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THESIS
22

20:0

 

LIBRARY
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This is to certify that the

dissertation entitled

The Effects of Competition and Resource
on Firm Performance in Local
Information Service on the Web

presented by

Ghee-Young Noh

has been accepted towards fulﬁllment
of the requirements for

Doctoral Mass Media

degree in

Major professor

 

 

Date May 30, 2000

 

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11100 W.“

THE EFFECTS OF COMPETITION AND RESOURCE ON FIRM PERFORMANCE
IN LOCAL INFORMATION SERVICE ON THE WEB

By

Ghee-Young Noh

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Mass Media Ph. D. Program

2000

ABSTRACT

THE EFFECTS OF COMPETITION AND RESOURCE ON FIRM PERFORMANCE
IN LOCAL INFORMATION SERVICE ON THE WEB

By

Ghee-Young Noh

The promise of a market for Web-based local information service (WLIS) has
attracted nearly all forms of media including broadcasters, newspapers, and Internet
ventures to a new cross-media competition and coordination. The present research is
the ﬁrst effort to empirically explore the mechanism of ﬁrms’ competitive advantage
and performance in the WLIS. Web survey research was used to collect 183 ﬁrm data
from 81 metropolitan markets.

The ﬁrst purpose was to assess how competition intensity affects ﬁrms’
ﬁnancial commitments and performance. The competitive environment was found to
strongly impact the resources available to the WLIS ﬁrm and its strategic choices. A
ﬁrm’s competition intensity was also positively related to market and ﬁnancial
growth performance controlling for market size, income, and service age.

The second purpose was to provide evidence that valuable and unique ﬁrm
resources and capabilities provide the key sources of competitive advantage. Firm
resources were found to be very important to the opportunity for competitive
advantages. In particular, the effects of a ﬁrm’s intangible resources were stronger on
ﬁrm performance and product quality than those of tangible resources controlling for

competition intensity.

Two competitive strategies were examined to identify the effects of ﬁrm
performance and product quality. A ﬁrm’s large-scale strategy was found to have a
strong impact on ﬁrm performance and product quality, while earlier entry strategy
was not. The result indicates that in the WLIS industry, without effective strategies
such as large-scale strategy and intangible resource commitment, ﬁrst mover
advantage has no effects on product quality and ﬁrm growth performance. In
addition, the effects of ﬁrm’s resources were found to be stronger than the effects of
market competition on ﬁrm performance.

Third, the present research identiﬁed three organizational goals in the WLIS:
service quality, proﬁtability, and business competition. The goals of ﬁrms with
different boundaries were found to be reﬂected in their allocation of resources and
competitive behavior, and result in different levels of ﬁrm performance

Finally, venture origin was found to be associated with a ﬁrm’s ﬁnancial
commitments, competitive strategies, and performance. Considering the market share
and market growth performance, the current WLIS market seems to be characterized
by strong competition between daily newspapers and Internet ventures.

This research elaborated the link between market structure and ﬁrm
performance by analyzing how market level effects are mediated by ﬁrm level
conduct for ﬁrm performance and product quality. It provides empirical evidence for
theoretical linkage between the ﬁnancial commitment and the resource-based theory

of the ﬁrm in a developing monopolistic competitive market.

Copyright by
Ghee-Young Noh
2000

T 0 My Mother and Father
With Thanks and Love

ACKNOWLEDGMENTS

I would like to acknowledge and thank the members of my committee for their
support and encouragement in pursuing this area of scholarship. Dr. Thomas
Baldwin, my dissertation and guidance committee chairman, was always helpful in
his ﬁiendly and indispensable guidance and advice to complete my doctoral program.
His warm encouragement and understanding provided me with a conﬁdence needed to
complete this research. Special thanks go to Dr. Stephen Lacy for his theoretical
guidance and instruction. My perspective on media theory has been inﬂuenced over
years by his scholarship. I had the beneﬁt of his close and critical comments and
suggestions during the entire period of my doctoral program. I am grateful to Dr.
Bradley Greenberg for his special attention to detail and his efforts in keeping me
learned about academic rigidity. My appreciation also goes to Dr. Carrie Heeter who
ﬁrst invited me into a multimedia production and offered a wonderful model of
quality as both a teacher and researcher on cyberspace. Finally, I sincerely would like
to thank Dr. Charles Salmon, director of the Mass Media Ph. D. program, who gave

me thoughtful consideration and needed support to complete this dissertation.

TABLE OF CONTENTS

LIST OF TABLES .................................................................................. x
LIST OF FIGURES .............................................................................. xvi
CHAPTER I
INTRODUCTION ................................................................................... 1
Purpose of the Research ................................................................... 2
CHAPTER II
LITERATURE REVIEW AND HYPOTHESES ............................................... 5
WLIS Product and Market Structure .................................................... 5
Financial Commitment .................................................................... 8
Competition and Performance .......................................................... 10
Resource-based Theory of the Firm ................................................... 12
Human Resources ............................................................... 15
Organizational Coordination ................................................... 17
Innovation Resources ............................................................ 18
Market and Resources ........................................................... 19
Competitive Strategies ................................................................... 20
Scale Strategy ..................................................................... 20
Time of Entry ..................................................................... 22
Product Quality ........................................................................... 23
Organizational Goals ..................................................................... 24
Venture Origin ............................................................................ 26
CHAPTER III
METHODS ......................................................................................... 29
Sampling and Data Collection .......................................................... 29
Operational Deﬁnition ................................................................... 32
Competition Intensity ............................................................ 32
Quantity of Human Resources ................................................. 33
Quality of Human Resources .................................................. 33
Organizational Coordination ................................................... 34
Innovation Resources ............................................................ 34
Market Size and Income ........................................................ 35
Product Quality .................................................................. 35
Market and Financial Growth Performance .................................. 36
Market Share Index .............................................................. 39
Organizational Goals ............................................................ 39
Service Age ....................................................................... 40
Scale Strategy ..................................................................... 40
Venture Origin ................................................................... 41

Data Cleaning for Multivariate Statistics .............................................. 41

Missing Data ..................................................................... 41
Multivariate Normality ......................................................... 42
Univariate Outliers .............................................................. 46
Statistical Analysis ....................................................................... 46
CHAPTER IV
RESULTS .......................................................................................... 50
Descriptives ................................................................................ 50
Market and WLIS Proﬁle ....................................................... 50
Product Quality and Performance ............................................. 52
Financial Commitment ................................................................... 54
Competition and Performance .......................................................... 58
Firm Resources ........................................................................... 62
Human Resources ............................................................... 62
Organizational Coordination ................................................... 75
Innovation Resources ........................................................... 77
Competitive Strategies ................................................................... 80
Scale Strategy .................................................................... 80
Time of Entry .................................................................... 83
Product Quality ........................................................................... 84
Resource and Competition ............................................................... 87
Organizational Goals ..................................................................... 89
Venture Origin ............................................................................ 97
Organizational Goals ............................................................ 97
Quantity of Human Resources ................................................. 99
Quality of Human Resources ................................................. 100
Organizational Coordination .................................................. 101
Innovation Resources .......................................................... 102
Product Quality ................................................................. 102
Market Growth Performance ................................................. 103
Financial Growth Performance ............................................... 104
Market Share .................................................................... 105
Additional Analysis: Intangible Resources .......................................... 106
CHAPTER V
SUMMARY AND DISCUSSION ............................................................. 111
Financial Commitment .................................................................. 111
Competition and Performance ......................................................... 113
Firm Resources .......................................................................... 116
Human Resources .............................................................. 116
Organizational Coordination and Innovation Resources .................. 119
Competitive Strategies ................................................................. 122
Product Quality ......................................................................... 124
Resource and Competition ............................................................ 124
Organizational Goals ................................................................... 126

viii

Venture Origin ........................................................................... 129

Synthetic Model and Implications for the WLIS Industry ........................ 130
Limitations and Recommendation for Future Research ........................... 137
APPENDIX A
MISSING VALUE ANALYSIS ............................................................... 141
APPENDIX B
MARKET CHARACTERISTICS ............................................................. 143
APPENDIX C
SURVEY INVITATION E-MAIL LETTER ................................................ 145
APPENDIX D
FIRST FOLLOW UP E-MAIL ................................................................. 146
APPENDD( E
SECOND FOLLOW UP E-MAIL ............................................................. 147
APPENDD( F
SURVEY QUESTIONNAIRE ................................................................. 148
BIBLIOGRAPHY ................................................................................ 156

ix

Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7
Table 8
Table 9
Table 10

Table 11

Table 11-1

Table 11-2

Table 1 1-3

Table 11-4

Table 12

Table 13
Table 14

LIST OF TABLES

Descriptive Statistics ............................................................. 44
Descriptive Statistics in Organizational Goal Items ........................ 45
Proﬁle: Service Age .............................................................. 50
Proﬁle: Number of Employees ................................................. 51
Quality of Human Resources ................................................... 51
Change in total employment ................................................... 51
Mean of Product Quality Items ................................................ 52
Mean Importance Scores in Seven Performance Measures ................ 53
Mean Success Scores in Seven Grth Performance Measures .......... 53

Pearson Product Moment Correlations of Independent Variables
for Financial Commitment Hypotheses ....................................... 54

Multiple Regression of Competition Intensity on Quantity of
Human Resources (log) ......................................................... 55

Multiple Regression of Competition Intensity on Quantity of

Editorial Staff (log) ............................................................... 55
Multiple Regression of Competition Intensity on Quantity of

Design Staﬁ‘ (log) ................................................................ 56
Multiple Regression of Competition Intensity on Quantity of

Technical Staff (log) ............................................................ 56
Multiple Regression of Competition Intensity on Quantity of

Marketing Staff (log) ............................................................ 56
Multiple Regression of Competition Intensity on Quality of

Human Resources ............................................................... 57
Multiple Regression of Competition Intensity on Scale Strategy .......... 58

Multiple Regression of Competition Intensity on Product Quality ....... 59

Table 15

Table 16

Table 17

Table 18

Table 19

Table 20

Table 21

Table 21-1

Table 21-2

Table 21-3

Table 21-4

Table 22

Table 23

Table 24

Table 24-1

Table 24-2

Multiple Regression of Competition Intensity on
Emphasis on Market Performance ............................................. 59

Multiple Regression of Competition Intensity on
Market Growth Performance ................................................... 60

Multiple Regression of Competition Intensity on
Financial Grth Performance ................................................ 61

Multiple Regression of Competition Intensity on
Market Share ..................................................................... 61

Pearson Product Moment Correlations of Independent Variables
for Firm Resource Hypotheses ................................................. 63

Multiple Regression of Quantity of Human Resources on
Product Quality .................................................................. 64

Multiple Regression of Quantity of Human Resources on
Market Grth Performance ................................................... 65

Multiple Regression of Quantity of Editorial Staff on
Market Grth Performance .................................................. 65

Multiple Regression of Quantity of Design Staff on
Market Growth Performance ................................................... 66

Multiple Regression of Quantity of Technical Staﬁ‘ on
Market Grth Performance ................................................... 66

Multiple Regression of Quantity of Marketing Staﬁ‘ on
Market Growth Performance .................................................. 66

Multiple Regression of Quantity of Human Resources on
Financial Growth Performance ................................................ 67

Multiple Regression of Quantity of Human resources on
Market Share ..................................................................... 68

Multiple Regression of Quality of Human Resources on
Product Quality .................................................................. 68

Multiple Regression of Quality of Editorial Staff on
Product Quality .................................................................. 69

Multiple Regression of Quality of Design Staﬂ‘ on
Product Quality .................................................................. 69

Table 24-3

Table 25

Table 25-1

Table 25-2

Table 25-3

Table 25-4

Table 26

Table 26-1

Table 26-2

Table 26-3

Table 26-4

Table 27

Table 28

Table 29

Table 30

Table 31

Multiple Regression of Product Quality on
Quality of Technical Staff ...................................................... 70

Multiple Regression of Quality of Human Resources on
Market Growth Performance ................................................... 70

Multiple Regression of Quality of Editorial Staff on
Market Grth Performance ................................................... 71

Multiple Regression of Quality of Design Staﬁ‘ on
Market Growth Performance ................................................... 71

Multiple Regression of Quality of Technical Staﬁ‘ on
Market Growth Performance ................................................... 72

Multiple Regression of Quality of Marketing Staﬁ‘ on
Market Grth Performance ................................................... 72

Multiple Regression of Quality of Human resources on
Financial Growth Performance ................................................ 73

Multiple Regression of Quality of Editorial Staff on
Financial Growth Performance ................................................ 73

Multiple Regression of Quality of Design Staff of
Financial Grth Performance ................................................ 74

Multiple Regression of Quality of Technical Staff of
Financial Growth Performance ................................................ 74

Multiple Regression of Quality of Marketing Staff on
Financial Growth Performance ................................................ 74

Multiple Regression of Organizational Coordination on
Product Quality .................................................................. 75

Multiple Regression of Organizational Coordination on
Market Grth Performance ................................................... 76

Multiple Regression of Organizational Resources on
Financial Growth Performance ................................................ 77

Multiple Regression of Innovation Resources on
Product Quality .................................................................. 78

Multiple Regression of Innovation Resources on
Market Growth Performance ................................................... 79

xii

Table 32

Table 33

Table 34

Table 35

Table 36

Table 37

Table 38

Table 39

Table 40

Table 41

Table 42

Table 43

Table 44

Table 45

Table 46

Table 47

Multiple Regression of Innovation Resources on
Financial Grth Performance ................................................ 79

Multiple Regression of Large-scale Strategy and Service Age on
Product Quality .................................................................. 80

Multiple Regression of Large-scale Strategy and Service Age on
Market Growth Performance .................................................. 81

Multiple Regression of Large-scale Strategy and Service Age on
Financial Growth Performance ................................................ 82

Multiple Regression of Large-scale Strategy and Service Age on
Market Share ..................................................................... 82

Multiple Regression of Product Quality on
Market Grth Performance ................................................... 85

Multiple Regression of Product Quality on
Financial Growth Performance ................................................ 86

Multiple Regression of Product Quality on
Market Share ..................................................................... 86

Comparison of Strength of Association with Product Quality
Between Four types of Resources and Competition Intensity ............. 87

Comparison of Strength of Association with Market Growth Performance
Between Four types of Resources and Competition Intensity ............. 88

Comparison of the Strength of Association with Financial
Growth Performance Between Four types of Resources and

Competition Intensity ............................................................ 88
Factor Loadings, Percents of Variance, Reliability For Principal Factor
Extraction and Oblique Rotation on Organizational Goal items .......... 91
Component Correlation Matrix ................................................ 91

Multiple Regression of Organizational Goals on
Quantity of Human Resources ................................................. 92

Multiple Regression of Organizational Goals on
Quality of Human Resources ................................................... 93

Multiple Regression of Organizational Goals on Organizational
Coordination ..................................................................... 94

Table 48

Table 49

Table 50

Table 51

Table 52

Table 53
Table 54
Table 54-1
Table 55
Table 55-1

Table 56

Table 57

Table 57-1

Table 58

Table 59

Table 59-1

Table 60
Table 60-1
Table 61
Table 61-1

Multiple Regression of Organizational Goals on

Innovation Resources ........................................................... 94
Multiple Regression of Organizational Goals on Scale of Entry .......... 95
Multiple Regression of Organizational Goals on
Product Quality .................................................................. 96
Multiple Regression of Organizational Goals on
Market Growth Performance ................................................... 96
Multiple Regression of Organizational Goals on
Financial Growth Performance ................................................ 97
Multiple Regression of Organizational Goals on Market Share ........... 97

Analysis of Covariance of Service Quality Goal by Venture Origin ...... 98
Mean Service Quality Goal for Five Categories of Venture Origin ....... 98
Analysis of Covariance of Proﬁtability Goal by Venture Origin ......... 99

Mean Proﬁtability Goal for Five Categories of Venture Origin ........... 99

Analysis of Covariance of Business Competition Goal by
Venture Origin ................................................................... 99

Analysis of Covariance of Quantity of Human Resources by
venture origin ................................................................... 100

Mean Quantity of Human Resources for Five Categories of
Venture Origin .................................................................. 100

Analysis of Covariance of Quality of Human Resources by
Venture Origin .................................................................. 101

Analysis of Covariance of Organizational Coordination by
Venture Origin .................................................................. 101

Mean Organizational Coordination for Five Categories of
Venture Origin ................................................................. 101

Analysis of Covariance of Innovation Resources by Venture Origin ... 102
Mean Innovation Resources for Five Categories of Venture Origin 102
Analysis of Covariance of Product Quality by Venture Origin .......... 103

Mean Product Quality by Five Categories of Venture Origin ............ 103

xiv

Table 62 Analysis of Covariance of Market Growth Performance by

Venture Origin .................................................................. 104
Table 62-1 Mean Market Growth Performance by Five Categories of

Venture Origin .................................................................. 104
Table 63 Analysis of Covariance of Financial Growth Performance by

Venture Origin .................................................................. 104
Table 63-1 Mean Financial Growth Performance for Five Categories of

Venture Origin .................................................................. 105
Table 64 Analysis of Covariance of Market Share by Venture Origin ............. 105
Table 64-1 Mean Market Share for Five Categories of Venture Origin .............. 106
Table 65 Pearson Product Moment Correlations of Independent Variables

for Intangible Resources Hypotheses ........................................ 107

Table 66 Multiple Regression of Tangible and Intangible Resources on
Product Quality ................................................................. 108

Table 67 Multiple Regression of Tangible and Intangible Resources on
Market Growth Performance ................................................. 109

Table 68 Multiple Regression of Tangible and Intangible Resources on
Financial Growth Performance ............................................... 110

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

LIST OF FIGURES

Scheme of the Research: The Eﬁ‘ects of Competition and
Resources on Firm Performance in Web-based Information Service ....... 4

Diagram of Hypotheses and Research Questions ............................ 28

Model of Competition Intensity on Relationship with Financial
Commitment, Product Quality, and F irm Performance Controlling
for Market Size, Market Income, and Service Age ........................ 115

Model of Human Resources on Relationship with Product Quality and
Firm Performance Controlling Competition Intensity, Market Size,
Market Income, and Service Age ............................................. 118

Model of Organizational Coordination and Innovation Resources on
Relationship with Product Quality and Firm Performance

Controlling Competition Intensity, Market Size,

Market Income, and Service Age ............................................. 121

Model of Large-scale and Early Entry Strategy on Relationship with
Product Quality and Firm Performance Controlling Competition Intensity,
Market Size, Market Income, and Service Age ............................. 123

Model of Organizational Goal on Relationship with Firm Resources,
Strategies, Product Quality, and Firm Performance Controlling Market
Size, Market Income, and Service Age ....................................... 128

Model of Market Growth Performance in the Web-based Local
Information Service ............................................................ 132

Model of Financial Growth Performance in the Web-based Local
Information Service ............................................................ 133

Model of Product Quality in the Web-based Local
Information Service ............................................................. 134

Chapter I

INTRODUCTION

In early 1998, 51 percent of Internet users had already accessed local
applications such as news, weather, sports, real estate, and e-commerce. Moreover,
46 percent indicated that they would use sites that aggregate local content at least
once a week (Krasilovsky, 1998). The promise of a market for Web-based local
information service (WLIS) has attracted nearly all forms of media including
television, radio broadcasters, newspapers, and Internet ventures to a new cross-
media competition and coordination. There are now more than 2,900 newspapers
online and more than 980 television station sites in the United States (Editor &
Publisher MediaLink, March 2000). In any given local market, there can be as
many as twenty local information service sites competing against one another. The
marketer of local information service may have a newspaper site, an alternative
weekly site, a local branch of a city guide chain such as CitySearch and Digital
Cities, several TV and radio station sites, and joint ventures between media groups.

