumber of

Respondents

Did not have sufficient time 3

Not sufficiently knowledgeable about e-commerce 3

Blank Questionnaire 3

Problems with questionnaire 2

Company does not fall into Travel and Tourism Sector 4

Company does not engage in e-commerce 4

Did not want to reveal confidential information 3

Appropriate person was away on leave 5

Did not want to participate in questionnaire (no reason given) 5

Other 22

Total 54

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4.4.2.2 Responses to Individual Questions

12 2

3 6

23

2

0

5

10

15

20

25

Nu

mb

er

of

Qu

est

ion

<50 51-60 61-70 71-80 81-90 91-100 >100

Response %

Question Response Rates

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Question Analysis Technique(s) Graphical Representation

3 Categorisation and Observation Counting Histogram

4 Sample Mean and Standard Deviation Frequency Distribution






























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4.5.1.1 Descriptive Statistics

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4.5.1.2 Multiple Correlations

4.5.1.3 Pairwise Regression Analyses

4.5.1.4 Hypothesis Testing

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4.5.3.1 Hypothesis I

4.5.3.2 Hypothesis II

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4.5.5.1 Analysing the Core Competencies and Website Key Success Factors

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Ranks

sponsesofNumberRankingesCompetenciCoreforAverageWeighted

Ranks

sponsesofNumberRankingFactorsSuccessKeyWebsiteforAverageWeighted

4.5.5.2 Analysing the Websites and Marketing & Promotion Strategies

4.5.5.3 Further Correlation Analysis

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4.5.5.4 Hypotheses III and IV

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5.1.1.1 Question 1

Do you perceive e-commerce to be important for the Travel and Tourism Industry in South

Africa? Why?

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5.1.1.2 Question 2

Do you think e-commerce will grow, both in terms of revenues and percentage of business, in

the South African Travel and Tourism Industry? How significantly do you think e-commerce

will grow?

5.1.1.3 Question 3

Where do you see the big e-commerce growth areas in the South African Travel and Tourism

Industry?

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5.1.1.4 Question 4

What do you see as some of the major trends and opportunities occurring currently in the

South African Travel and Tourism Industry?

5.1.1.5 Question 5

What do you feel are some of the major obstacles facing the growth of e-commerce initiatives

in the South African Travel and Tourism Industry?

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Who do you identify as the current and future major e-commerce players in the South African

Travel and Tourism Industry?

5.1.1.7 Question 7

How do you feel that current e-commerce initiatives in the South African Travel and Tourism

Industry compare to those in the USA and Europe? Do you feel that we lag behind or lead

these countries in terms of our e-commerce initiatives?

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5.1.2.1 Question 1

Reintermediation is occurring in the South African Travel and Tourism Industry.

5.1.2.2 Question 2

Disintermediation is occurring in the South African Travel and Tourism Industry

5.1.2.3 Quest on 3

To what extent do you think that reintermediation is occurring in the South African Travel

and Tourism Industry?

5.1.2.4 Question 4

What reintermediation models are predominant in the South African Travel and Tourism

Industry?

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5.1.2.5 Question 5

Which of the above models do you feel is, or will be, the most successful?

5.1.2.6 Question 6

What do you think are the key success factors for an e-commerce strategy for the South

African Travel and Tourism Industry?

5.1.2.7 Question 7

What core competencies do you think an e-commerce business needs to acquire to be

successful in the South African Travel and Tourism Industry?

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5.1.2.8 Question 8

What characteristics do you feel defines a "good" Travel and Tourism website?

5.1.2.9 Question 9

Most South African Travel and Tourism websites possess the characteristics you identified in

question 8? Do you:

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Company Location

1 2 2 22

3

8

10

17

Free State Eastern Cape Northern Province

Botswana Not Available Other

Kwazulu Natal Gauteng Western Cape

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Number of Employees

0

5

10

15

20

25

30

0-10 11-20 21-30 31-40 41-50 51-60 >61

Number of Employees

Nu

mb

er

of

Re

spo

nd

en

ts

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Annual Revenue 1999/2000

02468

101214161820

0-2 2-4 4-6 6-8 8-10 >10

R(million)

Nu

mb

er

of

Re

spo

nd

en

ts

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After-Tax Profits 1999/2000

0

2

4

6

8

10

12

14

loss 0-250 250-500 500-750 750-1000 >1000

R('000)

Nu

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f R

esp

on

den

ts

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Sectors Represented

33

20

20

16

7

11

17

Accommodation Safaris or Game Lodges

Corporate Functions Adventure Activities

Air Line Ticket Booking Car Rental

Other

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Business Models

34

3

1

9

Supplier Travel Portal DMO Online Travel/Booking Agent

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5.3.2.1 Number of Respondents Classified as Cybermediaries

Generic E-Commerce Business Models

34

13

Disintermediated Cybermediary

5.3.2.2 Relative Total Revenues Generated by Cybermediaries

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% Total Revenues

28%

72%

Cybermediary Disintermediated

5.3.2.3 Relative Total E-Commerce Revenues Generated by Cybermediaries

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% Total E-Commerce Revenues

20%

80%


5.3.2.4 Relative Total Number of Monthly Hits on Cybermediary Sites

Total Hits

96%

4%


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Cybermediary Business Models

3

1

9

Travel Portal DMO Online Travel/Booking Agent

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0%

5%

10%

15%

20%

25%

30%

Per

cen

tag

e o

f R

esp

on

ses

Total Numberof Hits

Number ofOn-lineQueries

E-CommerceGeneratedRevenues

Cost Savingsto Business

Number ofOn-line

Transactions

Other (PleaseSpecify)

Success Measure

Measuring E-Commerce Success

Cybermediaries Disintermediators

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Business Model E-commerce Revenues Monthly Hits

DMO Not Available 200

Online Travel/Booking Agent 10.0% 5








Online Travel/Booking Agent 0.0% Not Available

Travel Portal 50.0% 10

Travel Portal 90.0% 575000

Travel Portal Not Available Not Available

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Mean Percentage of E-Commerce Revenues

12.9%

22.8%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%


Per

cen

tag

e o

f E-C

om

mer

ce

Rev

enu

es

Mean Monthly Hits

965

55173

0

10000

20000

30000

40000

50000

60000


Nu

mb

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f Hits

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5.4.4.1 Hypothesis Test I: Percentage of Revenues Derived from E-Commerce