Some news companies reported annual new media proﬁts in the millions of
dollars. The Web site of WCCO of Minneapolis was the ﬁrst local TV site to claim
proﬁtability. Even if there have been incredible efforts to gain a competitive
advantage in the industry, a model of competitive advantage and ﬁrm performance
still should be established. The present research is the ﬁrst effort to empirically
explore the mechanism of ﬁrms’ competitive advantage and performance in the

WLIS.

In particular, this research focuses on local competition, ﬁrms' diverse
resources, and competitive strategies to explain ﬁrm performance in Web-based
local information services (WLIS). It will contribute to a ﬁrrther understanding of
the WLIS market and industry, expand the limited body of studies on ﬁrm behaviors
and resources in media research, and advance some important linkages between ﬁrm

resources and performance to theoretical foundations.

Purpose of the Research

The ﬁrst purpose of the present research is to identify organizational goals
and objectives in WLIS. According to system theory, before the inputs are released
into the systems and before the processes take place, direction in the form of goals
and objectives is given to the organizational system (Connellan, 197 8). In other
words, a ﬁrm should have some speciﬁc goals and objectives before the ﬁrm adopts
a speciﬁc conduct. The objectives of ﬁrms with different boundaries may differ
systematically, may be reﬂected in their competitive behavior, and could result in
different levels of ﬁrm performance. Exploring the organizational goals may be the
ﬁrst step to picture the industry and ﬁrm behavior for the emerging business and
market.

The second purpose is to identify which factor is the primary cause of ﬁrms'
performance in WLIS. Traditional economic research focuses on market structure
such as competition as the primary cause of performance,1 while the resource-based
view of the ﬁrm tends to emphasize the importance of ﬁrm-speciﬁc resources and

capabilities. McGahan and Porter (1997) conﬁrm that both market and ﬁrm effects

 

‘ Economists usually use the term market structure to describe how competition

are important in shaping proﬁtability. But they argue that the partitioning differs
across sectors of industry. The present research examines partitioning and
complementarity between ﬁrm and market effects on performance, and thereby
elaborates the link between market structure and ﬁrm performance.

The third purpose comes from theoretical concerns in media economics and
management. The ﬁnancial commitment approach is based on the competitive
newspaper and local television news markets, which are characterized as oligopolies
with product competition. An important theoretical issue is the applicability to
media markets that contain large numbers of ﬁrms, which is characteristic of perfect
competition (Lacy, 1992). This research attempts to apply the ﬁnancial
commitment approach to the emerging market of the WLIS.

Finally, for ﬁxture research in WLIS, this research methodologically attempts
to propose and develop several measures for economic and management variables
such as competition, ﬁrm resources, and performance. Figure 1 summarizes the

scheme of the present research.

 

takes place within a geographic area (Lacy, 1993).

 

 

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Chapter II

LITERATURE REVIEW AND HYPOTHESES

WLIS Product and Market Structure

The industrial organizational model allows the analysis of a particular
industry or market by examining the market structure. The structure-conduct-
performance (SCP) paradigm posits a causal relationship wherein external market
structure affects ﬁrm strategy or conduct, which in turn affects ﬁrm performance
(Bain 1956, 1968; Mason, 1957). Market structure models are derived ﬁ'om
microeconomic theories such as perfect competition, monopolistic competition,
oligopoly and monopoly. Given the geographic market, market structure is
determined by three dimensions: the number of ﬁrms, the homogeneity of products
within the market, and the extent of barriers to entry (Lacy & Simon, 1993).

According to three dimensions of market structure, traditional media markets
have been deﬁned as oligopolies with product competition (Prisuta, 1979; Litman,
1988; Lacy, 1992). For example, Litman (1988) places newspaper markets between
oligopolies and monopolies. An oligopoly is characterized by a market with a few
ﬁrms that recognize their strategic interdependence. The growing number of media
outlets in large markets, however, have made the application of oligopoly more and
more questionable (Lacy, Atwater, & Qin, 1989). Recently, Chan-Olmsted (1997)
proposed a multichannel media competition model by applying monopolistic

competition theory at the industry level and oligopoly theory at the group level.

Unlike the traditional media markets, the Web-based local information
service market has a larger number of ﬁrms and lower barriers to entry even in local
markets which are characteristic of perfect competition. Perfect competition
markets are those with many sellers, homogeneous products, and low barriers to
entry. In the perfect competition market, each ﬁrm assumes that the market price is
independent of its own level of output. At higher prices, the ﬁrm sells nothing, and
below the market price it faces the entire market demand curve (Varian, 1996).

WLIS products, however, are not homogeneous. Each WLIS ﬁrm attempts
to make consumers think that its product is different from the products of its
competitors to create some degree of market power. The primary incentive for
sellers to differentiate is the reduced substitutability between products. With
reduced substitutability between products, price-cutting does not result in a
complete loss of one's market share. Product differentiation thus gives a ﬁrm a
certain power within its own market. Such a market is called a monopolistically
competitive market (Chamberlin, 1962).

The ﬁrst step in examining the market structure is deﬁning the market in
terms of geography and product. WLIS markets are difﬁcult to strictly deﬁne by
geographic market. Chyi and Sylvie (1998) suggested electronic newspapers
perform in four separate submarkets: the local information market, the long-distance
information market, the local advertising market, and the long-distance advertising
market. Moreover, the local information market is not limited to the resident users
of the market. Long distance users can access speciﬁc local contents if they are

willing to pay their attention.

Another difﬁculty in deﬁning the WLIS market mainly comes from its joint
product nature which serves two markets with one production process. The WLIS
sells information and entertainment to consumers and space to advertisers. Even the
WLIS products are multiproducts that can serve markets other than the information
and advertising markets (Lacy & Noh, 1997). WLISs provide electronic commerce
transactions on their Web sites which may be an important revenue stream. For
example, New York Times' New York Today (http://www.nytoday.com) launched a
restaurant reservation service in February 2000. As Lacy and Noh (1997) argue,
electronic media have ceased being just about content and allow new behaviors
related to other markets.

WLIS products are quasi-public goods. Even if a user consumes information
or entertainment on WLIS, this action does not diminish its availability to others.
The WLIS products are not used up by the act of consumption, which is called non-
exhaustibility or joint of supply (Barry & Hardin, 1982). Every member also has a
chance to beneﬁt from the Web contents. In other words, it is impossible to exclude
some users from consuming the Web content. Public goods are not affected by
supply and demand in the same way that private goods are aﬁ‘ected (Picard, 1989).
Although most WLISs are available to all consumers, there exist some premium
Web services. When users begin to pay for the WLIS in the form of subscription
fees or pay-per-article, the price element begins to affect demand by losing viewers
who would surf only if the price were lower. Due to these hybrid characteristics,

WLISs are quasi-public goods.

The WLIS products are also experience goods. Experience goods are deﬁned
as “those whose qualities and characteristics can be discovered only after the actual
purchase of the good has occurred” (Ekelund & Saurman, 1988, p.63). Consumers
must experience or sample them to evaluate the quality of Web content. For many
experience goods, repeat purchases by consumers are commonplace. As a result, the
quality of the products can be quickly discovered. In the WLIS market, consumers
have a high degree of market power by repeat visiting. They can discontinue using
the WLIS and move to other WLISs. The important factor, therefore, may not be
the quantity of people a WLIS attracts but the quality of their experience. A WLIS
that attracts just a few thousand loyal customers may ultimately be more valuable
than one in which a million new people visit each month but never return (Schwartz,
1996). WLISs with loyal customers can extract higher advertising revenue from
advertisers. Customer information may be valuable to advertisers and direct
marketers (New York Times, October 20, 1998). The retention efforts are more

important to keep customers and deter them from switching to another service.

Financial Commitment

Media economics uses the term market structure to describe how competition
takes place. Various studies on the structure of media markets have indicated a
relationship between competition and ﬁrm conduct. Litman and Bridges (1986)
found that competition had an impact on the amount of money spent on the product.
Competitive newspapers tended to have more wire services and larger newsholes.
They called the tendency to increase ﬁnancial expenditures on the news product

"ﬁnancial commitment." Lacy (1989, 1992) conceptualized the ﬁnancial

commitment theory with a set of explicit theoretical statements, which are

applicable to newspaper and local TV news competition. According to the theory, as
intensity of competition increases, the amount of money committed to news content
increases (Lacy, 1992).

Newspaper research found that competition resulted in more ﬁnancial
commitment on product (Kenny & Lacy, 1987; Nixon & Jones, 1956; Litman &
Bridges, 1986; Lacy, 1987, 1988, 1990; Mullins, 1975; Everett & Everett, 1989).
The number of wire services carried and the number of reporters per amount of
news space were related positively to intensity of competition (Lacy, 1987). This is
because newspapers facing intense competition must increase expenditures on the
information product to attract readers (Lacy, Atwater, & Qin, 1989).

Even if the ﬁnancial commitment theory originated with the newspaper
industry, research also conﬁrmed the relationship between competition and ﬁnancial
commitment in local television news (Bustema, 1980, 1988a; Lacy, Atwater, Qin &
Powers, 1988; Lacy, Atwater, & Qin, 1989; Lacy & Bernstein, 1991; Litman, 1979;
Power and Lacy, 1991; Powers, 1993). For example, Litman (1979) found that an
increase in competition between television networks resulted in increased
programming expenditures. Bustema (1988) also found that competition in local
television news was signiﬁcantly related to staff size. Local news competition with
another station was positively related to the number of employees in a station’s
news department per daily newscast (Lacy, Atwater, & Qin, 1989).

The competitive environment impacts the resources available to the venture

and, as a result, its strategic choices (Chandler & Hanks 1994; Tsai, MacMillan, &

Low, 1991). Even if no published studies could be found concerning the impact of
competition on human resources commitment in the WLIS, traditional media
research suggests a possible connection between competition intensity and human
resources commitment. The rationale is that industry conditions, competition
intensity, inﬂuence the opportunities available for the ﬁrm. WLIS ﬁrms facing
intense competition are likely to try to diﬁ‘erentiate their products by hiring more
people and higher quality people who can do a better job. The following hypotheses
attempt to extend the relationship between competition and ﬁnancial commitment to
the WLIS market. Moreover, in proposing a hypothesis regarding this relationship,
we should point out that this relationship may be other market variable-dependent;
requiring controls for market size and market income level. The eﬁ‘ects of service
age are also controlled for these hypotheses (see p.21).

Hla: For Web-based local information service, a ﬁrm’s competition intensity

with other ﬁrms will be positively related to larger quantity of human

resources controlling for market size, income, and service age.

Hlb: For Web-based local information service, a ﬁrm’s competition intensity

with other ﬁrms will be positively related to higher quality of human

resources controlling for market size, income, and service age.

H2: For Web-based local information service, a ﬁrm’s competition intensity

with other ﬁrms will be positively related to larger scale of entry into market
controlling for market size, income, and service age.

Competition and Performance

Sandberg and Hofer (1987) suggest that industry conditions greatly impact a
ﬁrm’s performance. The competitive environment inﬂuences the market's
acceptance of its products. As the intensity of competition increases in a market,

the market will tend to accept the higher quality products. Previous studies support

10

the relationship between competition and content quality in the newspaper industry
(Wanta, Johnson, & Williams, 1990; White & Andsager, 1990; Sylvie, 1991). In the
competitive market, a ﬁrm will try to establish higher quality products for the
formation and survival of a new venture. A WLIS ﬁrm faces intense competition.
Therefore, it is likely to spend more money to differentiate its product which means
a higher quality of product.

Editorial content is the most important element to deﬁne the quality of WLIS
products. Without the visual impact of shape, color, and contrast, however, WLIS
content will not motivate the viewer to investigate their content. The Web design
concept has two dimensions: aesthetic and functional (N oh, 1998). The aesthetic
design concept refers to the extent to which Web design takes ﬁrll advantage of the
artistic potential of the medium. WLIS may also contain diverse levels of
functionality. Functionality refers to the quality of WLIS that is characterized by
increased control over the communications process by customers. For example, ease
of searching is often the most important ﬁrnctional attribute because users rarely
spend enough time on any individual Web site to become expert users (Nielson,
1998)

Research has supported to some degree that daily newspaper competition
increases product quality in the form of more news space, more news services, and
increased use of color and graphics. Kenny and Lacy (1987) found that the use of
color and graphics on the front page of newspapers increased when the intensity of
the competition increased. Lacy (1988) reported that increasing intensity of

intercity competition among daily newspapers resulted in increases in the

11

percentage of total newsholes given to local news. Research using an index of
quality in newspapers found a relationship between competition and quality (Lacy &
Fico, 1989). Competition in local television news was positively related to the
minutes of local news, as well as the number of minutes of all local programming
(Bustema, 1988). Intensely competitive WLISs are likely to have higher quality
ratings for their service products and ﬁrm performance.
H3: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to higher quality of product
controlling for market size, income, and service age.
H4a: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to stronger emphasis on market
performance within its business unit controlling for market size, income, and
service age.
H4b: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to higher level of market growth
performance controlling for market size, income, and service age.
H4c: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to higher level of ﬁnancial growth
performance controlling for market size, income, and service age.
H4d: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to higher level of market share
controlling for market size, income, and service age.
Resource-based Theory of the Firm
Resource-based theory emerged from dissatisfaction with the SCP paradigm
of industrial organization and neoclassical economics (Bogner, Mahoney, &
Thomas, 1998). The neoclassical theory of the ﬁrm was originally designed for the

theory of price determination and resource allocation. Within the industrial

organization model, ﬁrms in an industry were considered to be alike in all respects

12

except for scale. There is little heterogeneity of ﬁrms in the model. As a result, the
industrial organizational model emphasizes industry condition, market structure, as
the primary basis for superior ﬁrm performance.

The structural view has been criticized because the causal assumption
ignores the dynamic inﬂuences that a ﬁrm’s strategic actions have on the industry
structure (Chan-Olmsted, 1997). Penrose (1959) noted that it is inappropriate to try
to reconcile the neoclassical theory of the ﬁrm with organizational theory.
Neoclassical microeconomics is inadequate in dealing with dynamic growth
processes based on heterogeneity of ﬁrms. As Rumelt (1974) argued, "the central
concerns of business policy are the observed heterogeneity of ﬁrms and ﬁrm’s
choice of product-market commitments" (p.560). They require a focus on the
individual ﬁrm.

The resource-based theory of the ﬁrm takes the perspective that valuable and
unique ﬁrm resources and capabilities provide the key sources of sustainable
competitive advantage (Barney, 1991; Conner, 1991; Hart, 1995). The resource
base focuses on rents other than monopoly rents, the returns to market power. Thus
the resource-based theory assumes that business strategy and performance should be
viewed more as a quest for Ricardian rents (Grant, 1991). Unlike monopoly rents
by market power, Ricardian rents are based on the possession of scarce and valuable
resources (Barney, 1986: Peteraf, 1993). They suggest the returns to the resources
from competitive advantage are over and above the real costs of the resources. The
theory highlights how the deployment of unique and idiosyncratic organizational

resources and capabilities can result in superior performance. Even for the new

13

venturing ﬁrm, research has shown that resources and capabilities are the source of
competitive advantage (Chandler & Hanks, 1994; McGrath et. al., 1994).

Sustained competitive advantage grows from ﬁrm-speciﬁc resources that
cannot easily be imitated or substituted. Product strategies are dependent on
resources and therefore strategy formulation starts with a resource assessment.
Penrose (1959) provided a new conceptual schema for the ﬁrm as a collection of
resources. Mata, Fuerst, and Barney (1995) suggested that the ﬁrm's resources be
based on two characteristics: resource heterogeneity and immobility. Resource
heterogeneity means that the resources and capabilities possessed by competing
ﬁrms may differ; resource immobility means that these differences may be long
lasting. If a ﬁrm possesses a resource that is possessed by other competing ﬁrms,
that resource cannot be a source of competitive advantage. Thus common resources
do not meet the resource heterogeneity requirement. On the other hand, a resource is
immobile if ﬁrms without a resource face a cost disadvantage in developing,
acquiring, and using that resource compared to ﬁrms that already possess and use it
(Mata, Fuerst & Barney, 1995).

Firms are comprised of heterogeneous bundles of resources. Six major
categories of resources have been suggested: physical, ﬁnancial, human, reputation,
organizational, and technological resources (Barney, 1991; Conner, 1991; Peteraf,
1993, Grant, 1991). Although physical and ﬁnancial resources may produce a

temporary advantage for a ﬁrm, competitors or new entrants often can readily

14

acquire them on factor markets.2 Important resources in a ﬁrm must be difﬁcult to
replicate and therefore raise a "barrier to imitation" (Rumelt, 1984).

Human Resources

Brush and Chaganti (1998) showed that human and organizational resources
are more strongly associated with certain performance outcomes than strategy in
small service and retail ﬁrms. For example, technological and marketing skills
signiﬁcantly inﬂuence ﬁrm performance (Roberts, 1992). In particular, the most
important resources are tacit or socially complex. The tacit resources are skill-
based and people-intensive (Hart, 1995). Chandler (1962) highlights the importance
of critical human resources: “(O)f these resources, trained personnel with
manufacturing, marketing, engineering, scientiﬁc, and managerial skills often
become even more valuable than warehouses, plants, ofﬁces, and other physical
factors (p.383).”

Even if the translation of resources into content quality is not perfect because
some waste always occurs in the process (Lacy, 1992), media research has
supported the relationship between resource commitment and product quality (Lacy
& Fico, 1989; Lacy, Fico, & Simon, 1989). Lacy and Fico (1989) found that as
human resources for a given amount of space increased, the quality of the
newspaper increased. For direct performance effects, research has also suggested
that staff sizes are positively related to performance in the newspaper industry
(Lacy, Fico, & Simon, 1989). Power and Lacy (1991) found that size of newsroom

staff was positively related to audience share for the early evening newscast.

 

2 . . . . .
Moreover, ﬁnancial resources of a larger ﬁrm are not indicative of ﬁnancial
resources the ﬁrm has allocated or is willing to invest in the new venture. This

15

However, the quantity of human resources is only part of human resources
dimensions. Hiring more peOple does not guarantee higher quality which can do a
complete job for superior performance. Sometimes, the quality of human resources
has a stronger impact on ﬁrm performance than the quantity of human resources.
Therefore, the effects of each dimension should be examined for product quality and

ﬁrm performance.

H5a: The quantity of human resources will be positively related to higher
quality of product controlling for ﬁrm’s competition intensity, market size,
income, and service age.

H5b: The quantity of human resources will be positively related to higher
level of market growth performance controlling for ﬁrm’s competition
intensity, market size, income, and service age.

H5c: The quantity of human resources will be positively related to higher
level of ﬁnancial growth performance controlling for ﬁrm’s competition
intensity, market size, income, and service age.

HSd: The quantity of human resources will be positively related to higher
level of market share controlling for ﬁrm’s competition intensity, market
size, income, and service age.

H6a: The quality of human resources will be positively related to higher
quality of product controlling for ﬁrm’s competition intensity, market size,
income, and service age.

H6b: The quality of human resources will be positively related to higher
level of market growth performance controlling for ﬁrm’s competition
intensity, market size, income, and service age.

H60: The quality of human resources will be positively related to higher level
of ﬁnancial growth performance controlling for ﬁrm’s competition intensity,
market size, income, and service age.

 

would not be disclosed.

16

Organizational Coordination

Creating competitive advantage is not simply a matter of assembling a set of
resources. Rents are usually earned not necessarily because of better resources, but
because of better use of resources (Penrose, 1959). Resources are basic inputs into
the production process. Productive activity, however, requires the cooperation and
coordination of teams of resources (Chandler & Hanks, 1994). Socially complex
resources depend on large numbers of people engaged in coordinated action such
that few individuals have sufﬁcient breadth of knowledge to understand the overall
phenomenon (Barney, 1991; Reed & DeFillippi, 1990)

Organizational coordination involves processes that redeﬁne the ﬁrm’s
product strategies, reconﬁgure chains of resources the ﬁrm can use, and redeploy
resources through organizational structures (Sanchez, 1995). The organizational
coordination builds a pattern of organizational routines within the ﬁrm.
Organizational routines are regular and predictable patterns of activity which are
made up of a sequence of coordinated actions by individuals. The organizational
systems and routines such as relationship, planning, and structure, are central to
achieving efﬁciencies in operations and providing superior levels of service (Brush
& Chaganti, 1998). Andrews (1980) suggested that the essence of coordination is
the way in which subdivided functions and interests are resynthesized.

Sustainable competitive advantage involves complex patterns of coordination
between people and between people and other resources (Grant, 1991). Dynamic

product markets such as Web information service require frequent adjustments in

17

product strategies and coordinating the uses of product creation resources.
Therefore, the following hypotheses of organizational coordination were addressed.
H7a: Organizational coordination will be positively related to higher quality
of product controlling for ﬁrm’s competition intensity, market size, income,
and service age.
H7b: Organizational coordination will be positively related to higher level of
market growth performance controlling for ﬁrm’s competition intensity,
market size, income, and service age.
H7c: Organizational coordination will be positively related to higher level of
ﬁnancial growth performance controlling for ﬁrm’s competition intensity,

market size, income, and service age.

Innovjation Resources

Innovation in combining or deploying resources can lead to a competitive
advantage or superior performance (Grant, 1991; Barney, 1991). Being ﬁrst to
market allows the ﬁrm to establish its products as the standard, which forces later
entrants to follow the pioneer's rules of competition (Zahra, Nash, & Bickford,
1995). Rapid product introductions can meet customers' needs, generate proﬁts, and
preempt the competition (Zahra, 1996). In other words, they enhance the ﬁrm's
ability to differentiate itself from the competition.

In the early developmental stage of the WLIS, there is still much
experimentation, research, and development that need to be funded by WLIS ﬁrms.
For superior performance, it is essential to create a new and innovative application
that others will want to copy (Outing, October 23, 1999). Companies to compete
based on innovative products should be in the best position to take advantage of
opportunities in rapidly changing industries such as WLIS.

H8a: Innovation resources will be positively related to higher quality of

product controlling for ﬁrm’s competition intensity, market size, income, and
service age.

18

H8b: Innovation resources will be positively related to higher level of market
growth performance controlling for ﬁrm’s competition intensity, market size,
income, and service age.

H8c: Innovation resources will be positively related to higher level of
ﬁnancial growth performance controlling for ﬁrm’s competition intensity,
market size, income, and service age.

Market and Resource;

Market-level effects that promote homogeneity among ﬁrms coexist with
ﬁrm-level effects that generate heterogeneity, just as various forms of competition
coexist within the same industry (Mauri & Michaels, 1998). Thus there is a
complementarity between resource-based and industrial organizations perspectives,
including ﬁnancial commitment theory, to explain the mechanism of ﬁrm
performance. For example, Mauri and Michaels (1998) found the strong inﬂuence
of industry-level drivers on R & D and advertising investment, whereas the results
for performance conﬁrmed the strong effect of ﬁrm-level drivers from the variance
components methodology. Chandler and Hanks (1994) conﬁrmed that both
perceived market attractiveness and resource-based capabilities were signiﬁcantly
related to venture performance.

Although industry effects are important, the resource-based view places
primary emphasis on investigating ﬁrm-level effects on performance. Rumelt
(1991) found that the variance within markets is greater than variance across
markets. Roquebert, Phillips and Westfall (1996) also conﬁrmed the dominance of
ﬁrm effects on performance using variance components analysis. McGahan and
Porter (1997) found that there are signiﬁcant stable business segment-speciﬁc

effects of 32% while industry effects on ﬁrm proﬁtability was 19% of the total

19

variation. Even when ﬁrms follow similar strategies, the idiosyncrasy in their
resources leads to different performance outcomes (Lawless, Bergh, & Wilsted,
1989)

RQla: Are the effects of ﬁrm’s resources on product quality stronger than
those of ﬁrm’s competition intensity?

Rle: Are the effects of ﬁrm’s resources on market growth performance
stronger than those of ﬁrm’s competition intensity?

RQlc: Are the effects of ﬁrm’s resources on ﬁnancial growth performance

stronger than those of ﬁrm’s competition intensity?

Competitive Strategies
Scale Strategy

Literature on competitive strategies for new ventures differs signiﬁcantly on
the domain breadth for new ventures, small- versus large-scale entry. Small scale
position argued that new ventures should pursue ‘niche’ strategies concentrating on
specialized products, localized business operation (Hosmer, 1957) and customized
market segments, and high quality of customer services (Cohen and Lindberg,
1972). Thus they should avoid direct competition with large established ﬁrms.

On the other hand, MacMillan and Day (1987) found that large-scale entry
was more successful than small-scale entry. Large-scale strategies include high
levels of capacity, sales promotion, service quality, advertising, and sales force
expenditures. Cooper, Willard and Woo's (1986) also suggested that with the large-
scale combination of resources, new ventures may develop strategies of direct

competition with larger and established market leaders.

20

WLIS is deﬁned as increasing returns business. The essence of an increasing
returns business is that the product or service becomes more valuable as more
customers purchase it (Hagel, 1999). The increasing returns business of WLIS
mainly comes from the feature of discretionary database. Connolly and Thorn
(1993) deﬁned it as “a shared pool of data to which several participants may, if they
choose, separately contribute information.” When someone contributes information
to the Web, other consumers enjoy some beneﬁt from using the information on the
WLIS. WLIS include many kinds of discretionary databases such as local forum,
real-chatting, diverse opinion from customers, information of local events, and even
advertising. In the increasing returns business, the ﬁrm is likely to have to be
aggressive to drive success in this business. It is essential to achieve a critical mass
point to maximize WLIS utility, which results in product differentiation. Therefore
the following hypotheses were addressed.

H9a: For Web-based local information service, large-scale entry strategy will

be positively related to higher quality of product controlling for ﬁrm’s

competition intensity, market size, income, and service age.

H9b: For Web-based local information service, large-scale entry strategy

will be positively related to higher level of market growth performance

controlling for ﬁrm’s competition intensity, market size, income, and service
age.

H9c: For Web-based local information service, large-scale entry strategy will

be positively related to higher level of ﬁnancial growth performance

controlling for ﬁrm’s competition intensity, market size, income, and service
age.

H9d: For Web-based local information service, large-scale entry strategy will

be positively related to higher level of market share controlling for ﬁrm’s
competition intensity, market size, income, and service age.

21

Time of Entry

By engaging in pioneering, the ﬁrm takes the competition to a new arena
where its early mover status is hoped to create some basis for sustainable
competitive advantage (Covin, Slevin, & Heeley, 1999). Research supported that
ﬁrm age strongly impacts an organization’s resources and performance (Aldrich &
Auster, 1986; Venkataraman & Low, 1994; Brush & Chaganti, 1998). Mosakowski
(1993) found that ﬁrms of different ages have different combinations of human and
organizational resources correlated to performance.

Early entry into a young industry is also associated with higher levels of
long-term performance. Many researchers have found that ﬁrst entrants maintain
higher market performance and higher chance of survival (Mitchell, 1991; Lambkin,
1979; Robinson & Fornell, 1985; Mascarenhas, 1997). This is because the
investments made in an industry's early period create a set of strategic assets for
ﬁrms that conditions their later choices (Oster, 1994). Thus, the early entrants may
choose employees and agents and obtain equipment at lower market prices than later
entrants. Most of all, customers may view ﬁrst entrants as prototypical of the new
product category (Mascarenhas, 1997). Therefore, a ﬁrm’s earlier entry into market
is likely to have higher sustainable competitive advantage, which results in higher
ﬁrm performance.

H10a: For Web-based local information service, earlier entry into market will

be positively related to higher quality of product controlling for market size,

income, competition intensity, and scale of entry.

H10b: For Web-based local information service, earlier entry into market

will be positively related to higher level of market growth performance
controlling for market size, income, competition intensity, and scale of entry.

22

H10c: For Web-based local information service, earlier entry into market will
be positively related to higher level of ﬁnancial growth performance
controlling for market size, income, competition intensity, and scale of entry.
H10d: For Web-based local information service, earlier entry into market
will be positively related to higher level of market share controlling for
market size, income, competition intensity, and scale of entry.
Product Quality
Product quality has long been recognized and advocated as bases for
competitive differentiation in growing industries. Product quality may be described
as the characteristics of a product or service that can contribute to the fulﬁllment of
stated or implied customer needs and wants (Garvin, 1984, Reeves & Bednar, 1994).
The level of product quality reﬂects product differentiation. Product differentiation
has two dimensions: horizontal and vertical (Whinston, Stahl, & Choi, 1997).
Products are considered to be differentiated horizontally if the difference is based on
appearance or consumer preference. When all consumers prefer a product among
equally priced products, the products are considered to be vertically differentiated.
Thus products are differentiated vertically if their qualities are different.
Cost-reducing mass production technology is no longer a major concern for
digital products. If products are differentiated, each seller has some degree of
market power over those consumers who prefer their product. Customization is an
extreme example of product differentiation in which products are produced to match
the speciﬁc demand of a small group of consumers or even one individual
(Whinston, Stahl, & Choi, 1997).

Empirical evidence is supportive of the proposition that product quality

should enhance market performance in media industry. For example, the level of

23

newspaper quality was positively related to circulation (Lacy & Fico, 1989). Lacy
and Sohn (1990) found that as inches of copy devoted to stories about the suburbs
increased, metro daily penetration increased. Customers tend to be drawn to quality
outputs and form loyalties toward the providers (Kroll, Wright, & Heiens, 1999). A
WLIS competing for the same customers must match the quality of the competing
WLIS in most areas and differentiate themselves in other areas for higher level of
ﬁrm performance.

H1 1a: For Web-based local information service, product quality will be

positively related to higher level of market growth performance controlling

for ﬁrm’s competition intensity, market size, income, and service age.

H1 lb: For Web-based local information service, product quality will be

positively related to higher level of ﬁnancial growth performance controlling

for ﬁrm’s competition intensity, market size, income, and service age.

H1 1c: For Web-based local information service, product quality will be

positively related to higher level of market share controlling for ﬁrm’s

competition intensity, market size, income, and service age.
Organizational Goals

The ﬁrm's goal in traditional microeconomic theory is assumed as proﬁt
maximization. Criticizing the simplicity of the ﬁrm's assumed goals, other
alternative theories regarding the goals of ﬁrms suggests goals other than proﬁt
maximization. Baumol (1967) has suggested that ﬁrms work to maximize sales
revenue. Marris (1964) argued that the goals of the ﬁrm are to maximize
shareholder's capitalization.

All goals in the end may relate to maximize proﬁt or shareholder's value but

the ﬁrm has a number of subgoals. The ﬁrm cannot be sure of the relative

contribution of any single subgoal to the proﬁt or shareholder's value. Some of the

24

multiple goals involved in performance evaluation will be compatible, but others
may come into conﬂict. When multiple goals exit, how do we determine which one
is inﬂuencing strategic behavior? This may depend in part on the structure of the
set of goals. Research suggests that the structure of goals is hierarchical in nature
(Beach 1985; Maslow 1970). However, the organization of goals has a more ﬂuid
arrangement. One goal can sometimes be more or less important than alternatives.
For example, proﬁtability may not be an important goal in the initial stage of a new
venture. Regardless of the structure, research on the organization of goals suggests
that separate but compatible goals be grouped into relatively homogeneous
categories of goals (Murphy & Cleveland, 1995).

Demers (1996) reported three homogeneous categories from newspaper
management: product quality, proﬁt, and community involvement. Rhea (1970)
also conducted research on the goals of broadcast news directors. He included 11
task-oriented goals including proﬁt, product quality, community involvement,
licensing, and market position. Bates (1997) found that the primary focus of local
television sites remains promotional, with a secondary emphasis on providing
information content. Some WLIS ﬁrms are competing for the future. The ﬁrms
should be concerned not only with the present proﬁtability, but also with its ﬁxture
positions and source of competitive advantage (Hamel & Prahalad, 1994).

With ﬁrms that have different sets of goals, the impact of management
pursuing the goals can vary. The goals of ﬁrms with different boundaries may
differ systematically, may be reﬂected in their allocation of resources and

competitive behavior, and could result in different levels of ﬁrm performance. The

25

following research questions attempt to identify organizational goals and the effects
of the goals on resource allocation and competitive strategies in WLIS.
RQ2a: What kinds of organizational goals are most likely to be sought
through local Web-based local information service?
RQ2b: How do ﬁrm's organizational goals affect ﬁrm resources such as i)
quantity of human resources, ii) quality of human resources, iii)

organizational coordination, and iv) innovation resources?

RQ2c: How do ﬁrm's organizational goals affect large-scale entry to compete
with other ﬁrms?

RQ2d: How do ﬁrm's organizational goals affect i) product quality, ii)
market growth performance, iii) market share, and iv) ﬁnancial growth
performance?

Venture Origin

Five venture origins are found in Web-based local information service. The
market of WLIS may have daily, weekly newspaper sites, TV station sites, radio
station sites, and Internet ventures. The Internet venture is deﬁned as a local branch
of a city guide chain such as CitySearch or Digital Cities, independent city guide,
and joint venture between media groups.

Newspapers have a big advantage in being able to easily port over news
content composed for print directly to a Web site. TV stations have their most
valuable content in video form, which when ported to the Web presents serious
bandwidth problems. One successful business model is the 'cluster' strategy where
several weekly newspapers are grouped together on one site. By clustering groups
of weekly newspapers onto shared regional sites, the content is updated more

frequently—either every day or every few days. This schedule means that the Web

26

site gets fresh news almost every day from at least one of the cluster weeklies on the
site (Sullivan, 1998).

Oster (1994) deﬁned a strategic alliance broadly to include any arrangement
in which two or more ﬁrms combine resources outside of the market in order to
accomplish a particular task or set of tasks. In a joint venture, a separate entity is
set up and the contributing ﬁrms each transfer resources to the partnership and
receive ownership rights over the common property. Strategic alliances have been
particularly common in the WLIS industry. For example, TV-newspaper joint
ventures can combine newspaper reporting with video from the TV station ported to
the Web. Research suggests that venture origin is an important source of resource
differences, and a source of differences in strategies and performance (Knight,
1989; Miller & Camp, 1985; Shrader & Simon, 1997). The origins may be a
powerful variable in explaining the differences of resources as well as ﬁrm
performance. Figure 2 displays a diagram of hypotheses and research questions for
the present research.

RQ3a: How does variation in venture origin affect ﬁrm's organizational

goals?

RQ3b: How does variation in venture origin affect ﬁrm's i) quantity of

human resources, ii) quality of human resources, iii) organizational

coordination, and iv) innovation resources?

RQ3c: How does variation in venture origin affect ﬁrm’s i) product quality,

ii) market growth performance, iii) ﬁnancial growth performance, and iv)
market share?

27

 

 

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28

Chapter III

METHODS

Sampling and Data Collection

The ﬁrst step was to identify markets for Web-based local information service
(WLIS). The t0p 100 metropolitan statistical areas (MSAs) were chosen from the
Editor & Publisher Market 1999 Guide. To create a master list of WLISs in the top
100 MSAs, media Web search engines were used, the E & P Online Media Links’,
Ultimate TV‘, and Yahoo5 search engines. Even though the E & P Online Media
search engine includes comprehensive URL information across all types of media, it is
specialized in newspapers and city guides. The E & P Online Media Links were used
to generate the URLs of daily, weekly newspapers and city guides. Television station
URLs were collected from the Ultimate TV search engine and radio station URLs
from the Yahoo search engine. Three search engines allow for a search of an
individual WLIS’s URL based on completing the search ﬁelds: name, city, county, or
state. Each search engine was used as a complementary frame to obtain a complete
list of WLIS population in the top 100 MSA markets. The master list included daily
newspapers, weekly newspapers, local television stations, city-guides, and radio
station sites, which provide local applications such as local news, classiﬁeds, local

entertainment, weather, and local forums. The list incorporated 271 TV stations, 326

 

3 URL: http://emedial .mediainfo.com/emedia/
‘ URL: http://www.ultimatetv.com/tv/us/
5 URL: http://dir.yahoo.com/News_and_Media/Radio/By_Region/U_S__States/

29

radio stations‘, 296 newspapers, and 84 Internet venture, totaled 977 WLISs in the
top 100 MSA markets.7

The highest-ranking managers in WLIS (e. g. general manager, new media
director, online editor) were selected as respondents to the survey for the ﬁrm data.
The e-mail addresses of the managers were collected by searching contact information
on their Web sites. If a personalized e-mail address of the manager was not found on
the Web site, a representative e-mail address was chosen.

A small-scale pilot study was conducted to test the survey instrument and
implementation. Twenty ﬁve sites from smaller markets were selected for the pilot
study. It was conducted the same as the main study procedure. A review and
evaluation of the pilot study suggested a few minor changes in wording for the ﬁnal
survey questionnaire and invitation e-mail letter.

The self-report questionnaire was administered to the highest-ranking manager
sample using Web format during January and February in 2000. A four-wave data
collection procedure for the Web format survey was employed. Each wave procedure
had an interval of two weeks. The ﬁrst step in data collection was sending e-mail
"Response Invitation" letters, which described the study and invited respondents to
participate in the survey on the Web. This included a hyperlink to the survey

questionnaire in the body of the e-mail message. The response invitation allowed

 

6 Many local radio Web sites are operated by the same Web site management team
under one general manager in a local market. In the case of multiple ownership in a
local market, just one site was selected for a master list of WLIS.

7 The total number was extended after a pilot study that revealed a lower response
rate (about 20%) than the researcher expected, to achieve the required case size for
this study.

30

people to either click through to the survey or enter the URL in their web browser
because URL links are not supported by all e-mail software packages. Those
individuals not responding after two weeks were sent a reminder e-mail message with
a hyperlink to the survey questionnaire. The people not responding after the ﬁrst and
second step were sent a third reminder message. For the fourth wave, those
individuals not responding even after the third wave were sent the original response
invitation letter.

The response invitation letters were distributed to 977 WLIS site managers in
the top 100 MSA markets by e-mail. The Web survey questionnaire was located at
Michigan State University Web server (http://www.msu.edu/~nohgheey/issurvey.htm).
Of the 977, 93 8 were returned not deliverable with reasons of unknown destination
address, permanent connection timeout, and quota exceeding. Of the remaining 884
managers, 110 submitted their responses on the Web at ﬁrst wave, 45 at second wave,
20 at third wave, and 12 at fourth wave for a response rate of 21 .2 %.9 Four invalid
responses were excluded in statistical analysis. For the multiple correlation testing,
required case size depends on a number of issues, including the desired power, alpha
level, number of predictors, and expected effect size. According to rules of thumb

(Tabachnick & Fidell, 1996), more than 162 cases are needed for this research with a

 

8 For 121 undelivered e-mails through the third wave, the e-mail addresses were
rechecked on the Web sites. The e-mail address of the next highest-ranking manager
in the same ﬁrm was collected if it was available. Thirty one new addresses were
found and incorporated in the fourth wave. Of the 31, three were returned not
deliverable.