5.4.4.2 Hypothesis Test II: Number of Monthly Hits

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Origin of Site Hits

45%

20%

14%

13%8%

Search Engines Travel Portals

Destination Marketing Orgs Dont Know

Other (Please Specify)

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Analysis of Source of Hits versus Business Model

0

5

10

15

20

25

DMO Travel Portal OnlineBooking/Travel

Agent

Supplier

Res

po

nse

s

Search EnginesTravel PortalsDestination Marketing OrgsDont KnowOther (Please Specify)

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5.6.1.1 Respondents' Ranking of Core Competencies

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8.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

We

igh

ted

Ave

rag

e

1 2 3 4 5 6 7 8

Competency

Weighted Average of Core Competencies(Cybermediary)

0.000.501.001.502.002.503.003.504.004.505.00

We

igh

ted

Ave

rag

e

1 2 3 4 5 6 7 8

Competency

Weighted Average of Core Competencies(Disintermediator)

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5.6.1.2 Respondents' Ranking of Website Key Success Factors

8.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

We

igh

ted

Ave

rag

e

1 2 3 4 5 6 7 8

Website Success Factors

Weighted Average of Website Success Factors(Cybermediary)

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0.50

1.00

1.50

2.00

2.50

3.00

We

igh

ted

Ave

rag

e

1 2 3 4 5 6 7 8

Website Success Factors

Weighted Average of Website Success Factors(Disintermediator)

5.6.1.3 Cybermediary Respondent's Ranking of Value adding Services

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0.000

0.200

0.400

0.600

0.800

1.000

1.200

We

igh

ted

A

vera

ge

1 2 3 4 5

Value-Added Services

Weighted Averages of Value-Added Services to Suppliers (Cybermediaries)

5.6.1.4 Analysing Correlations Between Core Competency and Website Key Success Factor

Rankings

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Core Competency Website Success Factor Correlation

Customer Relationship Management Seamless Integration with Other Systems 0.744

Customer Relationship Management Ability to Capture and Track Customer Information 0.823

Customer Relationship Management Depth Of Content And Information 0.713

Customer Relationship Management Powerful Navigation Features 0.657

Supply Chain Management Powerful Information Search and Sort Features 0.687

Distribution Chain Management Powerful Navigation Features 0.806

Logistics Management Seamless Integration with Other Systems 0.834

Brand Building Delivery of Customisable Content 0.743

5.6.2.1 Level of Integration with Other Systems

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Level of Integration of Back-Office Systems with Web Site

15%

17%

26%

42%

Extensively ModeratelyTo a Limited Degree Not At All

5.6.2.2 Online Facilities Offered on Respondents' Websites

On-Line Facilties

0

5

10

15

20

25

30

35

Online Reservatio

ns/Bookings

Sort and Search Facilit

ies

Customer Feedback Form

s

Online payment fa

cilities

Online Discussion Foru

m

Fulfilment G

uarantees

Frequently

Asked Questions

Customer Ranking of S

uppliers

Transaction Securit

y Guarantee

Privacy Statement

Other (Please Specify

)

Nu

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of

Re

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en

t

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5.6.2.3 Website Design and Maintenance

Web Site Design

33%

47%

16%4%

In-house Design

Professional Web Design House

Internet Service Provider

Other

Web Site Maintenance

43%

34%

16%7%

In-house Maintenance

Professional Web Design House

Internet Service Provider

Other

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5.6.2.4 Total Website Costs

0

2

4

6

8

10

12

14

16

18

Nu

mb

er

of

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spo

nd

en

ts

0-10 10-20 20-30 30-40 40-50 >50

Web Cost (R000)

Total Web Costs

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5.6.2.5 Number of Content Pages

0

5

10

15

20

25

Nu

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of

Re

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ts

0-10 11-20 21-30 31-40 41-50 >50

Pages

Number of Content Pages

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5.6.3.1 Capturing and Using Customer Information

Customer Information Captured

23%

20%

22%

20%

12%3%

Name Postal Address

E-mail Address Telephone Number

Demographic Information Other (Please Specify)

Use of Customer Information

32%

15%9%

21%

14%

9%

Generate Mailing Lists Statistical Customer Analysis

Customisable Content Deliver Targeted Advertising

Data Warehousing Other (Please Specify)

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5.6.3.2 Primary Promotion Channel for Websites

Main Promotional Channels

20%

47%

31%

2%

On-line Advertising Traditional Media

Do Not Advertise Other (Please Specify)

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0

5

10

15

20

25

Nu

mb

er

of

Re

spo

nd

en

ts

0-200 201-400 401-600 601-800 801-1000

>1000

Hits

Monthly Hits

5.6.3.3 Online Advertising

Number of Search Engine Listings

0

2

4

6

8

10

12

14

16

18

0-2 3-5 6-8 9-11 >11

Number of Search Engines

Nu

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er

of

Re

spo

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en

ts

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Percentage of Revenue Spent on On-Line Advertising

0

5

10

15

20

25

30

<2% 2-4% 4-6% 6-8% 8-10% >10%

% of Revenue

Nu

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of

Re

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en

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5.6.3.4 Offline Advertising

Percentage of Revenue Spent on Off-Line Advertising

0

2

4

6

8

10

12

14

16

<2% 2-4% 4-6% 6-8% 8-10% >10%

% of Revenue

Nu

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of

Re

spo

nd

en

ts

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Variable Variable Correlation

Size Revenue 0.988

Size Total web costs 0.693

Revenue Total web costs 0.633

E-commerce revenues Monthly hits 0.633

E-commerce revenues Number of content pages 0.726

E-commerce revenues Length of web presence 0.618

Number of regions represented Number of online features 0.674

Monthly hits Number of online features 0.704

Monthly hits Number of online advertising channels 0.713

Number of online features Number of content pages 0.885

Number of online advertising channels Number of content pages 0.873

Total web costs Number of online advertising channels 0.603

% of revenue spent on online advertising % of revenue spent on offline advertising 0.725

5.6.4.1 Correlations with Annual 1999/2000 Revenues

5.6.4.2 Correlations with Percentage of Revenues Derived from E-Commerce

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5.6.4.3 Correlations with Monthly Hits

5.6.4.4 Correlations with Number of Content Pages

5.6.4.5 A Causal Hypothesis for the Correlations

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"The Impact of Electronic Commerce on the Travel Industry"

"The 1999 South African Electronic Commerce Survey"

“Wireless Web Whirlwind - Diversity of devices alters e-

commerce business models

Winning Internet Business Models

Web Strategies for Promoting and Selling Tourism Destinations

“Strategies for Internet Middlemen in the

Intermediation/Disintermediation/ Reintermediation Cycle

"Strategy and the New Economics of Information"

"Getting Real About Virtual Commerce"

Travelling via the Web: The Changing Structure of an

Industry

“Making Business Sense of the Internet”.