9 The response rates by venture origin were 17.3 % of TV stations, 15.0% of radio
stations, 22.3% of newspapers, and 25% of Internet ventures indicating a good
reprentation of each venture type.

31

total of 14 independent variables to test multiple regression. '0 With 183 valid
respondents, the number of cases is well above the minimum requirement for testing
overall correlation and individual predictors. Market data for 81 MSAs were

collected from the E & P Market 1999 Guide.

Operational definition

Each of the relevant constructs for the present research was measured by
multiple-item scales. Because several original scales vital to this research are
introduced, no previous reliability estimates exist. Therefore coefﬁcient alphas were
calculated as an indicator of internal consistency on the scales. All composite scales
were above the minimum reliability level of .65.11
Competition InterLity

Competition intensity was measured by asking the following question about
ﬁve statements: “At your Web site, how much concern do you have for competition
from other local Web sites?” A seven-point scale, with a range ﬁ'om "strongly
disagree" to "strongly agree", was used. One statement of “Our site overall is very
different from others in the Web service” was found not to contribute to the scale and
reacted in reverse direction of what had been anticipated. As the statement was
dropped out, the competition intensity scale demonstrated a higher internal reliability.
The scale of competition intensity was reduced to four statements. The coefficient

Cronbach alpha for the scale was .74. The overall mean of competition intensity

 

1° For testing individual predictors, only 118 cases are needed.

'1 No standard rules exist for evaluating the magnitude of reliability coeﬁicients.
However, Nunnally (1978)’s guideline suggests that in the early stages of research,
modest reliability in the .50 to .60 is acceptable.

32

scale was 19.295.12

At your Web site, how much concern do you have for competition from other local Web sites?
Please select the number that best describes your approach.

 

Strongly Strongly
Disagree Agree

 

Our budget Is increased as competition develops. 1 2 3 4 5 6 7
‘ Eliminated from the scale.

Quantity of Human Resources

 

The quantity of human resources was operationalized by indexing the number
of full-time equivalent employees in four task areas: editorial, design, technical and
marketing. The quantity was measured by asking the question: "In the following
areas, how many full-time equivalent do you have for your Web site?" Respondents
were asked to consider two half time staffs as one full-time staff for the total number
of employees.

Qu_ality of Human Resources

Respondents were also asked to rate their employees for overall quality with
the following question: "Compared to your top market competitor in your city
market, how do you evaluate the quality of your employees working on the following
areas?" A seven-point response scale was used with a range from "much worse" to

"much better." The standardized item alpha for the quality of human resources scale

 

’2 Group means by venture origin were Internet venture 20.95, daily newspaper 20.91,
television station 19.42, weekly newspaper 19.16, and radio 17.11. The fact that
there is variability across the ﬁve categories, suggests validity of the competition
intensity measure.

33

was .72.

Compared to the top competitor in your city market, how do you evaluate the quality of your
employees working on the following areas? Please select the number that best describes your
Web business unit.

 

 

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Organizationg Coordination

The level of organizational coordination was measured by the following four
items. Respondents were asked to respond to “To the best of your knowledge, how
do you evaluate your organizational routines in the following areas?” Response scale
was seven-point scale with a range from “very poor” to “excellent”. The standardized

item alpha for the scale was .76.

To the best of your knowledge, how do you evaluate your organizational routines in the
following areas?

 

Very Poor Excellent

Coordination between editorial staff and 1 2 3 4 5 6 7

 

 

Innovation Resources
The level of innovation resources was measured by six statements.
Respondents were asked to respond to “The following statements deal with speciﬁc

resources that a business unit may have. To the best of your knowledge, please check

34

the number that represents how much you agree with the following statements.” A
seven-point response scale was used with a range from "strongly disagree" to
"strongly agree". The standardized item alpha for the scale of innovation resources

was .84.

The following statements deal with speciﬁc resources that a business unit may have. To the best
of your knowledge, please select the number that represents how much you agree with the
following statements. Our business unit:

 

Strongly Strongly
Disalee Agree

 

Is very eager to try fresh ideas. _ , 7 1 2 3 4 . 5 6 . 7

 

Is respected by our Industry for successful use of 1 2 3 4 5 6 7
anew Ideas , ,_

 

 

Market Size and Income

Market size was measured by the number of Internet users. Respondents were
asked to provide the estimated percentage of Internet users in their metropolitan
market. Then the estimated percentage was multiplied by the population of the MSA
market. Because more than one person responded from each market, some variation
in responses to the estimates of Internet user penetration was found. In that case, the
mean estimates of the responses at the same market were used. Gross income per
household was used for market income level.

Product Quality

Respondents rated their product quality compared with the top competitor in

their local market on a seven-point response scale ("much worse" to "much better")

for three aspects of product quality: editorial, aesthetic, and ﬁmctional quality. The

35

editorial quality was measured by the following four items: depth of the information,
original content, immediacy of the information, and breadth of the information (or =
.85). Four items were used for the aesthetic quality measure: visual effects, aesthetic
design, vividness of images, and layout (or = .92). The functional quality was
measured by the following four items: easy of use, degree of customization, security
of transaction, and bulletin board ﬁsnction (or = .69). Overall product quality was
operationalized and measured by the sum of the three subscales of product quality

including 12 items (or = .87).

Compared to the top competitor in your city market, how would you rate your service quality in
the following areas?

 

Much

 

Depth of the information

 

 

Market and Financial Growth Performﬂiaa

Respondents were asked to rank their ﬁrm performance on four market and
three ﬁnancial growth performance measures for the previous twelve-month period.
In principle, more objective measures would be preferred. However, the objective

data are not available to the public and no standard measure has been established for

36

ﬁrm performance in the Web-based information service. Moreover, past studies of
business policy and strategy have shown that self-reported ratings by knowledgeable
respondents provide valid measures of ﬁrm performance (Conant, Mokwa, &
Varadarajan, 1990; Dess & Robinson, 1984; Robinson & Prearce, 1988; Venkatramna
& Ramnujam, 1987).

There is no commonly accepted measure for performance, especially in new
ventures. The use of proﬁtability measures such as return of equity to evaluate new
venture may be questioned, because it is affected by owner's salaries and other cost
items that are frequently not disclosed by the venture (Zahra, 1996). Strong
proﬁtability may or may not be an important objective for a new venture (McDougall
& Oviatt, 1996). For example, the development of a new product and brand
identiﬁcation may be a more important performance criterion than proﬁtability in the
initial stage of venture's progress. The multiple measures were used to reﬂect the
multidimensionality of the performance construct (Priem, Rasheed, & Kotulic, 1995)
focusing on ﬁrm’s growth.

The seven-item performance scale gauged managers' perceptions of their
venture's performance. Two evaluations per performance item were used. The ﬁrst
indicates the importance of each performance measure in their ﬁrm. Respondents
rated the importance of performance measures in their business unit on a seven-point
response scale (1= not at all important to 7 = extremely important). The second
indicates the extent managers evaluated their ﬁrm’s success in the seven performance
measures. Respondents were asked to rate their performance success during the last

12 months on a seven-point scale (1= not at all successful to 7: extremely successful).

37

Importance scores were multiplied by their corresponding performance success
scores. Each multiplied score was summated for multiple performance measure. The
market growth performance was measured by the following four items: advertising
sales growth, e-commerce sales growth, brand identiﬁcation, and market share. The
standardized item alpha for the market growth performance scale was .76. The
ﬁnancial growth performance was measured by the following three items: return on
investment, return on equity, and net proﬁt margin. The standardized item alpha for

the ﬁnancial growth performance scale was .97.

How much importance does the top management at your organization place on the following
performance areas?

 

Not at all Extremely
important important

 

Advertising sales growth . 1 2 ‘_ 3 4 5 6 7 f

 

Net proﬁt margin 1 2 3 4 5 6 7

How successful was your Web service in the following performance areas during the last 12
months?

 

Not at all Extremely
Increasing successful successful

 

Net Jroﬁt margL 1 2 3 4 5 6 7

38

Mt Share Index

In addition, respondents were asked to provide the average number of visitors
a day on their Web site. For market share index, the average number of visitors was
divided by the market size deﬁned as the number of Internet user in a speciﬁc market.
Organizational Goals

The organizational goals were measured by the following 22 statements which
comprise of measures for newspaper organizational goals suggested by Demers

(1996), for local television station suggested by Rhee (1971) and original measures. ‘3

The following statements represent organizational goals that a business unit may pursue.
Please indicate how much importance top management at your Web-based service places on
the following beliefs or values by selecting the number that best represents the importance.

 

Not At All Extremely
Important Important

.5
N
(A)
#1
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..................................................................................................................................................................................................................................................................

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'3 This study uses the informant approach to measure organizational goals which asks
employees to deﬁne the goals or rank-order them (Demers, 1996).

39

The adopted measures were partly reworded for the present study.
Respondents were given a series of statements and asked to indicate the extent to
which they agree with each statement on 7-point scales ranging from "not very
important" to "extremely important." Due to the use of the adaptive and original
measures, a conﬁrmatory stage was necessary to establish the reliability of
components in the individual scales (See statistical analysis and results chapter).
Service Age

Service age was measured by asking the following question: "At what date was
your Web site ﬁrst operational as service (after any trial periods)? The Editor &
Publisher MediaLink database also provides the starting date of television station and
newspaper sites. The information from the MediaLink was used for incomplete
responses.

Scale Strategy

Respondents were asked to evaluate the following scale strategy items adapted
from McDougall and Robinson (1990). Respondents were given a series of
statements, which represent the methods by which business may compete and asked to
indicate the extent to which they agree with the following statements on a seven-point
scale ranging from "never emphasized" to "always emphasized." Two items, “
Serving limited or speciﬁc geographic markets” and “Providing a narrow range of
services,” were evaluated to have a weak empirical and construct validity, even
though they were recoded in a reverse direction during the analysis phase. The two

items were deleted from the index and the index reduced to four items.

40

Each of the following items represents different methods by which businesses may compete.
Please select the number that best describes the emphasis your business unit has placed on the
means of competition.

 

 

Never Always

emphasized emphasized
.................... 123 4567
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'Eliminated from the index

Venture Oragia

Venture origin was coded by the classiﬁcation of the E & P's MediaLink
directory (1: TV station, 2: Daily Newspaper, 3: Radio station, 4: Internet Venture, 5:
Weekly Newspaper). Internet venture includes city guides and joint ventures between
media groups. The venture origin of WLIS was also checked by visiting the Web

sites.

Data cleaning for Multivariate Statistics

Prior to statistical analysis, all items and scales were examined through various
SPSS programs for accuracy of data entry, missing values, ﬁt between their
distributions, and the assumptions for multivariate analysis.
Missing Data

Reviewing and examining the pattern of missing data suggested that missing
values scattered randomly through the data matrix. All variables on the data matrix

had missing values on less than ﬁve percent of the cases except ‘User’ variable which

41

is used for “Market Share.” Regression method was used to estimate missing values,
except the “User” variable. For the estimation, other variables were used as
independent variables to write a regression equation for the variable with missing data
serving as dependent variable. Then the missing values were replaced by the
regression estimates for the cases.
For ‘User’ variable, however, there are 30 cases with missing values--more than 5%
of the cases. Since the variable is used for a dependent variable and considered to be
critical to the hypotheses, the 30 cases that failed to provide scores were just deleted
instead of replacing the missing data for statistical analysis. The results of missing
value analysis are displayed in Appendix 1.
Multivariate Normality

Multivariate normality is the assumption that each variable and all linear
combinations of the variables are normally distributed. The multivariate normality of
variables was assessed by both statistical and graphical methods. After screening
continuous variables for multivariate normality, four variables were found to have
substantial positive skewness with nonnormal kurtosis: “Market Size,” “Income,”
“Market Share,” and “Quantity of Human Resources.” With nonnormality of the
variables, a logarithmic transformation was applied to four variables. For the “Market
Size” variable, skewness was reduced from 2.063 to .368 and kurtosis reduced from
4.326 to -.527 by the logarithmic transformation. For the ‘Income’ variable,
skewness was reduced from .995 to .515 and kurtosis reduced from 1.627 to .502 by
the logarithmic transformation. For the ‘Market Share’ variable, skewness was

reduced from 2.856 to -.332 and kurtosis reduced from 9.144 to -.475 after data

42

transformation. For the ‘Quantity of Human Resources’ variable, skewness was
reduced from 2.395 to .285 and kurtosis reduced from 7.377 to -.501. Table 1
displays descriptive statistics of continuous variables used for the present study.

On the other hand, 22 items of the ‘Organizational Goal’ were checked for
multivariate normality before factoring the items.14 The distribution of all the
variables showed substantial negative skewness. A reﬂected logarithmic
transformation was applied to the organizational goal items to improve their
normality. Thus the items with negative skewness were converted to those with
positive skewness prior to logarithmic transformation. Skewness and kurtosis were
substantially reduced after the transformation of data. Table 2 shows descriptive

statistics for organizational goal items.

 

" As long as factor analysis is used descriptively to summarize the relationship in a
large set of observed variables, assumptions regarding the distributions of variables
are not in force. However, even when the statistics are used purely descriptively,
normality of variables enhances the analysis (Tabachinick & Fidel, 1996).

43

 

 

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45

Univariate Outliers

Searching for univariate outliers was performed by Missing Value Analysis and
Explore Analysis (See Appendix 1). The inﬂuence of univariate outliers was reduced
by assigning a raw score on the offending variable that is one unit lager or smaller
than the next most extreme score in the distribution as Tabachicnick and Fidell (1996)
suggested. The search of multivariate outliers was performed by computation of
Mahalanobis distance for each case on regression procedure. Mahalanobis distance is
the distance of a case from the centroid of the remaining cases where the centroid is
the point created by the means of all the variables (Tabchnick & Fidell, 1996).
Because Mahalanobis distance is distributed as a chi square variable with degree of
freedom equal to the number of independent variables, multivariate outliers were

determined by the value of x 2 at the .001 alpha level.

Statistical Analysis

Standard multiple regression was performed to test hypotheses and assess
relationships among variables for research questions. Multiple regression is used to
control possible confounding variables that may inﬂuence the hypothesized
relationship between variables.

The WLISs which responded are not a random sample even though the survey
attempted a census of the top 100 MSAs. Therefore, tests of substantive signiﬁcance
were used to interpret the regression analysis and test the hypotheses. Even though
statistical signiﬁcance was reported in each result table, it was not used for purpose of

hypothesis testing and inference because inferential statistics assume random sampling

46

with a perfect response rate (Babbie, 1992). The test for hypothesis support was set
at a semipartial correlation of .20 or higher.15 Semipartial correlation expresses the
unique contribution of the independent variable to the total variance of the dependent
variable. On the standard multiple regression, the value of semipartial correlation,
when squared, indicates the amount by which R would be reduced if an independent
variable were omitted from the regression equation. This criterion of the .20
semipartial correlation indicates that 4% of the variance in the dependent variable is
exclusively shared with an independent variable.

For research question 1, squared semipartial correlations on each regression
were used to determine the degree of association. As two values of squared
semipartial correlation showed a difference of more than .02 (2%), the degree of
association for each variable was interpreted to be different.

For research question 2, a principal components factor analysis was performed
to reduce a large number of organizational goal items and identify main organizational
goals because none of the variables was designed as dependent, and no grouping of
observations was assumed (Rencher, 1995). The principal components analysis uses
units (1.00) in the principal diagonal of the correlation matrix. Oblique rotation was
judged to be more reasonable than orthogonal rotation because it seems more likely
that factors generated from organizational goal items are correlated than they are not.
The factor correlation matrix showed that two component correlations exceed .32,

indicating there is enough variance to warrant oblique rotations.

 

1’ A higher standard would increase the chance of type II error, which should be
avoided at this stage of research because this is an under-researched topic (See Shaver

47

Before factoring organizational goal items, screening mean scores of the items
was performed to ﬁnd inappropriate items for the organizational goal. One item
“Supporting local community leaders” was found to be inappropriate because the
mean score of the item was less than 4.0, a center point of response scale. In other
words, the item was least used for organizational goal. Then each factor was
obtained using oblique rotation with a cut-off eigenvalue of 1.0. The criterion for a
variable to remain in the factor analysis was a loading of at least .50 (Comrey & Lee,
1992)

A factor with less than 2 items was removed to produce reliable scales. A
principal components analysis of the 21 items resulted in four eigenvalues greater than
1.00. Four-factor solution contained a factor with just two items. The two items
were removed for next step factoring. On the three-factor solution, one item
“Keeping the service growing and expanding” was loaded less than .50. Another item
“Being the best” was multi-loaded more than .40. Therefore two items were deleted
for the ﬁnal solution. The present study reports the results of the oblique rotated
three-factor solution with 17 organizational goal items. To assess the internal
consistency of multiple-item measures, the Cronbach coefﬁcient alpha was estimated.
The standardized item alphas for three factors were above .80 (See Table 45). Each
factor scores was estimated by regression methods for further analysis.

Finally, research question 3 was assessed by performing analysis of covariance
(ANCOVA) to examine the main effects of venture origin on a ﬁrm’s resource

commitment, strategies, goals, and performances controlling for possible confounding

 

& Lacy, 1999).

48

variables. Differences between ﬁrms on covariates such as competition intensity and
service age are removed so that the only differences that remain are related to the
effects of venture origin on ANCOVA. Method 3, sequential approach, for survey
data and unequal cell size was used for the ANCOVA. Strength of association is
usually assessed as the percentage of variance in the dependent variables that is
associated with the independent variable. A partial eta-squared was used to test the
strength of association for each effect. The eta-squared statistic describes the
proportion of total variability attributable to a factor. The test for hypothesis support
was set at a partial eta-squared of .04 or higher. Homogeneity of variance was

assessed using Levene’s test of equality of error variance.

49

 

Chapter IV

RESULTS

Descriptives

Market and WLIS Proﬁle

The Web-based local information services (WLISs) responded to the survey
cover 81 Metropolitan markets with a population range of 9 million to 500
thousand. Household income was ranged from average 41,200 dollars to 90,900
dollars. Income per household, estimated number of Internet user, and frequency of
response are seen in Appendix 2.

As for venture origin of the WLISs, 47 were television station sites, 43 daily
newspaper sites, 49 radio station sites, 23 weekly newspaper sites, and 21 Internet
venture sites. The average service age was about 36 months (See Table 3). They

had average 20,250 visitors a day on their Web sites.

Table 3 Proﬁle: Service Age

 

Frequency Valid Percent Cumulative Percent

 

1994 12 6.7 6.7
1995 36 20.0 26.7
1996 45 25.0 51.7
1997 30 16.7 68.3
1998 28 15.6 83.9
1999 28 15.6 99.4
2000 1 .6 100.0
Total 180 100.0

 

Respondents also reported to have at least average 7 dedicated staffs to their
Web information service. As displayed in Table 4, the WLISs employed average

three persons for editorial task, more than one person for design and technical task,

50

and about two persons for marketing. As for quality level of human resources,
editorial staff was ranked ﬁrst (M = 5.6) and design staff received the second
highest rating (M = 5.5) by respondents. The quality of human resources is

displayed in Table 5.