“Developing a model which identifies the essential strategic elements of

success in Business-to-consumer E-commerce and testing whether this model can be applied to

South African E-Commerce Companies

“Internet-driven business models

"The Coming Battle for Customer Information"

"The E-CRM Extended Enterprise: The Myth of Disintermediation

“The Virtual Value Chain: South African Tourism’s Extent of Development

and Areas of Benefit”

“Internet-enabled International Marketing for South African SME Travel

Businesses”

http://www.crmproject.com/crm/wp/hayes.html

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"E-Business: Roadmap for Success"

"Statistics for Management & Economics"

The New e-Tourism Destination of the Future

"Management Information Systems: Organization and

Technology in the Networked Enterprise"

"Airlines Take to the Internet"

“Key E-Commerce Issues facing the South African

Tourism Industry”.

"New Tourism Distribution Players and Business Models".

“The role of electronic commerce in creating virtual tourism

destination marketing organisations”

“Distribution Channels in Electronic Markets: A

Functional Analysis of the Disintermediation Hypothesis”

Competitive Advantage"

“Managing in the Marketspace”

“Exploiting the Virtual Value Chain”

"Digital Darwinism - 7 Business Strategies for Surviving In the Cut-

throught Web Economy"

The Future of

Commerce

"South African Tourism: Resources for the Media and Travel Trade".

Marketing Channels

“Emerging Business Models: Intentions-Based Business-to-Business E-

Commerce”

"Disintermediation: The Buzzword from Hell"

http://www.satour.com/trade/market/statistics.html

http://www.zdnet.com/pcmag/insites/willmott/dw970910.htm

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First Round E-Mail Sent to Respondents

University of Cape Town

Graduate School of Business

Portswood Road

Green Point

8005

To The Owner, CEO or Marketing Director

Your company has been approached to participate in bona-fide research on the South African Travel

and Tourism Industry as part of a U.CT. Graduate School of Business study. The purpose of this

study is to uncover the current trends and success factors for e-commerce in the South African Travel

and Tourism Industry. In order to gather data for this research, we kindly request that you complete a

short on-line questionnaire.

The questionnaire cab be accessed at the following Web Site Address:

http://www.freesite.co.za/business/uctmba/questionnaire/

All companies that complete the questionnaire will be able to receive the findings and report as an

Adobe PDF file. This document will contain findings that will be very useful to most South African

Travel and Tourism companies who are currently engaged in any form of e-commerce. It is critical

that we receive as many responses as possible (from both LARGE and SMALL organisations) in order

to make the findings statistically relevant. Therefore, we greatly appreciate you taking 15 to 20

minutes to complete the questionnaire.

Certain of the questions require the respondent to have a good overall knowledge of many of the

business aspects of the company. Consequently, we would encourage the Managing Director, CEO,

or someone who has a very good idea of the company's e-commerce strategy to complete the

questionnaire.

The confidentiality of responses is guaranteed, and is further explained in the preamble to the

questionnaire for those concerned about confidentiality of responses.

Lastly, to make the research findings statistically relevant, we need as many responses as possible.

We would, therefore, encourage you to forward this e-mail to other Travel and Tourism companies to

increase the survey sample size.

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Thank you for cooperating in this research.

Researchers

Graeme Newcomb B.Sc. (Chemical Engineering) (UCT)

E-mail: [email protected]

Cell: (083) 206 8684

Neil Botes B.Sc. (Eng.) (Stellenbosch)


Cell: (083) 324 3966

Supervisor

Lance Stringer


Tel: (021) 406 1911

CIO



Second Round E-Mail Sent to Respondents



MBA Programme 2000

To The Owner, CEO or Marketing Director

The first round response from our research questionnaire did not yield sufficient responses to allow for

meaningful statistical analysis. As researchers, we are totally dependent on the goodwill of

companies in the industry to supply research data. We, therefore, ask you to please take 15-20

minutes to complete the questionnaire.

The questionnaire can be either accessed on-line at the following Web Site Address:

http://www.freesite.co.za/business/uctmba/questionnaire/ OR BY OPENING the attached HTML

document (offlineform.html). In both instances, when you are finished the questionnaire, please click

on the "Send Responses" button at the bottom of the questionnaire.

This research is highly relevant to the South African Travel and Tourism Industry because the Internet

(and e-commerce) is one of the most significant factors that WILL change the nature of the entire

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industry. Consequently, data on this issue, from both large AND small companies, is critical to gain a

better understanding of e-commerce and the Travel and Tourism Industry as a whole. Your

responses will enable us to better understand how the Internet is affecting the Travel and Tourism

Industry.

All participating companies will receive an electronic copy of the final report, which will include an

analysis of the data from the questionnaire, as well as results of recent interviews we have had with

local and international industry experts.

Thank you for co-operating in this academic research initiative.

Yours sincerely

Graeme Newcomb

UCT Graduate School of Business MBA Programme


Cell: (083) 206 8684

Neil Botes

UCT Graduate School of Business MBA Programme


Cell: (083) 324 3966

Supervisor

Lance Stringer


Tel: (021) 406 1911

CIO



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Item Data

Total E-mails Sent 1196

"Bounced"1 E-Mails 153

Successful E-mails 1043

Total Responses 105

% Responses 10.07%

Responses with Questionnaire 51

% Questionnaire Responses 4.89%

Question Conditional Requirements Number of Responses Response Rate

1 None 47 100.0%

2a None 44 93.6%

2b None 46 97.9%

3 None 45 95.7%

4 None 47 100.0%

5 None 40 85.1%

6 None 28 59.6%

7 None 47 100.0%

8 None 32 68.1%

9 None 47 100.0%

10 Only respondents who are cybermediaries 5 38.5%

11 None 41 87.2%

12 None 45 95.7%

13 None 45 95.7%

14 None 43 91.5%

15 Only respondents who are cybermediaries 7 53.8%

16 None 44 93.6%

17 None 47 100.0%

18 None 46 97.9%

19 None 38 80.9%

20 None 45 95.7%

21 None 43 91.5%

22 None 47 100.0%

23 None 42 89.4%

24 Only respondents with on-line payment facilities 10 125.0%

25 Only respondents with on-line payment facilities 13 162.5%

26 Only respondents with on-line reservation facilities 18 58.1%

27 None 45 95.7%

28 None 44 93.6%

29 None 35 74.5%

30 None 46 97.9%

31 None 44 93.6%

32 None 41 87.2%

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33 None 37 78.7%

34 None 45 95.7%

35 None 37 78.7%

36 None 43 91.5%

37 None 41 87.2%

38 None 45 95.7%

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Analysis of Quantitative Demographic Questions Question Number Question 4 Question 5 Question 6 Question 12

Data Size Revenue Profit Proportion of E-Commerce Revenues

Mean 64.40425532 11812200 5918991.667 0.153

Standard Error 36.27878817 4526552.999 5171648.179 0.034453306

Median 7 2250000 230000 0.05

Mode 2 1000000 5000 0

Standard Deviation 248.714841 28628434.85 25335798.33 0.231119804

Sample Variance 61859.07216 8.19587E+14 6.41903E+14 0.053416364

Kurtosis 43.1298057 13.75273333 23.84696216 3.549911172

Skewness 6.459056254 3.651550689 4.876888533 2.101187074

Range 1699 144990000 124695000 0.9

Minimum 1 10000 5000 0

Maximum 1700 145000000 124700000 0.9

Sum 3027 472488000 142055800 6.885

Count 47 40 24 45

Confidence Level(95.0%) 73.02534322 9155808.65 10698354.8 0.069436073

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Data for Frequency Distributions Revenue (Rm) Number of Responses Organisation Size (Employees) Number of Responses After Tax Profit (R1000) Number of Responses

0-2 18 0-10 27 Loss 4

2-4 7 11-20 7 0-250 12

4-6 4 21-30 1 250-500 5

6-8 2 31-40 0 500-750 1

8-10 2 41-50 4 750-1000 1

>10 7 51-60 2 >1000 5

>61 5

Analysis of Qualitative Demographic QuestionsQuestion 3 Question 7 Question 9

Company Location Number of Responses Sectors Number of Responses Business Model Number of Responses

Mpumalanga 0 Accommodation 33 Supplier 34

Zimbabwe 0 Safaris or Game Lodges 20 Travel Portal 3

Namibia 0 Corporate Functions 20 DMO 1

Mozambique 0 Adventure Activities 16 Online Travel/Booking Agent 9

Free State 1 Air Line Ticket Booking 7

Eastern Cape 2 Car Rental 11

Northern Province 2 Other 17

Botswana 2

Not Available 2

Other 3

Kwazulu Natal 8

Gauteng 10

Western Cape 17

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Question 11 Question 18 Question 9

Revenue Model Number of Responses Regions Represented on Web Site Number of Responses Business Model Number of Responses

Direct Revenue 32 Gauteng 23 Disintermediated 34

Hosting Fee 1 Western Cape 30 Cybermediary 13

Advertising 2 Eastern Cape 17

Commission Based 4 Kwazulu Natal 22

Other (Please Specify) 2 Free State 14

Not Available 6 Northern Province 17

Mpumalanga 20

Botswana 14

Namibia 14

Zimbabwe 12

Mozambique 10

Other 6

Business Model versus Revenue Model Business Model

Revenue Model Supplier Travel Portal DMO Online Travel/Booking Agent

Direct Revenue 24 3 0 5

Commission Based 4 0 1 0

Advertising 0 0 0 2

Hosting Fee 0 0 0 1

Other (Please Specify) 1 0 0 0

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Analysis of Success Measures Revenue E-Commerce Revenues E-Commerce Revenues Monthly Hits

Statistic Cybermediary Disintermediated Cybermediary Disintermediated Cybermediary Disintermediated Cybermediary Disintermediated

Mean R13,033,000 R11,405,267 22.8% 12.9% R733,950 R993,728 55173 965

Std Deviation R30,138,231 R28,629,148 33.1% 18.9% R1,538,752 R3,943,324 172606 1999

Sum R130,330,000 R342,158,000 R7,339,500 R29,811,850 606905 26061

n 10 30 11 34 10 30 11 27

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Hypothesis 1 Test: Percentage E-Commerce Revenues

Ho: (µ1 - µ2) = 0

H1: (µ1 - µ2) < 0

Where:

µ1 is the mean percentage of total revenues derived from e-commerce by

disintermediated companies

µ2 is the mean percentage of total revenues derived from e-commerce by

cybermediaries

Tested at 5% significance level using the Microsoft Excel 2-sample t-test

assuming unequal variances.

Hypothesis 1 Test Results


Mean 12.882% 22.773%

Variance 3.556% 10.957%

Observations 34 11

Hypothesized Mean Difference 0

Df 12

T Stat -0.942736455

P(T<=t) one-tail 0.182204527

T Critical one-tail 1.782286745

P(T<=t) two-tail 0.364409055

T Critical two-tail 2.178812792

Hypothesis 2 Test: Monthly Hits

Ho: (µ1 - µ2) = 0

H1: (µ1 - µ2) < 0

Where:

µ1 is the mean number of monthly hits on disintermediated companies'

websites

µ2 is the mean number of monthly hits on cybermediaries' websites

Tested at 5% significance level using the Microsoft Excel 2-sample t-test

assuming unequal variances.