Table 4 Proﬁle: Number of Employees

 

N Min. Max. Mean 8.0.
Editorial Staff 177 .00 20.00 2.7839 3.7346
Design Staff 177 .00 15.00 1 .4319 1 .9569
Technical Staff 177 .00 15.00 1.4090 2.0813
Marketing Staff 177 .00 20.00 1.8828 2.8719

 

Table 5 Quality of Human Resources

 

N Min. Max. Mean SD.
Editorial Staff 181 1.00 7.00 5.6243 1.4307
Design Staff 181 1.00 7.00 5.5028 1.5078
Technical Staff 182 1.00 7.00 5.3571 1.4485
Marketing Staff 181 1.00 7.00 4.9558 1.7056
* 1= Very Poor 7= Excellent

More than 40% of the WLISs were reported to increase their employees over
50% during last two years. Further 21 % employed their staffs more than 100%.
Only 4.6% reduced their employees for the WLIS. Table 6 shows the pattern of

change in total employment.

Table 6 Change in total employment

 

Frequency Percent Cum Percen

 

More than 50% Decrease 3 1.7 1.
25% Decrease 5 2.9 4.
Unchanged 66 37.7 42.
25% Increased 29 16.6 58.
50% Increased 27 15.4 74.
75% Increased 6 3.4 77.
More than 100% Increased 39 22.3 100.
Total 175 100.0

 

51

Product Quality and Performance

The mean values of product quality assessed by the respondents are reported
in Table 7. Editorial quality items were evaluated higher than ﬁmctional quality

items. Table 7 shows a rank order of the product quality items.

Table 7 Mean of Product Quality Items

 

 

N Min Max Mean SD.
Original content 182 1 .00 7.00 5.5824 1 .5382
Immediacy of the information 182 1 .00 7.00 5.4780 1 .51 13
Depth of the information 182 1.00 7.00 5.3736 1.5917
Breadth of the information 182 1.00 7.00 5.3242 1.4752
Layout 182 1 .00 7.00 5.1923 1 .4722
Aesthetic design 182 1.00 7.00 5.1813 1.5142
\frvidness of images 182 1.00 7.00 5.0604 1.5562
Ease of searching 182 1 .00 7.00 4.8516 1 .6933
Visual effects 182 1.00 7.00 4.7143 1 .6134
Security of transaction 182 1.00 7.00 4.2143 2.0174
Degree of customization 182 1 .00 7.00 4.1484 1.9340
Bulletin board function 182 1 .00 7.00 3.4945 2.0991

 

As for the importance of performance measures, brand identiﬁcation was
ranked ﬁrst (mean of 6. l4) and advertising sales growth received the second highest
rating (mean of 5.96). E-commerce sales growth (mean of 4.58) and return on
equity (mean of 5.09) were rated lowest. Except e-commerce sales growth, market
performance was perceived to be more important than ﬁnancial performance in the
current stage of WLIS industry. On the other hand the WLISs responded to the
survey were evaluated to make a success in brand identiﬁcation (mean of 5.16) and
market share (mean of 4.86). However all measures of ﬁnancial performance were
below a central point (4.00). This indicates that they made no ﬁnancial success in
their WLIS. Table 8 and 9 show mean importance and success scores in seven

performance measures.

52

Table 8 Mean Importance Scores in Seven Performance Measures

 

 

N Min Max Mean SD.

Brand identiﬁcation 183 1 .00 7.00 6.1421 1 .2005
Advertising sales growth 182 1.00 7.00 5.9615 1.5710
Market share 182 1.00 7.00 5.9121 1.4539
Net profit margin 182 1 .00 7.00 5.4396 1.6468
Return on investment 183 1 .00 7.00 5.3169 1 .7691
Return on equity 182 1.00 7.00 5.0934 1.8107

182 1 .00 7 .00 4.5769 1 .9587

E-commerce sales growth

Table 9 Mean Success Scores in Seven Growth Performance Measures

 

 

Increasing N Min Max Mean SD.

Brand identification 181 1.00 7.00 5.1602 1.4764
Market share 179 1 .00 7.00 4.8547 1.5904
Advertising sales growth 179 1.00 7.00 4.2961 1.9736
Return on investment 179 1.00 7.00 3.8603 1.8383
Net proﬁt margin 178 1.00 7.00 3.7978 1.8937
Return on equity 178 1.00 7.00 3.6798 1.8392

176 1.00 7.00 3.0511 1.8586

E-commerce sales growth

53

Financial Commitment

All variables entered the equation without violating the default value for
tolerance. Further, as Table 10 displays, the highest correlation among the
independent variables, between Market Size (log) and Income (log), was .50.
Therefore, no evidence of multicollinearity and singularity among the independent
variables was found. None of cases had the Mahalanobis distance value in excess of

14.52. This shows no multivariate outliers in the solution.

Table 10 Pearson Product Moment Correlations of Independent Variables for Financial
Commitment Hypotheses

 

Competition Market Size Income Service age

 

Intensity (log) (log)
Competition Intensity 1.000 .005 -.046 .122
Market Size (log) .005 1 .000 .503 -.146
Income (log) -.046 .503 1.000 .085
Service age .122 -.146 .085 1.000

 

Hla: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to larger quantity of human
resources controlling for market size, income, and service age.

The hypothesis was supported, as the semipartial correlation for the
relationship was .303 in the direction hypothesized, which exceeds the .200 cut-off
point (B = .306, SE = .005). This indicates that a ﬁrm’s competition intensity
accounts for about 9.2 % of variance in quantity of human resources (log) even
when market size and service age are constant. The regression result suggests that
as a ﬁrm’s competition intensity becomes stronger, the ﬁrm tends to employ more
people. Table 11 displays the unstandardized regression coefﬁcients (B), the

standardized regression coefficients ([3), zero-order correlations (r), the semipartial

correlations (sri) and R2.

54

Table 11 Multiple Regression of Competition Intensity on Quantity of Human Resources (log)

 

 

 

 

 

B S. E B t Sig. l' pr. sri

(Constant) .000 2.237 .030 .976
Market Size (log) .230 .079 .235 2.892 .004 .192 .212 .197
Income (log) -.251 .513 -.039 -.489 .625 .079 -.037 -.033
Service age .000 .001 .169 2.376 .018 .168 .175 .162
Competition intensity .000 .005 .306 4.440 .000 .330 .316 .303
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.414 .171 .153 .3453

 

To examine the pattern of association, the quantity of four task areas was
entered into regression equation. The degree of association in each task area also
showed supporting the hypothesis. In particular, the semipartial correlation for the

quantity of marketing staffs (B = .329, sri = .305) was found to be stronger than the

quantity of editorial (B = .205, sr- = .203) and technical staffs (B = .236, sri = .233)
in the relationship of WLIS ﬁrm’s competition intensity. The semipartial

correlation for the quantity of design staff was .272 (B = .275). In other words, with
a stronger competition, WLIS ﬁrms appear to employ more marketing staff rather
than editorial and technical staff. Table 11-1, Table “-2, Table 11-3, and Table 11-
4 show standardized regression coefﬁcients and semipartial correlations of the
quantity of each human resource area on a ﬁrm’s competition intensity and other

covariates.

Table 11-1 Multiple Regression of Competition Intensity on Quantity of Editorial Staff (log)

 

 

 

 

 

Coefﬁcients

B SE B t Sig. r pr. sri
(Constant) -.390 2.005 -.194 .846
Market Size (log) .154 .071 .181 2.161 .032 .142 .160 .152
Income (log) .000 .460 -.017 -.208 .835 .082 -.016 -.015
Service age .000 .001 .215 2.942 .004 .212 .215 .207
Competition intensity .000 .004 .205 2.884.004 .233 .211 .203
Model Summary
R R‘ Adjusted R7 S. E of the Estimate
.343 .118 .098 .3095

 

55

Table 11-2 Multiple Regression of Competition Intensity on Quantity of Design Staff (log)

 

 

 

 

 

Coefﬁcients
B S. E [3 t Sig. r pri sr.

(Constant) -.694 1.318 -.527 .599
Market Size (log) .149 .047 .258 3.175 .002 .229 .232 .217
Income (log) .000 .302 -.009 -.115 .909 .123 -.009 -.008
Service age .000 .001 .175 2.474 .014 .171 .182 .169
Competition intensity .000 .003 .275 3.983 .000 .298 .286 .272
Model Summary

R R" Adjusted R2 S. Eof the Estimate

.412 .170 .151 .2035

 

Table 11-3 Multiple Regression of Competition Intensity on Quantity of Technical Staff (log)

 

 

 

 

 

Coefﬁcients

B S. E B t Sig. r pr. sr.
(Constant) -1.624 1.558 -1.043 .298
Market Size (log) .148 .055 .222 2.669 .008 .226 .196 .187
Income (log) .161 .357 .037 .452 .652 .148 .034 .032
Serviceage .000 .001 .110 1.514 .132 .110 .113 .106
Competition intensity .000 .003 .236 3.331 .001 .249 .242 .233
Model Summary
R R1 Adjusted R‘ S. Eof the Estimate
.356 .127 .107 .2405

 

Table 11-4 Multiple Regression of Competition Intensity on Quantity of Marketing Staff (log)

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sr.

(Constant) 1.321 1.739 .760 .448
Market Size (log) .110 .062 .147 1.780 .077 .091 .132 .124
Income (log) -.425 .399 -.088 -1.065 .288 -.020 -.080 -.O74
Service age .000 .001 .097 1.349 .179 .109 .101 .094
Competition intensity .000 .004 .329 4.671 .000 .345 .330 .325
Model Summary

R R1 Adjusted R1 S. Eof the Estimate

.373 .139 .120 .2684

 

56

Hlb: For Web-based local information service, a ﬁrm’s competition intensity

with other ﬁrms will be positively related to higher quality of human

resources controlling for market size, income, and service age.

The hypothesis was not supported, as the semipartial correlation equaled .173
(B = .175). Even though the semipartial correlation for the relationship did not
exceed the cut-off point of .200, the result explains that about 3.1 % of variance in
quality of human resources (log) is associated with a ﬁrm’s competition intensity in
the direction hypothesized. Table 12 displays unstandardized regression

coefficients (B), the standardized regression coefﬁcients (B), zero-order correlations

(r), the semipartial correlations (sri) and R2.

Table 12 Multiple Regression of Competition Intensity on Quality of Human Resources

 

 

 

 

 

B S.E ﬁ t Sig. r prl sr,

(Constant) -21.456 27.518 -.780 .437
Market Size (log) -2.137 .977 -.185 -2.186 .030 -.134 -.162 -.156
Income (log) 10.756 6.307 .143 1.705 .090 .055 .127 .121
Service age .000 .018 .153 2.070 .040 .214 .153 .147
Competition intensity .148 .061 .175 2.435 .016 .186 .180 .173
Model Summary

R R1 Adjusted R‘ S. E of the Estimate

.313 .098 .078 4.2478

 

H2: For Web-based local information service, a ﬁrm’s competition intensity

with other ﬁrms will be positively related to larger scale of entry into market

controlling for market size, income, and service age.

The hypothesis was supported, as the semipartial correlation for the
relationship was .290 in the direction hypothesized, which exceeds the .200 cut-off
point (B = .293). This shows that 8.4 % of variance in large-scale strategy is

associated with a ﬁrm’s competition intensity. Thus as a ﬁrm’s competition

intensity becomes stronger, WLIS ﬁrms are likely to conduct larger scale strategies

57

in advertising, specialty services, customer service, and entry. Information from

this analysis is summarized in Table 13.

Table 13 Multiple Regression of Competition Intensity on Scale Strategy

 

 

 

 

 

B S. E B t Sig. r pri sri

(Constant) 46.966 26.371 1.781 .077
Market Size (log) .000 .937 .007 .081 .935 -.019 .006 .006
Income (log) -6.708 6.044 -.093 -1.110 .269 -.114 -.083 -.079
Service age .000 .017 -.133 -1.806 .073 -.106 -.134 -.128
Competition intensity .239 .058 .293 4.095 .000 .281 .293 .290
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.327 .107 .087 4.0707

 

Competition and Performance
H3: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to higher quality of product
controlling for market size, income, and service age.
The hypothesis was not supported, as the semipartial correlation equaled .188
(B = .190). Even though the semipartial correlation for the relationship did not
exceed the cut-off point, the result was consistent with the hypothesized direction.
About 3.5 % of variance in product quality was found to be associated with a ﬁrm’s
competition intensity in the direction hypothesized. Table 14 displays the

unstandardized regression coefﬁcients (B), the standardized regression coefﬁcients

(B), zero-order correlations (r), the semipartial correlations (sri) and R2.

58

Table 14 Multiple Regression of Competition Intensity on Product Quality

 

 

 

 

 

Coefﬁcients

B S. E B t Sig. r prl 5r.
(Constant) 5.865 81.638 .072 .943
Market Size (log) -1.663 2.899 -.049 -.573 .567 -.043 -.043 -.042
Income (log) 10.547 18.712 .048 .564 .574 .025 .042 .041
Service age .000 .053 .126 1.677 .095 .161 .125 .122
Competition intensity .467 .181 .190 2.588 .010 .203 .190 .188
Model Summary
R R’ Adjusted R‘ S. Eat the Estimate
.249 .062 .041 12.6021

 

H4a: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to stronger emphasis on market
performance within its business unit controlling for market size, income, and
service age.

The hypothesis was supported as the semipartial correlation for the
relationship between ﬁrm’s competition intensity and emphasis on market
performance was .296 (B = .299). The result was consistent with the hypothesized
direction. This indicates that 8.8 % of variance in emphasis on market performance
is associated with a ﬁrm’s competition intensity. A ﬁrm’s competition intensity

with other ﬁrms is positively related to a stronger emphasis on market performance.

Table 15 displays the result of the regression analysis.

Table 15 Multiple Regression of Competition Intensity on Emphasis on Market Performance

 

 

 

 

 

B S. E B t Sig. r pr. sr.

(Constant) 42.949 28.295 1.518 .131
Market Size (log) .586 1.005 .049 .583 .561 .030 .044 .041
Income (log) -5.819 6.486 -.075 -.897 .371 -.075 -.067 -.064
Service age .000 .018 -.121 -1.637 .103 -.098 —.122 -.116
Competition intensity .261 .063 .299 4.172 .000 .288 .298 .296
Model Summary

R R1 Adjusted R1 S. Eof the Estimate

.324 .105 .085 4.3678

 

59

H4b: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to higher level of market growth
performance controlling for market size, income, and service age.

The hypothesis was strongly supported, as the semipartial correlation for the
relationship was .333 in the direction hypothesized, which exceeds the .200 cut-off
point (B = .336). This indicates that 11.1 % of variance in market growth
performance is associated with a ﬁrm’s competition intensity. A ﬁrm’s competition
intensity with other competitors considerably contributed to regression. Thus as a
firm’s competition intensity becomes stronger, the ﬁrrn’s market growth

performance is likely to be better. Table 16 displays the regression result of market

growth performance on competition intensity and other covariates.

Table 16 Multiple Regression of Competition Intensity on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. I‘ pri sr.

(Constant) 448.918 236.429 1.899 .059
Market Size (log) 14.551 8.397 .142 1.733.085 .053 .129 .120
Income (log) -102.115 54.192 -.154 -1.884 .061 -.090 -.140 -.130
Service age .193 .153 .090 1.258 .210 .097 .094 .087
Competition intensity 2.515 .523 .336 4.812.000 .355 .339 .333
Model Summary

R R1 Adjusted R2 S. E of the Estimate

.387 .150 .131 36.4964

 

H4c: For Web-based local information service, a ﬁrm’s competition intensity

with other ﬁrms will be positively related to higher level of ﬁnancial growth

performance controlling for market size, income, and service age.

The semipartial correlation between a ﬁrm’s competition intensity and
ﬁnancial performance was .227 in the direction hypothesized (B = .230). Therefore,

the hypothesis was supported. In other words, about 5.5% of variance in ﬁnancial

growth performance is associated with a ﬁrm’s competition intensity. Table 17

60

displays the unstandardized regression coefﬁcients (B), the standardized regression

coefﬁcients (B), zero-order correlations (r), the semipartial correlations (sri) and R2.

Table 17 Multiple Regression of Competition Intensity on Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) 158.464 246.151 .64 .521
Market Size (log) 16.246 8.742 .159 1.85 .065 .127 .138 .134
Income (log) 46.823 56.421 -.071 -.83 .408 -.003 -.062 -.060
Service age .000 .159 -.O16 -.21 .831 -.017 -.016 -.015
Competition intensity 1.714 .544 .230 3.15 .002 .232 .230 .227
Model Summary

R R1 Adjusted R1 S. E of the Estimate

.272 .074 .053 37.9972

 

H4d: For Web-based local information service, a ﬁrm’s competition intensity
with other ﬁrms will be positively related to higher level of market share
controlling for market size, income, and service age.

The hypothesis was supported, as the semipartial correlation for the
relationship was .207 in the direction hypothesized, which exceeds the .200 cut—off
point (B = .210). This indicates that 4.3 % of variance in market share (log) is
associated with individual ﬁrm’s competition intensity. A ﬁrm’s competition

intensity with other ﬁrms appears positively related to higher level of market share

(log). Table 18 displays the result of multiple regression analysis.

Table 18 Multiple Regression of Competition Intensity on Market Share

 

 

 

 

 

B S. E B t Sig. r pri sn

(Constant) 1.345 4.644 .290 .772
Market Size (log) -.487 .175 -.239 -2.779 .006 -.268 -.223 -.203
Income (log) .000 1.065 .007 .084 .934 -.095 .007 .006
Service age .000 .003 .275 3.606.000 .341 .284 .264
Competition intensity .000 .011 .210 2.823.005 .242 .226 .207
Model Summary

R RZ Adjusted R‘ S. Eof the Estimate

.456 .208 .186 .6919

 

61

Firm Resources

Human Resources

As Table 19 displays, the highest correlation among the independent
variables, between ‘Organizational Resources’ and ‘Innovation Resources’, was
.617. ‘Quality of human resources’ was high correlated with ‘Organizational
Resources’ and ‘Innovation Resources’. The inclusion of high correlated variables
in the same analysis actually weaken an analysis because they inﬂate the size of
error terms. The resource variables, however, are not redundant because they reﬂect
different measures in four aspects in ﬁrm resources. Further, the resource variables
are not using their subscale. Therefore, to resolve doubts about possible
multicollinearity among the resource variables, each resource independent variable
was separately entered into regression equation with a ﬁrm’s competition intensity
and other four covariates. Mahalanobis distances showed no multivariate outliers in
the solution for the hypothesis 5 examining the effects of the quantity of human
resources. For the hypothesis 6, Mahalanobis distances also showed no
multivariate outliers in the solution with the quality of human resources. None of
cases shows the Mahalanobis distance score in excess of 16.31. The variable to case
ratio for dependent variables was 1 to 36.6 exceeding the minimum ratio for

multiple regression.

62

0802-00 0.6. .0.0 05 .0 28500.0 0. 00002.00 ...

 

 

 

000.. 0.0. .03. ~00. 0.0.- .000. 000.- 000. 0020000.. 00:05:...
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0000200000

00005000.: 0200001 :50 00. 00.00_._m> 0000000005 .0 000050.000 .5822 8:090 0000000.

2. 032.

63

H5a: The quantity of human resources will be positively related to higher
quality of product controlling for ﬁrrn’s competition intensity, market size,
income, and service age.