Hypothesis 2 Test Results


Mean 965.22 55173.18

Variance 3996834.41 29792831751.36

Observations 27 11

Hypothesized Mean Difference 0

df 10

t Stat -1.041577628

P(T<=t) one-tail 0.161067218

t Critical one-tail 1.812461505

P(T<=t) two-tail 0.322134436

t Critical two-tail 2.228139238

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Analysis of Quantitative Website QuestionsQuestion Number Question 19 Question 21 Question 23 Question 29 Question 30

Data Number of Monthly Hits Number of Content Pages Number of on-line Features Total Web Costs Length of Web Presence

Mean 16657 100.3255814 2.382978723 22600 24.65217391

Standard Error 15109.81507 66.85772501 0.276346575 11288.91849 2.620288252

Median 200 12 2 10000 24

Mode 100 6 2 10000 36

Standard Deviation 93143.1556 438.4154217 1.89453667 66786.14245 17.77165958

Sample Variance 8675647436 192208.0819 3.589269195 4460388824 315.8318841

Kurtosis 37.78802227 36.44703085 1.16516911 32.50697231 7.567157651

Skewness 6.139964023 5.924327443 1.220955163 5.618136524 1.952294704

Range 574999 2799 8 400000 103

Minimum 1 1 0 0 1

Maximum 575000 2800 8 400000 104

Sum 632966 4314 112 791000 1134

Count 38 43 47 35 46

Confidence Level(95.0%) 30615.36355 134.9243942 0.556256273 22941.82754 5.27753122

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Frequency DistributionsNumber of Hits Number of

Respondents

Number of Content

Pages

Number of

Respondents

Total Web Costs

(R1000)

Number of

Respondents

Length of Web

Presence

Number of

Respondents

0-200 21 0-10 21 0-10 17 0-6 6

201-400 5 11-20 9 10-20 12 6-12 8

401-600 3 21-30 3 20-30 3 12-18 6

601-800 0 31-40 5 30-40 0 18-24 7

801-1000 0 41-50 2 40-50 2 24-30 5

>1000 9 >50 3 >50 1 30-36 8

>36 6

Analysis of Qualitative Website QuestionsQuestion 20 Question 22 Question 23

Origin of Hits Number of Responses Level of System Integration Number of Responses On-line Faciltiies Number of Responses

Search Engines 34 Extensively 7 Online Reservations/Bookings 31

Travel Portals 15 Moderately 8 Sort and Search Facilities 15

DMO's 11 To a Limited Degree 12 Customer Feedback Forms 20

Don't Know 10 Not At All 20 Online payment facilities 8

Other (Please Specify) 6 Online Discussion Forum 6

Fulfilment Guarantees 10

Frequently Asked Questions 8

Customer Ranking of Suppliers 2

Transaction Security Guarantee 3

Privacy Statement 5

Other (Please Specify) 4

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Question 27 Question 28

Website Design Number of Respondents Web Site Maintenance Number of Responses

In-house Design 15 In-house Maintenance 19

Professional Web Design House 21 Professional Web Design House 15

Internet Service Provider 7 Internet Service Provider 7

Other 2 Other 3

Analysis of Business Models versus Origin of Hits Business Model

Source of Hits DMO Travel Portal Online Booking/Travel Agent Supplier Total

Search Engines 1 3 7 23 34

Travel Portals 1 1 4 9 15

Destination Marketing Organisations 0 1 1 9 11

Don't Know 0 0 3 7 10

Other (Please Specify) 0 0 0 6 6

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Summary of Responses to Question 3

Index of Core Competencies

9. Marketing And Promotion Management (using effective online and

offline marketing strategies)

10. Customer Relationship Management (capturing and using customer

information to know customer better)

11. Supply Chain Management (integrating supply chain, managing supply

chain partners)

12. Distribution Chain Management (integrating distribution channels,

managing partnerships)

13. Logistics Management (managing of travel logistics)

14. Customer Service Excellence (focussing one delivering excellent

customer service)

15. Technology Management (integration of systems, continual technology

innovation)

16. Brand Building (building a sustainable brand name and image)

17. Other (Please Specify)

All Responses

Core Competencies Rank

1 2 3 4 5 6 7 8 9

1 15 10 3 3 8 30 6 16 0

2 8 6 2 3 4 4 3 9 1

3 8 11 2 2 6 2 7 3 0

4 7 4 1 1 1 2 1 4 0

5 4 6 6 5 3 1 3 1 0

6 0 0 5 3 1 0 5 1 0

7 2 0 3 4 2 1 1 4 0

8 1 0 2 1 1 0 5 1 0

9 0 3 3 4 3 3 2 2 0

Not Ranked 0 5 18 18 16 2 12 4 44

Weighted ave. 2.27 3.24 5.84 5.71 4.87 2.02 4.85 2.98

Cybermediary Responses


1 2 3 4 5 6 7 8 9

1 4 5 2 2 3 10 3 5 0

2 3 0 0 2 1 1 2 4 1

3 3 3 0 0 2 0 2 0 0

4 2 0 0 1 0 0 0 2 0

5 0 3 2 0 0 1 0 0 0

6 0 0 1 0 0 0 1 0 0

7 0 0 0 1 0 0 0 0 0

8 0 0 1 0 0 0 0 0 0

9 0 1 1 1 1 0 1 0 0

Not Ranked 0 0 5 4 5 0 3 1 11

Weighted ave. 0.49 0.69 1.55 1.20 1.27 0.31 1.05 0.56

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Disintermediated Responses


1 2 3 4 5 6 7 8 9

1 11 5 1 1 5 20 3 11 0

2 5 6 2 1 3 3 1 5 0

3 5 8 2 2 4 2 5 3 0

4 5 4 1 0 1 2 1 2 0

5 4 3 4 5 3 0 3 1 0

6 0 0 4 3 1 0 4 1 0

7 2 0 3 3 2 1 1 4 0

8 1 0 1 1 1 0 5 1 0

9 0 2 2 3 2 3 1 2 0

Not Ranked 0 5 13 14 11 2 9 3 33

Weighted ave. 1.78 2.55 4.29 4.51 3.60 1.71 3.80 2.42

Summary of Responses to Question 14Success Measure Total Responses Total Responses Cybermediaries Cybermediaries Disintermediated Disintermediated

Total Number of Hits 13 15.9% 6 20.7% 7 12.7%

Number of On-line Queries 18 22.0% 5 17.2% 13 23.6%

E-Commerce Generated Revenues 23 28.0% 8 27.6% 16 29.1%

Cost Savings to Business 10 12.2% 3 10.3% 7 12.7%

Number of On-line Transactions 14 17.1% 4 13.8% 11 20.0%

Other (Please Specify) 4 4.9% 3 10.3% 1 1.8%

Total 82 100.0% 29 100.0% 55 100.0%

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Index of Value Added Services

6. Generation Of Sales Leads For Suppliers

7. Effective Marketing On Behalf Of The Supplier

8. Automation Of Reservations Or Bookings For Supplier

9. Automation Of Payments For Supplier

10. Automation Of Back-Office Functions (booking, accounting, billing etc.) for your suppliers



Value-Adding Services Rank

1 2 3 4 5 6

1 3 2 2 0 0 0

2 0 2 1 1 1 0

3 0 0 0 1 1 0

4 1 0 0 0 0 0

5 0 0 0 0 0 0

6 0 0 0 0 0 0

Not Ranked 1 1 2 2 3 5

Weighted ave. 0.548 0.516 0.774 0.806 1.129 1.613

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Index of Key Website Success Factors

9. Attractive Site Layout And Design

10. Seamless Integration with Other Systems (databases, data

warehouses etc.)

11. Ability to Capture and Track Customer Information

12. Delivery of Customisable Content

13. Depth Of Content And Information

14. Powerful Information Search and Sort Features

15. Powerful Navigation Features

16. Efficient And Secure Booking And Payment Facilities


All Responses

Key Success Factors Rank

1 2 3 4 5 6 7 8 9

1 17 9 10 10 15 12 5 14 4

2 12 3 6 6 7 5 9 5 0

3 6 4 7 6 6 3 2 5 0

4 4 3 3 2 6 3 4 2 0

5 1 5 5 4 5 5 8 3 0

6 0 3 1 3 0 1 6 2 0

7 2 2 1 3 2 4 2 3 0

8 0 3 4 2 0 2 2 1 0

9 0 2 2 1 0 4 1 2 0

Not Ranked 2 9 5 7 3 5 5 7 38

weighted ave. 2.07 4.15 3.51 3.67 2.55 3.71 3.82 3.47



1 2 3 4 5 6 7 8 9

1 5 4 3 4 3 5 3 5 2

2 4 0 2 3 3 0 3 1 0

3 0 0 2 2 1 2 0 3 0

4 2 1 2 0 4 0 1 1 0

5 0 1 1 2 1 2 2 0 0

6 0 2 0 0 0 1 1 1 0

7 0 1 0 1 0 0 1 0 0

8 0 0 1 0 0 1 0 0 0

9 0 1 0 0 0 0 0 1 0

Not Ranked 2 3 2 1 1 2 2 1 11

weighted ave. 0.75 1.29 0.98 0.78 0.78 1.00 1.02 0.82

Disintermediated Responses


1 2 3 4 5 6 7 8 9

1 12 5 7 6 12 7 2 9 2

2 8 3 4 3 4 5 6 4 0

3 6 4 5 4 5 1 2 2 0

4 2 2 1 2 2 3 3 1 0

5 1 4 4 2 4 3 6 3 0

6 0 1 1 3 0 0 5 1 0

7 2 1 1 2 2 4 1 3 0

8 0 3 3 2 0 1 2 1 0

9 0 1 2 1 0 4 1 1 0

Not Ranked 0 6 3 6 2 3 3 6 27

Weighted ave. 1.33 2.85 2.53 2.89 1.76 2.71 2.80 2.65

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Correlations Between Core Competencies and Website Key Success Factors

Cybermediaries

Core Competencies Web Success Factors

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

Corecomp1 1.000

Corecomp2 0.177 1.000

Corecomp3 0.341 0.423 1.000

Corecomp4 0.504 0.500 0.781 1.000

Corecomp5 0.349 0.556 0.660 0.615 1.000

Corecomp6 -0.429 0.037 -0.168 -0.309 -0.261 1.000

Corecomp7 0.072 0.381 0.728 0.710 0.683 -0.205 1.000

Corecomp8 -0.085 0.354 0.414 0.009 0.418 -0.149 0.056 1.000

Web Success 1 -0.122 -0.007 -0.324 -0.477 -0.028 -0.182 -0.480 0.502 1.000

Web Success 2 0.142 0.744 0.579 0.479 0.834 -0.002 0.492 0.521 0.017 1.000

Web Success 3 0.244 0.823 0.335 0.483 0.533 0.064 0.324 0.160 -0.165 0.824 1.000

Web Success 4 0.158 0.198 0.280 -0.200 0.309 0.126 -0.067 0.743 0.418 0.271 -0.044 1.000

Web Success 5 0.473 0.713 0.308 0.599 0.234 -0.144 0.320 -0.144 -0.244 0.315 0.640 -0.116 1.000

Web Success 6 0.146 0.552 0.687 0.398 0.502 -0.259 0.423 0.504 0.057 0.659 0.459 0.193 0.295 1.000

Web Success 7 0.644 0.657 0.429 0.806 0.450 -0.297 0.363 -0.016 -0.295 0.454 0.711 -0.089 0.861 0.217 1.000

Web Success 8 0.107 0.079 0.410 0.489 0.001 -0.268 0.274 0.099 -0.273 -0.127 -0.134 0.005 0.403 0.103 0.370 1.000

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Disintermediated Respondents

Core Competencies Web Success Factors

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

Corecomp1 1.000

Corecomp2 0.357 1.000

Corecomp3 -0.039 0.355 1.000

Corecomp4 0.301 0.425 0.766 1.000

Corecomp5 0.123 0.263 0.602 0.563 1.000

Corecomp6 0.469 0.735 0.106 0.242 0.370 1.000

Corecomp7 0.278 0.125 0.510 0.580 0.705 0.181 1.000

Corecomp8 0.482 0.438 0.208 0.463 0.394 0.566 0.423 1.000

Web Success 1 0.387 0.350 0.044 -0.047 0.096 0.471 0.011 0.117 1.000

Web Success 2 -0.279 0.365 0.345 0.406 0.028 0.216 0.093 0.139 -0.031 1.000

Web Success 3 0.102 0.245 0.246 0.328 0.330 0.195 0.366 0.216 0.353 0.473 1.000

Web Success 4 -0.082 0.014 0.330 0.114 0.114 0.017 0.031 -0.122 0.059 0.310 0.226 1.000