The hypothesis was not supported, as the semipartial correlation for the
relationship failed to prove substantially signiﬁcant (B = .145, sr; = .132). Even if
the relationship was positive in the direction hypothesized, only 1. 7 % of variance
in product quality is associated with the quantity of human resources (log) after
controlling for ﬁrms’ competition intensity and other covariates. The quantity of

human resources was not related to product quality. Table 20 displays the result of

multiple regression of product quality.

Table 20 Multiple Regression of Quantity of Human Resources on Product Quality

 

 

Coefﬁcients

B S. E B t Sig. r pr. sri
(Constant) 5.532 81.10 .068 .946
Market size (log) -2.803 2.94 -.083 -.951 .343 -.043 -.071 -.069
Income (log) 11.791 18.60 .054 .634 .527 .025 .048 .046
Competition intensity .358 .18 .146 1.895 .060 .203 .141 .137
Service age .000 .05 .102 1.341 .182 .161 .100 .097

Quantity of human resources (log) 4.963 2.71 .145 1.826 .070 .198 .136 .132
Model Summary

R R1 Adjusted R‘ S. E of the Estimate

.282 .080 .054 12.5202

 

 

 

H5b: The quantity of human resources will be positively related to higher

level of market growth performance controlling for ﬁrm’s competition

intensity, market size, income, and service age.

The hypothesis was supported, as the relationship between the quantity of
human resources (log) and market growth performance was found to be positive (B
= .314, sri = .286). This result explains that the quantity of human resources (log)

accounts for about 8.2% of variance in market growth performance even after

controlling for the ﬁrms’ competition intensity and other covariates. Table 21

64

displays the unstandardized regression coefficients (B), the standardized regression

coefﬁcients (B), zero-order correlations (r), the semipartial correlations (sri) and R2.

Table 21 Multiple Regression of Quantity of Human Resources on Market Growth Performance

 

 

Coefﬁcients

B S. E B t Sig. r pr. sri
(Constant) 446.723 225.445 1.982 .049
Market size (log) 7.033 8.193 .069 .858 .392 .053 .064 .057
Income (log) -93.910 51.709 -.142 -1.816 .071 -.090 -.135 -.120
Competition intensity 1.797 .525 .240 3.420 .001 .355 .249 .225
Service age .000 .148 .037 .539 .591 .097 .040 .036

Quantity of human resources (log) 32.727 7.554 .314 4.332.000 .401 .310 .286
Model Summary

R R2 Adjusted R1 S. Eof the Estimate

.481 .231 .210 34.8008

 

To examine the pattern of association, each quantity in editorial, design,
technical, and marketing staff was entered into regression equation. The quantity of
editorial (B = .274, sr; = .257), design (B = .270, sr; = .246), and marketing staff (B =
.289, sri = .268) were positively related to higher level of market growth
performance except the quantity of technical staff (B = .191, sr; = .179). There
seems to be no difference in the degree of association across three task areas. Table
21-1, Table 21-2, Table 21-3, and Table 21-4 summarize the results of each multiple

regression analysis with regression coefﬁcients and model summary.

Table 21-1 Multiple Regression of Quantity of Editorial Staff on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S.E B t Sig. r pr. 80

(Constant) 461.740 227.702 2.028 .044

Market size (log) 9.487 8.192 .093 1.158 .248 .053 .087 .077
Income (log) -98.962 52.193 -.149 -1.896 .060 -.090 -.141 -.126
Competition intensity 2.095 .515 .280 4.067.000 .355 .292 .271
Service age .000 .151 .031 .443 .658 .097 .033 .030
Quantity of editorial staff (log) 32.907 8.512 .274 3.866.000 .347 .279 .257
Model Summary

R R‘ Adjusted R‘ S. Eof the Estimate

.465 .216 .194 35.1455

 

65

Table 21 -2 Multiple Regression of Quantity of Design Staff on Market Growth Performance

 

 

Coefﬁcients

B S. E B t Sig. r pr, sr.
(Constant) 482.174 228.658 2.109 .036
Market size (log) 7.429 8.341 .073 .891 .374 .053 .067 .059
Income (log) -100.455 52.372 -.151 -1.918 .057 -.090 -.143 -.128
Competition intensity 1.959 .527 .262 3.716 .000 .355 .269 .248
Service age .000 .151 .043 .608 .544 .097 .046 .041
Quantity of design staff (Igg) 47.906 12.990 .270 3.688 .000 .354 .267 .246

 

 

Model Summaer
R R1 Adjusted R‘ S. E of the Estimate
.459 .210 .188 35.2694

 

Table 21-3 Multiple Regression of Quantity of Technical Staff on Market Growth Performance

 

 

Coefﬁcients

B S.E B t Sig. r pri sn
(Constant) 496.687 233.312 2.129 .035
Market size (log) 10.208 8.425 .100 1.212.227 .053 .091 .082
Income (log) -106.856 53.346 -.161 -2.003 .047 -.090 -.149 -.136
Competition intensity 2.178 .530 .291 4.108 .000 .355 .295 .279
Service age .148 .152 .069 .975 .331 .097 .073 .066

Quantity of technical staff (log) 29.407 11.192 .191 2.627 .009 .270 .194 .179
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.426 .182 .159 35.9059

 

Table 21-4 Multiple Regression of Quantity of Marketing Staff on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr, sri

(Constant) 396.679 227.221 1.746 .083
Market size (log) 10.206 8.128 .100 1.256.211 .053 .094 .083
Income (log) -85.330 52.163 -.129 -1.636 .104 -.090 -.122 -.108
Competition intensity 1.805 .531 .241 3.397.001 .355 .247 .225
Service age .133 .148 .062 .898 .370 .097 .067 .060
Quantity of marketing staff (lgg) 39.532 9.778 .289 4.043.000 .391 .291 .268
Model Summary

R R“ Adjusted R‘ S. Eof the Estimate

.471 .222 .200 35.0183

 

 

66

H5c: The quantity of human resources will be positively related to higher

level of ﬁnancial growth performance controlling for ﬁrm’s competition

intensity, market size, income, and service age.

The hypothesis was not supported, as the semipartial correlation only
equaled .109, which did not exceed the cut-off point. As can be seen in the Table
22, although the relationship was positive (B = .120), the quantity of human

resources (log) does not seem to be substantially related to ﬁnancial growth

performance.

Table 22 Multiple Regression of Quantity of Human Resources on Financial Growth Performance

 

 

Coefﬁcients

B S. E B t Sig. r pr, 5r.
(Constant) 157.625 245.245 .643 .521
Marketsize (log) 13.371 8.912 .131 1.500 .135 .127 .112 .108
Income (log) -43.686 56.251 -.066 -.777 .438 -.003 -.058 -.056
Competition intensity 1.440 .571 .193 2.519 .013 .232 .186 .181
Service age .000 .161 -.036 -.478 .633 -.017 -.036 -.034

Quantityof human resource (log) 12.512 8.218 .120 1.523 .130 .198 .114 .109
Model Summary

R R1 Adjusted R‘ S. Eof the Estimate

.293 .086 .060 37.8573

 

H5d: The quantity of human resources will be positively related to higher
level of market share controlling for ﬁrm’s competition intensity, market
size, income, and service age.

The hypothesis was strongly supported, as the semipartial correlation for the
relationship was .555 in the direction hypothesized (B = .612). This indicates that
30.8 % of variance in market share is associated with the quantity of human
resources (log). The quantity of human resources was positively related to higher

level of market share even after controlling for the ﬁrms’ competition intensity and

other covariates. Table 23 displays the result of multiple regression analysis.

67

Table 23 Multiple Regression of Quantity of Human resources on Market Share

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr, sr.

(Constant) .280 3.64 .077 .939
Market size (log) -.763 .14 -.375 -5.434 .000 -.268 -.409 -.312
Income (log) .591 .83 .047 .706 .481 -.095 .058 .041
Competition intensity .000 .00 .005 .079 .937 .242 .007 .005
Service age .000 .00 .193 3.199 .002 .341 .255 .184
Quantity of human resource (lgg) 1.266 .13 .612 9.662.000 .576 .623 .555
Model Summary

R R1 Adjusted R‘ S. E of the Estimate

.718 .515 .499 .5429

 

H6a: The quality of human resources will be positively related to higher
quality of product controlling for ﬁrm’s competition intensity, market size,
income, and service age.

The semipartial correlation between ﬁrm’s quality of human resources and
product quality, as summarized in Table 24, was .359 in the direction hypothesized
(B = .378). Therefore, the hypothesis was strongly supported. This result indicates
that a ﬁrm’s quality of human resources accounts for about 12.7 % of variance in
product quality even after controlling for the ﬁrm’s competition intensity. As the

quality level of human resources in the WLIS ﬁrms becomes higher, product quality

is likely to be higher.

Table 24 Multiple Regression of Quality of Human Resources on Product Quality

 

 

 

 

 

_C_oefﬁcients

... B S.E B t Sig. r Pit sr,
(Constant) 29.479 76.156 .387 .699

Market size (log) .689 2.736 .021 .252 .801 -.043 .019 .017
Income (log) -1.291 17.568 -.006 -.074 .941 .025 -.006 -.005
Competition Intensity .304 .171 .124 1.779.077 .203 .133 .120
Service age .000 .050 .069 .964 .336 .161 .072 .065
_Q_uality of human resources 1.101 .207 .378 5.315.000 .413 .371 .359
Mgdel Summary

j R1 Adjusted R2 S. E of the Estimate

A37 .191 .168 11.7358

 

68

To examine the strength of association on the quality of editorial, design, and
technical staff, each semipartial correlation were computed. As displayed in Table
24-1, Table 24-2 and Table 24-3, the quality of design staff (B = .372, sri = .363)
had a stronger association with product quality than the quality of editorial staff (B

= .292, sr; = .279) and technical staff (B = .320, sri = .314).

Table 24-1 Multiple Regression of Quality of Editorial Staff on Product Quality

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r prI Sn

(Constant) 40.074 78.856 .508 .612
Market size (log) -.346 2.804 -.010 -.124 .902 -.043 -.009 -.009
Income (log) -.724 18.187 -.003 -.040 .968 .025 -.003 -.003
Competition Intensity .376 .175 .153 2.150.033 .203 .160 .150
Service age .000 .052 .075 1.021 .309 .161 .077 .071
Quality of editorial staff 2.638 .659 .292 4.006.000 .331 .288 .279
Model Summary

R R1 Adjusted R7 S. Eof the Estimate

.374 .140 .116 12.1010

 

Table 24-2 Multiple Regression of Quality of Design Staff on Product Quality

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pri sr,

(Constant) 31.873 76.037 .419 .676
Market size (log) -.236 2.708 -.007 -.087 .931 -.043 -.007 -.006
Income (log) .381 17.495 .002 .022 .983 .025 .002 .001
Competition Intensity .337 .170 .137 1.987 .048 .203 .148 .134
Service age .000 .049 .095 1.348 .179 .161 .101 .091
Quality of design staff 3.167 .588 .372 5.387 .000 .405 .375 .363
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.441 .194 .172 11.7138

 

69

Table 24-3 Multiple Regression of Product Quality on Quality of Technical Staff

 

 

 

 

 

Coefﬁcients

B S. E B t Sig. r pr. sri
(Constant) 2.272 77.441 .029 .977
Market size (log) 7.817E-02 2.777 .002 .028 .978 -.043 .002 .002
Income (log) 624817.774 .029 .352 .726 .025 .026 .024
Competition Intensity .436 .171 .177 2.545 .012 .203 .188 .175
Service age 6.601E-02 .050 .094 1.310.192 .161 .098 .090
Quality of technical staff 2.853 .625 .320 4.565.000 .341 .325 .314
Model Summary
R R‘ Adjusted R‘ S. E of the Estimate
.401 .161 .137 11.9536

 

H6b: The quality of human resources will be positively related to higher

level of market growth performance controlling for ﬁrm’s competition

intensity, market size, income, and service age.

The hypothesis was supported, as the semipartial correlation for the
relationship was .277 in the direction hypothesized, which exceeds the .200 cut-off
point (B = .291). About 7.7 % of variance in market growth performance was found
to be associated with the quality of human resources. This indicates that as the
quality of human resources becomes higher, market growth performance is better
even when the ﬁrms’ competition intensity is constant. Table 25 displays the

unstandardized regression coefﬁcients (B), the standardized regression coefﬁcients

(B), zero-order correlations (r), the semipartial correlations (sri) and R2.

Table 25 Multiple Regression of Quality of Human Resources on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) 504.219 226.56 2.226 .027
Market Size (log) 20.059 8.14 .196 2.464 .015 .053 .182 .163
Income (log) 429.837 52.26 -.196 -2.484 .014 -.090 -.184 -.164
Competition intensity 2.133 .50 .285 4.197 .000 .355 .301 .277
Service age .000 .14 .046 .658 .511 .097 .049 .044
Quality of human resources 2.577 .61 .291 4.184 .000 .317 .300 .277
Model Summary .

R R1 Adjusted R‘ S. E of the Estimate

.476 .226 .204 34.9139

 

7O

In particular, the quality of editorial staff was found to be the most important
contributor to higher level of market growth performance among the quality levels

of four task areas. As can be seen in Table 25-1 through Table 25-4, the semipartial
correlation of the quality of editorial staff (B = .281, sri = .269) was higher than the
quality of design (B = .194, sr; = .190), marketing (B = .176, sri = .170), and

technical staff(B = .169, sri = .165).

Table 25-1 Multiple Regression of Quality of Editorial Staff on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) 548.987 228.163 2.406 .017
Market Size (log) 18.402 8.112 .180 2.269.025 .053 .168 .150
Income (log) 435.085 52.623 -.204 -2.567 .011 -.090 -.189 -.170
Competition intensity 2.248 .506 .301 4.445 .000 .355 .317 .295
Service age .000 .149 .041 .586 .559 .097 .044 .039
Quality of editorial staff 7.717 1.906 .281 4.050.000 .296 .291 .269
Model Summary

R R7 Amsted R‘ S. E of the Estimate

.471 .222 .200 35.0133

 

Table 25-2 Multiple Regression of Quality of Design Staff on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr, sri

(Constant) 490.182 232.499 2.108 .036

Market size (log) 16.815 8.281 .165 2.031 .044 .053 .151 .138
Income (log) 418.243 53.496 -.178 -2.210 .028 -.090 -.164 -.150
Competition Intensity 2.309 .518 .309 4.454 .000 .355 .317 .302
Service age .158 .151 .074 1.044.298 .097 .078 .071
Quality of design staff 5.025 1.798 .194 2.795.006 .226 .206 .190
Model Summary

R R“ Adjusted R‘ S. E of the Estimate

.431 .186 .163 35.8174

 

71

Table 25-3 Multiple Regression of Quality of Technical Staff on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. 5r,

(Constant) 443.169 233.265 1.900 .059

Market size (log) 17.337 8.364 .170 2.073.040 .053 .154 .141
Income (log) -108.992 53.539 -.164 -2.036 .043 -.090 -.151 -.139
Competition Intensity 2.466 .516 .330 4.777 .000 .355 .338 .326
Service age .156 .152 .073 1.030 .304 .097 .077 .070
Quality of technical staff 4.564 1.882 .169 2.425.016 .172 .179 .165
Model Summary

R R1 Adjusted R‘ S.Eof the Estimate

.421 .177 .154 36.0062

 

Table 25-4 Multiple Regression of Quality of Marketing Staff on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pri 5r.

(Constant) 457.255 233.064 1.962 .051
Market size (log) 17.202 8.345 .168 2.061 .041 .053 .153 .140
Income (log) -110.118 53.512 -.166 -2.058 .041 -.090 -.153 -.140
Competition Intensity 2.278 .524 .305 4.347 .000 .355 .311 .296
Service age .159 .152 .074 1.047.296 .097 .078 .071
Quality of marketing staff 4.036 1.619 .176 2.493.014 .223 .184 .170
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.423 .179 .155 35.9733

 

H6c: The quality of human resources will be positively related to higher level
of ﬁnancial growth performance controlling for ﬁrm’s competition intensity,
market size, income, and service age.

The hypothesis was also supported, as the semipartial correlation for the
relationship equaled .264 (B = .278), which exceeds the .200 cut-off point. This is
consistent with the hypothesized direction. The result indicates that about 7.0 % of
variance in ﬁnancial growth performance is associated with the quality of human

resources even after controlling for WLIS ﬁrms’ competition intensity and market

size. As the quality of human resources becomes higher, ﬁnancial growth

72

performance is likely to be better even when the ﬁrms’ competition intensity and
market size are equal. Table 26 displays the result of multiple regression of
ﬁnancial growth performance on quality of human resources.

As displayed in Table 26-1 through Table 26-4, the quality of design (B =
.259, sri = .253) and marketing staff (B = .215, sri = .208) were positively related to
higher level of ﬁnancial growth performance. However, the strength of association
in editorial staff (B = .151, sri = .144) and technical staff (B = .148, sri = .145) were

tenuous to predict a ﬁrm’s ﬁnancial grth performance.

Table 26 Multiple Regression of Quality of Human resources on Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr, sr.

(Constant) 211.135 237.773 .888 .376
Market Size (log) 21.492 8.543 .211 2.516 .013 .127 .186 .175
Income (log) -73.228 54.850 -.111 -1.335 .184 -.003 -.100 -.093
Competition intensity 1.351 .533 .181 2.532 .012 .232 .187 .176
Service age -.125 .156 -.059 -.801 .424 -.017 -.060 -.056
Quality of human resource 2.455 .647 .278 3.797 .000 .265 .274 .264
Model Summary

R R‘ Adjusted R1 S. E of the Estimate

.379 .144 .120 36.6415

 

Table 26-1 Multiple Regression of Quality of Editorial Staff on Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r prI sn

(Constant) 212.021 245.508 .864 .389
Market size (log) 18.307 8.728 .180 2.097.037 .127 .156 .150
Income (log) —64.469 56.624 -.097 -1.139 .256 -.003 -.085 -.081
Competition Intensity 1.572 .544 .211 2.887 .004 .232 .212 .206
Service age .000 .161 -.042 -.563 .574 -.017 -.042 -.040
Quality of editorial staff 4.130 2.051 .151 2.014 .045 .147 .150 .144
Model Summary

R R‘ Adjusted R‘ S. Eat the Estimate

.308 .095 .069 37.6751

 

73

Table 26-2 Multiple Regression of Quality of Design Staff of Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr, sf.

(Constant) 213.395 238.644 .894 .372
Market size (log) 19.259 8.499 .189 2.266 .025 .127 .168 .158
Income (log) -68.294 54.910 -.103 -1.244 .215 -.003 -.093 -.087
Competition Intensity 1.439 .532 .193 2.706 .007 .232 .199 .189
Service age .000 .155 -.038 -.522 .602 -.017 -.039 -.036
Quality of design staff 6.690 1.845 .259 3.625 .000 .264 .263 .253
Model Summary

R R‘ Adjusted R2 S. E of the Estimate

.372 .138 .114 36.7641

 

Table 26-3 Multiple Regression of Quality of Technical Staff of Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr; sri

(Constant) 153.438 244.043 .629 .530
Market size (log) 18.682 8.750 .183 2.135.034 .127 .158 .153
Income (log) -52.837 56.013 -.080 -.943 .347 -.003 -.071 -.067
Competition Intensity 1.671 .540 .224 3.095 .002 .232 .227 .221
Service age .000 .159 -.031 -.414 .680 -.017 -.031 -.030
Quality oftechnical staff 3.991 1.969 .148 2.027.044 .129 .151 .145
Model Summary

R R‘ Adjusted R1 S. E of the Estimate

.308 .095 .069 37.6699

 

Table 26-4 Multiple Regression of Quality of Marketing Staff on Financial Growth Performance

 

 

 

 

 

Coefficients
B S. E B t Sig. r pr. sri

(Constant) 168.644 241.042 .700 .485
Market size (log) 19.483 8.630 .191 2.257.025 .127 .167 .159
Income (log) -56.597 55.343 -.086 -1.023 .308 -.003 -.077 -.072
Competition Intensity 1.424 .542 .191 2.628 .009 .232 .194 .186
Service age .000 .157 -.035 -.481 .631 -.017 -.036 -.034
Quality of marketing staff 4.928 1.674 .215 2.943.004 .222 .216 .208
Model Summary

R R1 Adjusted R‘ S.Eof the Estimate

.342 .117 .092 37.2047

 

74

Organizational Coordinagon

None of cases had the Mahalanobis distance value in excess of 15.92. This
shows no multivariate outliers in the solution for the hypothesis 7. No evidence of
multicollinearity and singularity among the independent variables was found.