Web Success 5 0.294 0.317 0.188 -0.043 0.190 0.362 0.145 0.101 0.366 -0.096 0.072 0.565 1.000

Web Success 6 0.093 0.277 0.081 0.052 0.198 0.250 0.115 -0.079 0.200 0.193 0.189 0.469 0.436 1.000

Web Success 7 0.124 0.325 0.146 0.354 0.265 0.191 0.264 0.208 0.107 0.562 0.801 0.252 0.097 0.221 1.000

Web Success 8 0.053 0.426 0.352 0.279 0.163 0.261 0.326 0.089 0.453 0.541 0.661 0.331 0.208 0.354 0.611 1.000

Correlations Between Core Competencies and Value Adding ServicesCore Competencies

1 2 3 4 5 6 7 8

Value Added 1 -0.689367944 -0.54143494 -0.640183369 -0.486077471 -0.718167812 0.037582301 -0.184749586 -0.31967401

Value Added 2 0.663131653 0.207104217 -0.045825757 0.439566514 0.368008066 -0.139443338 -0.266577448 -0.048412292

Value Added 3 0.286896276 0.379242523 0.438938113 0.041884974 0.541411421 -0.333911536 -0.198477747 0.891056385

Value Added 4 0.198838988 0.445131907 0.387306828 0.002170878 0.482909603 -0.280975743 -0.230206552 0.891497437

Value Added 5 0.27740343 0.418573315 -0.156116327 -0.046023252 0.104866869 -0.544435723 -0.333817823 0.29835285

Value Added 6 0.509524665 0.544581149 0.803326418 0.602644602 0.749380494 0.166666667 0.455172847 0.270030862

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Correlations Between Website Key Success Factors and Value Adding Services

Web Site Success Factors

1 2 3 4 5 6 7 8

Value Added 1 -0.392565459 -0.711772269 -0.491421894 -0.397224683 -0.199310251 -0.505835729 -0.348016981 0.451271186

Value Added 2 0.136285721 0.519282282 0.660424249 -0.213085442 0.262936879 -0.102839755 0.619279375 -0.20438346

Value Added 3 0.959853806 0.706377556 0.2608793 0.727864023 -0.092592593 0.704377342 0.051967464 -0.141731734

Value Added 4 0.95256455 0.752371293 0.349561018 0.715605365 0.022075539 0.714477985 0.108411325 0.007919786

Value Added 5 0.483200359 0.412307042 0.459980382 -0.069330028 0.271746488 0.56980765 0.264787046 -0.041524093

Value Added 6 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

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Analysis of Quantitative Marketing and Promotion QuestionsQuestion Number Question 33 Question 36 Question 38

Data Number of Search Engine Listings % of Revenue Spent on On-Line Advertising % of Revenue Spent on Off-Line Advertising

Mean 5.106382979 0.047627907 0.173444444

Standard Error 0.670671473 0.015031124 0.039493582

Median 4 0.01 0.05

Mode 0 0 0

Standard Deviation 4.597891968 0.098565671 0.264930999

Sample Variance 21.14061055 0.009715192 0.070188434

Kurtosis -0.964061069 11.39927292 2.838774828

Skewness 0.642036628 3.238003254 1.918010337

Range 13 0.5 0.99

Minimum 0 0 0

Maximum 13 0.5 0.99

Sum 240 2.048 7.805

Count 47 43 45

Confidence Level(95.0%) 1.349990365 0.030334046 0.079594081

Data for Frequency DistributionsNumber of Search Engine

Listings

Number of Respondents % of Revenue Spent on

Online Advertising

Number of Respondents % of Revenue Spent on

Offline Advertising

Number of Respondents

0-2 16 <2% 26 <2% 14

3-5 16 2-4% 4 2-4% 4

6-8 2 4-6% 6 4-6% 6

9-11 5 6-8% 0 6-8% 3

>11 8 8-10% 0 8-10% 1

>10% 7 >10% 14

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Analysis of Qualitative Website Questions Question 17 Question 31 Question 32

Market Segmentation Bases Number of Responses Customer Information

Captured

Number of Responses Use of Customer Information Number of Responses

Geographic Regions 32 Name 44 Generate Mailing Lists 26

Age Groups 7 Postal Address 39 Statistical Customer Analysis 12

Specialist Activities 27 E-mail Address 43 Customisable Content 7

Income Groups 31 Telephone Number 38 Deliver Targeted Advertising 17

Other (Please Specify) 14 Demographic Information 23 Data Warehousing 11

Other (Please Specify) 6 Other (Please Specify) 7

Question 33 Question 34 Question 35

Search Engines Listed On Number of Responses Main Promotional Channel Number of Responses Online Promotion Channels Number of Responses

Yahoo 33 On-line Advertising 9 Local Portals (M-Web etc) 10

Alta Vista 28 Traditional Media 21 Global Portals 7

Hotbot 14 Do Not Advertise 14 Global Travel Portals 8

Lycos 20 Other (Please Specify) 1 Local Travel Portals 13

Northern Light 11 Advertising on Search Engine 6

Webcrawler 15 Advertising on Partner Sites 12

Excite 21 Banner Exchanges 5

Infoseek 18 Do Not Advertise Online 9

MSN 17 Other (Please Specify) 1

Max 9

Google 17

Ananzi 22

Aardvark 15

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Question 37

Off-Line Promotion Channels Number of Responses

Travel Magazines 35

Newspapers 14

Radio 5

Television 2

Billboards 3

Do Not Advertise in Offline Media 4

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CORRELATION MATRIX: CYBERMEDIARIES (n=23)

1 2 3 4 5 6 7 8 9 10 11 12 13

1 size 1.00

2 revenue 0.64 1.00

3 E-commerce Revenues -0.42 0.32 1.00

4 Total No of regions -0.21 -0.30 -0.49 1.00

5 Monthly Hits -0.02 0.18 -0.26 0.86 1.00

6 Content Pages 0.22 0.48 -0.19 0.65 0.94 1.00

7 Number of On-line Features 0.43 0.63 -0.22 0.43 0.80 0.95 1.00

8 Total Web Costs 0.96 0.80 -0.26 -0.22 0.09 0.38 0.60 1.00

9 Length of Web Presence -0.11 0.61 0.64 -0.26 0.21 0.39 0.48 0.16 1.00

10 Total Search Engines -0.57 0.14 0.48 0.42 0.65 0.59 0.43 -0.35 0.68 1.00

11 Number of On-Line Advertising Channels 0.91 0.39 -0.61 0.12 0.14 0.26 0.37 0.80 -0.43 -0.59 1.00

12 On-line Ad Spend -0.26 -0.21 -0.38 0.52 0.58 0.52 0.52 -0.15 0.29 0.48 -0.27 1.00

13 Off-line Ad Spend -0.42 -0.64 -0.43 0.23 0.06 -0.08 -0.06 -0.43 -0.06 0.08 -0.41 0.80 1.00

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CORRELATION MATRIX: DISINTERMEDIATED (n=23)