H7a: Organizational coordination will be positively related to higher quality

of product controlling for ﬁrm’s competition intensity, market size, income,

and service age.

The hypothesis was strongly supported, as the semipartial correlation for the
relationship was .315 (B = .320) in the direction hypothesized, which exceeds the
.200 cut-off point. This shows that a ﬁrm’s organizational resources account for
about 10 % of variance in product quality even after controlling for the ﬁrrns’
competition intensity. Thus a ﬁrm’s organizational coordination was positively
related to higher quality of product. Table 27 displays the unstandardized

regression coefﬁcients (B), the standardized regression coefficients (B), zero-order

correlations (r), the semipartial correlations (sri) and R2.

Table 27 Multiple Regression of Organizational Coordination on Product Quality

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sr.

(Constant) -29.692 77.791 -.382 .703

Market Size (log) -1.628 2.749 -.048 -.592 .555 -.043 -.044 -.041
Income (log) 14.59417.764 .067 .822 .412 .025 .062 .057
Competition Intensity .350 .173 .142 2.021 .045 .203 .150 .139
Serviceage .000 .050 .118 1.657 .099 .161 .124 .114
grggnizational coordination .916 .200 .320 4.583.000 .344 .326 .315
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.402 .162 .138 11.9485

 

75

H7b: Organizational coordination will be positively related to higher level of
market growth performance controlling for ﬁrm’s competition intensity,
market size, income, and service age.

The semipartial correlation between a ﬁrm’s level of organizational
coordination and market performance was .279 (B = .283) in the direction
hypothesized. Therefore, the hypothesis was supported. About 7.8 % of variance in
market performance was associated with organizational resources even after
controlling the ﬁrm’s competition intensity. The result indicates that as the level of
organizational coordination becomes higher, market growth performance is likely to
be better even when the ﬁrms’ competition intensity is constant. Table 28

summarizes the result of multiple regression analysis.

Table 28 Multiple Regression of Organizational Coordination on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pri sri

(Constant) 353.361 227.138 1.556 .122
Market Size (log) 14.645 8.027 .143 1.824.070 .053 .136 .121
Income (log) -91.237 51.867 -.138 -1.759 .080 -.090 -.131 -.116
Competition Intensity 2.200 .505 .294 4.354 .000 .355 .311 .288
Service age .178 .146 .083 1.214.227 .097 .091 .080
Eganizational coordination 2.461 .583 .283 4.219.000 .335 .302 .279
Model Summary

R RZ Adjusted R‘ S. Eof the Estimate

.477 .227 .206 34.8875

 

H7c: Organizational coordination will be positively related to higher level of

ﬁnancial growth performance controlling for ﬁrm’s competition intensity,

market size, income, and service age.

The hypothesis was again supported, as the semipartial correlation for the
relationship was .256(B = .260), which exceeds the .200 cut-off point (see Table

29). This is consistent with the hypothesized direction. The result indicates that

about 6.6 % of variance in ﬁnancial growth performance is associated with the

76

ﬁrm’s organizational coordination even aﬁer controlling for competition intensity
and market size. As the degree of organizational coordination becomes higher, the
ﬁrm’s ﬁnancial growth performance is likely to be higher even when competition

intensity and market size are equal.

Table 29 Multiple Regression of Organizational Resources on Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sr.

(Constant) 70.837 239.129 .296 .767
Market Size (log) 16.332 8.451 .160 1.933.055 .127 .144 .135
Income (log) -36.848 54.605 -.056 -.675 .501 -.003 -.051 -.047
Competition Intensity 1.425 .532 .191 2.679.008 .232 .197 .187
Service age .000 .154 -.022 -.310 .757 -.017 -.023 -.022
9_rganizational coordination 2.257 .614 .260 3.674 .000 .286 .266 .256
Model Summary

R R‘ Adjusted R1 S. Eof the Estimate

.374 .140 .115 36.7293

 

Innomtion Resources

All variables entered the equation without violating the default value for
tolerance. Therefore, no evidence of multicollinearity and singularity among the
independent variables was found. None of cases had a Mahalanobis distance value
in excess of 16.9. This shows no multivariate outliers in the solution.

H8a: Innovation resources will be positively related to higher quality of

product controlling for ﬁrm’s competition intensity, market size, income, and

servrce age.

The hypothesis was strongly supported, as the semipartial correlation for the
relationship was .356 in the direction hypothesized (B = .366). This indicates that
about 12.7 % of variance in product quality is associated with ﬁrm’s innovation
resources even after controlling for competition intensity and service age.

Innovation resources are positively related to higher quality of product even when

77

the ﬁrms’ competition intensity and service age are constant. Table 30 displays the

unstandardized regression coefﬁcients (B), the standardized regression coefﬁcients

(B), zero-order correlations (r), the semipartial correlations (sri) and R2.

Table 30 Multiple Regression of Innovation Resources on Product Quality

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) -11.704 76.209 -.154 .878
Market Size (log) -1.603 2.704 -.048 -.593 .554 -.043 -.045 -.040
Income (log) 9.631 17.452 .044 .552 .582 .025 .041 .037
Competition Intensity .259 .173 .105 1.500.135 .203 .112 .102
Service age .100 .049 .143 2.029.044 .161 .151 .137
Innovation resources .800 .152 .366 5.259 .000 .387 .368 .356
Model Summary

R R1 Adjusted R‘ S. Eof the Estimate

.435 .189 .166 11.7528

 

H8b: Innovation resources will be positively related to higher level of market
growth performance controlling for ﬁrrn’s competition intensity, market size,
income, and service age.

The semipartial correlation between ﬁrm’s innovation resources and market
growth performance was .331 in the direction hypothesized (B = .340). Therefore,
the hypothesis was strongly supported. About 11 % of variance in market growth
performance was associated with the ﬁrm’s innovation resources even after
controlling for competition intensity. The result indicates that as the level of
innovation resources becomes higher, the ﬁrm’s market growth performance is

likely to be better even when competition intensity is equal. The summary of

multiple regression analysis is displayed in Table 31.

78

Table 31 Multiple Regression of Innovation Resources on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sr.

(Constant) 399.240 221.510 1.802 .073
Market Size (log) 14.719 7.860 .144 1.873.063 .053 .139 .121
Income (log) -104.704 50.726 -.158 -2.064 .040 -.O90 -.153 -.134
Competition Intensity 1.928 .503 .258 3.836 .000 .355 .277 .248
Serviceage .225 .143 .106 1.570.118 .097 .117 .102
Innovation resources 2.262 .442 .340 5.116.000 .399 .359 .331
Model Summary

R R‘ Adjusted R2 S. E of the Estimate

.509 .259 .238 34.1607

 

H8c: Innovation resources will be positively related to higher level of
ﬁnancial growth performance controlling for ﬁrm’s competition intensity,
market size, income, and service age.

The hypothesis was again strongly supported, as the semipartial correlation
for the relationship was .353 (B = .363), which exceeds the .200 cut-off point (see
Table 32). This is consistent with the hypothesized direction. The result explains
that a ﬁrm’s innovation resources account for about 12.5 % of variance in ﬁnancial
growth performance even after controlling for competition intensity and market
size. Innovation resources are positively related to higher level of ﬁnancial growth

performance even when the ﬁrms’ competition intensity and market size are equal.

Table 32 Multiple Regression of Innovation Resources on Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r prI srI

(Constant) 105.570 229.836 .459 .647
Market Size (log) 16.425 8.155 .161 2.014.046 .127 .150 .136
Income (log) 49.580 52.633 -.075 -.942 .347 -.003 -.071 -.063
Competition Intensity 1.089 .521 .146 2.088 .038 .232 .155 .141
Service age .000 .149 .000 .004 .997 -.017 .000 .000
Innovation resources 2.409 .459 .363 5.250.000 .398 .367 .353
Model Summary

R R1 Adjusted R7 S. E of the Estimate

.446 .199 .176 35.4446

 

79

Competitive Strategies

Scale Strategy

The Mahalanobis distance values for each case showed no multivariate
outlier in the solution for the hypothesis 9 and 10. No evidence of multicollinearity
among the independent variables was found. All variables entered the equation
without violating the default value for tolerance.

H9a: For Web-based local information service, large-scale entry strategy will

be positively related to higher quality of product controlling for ﬁrm’s

competition intensity, market size, income, and service age.

The hypothesis was supported, as the semipartial correlation for the
relationship equaled .208 (B = .221) in the direction hypothesized, which exceeds
the .200 cut-off point. This indicates that a ﬁrm’s large-scale entry strategy
accounts for about 4.3 % of variance in product quality even when ﬁrm’s
competition intensity and service age are constant. A ﬁrm’s large-scale entry was
positively related to higher level of product quality. Table 33 displays the

unstandardized regression coefficients (B), the standardized regression coefﬁcients

(B), zero-order correlations (r), the semipartial correlations (sri) and R2.

Table 33 Multiple Regression of Large-scale Entry Strategy and Service Age on Product Quality

 

 

 

 

 

Coefﬁcients

B S.E B t Sig. r pri 5r,
(Constant) -25.420 80.660 -.315 .753
Market Size (log) -1.713 2.840 -.051 -.603 .547 -.043 -.045 -.043
Income (log) 1501518389 .069 .817 .415 .025 .061 .058
Competition .308 .185 .125 1.666.097 .203 .124 .118
Service age .109 .052 .156 2.090.038 .161 .155 .149
large-scale entry .666 .227 .221 2.931 .004 .232 .215 .208
Model Summary
R R’ Adjusted R‘ S.Eof the Estimate
.325 .106 .080 12.3416

 

80

H9b: For Web-based local information service, large-scale entry strategy will
be positively related to higher level of market growth performance
controlling for ﬁrm’s competition intensity, market size, income, and service
age.

The semipartial correlation between a ﬁrm’s large-scale entry and market
growth performance was .327 in the direction hypothesized (B = .346). Therefore,
the hypothesis was strongly supported. This explains that about 10.7 % of variance
in market growth performance is associated with a ﬁrm’s large-scale entry even

when competition intensity and service age are equal. The result of multiple

regression analysis is summarized in Table 34.

Table 34 Multiple Regression of Large-scale Entry Strategy and Service Age on Market Growth
Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. Si'r

(Constant) 299.782 223.694 1.340 .182
Market Size (log) 14.310 7.875 .140 1.817 .071 .053 .135 .118
Income (log) -80.815 50.998 -.122 -1.585 .115 -.090 -.118 -.103
Competition 1.757 .513 .235 3.426 .001 .355 .249 .222
Service age .291 .145 .136 2.006 .046 .097 .149 .130
Erge-scale entry 3.175 .630 .346 5.039.000 .409 .354 .327
Model Summary

R R‘ Adjusted R1 S. Eof the Estimate

.506 .256 .235 34.2270

 

H9c: For Web-based local information service, large-scale entry strategy will

be positively related to the higher level of ﬁnancial growth performance

controlling for ﬁrm’s competition intensity, market size, income, and service

age.

The hypothesis was supported, as the semipartial correlation for the
relationship equaled .263 (B = .278), which exceeds the .200 cut-off point (see Table

35). This is consistent with the hypothesized direction. The result indicates that

about 6.9 % of variance in ﬁnancial growth performance is associated with large-

81

scale entry even after controlling for the ﬁrms’ competition intensity and market
size. A ﬁrm’s large-scale entry strategy was positively related to higher level of

ﬁnancial growth performance.

Table 35 Multiple Regression of Large-scale Entry Strategy and Service Age on Financial
Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) 38.593 239.545 .161 .872
Market Size (log) 16.052 8.433 .157 1.903.059 .127 .142 .132
Income (log) -29.704 54.612 -.045 -.544 .587 -.003 -.041 -.038
Competition 1.105 .549 .148 2.012.046 .232 .150 .140
Service age .000 .155 .021 .288 .773 -.017 .022 .020
age-scale entry 2.552 .675 .278 3.782 .000 .320 .273 .263
Model Summary

R R‘ Adjusted R1 S. Eof the Estimate

.378 .143 .119 36.6523

 

H9d: For Web-based local information service, large-scale entry strategy will
be positively related to higher level of market share controlling for ﬁrm’s
competition intensity, market size, income, and service age.

As shown in the Table 36, the semipartial correlation did not exceed the cut-

off point (B = -.010, sr; = -.010). The hypothesis was not supported. This indicates

that there is little association between a ﬁrm’s large-scale entry and market share.

Table 36 Multiple Regression of Large-scale Entry Strategy and Service Age on Market Share

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) 1.416 4.688 .302 .763
Market Size (log) -.485 .176 -.239 -2.761 .007 -.268 -.222 -.203
Income (log) .000 1.071 .006 .074 .941 -.095 .006 .005
Competition .000 .011 .213 2.766.006 .242 .222 .203
Service age .000 .003 .273 3.547 .001 .341 .281 .260
Laﬂe-scale entg .000 .014 -.010 -.137 .892 -.004 -.011 -.010
Model Summary

R R1 Adjusted R‘ S. Eof the Estimate

.456 .208 .181 .6942

 

82

Time of Entry

H10a: For Web-based local information service, earlier entry into market will

be positively related to higher quality of product controlling for market size,

income, competition intensity, and scale of entry.

As Table 33 displays, the hypothesis was not supported, as the semipartial
correlation for the relationship failed to prove meaningﬁil (B = .156, sri = .149).
Even if the relationship was positive in the direction hypothesized, only 2.2 % of
variance in product quality was associated with a ﬁrm’s earlier entry after
controlling for competition intensity and scale of entry. A ﬁrm’s entry of time was
not related to product quality.

H10b: For Web-based local information service, earlier entry into market

will be positively related to higher level of market growth performance

controlling for market size, income, competition intensity, and scale of entry.

The hypothesis was not supported, as the semipartial correlation only
equaled .130 (B = .136), which did not exceed the cut-off point, even though the
relationship was positive. This indicates that only 1. 7 % of variance in market
grth performance is associated with a ﬁrm’s earlier entry after controlling for
competition intensity and other covariates (see Table 34).

H10c: For Web-based local information service, earlier entry into market will

be positively related to higher level of ﬁnancial growth performance

controlling for market size, income, competition intensity, and scale of entry.

The hypothesis was also not supported, as the semipartial correlation only
equaled .020, which did not exceed the cut-off point (see Table 35). This shows

that variance in ﬁnancial growth performance is least associated with a ﬁrm’s

earlier entry aﬁer controlling for competition intensity and scale of entry.

83

H10d: For Web-based local information service, ﬁrm’s earlier entry into
market will be positively related to higher level of market share controlling
for market size, income, competition intensity, and scale of entry.

The hypothesis was supported, as the relationship between a ﬁrm’s earlier
entry and market share was found to be positive (B = .274, sri = .260). The result of
the regression analysis is summarized in Table 36. This result explains that a ﬁrrn’s
time of entry into market accounts for about 6.8% of variance in market share even

after controlling for competition intensity. Thus a ﬁrm’s earlier entry was

positively related to the ﬁrm’s market share.

Product Quality

H1 1a: For Web-based local information service, product quality will be positively

related to higher level of market growth performance controlling for ﬁrm’s

competition intensity, market size, income, and service age.

The hypothesis was strongly supported, as the semipartial correlation for the
relationship was .358 in the direction hypothesized (B = .370). This indicates that
product quality accounts for about 12.8 % of variance in market growth
performance even when ﬁrm’s competition intensity is constant. Product quality

was positively related to higher level of market growth performance. Table 37

displays the result of multiple regression analysis.

84

Table 37 Multiple Regression of Product Quality on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) 442.323 218.497 2.024 .044
Market Size (log) 16.421 7.767 .161 2.114.036 .053 .157 .135
Income (log) -113.974 50.126 -.172 -2.274 .024 -.090 -.168 -.145
Competition 1.990 .492 .266 4.044 .000 .355 .291 .258
Service age .000 .143 .044 .652 .515 .097 .049 .042
Product Quality 1.124 .201 .370 5.605.000 .419 .388 .358
Model Summary

R R‘ Adjusted R7 S. Eof the Estimate

.527 .278 .258 33.7279

 

H1 1b: For Web-based local information service, product quality will be
positively related to higher level of ﬁnancial growth performance controlling
for ﬁrrn’s competition intensity, market size, income, and service age.

The hypothesis was supported, as the semipartial correlation for the
relationship equaled .273 (B = .282), which exceeds the .200 cut-off point. This is
consistent with the hypothesized direction. This indicates that about 7.5 % of
variance in ﬁnancial growth performance is associated with product quality even
aﬁer controlling for ﬁrm’s competition intensity and market size. Thus as the level
of product quality becomes higher, ﬁnancial growth performance is likely to be
better even when competition intensity and market size are equal. Table 38 displays

the unstandardized regression coeﬂicients (B), the standardized regression

coefﬁcients (B), zero-order correlations (r), the semipartial correlations (sri) and R2.

85

Table 38 Multiple Regression of Product Quality on Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pri sr,

(Constant) 153.454 236.736 .648 .518
Market Size (log) 17.666 8.416 .173 2.099.037 .127 .156 .146
Income (log) -55.832 54.310 -.084 -1.028 .305 -.003 -.077 -.071
Competition 1.315 .533 .176 2.467.015 .232 .182 .171
Service age -.110 .155 -.052 -.710 .479 -.017 -.053 -.049
Product quality .854 .217 .282 3.930.000 .299 .283 .273
Model Summary

R R2 Adjusted R‘ S. E of the Estimate

.385 .148 .124 36.5433

 

H1 1c: For Web-based local information service, product quality will be

positively related to higher level of market share controlling for ﬁrm’s

competition intensity, market size, income, and service age.

The hypothesis was not supported, as the semipartial correlation only
equaled .085 (B = .088), which did not exceed the cut-off point. This shows that

variance in market share is little associated with product quality after controlling for

competition intensity, service age and market size (see Table 39).

Table 39 Multiple Regression of Product Quality on Market Share

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sri

(Constant) 1.122 4.643 .242 .809
Market Size (log) -.480 .175 -.236 -2.741 .007 -.268 -.220 -.200
Income (log) .000 1.064 .006 .071 .943 -.095 .006 .005
Competition .000 .011 .197 2.619 .010 .242 .211 .191
Service age .000 .003 .258 3.344 .001 .341 .266 .244
Product quality .000 .005 .088 1.168 .245 .190 .096 .085
Model Summary

R R1 Adjusted R‘ S. E of the Estimate

.464 .215 .188 .6910

 

86

Resources and Competition

RQla: Are the effects of ﬁrm’s resources on product quality stronger than
those of ﬁrm’s competition intensity?