1 2 3 4 5 6 7 8 9 10 11 12 13

1 size 1.00

2 revenue 0.99 1.00

3 E-commerce Revenues -0.03 -0.05 1.00

4 Total No of regions 0.10 0.14 0.50 1.00

5 Monthly Hits -0.02 -0.04 -0.24 -0.04 1.00

6 Content Pages 0.36 0.40 0.18 0.66 -0.07 1.00

7 Number of On-line Features 0.46 0.46 0.01 0.30 0.17 0.18 1.00

8 Total Web Costs 0.99 0.98 0.00 0.17 0.02 0.38 0.49 1.00

9 Length of Web Presence 0.13 0.10 0.13 0.08 0.57 0.03 -0.16 0.17 1.00

10 Total Search Engines -0.08 -0.06 0.15 0.42 -0.15 0.49 -0.19 -0.04 -0.11 1.00

11 Number of On-Line Advertising Channels -0.23 -0.23 -0.15 0.07 -0.10 0.10 -0.05 -0.16 -0.03 0.10 1.00

12 On-line Ad Spend -0.13 -0.14 0.60 0.37 -0.17 0.52 -0.16 -0.09 -0.01 0.62 0.13 1.00

13 Off-line Ad Spend 0.55 0.53 0.19 0.05 -0.16 0.32 0.24 0.59 -0.02 0.20 -0.28 0.37 1.00

CORRELATION MATRIX: CYBERMEDIARIES (n=31)

1 2 3 4 5 6 7 8 9 10 11

1 size 1.00

2 E-commerce Revenues -0.25 1.00

3 Total No of regions 0.18 0.33 1.00

4 Monthly Hits -0.14 0.72 0.79 1.00

5 Content Pages -0.14 0.72 0.77 1.00 1.00

6 Number of On-line Features 0.03 0.54 0.82 0.89 0.88 1.00

7 Length of Web Presence -0.60 0.58 -0.11 0.25 0.24 0.32 1.00

8 Total Search Engines -0.58 0.67 0.44 0.57 0.55 0.56 0.76 1.00

9 Number of On-Line Advertising Channels -0.25 0.45 0.63 0.86 0.86 0.81 0.16 0.36 1.00

10 On-line Ad Spend -0.23 -0.16 0.31 0.13 0.11 0.31 0.35 0.47 0.00 1.00

12 Off-line Ad Spend -0.36 -0.45 -0.24 -0.32 -0.32 -0.35 0.08 0.04 -0.37 0.72 1.00

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CORRELATION MATRIX: DISINTERMEDIATED (n=31)

1 2 3 4 5 6 7 8 9 10 11

1 size 1.00

2 E-commerce Revenues -0.06 1.00

3 Total No of regions 0.10 0.28 1.00

4 Monthly Hits -0.01 -0.28 -0.06 1.00

5 Content Pages 0.37 0.05 0.61 -0.01 1.00

6 Number of On-line Features 0.37 -0.11 0.38 0.06 0.09 1.00

7 Length of Web Presence 0.13 0.21 0.00 0.49 0.07 -0.28 1.00

8 Total Search Engines -0.04 -0.03 0.28 0.08 0.49 -0.30 -0.06 1.00

9 Number of On-Line Advertising Channels -0.17 -0.22 0.06 0.01 0.17 -0.08 -0.02 0.19 1.00

10 On-line Ad Spend -0.12 0.50 0.34 -0.14 0.51 -0.17 0.03 0.52 0.17 1.00

12 Off-line Ad Spend 0.56 0.12 0.06 -0.09 0.37 0.13 0.02 0.25 -0.16 0.38 1.00

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Matrices of Microsoft Excel Linear Regression Results

Multiple R (Coefficient of Correlation):

1 2 3 4 5 6 7 8 9 10 11 12 13

1 size

2 revenue 0.988

3 E-commerce Revenues

4 Total No of regions

5 Monthly Hits 0.663 0.441

6 Content Pages 0.726 0.576 1.000

7 Number of On-line Features 0.409 0.674 0.704 0.885

8 Total Web Costs 0.693 0.633 0.313

9 Length of Web Presence 0.618

10 Total Search Engines 0.482 0.530 0.598

11 Number of On-Line Advertising Channels 0.713 0.873 0.603

12 On-line Ad Spend 0.555

13 Off-line Ad Spend 0.725

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R Squared (Coefficient of Determination):

1 2 3 4 5 6 7 8 9 10 11 12 13

1 size

2 revenue 0.977




6 Content Pages 0.527 0.332 0.999


8 Total Web Costs 0.481 0.401 0.098






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T-Test:

1 2 3 4 5 6 7 8 9 10 11 12 13

1 size

2 revenue 18.35




6 Content Pages 2.59 1.99 93.92


8 Total Web Costs 2.55 2.01 -0.81






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P-value:

1 2 3 4 5 6 7 8 9 10 11 12 13

1 size

2 revenue 8.02E-08



5 Monthly Hits 3.66E-02 1.75E-01

6 Content Pages 4.14E-02 8.12E-02 4.09E-12

7 Number of On-line Features 2.12E-01 1.16E-02 1.55E-02 6.71E-04

8 Total Web Costs 3.83E-02 9.18E-02 4.51E-01

9 Length of Web Presence 5.70E-02

10 Total Search Engines 1.33E-01 1.15E-01 3.98E-02

11 Number of On-Line Advertising Channels 1.38E-02 9.61E-04 8.54E-02

12 On-line Ad Spend 7.65E-02

13 Off-line Ad Spend 1.17E-02

Date post:	13-Aug-2018
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