Firm resources were far stronger than competition intensity in the strength of
association with product quality. As summarized in Table 40, all intangible
resources including the quality of human resources (difference = .115),
organizational coordination (difference = .080), and innovational resources
(difference = .117), are stronger contributors to higher quality of product than
competition intensity. The difference exceeded the criteria of .02 except the
quantity of human resources. For the quantity of human resources (difference = -
.002), there was little difference from competition intensity in the strength of

association with product quality.

Table 40 Comparison of Strength of Association with Product Quality Between Four types of
Resources and Competition Intensity

 

 

Resources Competition Diff.
sn sri2 sr- sr-2 R(sr;2)-C(sri2)
Quantity of Human Resources Regression .132 .017 .137 .019 -.002
Quality of Human Resources Regression .359 .129 .120 .014 .115"
Organizational Coordination Regression .315 .099 .139 .019 .080'
lnnovational Resources Regression .356 .127 .102 .010 .1 17*

 

* Difference of Squared Semipartial Correlation between Resources and Competition is above .02
(2%).

Rle: Are the effects of ﬁrm’s resources on market growth performance

stronger than those of ﬁrm’s competition intensity?

The quantity of human resources (difference = .031) and innovation
resources (difference = .048) were stronger contributors to higher level of market

growth performance than competition intensity as displayed in Table 41. However,

87

there was no difference between other two resources and market growth

performance in the strength of association.

Table 41 Comparison of Strength of Association with Market Growth Performance Between Four
types of Resources and Competition Intensity

 

Resources Competition Diff.

 

sr- sr.2 sr. sr.2 R(sr-2)-C(sr.2L
Quantity of Human Resources Regression .286 .082 .225 .051 .031"
Quality of Human Resources Regression .277 .077 .277 .077 .000
Organizational Coordination Regression .279 .078 .288 .083 .-.005
Innovational Resources Regression .331 .110 .248 .062 .048*

 

* Difference of Squared Semipartial Correlation between Resources and Competition is above .02
(2%).

RQlc: Are the effects of ﬁrm’s resources on ﬁnancial growth performance
stronger than those of ﬁrm’s competition intensity?

Only the quantity of human resources was found to be weaker than
competition intensity in terms of strength of association with ﬁnancial grth
performance. The associations of three resources were stronger than competition
intensity. For the quantity of human resources, the difference between resources
and competition intensity was only .021. Innovation resources (difference = .105)
were far stronger predictor for ﬁnancial growth performance than competition
intensity. Table 42 displays a comparison of the strength of association with

ﬁnancial performance between four types of resources and competition intensity.

Table 42 Comparison of the Strength of Association with Financial Growth Performance Between
Four types of Resources and Competition Intensity

 

 

Resources Competition Diff.
sr. sr.2 sn sr.2 R(sr-2)-C(sr-2)
Quantity of Human Resources Regression .109 .012 .181 .033 -.021*
Quality of Human Resources Regression .264 .070 .176 .031 .039'
Organizational Coordination Regression .256 .066 .187 .035 .031*
Innovational Resources Regression .353 .125 .141 .020 .105"

 

* Difference of Squared Semipartial Correlation between Resources and Competition is above .02
(2%).

88

Organizational Goals

RQ2a: What kinds of organizational goals are most likely to be sought

through local Web contents service?

Principal components extraction with oblique rotation was performed on 17
organizational goal items aﬁer a preliminary extraction to estimate number of
factors. Kaiser’s measure of sampling adequacy was used to test factorability. The
Kaiser’s measure of .88 showed that there was no problem of factorability on the
ﬁnal solution. Three factors were extracted. The lowest of Cronbach alphas for
three factors was .80. These factors accounted for about 61.8 percent of the
variance. Loadings of variables on factors, communalities, and percents of variance
are shown in Table 43. Variables are ordered and grouped by size of loading to
facilitate interpretation. As can be seen in Table 43, the loadings of factor 3 were
reﬂected during the rotation. Thus to interpret the factor 3, caution is necessary to
reverse the direction of the interpretation. Component correlations among factors
were shown in Table 44.

Factor 1 included “Providing high quality information,” “Improving the
information product,” “Providing superior information to the community,”
“Responding to user's needs,” “Doing the job well,” “Serving the general needs of
the community,” “Hiring the best employees,” and “Maintaining high quality
transmission standards.” This factor accounts for 37.4 % of total variance. (or =

.89). The factor 1 was termed “service quality.”

89

Factor 2 included “Maximizing proﬁts,” “Increasing proﬁt,” “Increasing the
gross revenue,” and “Reducing costs.” This factor accounts for 15.9 % of total
variance. (or = .86). The factor 2 was named “proﬁtability.”

Factor 3 was deﬁned by “Beating the competition,” “Protecting our franchise
in the market,” “Maintaining our ﬁrm's position in the market,” “Attaining a
position of leadership in the business community,” “Maximizing growth of
organization.” This factor accounts for 8.49 % of total variance. (or = .80). The
factor 3 was termed “business competition.”

Principal components extraction with oblique rotation identiﬁed three

,3 ‘5

components labeled “service quality, proﬁtability,” and “business competition.”
All factors were internally consistent and well deﬁned by the organizational items.
Factor scores by regression method were used to examine the effects of three

factors. The factor 3 scores were transformed to cancel out the reﬂection for a

uniﬁed interpretation with other factors.

90

Table 43 Factor Loadings, Percents of Variance, Reliability For Principal Factor Extraction and

Oblique Rotation on Organizational Goal items

 

 

Pattern Matrix
Service Proﬁtability Business

Quality Competition
Providing high quality information .850 -.128 .000
Improving the information product .845 .000 .000
Providing superior information to the community .825 -.135 .000
Responding to user‘s needs .770 .000 .000
Doing the job well .734 -.101 .000
Serving the general needs of the community .678 .156 .102
Hiring the best employees .570 .000 -.216
Maintaining high quality transmission standards .547 .248 -.162
Maximizing proﬁts .000 .932 .000
Increasing proﬁt .000 .928 .000
Increasing the gross revenue .000 .886 .000
Reducing costs .000 .599 .000
Beating the competition .000 .000 -.833
Protecting our franchise in the market .000 .000 -.808
Maintaining our ﬁrm's position in the market .000 .110 -.698
Attaining a position of leadership in the business community .000 .000 -.687
Maximizing growth of organization .192 .130 -.543
Eigenvalue 6.36 2.70 1 .44
% of Variance 37.40 15.88 8.49
Cronbach alpha .89 .86 .80

 

Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.

Table 44 Component Correlation Matrix

 

Component Service Quality Proﬁtability Business Competition

 

 

Service Quality 1.000 .230 -.441
Proﬁtability .230 1 .000 -.341
Business Competition -.441 -.341 1.000

91

RQ2b: How do ﬁrm's organizational goals affect ﬁrm resources such as i)

quantity of human resources, ii) quality of human resources, iii)

organizational coordination, and iv) innovation resources?

No relationship was found between organizational goals and the quantity of
human resources (log). Three organizational goals do not seem to affect the

quantity of human resources (log). Their semipartial correlations for the association

with the quantity of human resources were very low as summarized in Table 45.

Table 45 Multiple Regression of Organizational Goals on Quantity of Human Resources

 

 

 

 

 

Coefﬁcients
(Constant) 1.336 2.32 .575 .566
Market size (log) .250 .08 .255 2.999 .003 .192 .220 .214
Income (log) -.460 .53 -.072 -.861 .390 .079 -.065 -.061
Service age .000 .00 .217 2.912.004 .168 .214 .208
Goal1: Service Quality .000 .03 -.065 -.812 .418 -.076 -.061 -.058
Goal2: Proﬁtability .000 .02 .080 1.040 .300 .055 .078 .074
Goal3: Business competition .000 .03 -.131 -1.576 .117 -.123 -.118 -.112
Model Summary

R R1 Adjusted R‘ S. Eof the Estimate

.324 .105 .075 .3608

 

For the quality of human resources, the service quality (B = -.224, sri = -.194)
and business competition goals (B = -.228, sri = -. 196) were found to be important
predictors. The semipartial correlations of both goals were considerably reduced
relatively to zero-order correlations, as can be seen in Table 46. This is due to high
correlation (r = .441) between the service quality and business competition,
indicating that both variables have a sizable shared variance. A cautious
interpretation is also necessary to interpret the standardized regression coefﬁcient
and semipartial correlation. Because the organizational items were transformed by a

reﬂected logarithm prior to principal component extraction, the coefﬁcients should

92

be interpreted in the reverse direction. Thus both goals appeal positively related to

the quality of human resources even when service age is constant.

Table 46 Multiple Regression of Organizational Goals on Quality of Human Resources

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pri ST:

(Constant) -11.264 25.622 -.440 .661
Market size (log) -1.868 .920 -.162 -2.031 .044 -.134 -.151 -.135
Income (log) 8.761 5.894 .117 1.486 .139 .055 .111 .099
Service age .000 .017 .224 3.216 .002 .214 .236 .214
Goal1: Service Quality -.965 .332 -.218 -2.906 .004 -.318 -.214 -.194
Goal2: Proﬁtability -.119 .319 -.027 -.372 .710 -.114 -.028 -.025
Goal3: Business competition -1.008 .344 -.228 -2.932 .004 -.301 -.216 -.196
Model Summary

R R“ Adjusted R‘ S. E of the Estimate

.466 .217 .190 3.9807

 

The service quality goal was also observed to be a meaningful predictor for
the level of organizational coordination (B = -.412, sri = -.367) and innovation
resources (B = -.328, sr; = -.291). The service quality goal accounted for about 13.5
% of variance in the level of organizational coordination and about 8.5 % of
variance in the level of innovation resources. A positive relationship was found
between the service quality and the level of organizational coordination and
innovation resources. The proﬁtability and business competition goals, however,
did not seem to affect the level of organizational coordination and innovation
resources (see Table 47, 48).

The organizational goals appear to affect the level of resource commitment,
particularly in intangible resources. Especially, a ﬁrm’s service quality goal seems
to positively affect the level of the quality of human resources, organizational

coordination, and innovation resources.

93

Table 47 Multiple Regression of Organizational Goals on Organizational Coordination

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sf.

(Constant) 48.622 26.510 1.834 .068
Market size (log) .502 .952 .043 .527 .599 -.035 .040 .036
Income (log) -6.722 6.099 -.088 -1.102 .272 -.064 -.083 -.075
Service age .000 .017 .081 1.148 .253 .039 .086 .078
Goal1: Service Quality -1.854 .343 -.412 -5.398 .000 -.421 -.377 -.367
60812: Proﬁtability .000 .330 .019 .255 .799 -.080 .019 .017
Goal3: Business competition -.197 .356 -.044 -.553 .581 -.211 -.042 -.038
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.434 .189 .161 4.1187

 

Table 48 Multiple Regression of Organizational Goals on Innovation Resources

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sr.

(Constant) 38.841 34.401 1.129 .260
Market size (log) .583 1.235 .038 .472 .637 .009 .036 .032
Income (log) -2.372 7.914 -.024 -.300 .765 -.005 -.023 -.020
Service age .000 .023 .047 .667 .505 -.015 .050 .045
Goal1: Service Quality -1.929 .446 -.328 -4.328 .000 -.410 -.310 -.291
Goal2: Proﬁtability -.581 .428 -.099 -1.359 .176 -.217 -.102 -.092
Goal3: Business competition -.854 .462 -.145 -1.849 .066 -.320 -.138 -.125
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.449 .202 .175 5.3447

 

RQ2c: How do ﬁrm's organizational goals affect large-scale entry to compete
with other ﬁrms?

Both service quality and business competition goal were associated with the
large-scale entry. The semipartial correlation of service quality goal was -.213 (B =
-.239) and business competition goal -.221(B = -.257). Both goals were positively
related to the large-scale entry into market. Surprisingly, the proﬁtability goal was
found not to affect a ﬁrm’s scale of entry (B = -.066, sri = -.061). Table 49

summarizes the result of multiple regression analysis using factor scores.

94

Table 49 Multiple Regression of Organizational Goals on Scale of Entry

 

 

Coefﬁcients
B S. E B t Sig. r pr. srr

(Constant) 61.927 24.617 2.516 .013

Market size (log) .450 .884 .040 .509 .612 -.019 .038 .034
Income (log) -9.510 5.663 -.132 -1.679 .095 -.114 -.126 -.112
Service age .000 .016 -.034 -.483 .630 -.106 -.036 -.032
Goal1: Service Quality -1.020 .319 -.239 -3.198 .002 -.370 -.234 -.213
Goal2: Proﬁtability -.282 .306 -.066 -.923 .357 -.219 -.069 -.061

Goal3: Business competition -1.095 .330 -.257 -3.314 .001 -.389 -.242 -.221
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.470 .221 .194 3.8245

 

RQ2d: How do ﬁrm's organizational goals affect i) product quality, ii)
market growth performance, iii) market share, and iv) ﬁnancial growth
performance?

As displayed in Table 50, only the service quality goal was positively related
to the level of product quality, as the semipartial correlation of service quality was -
.243 (B = -.274). For the market growth performance (see Table 51), the business
competition goal was found to be a meaningﬁil predictor (B = -.338, sr; = -.291).
Although zero-order correlation between service quality and market growth
performance was -.251, the service quality goal did not contribute to the regression.
The relationship between service quality goal and market growth performance was
mediated by the relationship between business competition goal and market growth
performance.

On the other hand, a ﬁrm’s proﬁtability goal was associated with higher level

of ﬁnancial growth performance (B = -.292, sri = -.270). The proﬁtability goal

accounted for about 7.3 % of variance in ﬁnancial growth performance. Even

95

though the business competition goal showed a positive relationship with a zero-
order correlation of -.294, the relationship was mediated by the proﬁtability goal on
the multiple regression (see Table 52). Finally, no relationship was found between

three organizational goals and market share as shown in Table 53.

Table 50 Multiple Regression of Organizational Goals on Product Quality

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr, Si'r

(Constant) 36.199 78.324 .462 .645
Market size (log) -.439 2.812 -.013 -.156 .876 -.043 -.012 -.011
Income (log) 4.229 18.019 .019 .235 .815 .025 .018 .016
Serviceage .134 .051 .192 2.617.010 .161 .194 .183
Goal1: Service Quality -3.521 1.015 -.274 -3.470 .001 -.306 -.253 -.243
Goal2:ProﬁtabiIity -.567 .974 -.044 -.582 .561 -.101 -.044 -.041
Goal3: Business competition -1.016 1.051 -.079 -.967 .335 -.193 -.073 -.068
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.368 .135 .106 12.1687

 

Table 51 Multiple Regression of Organizational Goals on Market Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr. sr.

(Constant) 602.633 229.030 2.631 .009
Market size (log) 16.669 8.224 .163 2.027 .044 .053 .151 .137
Income (log) -128.220 52.689 -.193 -2.434 .016 -.090 -.180 -.164
Service age .387 .150 .181 2.577 .011 .097 .191 .174
Goal1: Service Quality -4.818 2.967 -.123 -1.624 .106 -.251 -.121 -.109
Goal2: Proﬁtability .377 2.848 .010 .132 .895 -.109 .010 .009
Goal3: Business competition -13.248 3.073 -.338 -4.311 .000 -.376 -.309 -.291
Model Summary

R R1 Adjusted R2 S. E of the Estimate

.448 .201 .174 35.5828

 

96

Table 52 Multiple Regression of Organizational Goals on Financial Growth Performance

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r pr, sri

(Constant) 251.977 227.869 1.106 .270
Market size (log) 19.575 8.182 .192 2.392 .018 .127 .177 .161
Income (log) -65.306 52.422 -.099 -1.246 .215 -.003 -.093 -.084
Service age .197 .149 .092 1.317 .190 -.017 .099 .088
Goal1: Service Quality -6.025 2.952 -.154 -2.041 .043 -.261 -.152 -.137
Goal2: Proﬁtability -11.406 2.834 -.292 -4.025 .000 -.358 -.290 -.270
Goal3: Business competition -4.942 3.057 -.127 -1.616 .108 -.294 -.121 -.109
Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

.453 .205 .178 35.4024

 

Table 53 Multiple Regression of Organizational Goals on Market Share

 

 

 

 

 

Coefﬁcients
B S. E B t Sig. r' prl sn

(Constant) 3.073 4.719 .651 .516
Market size (log) -.471 .180 -.232 -2.616 .010 -.268 -.212 -.196
Income (log) -.178 1.085 -.014 -.164 .870 -.095 -.014 -.012
Service age .000 .003 .309 3.933 .000 .341 .309 .294
Goal1: Service Quality .000 .065 .012 .138 .890 -.038 .011 .010
Goal2: Proﬁtability .000 .063 .071 .865 .388 .093 .071 .065
Goal3: Business competition -.112 .069 -.145 -1.635 .104 -.054 -.134 -.122
Model Summary

R R7 Adjusted R1 S. E of the Estimate

.427 .183 .149 .7075

 

Venture Origin
Organizational Goals
RQ3 a: How does variation in venture origin affect ﬁrm's organizational goals?
A ﬁrm’s service quality goal varied with venture origin, as summarized in
Table 54, with an eta squared of .047. Venture origin was deﬁned by daily, weekly
newspapers, local television stations, radio stations, and Internet ventures. Internet
ventures showed higher service quality goals than any other service (see Table 54-

1). The assumption of homogeneity of variance was not violated on this ANCOVA.

97

Table 54 Analysis of Covariance of Service Quality Goal by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared
Corrected Model 19.339 7 2.763 2.972 .006 .106
Intercept .244 1 .244 .263 .609 .001
Market Size (log) 2.134 1 2.134 2.296 .132 .013
Income (log) .363 1 .363 .391 .533 .002
Competition 12.033 1 12.033 12.946 .000 .069
Venture Origin 8.058 4 2.014 2.167 .075 .047
Error 162.661 175 .929

 

Table 54-1 Mean Service Quality Goal for Five Categories of Venture Origin

 

 

Origin M 8.0 N
TV .000 .93 47
Daily .230 1.16 43
Radio .000 .99 49
I-Venture -.405 .86 21
Weekly .000 .86 23
Total .000 1.00 183

 

Venture origin deﬁned by ﬁve media groups was different in the degree of
proﬁtability goal (1]2 = .098). The strength of association for the effect was found
to be positive. As displayed in Table 55-1, Internet venture and radio groups had a
higher proﬁtability goal than the weekly and daily newspaper groups. There was no
violation of homogeneity of variance on this ANCOVA. Table 55 reports the results
of ANCOVA of proﬁtability goal by venture origin.

On the other hand, the business competition goal did not vary with venture
origin (7]2 = .026). The ﬁve groups were not different in the strength of the business

competition goal (see Table 56).

98

Table 55 Analysis of Covariance of Proﬁtability Goal by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 18.093 7 2.585 2.760 .010 .099

Intercept .000 1 .000 .000 .996 .000

Market Size (log) .274 1 .274 .293 .589 .002

Income (log) .000 1 .000 .003 .953 .000

Competition .727 1 .727 .776 .380 .004

Venture Origin 17.774 4 4.443 4.744 .001 .098
Error 163.907 175 .937

 

Table 55-1 Mean Proﬁtability Goal for Five Categories of Venture Origin

 

 

Origin M SD. N
TV -.128 .886 47
Daily .211 1 .078 43
Radio -.257 .930 49
I-Venture -.256 .968 21
Weekly .651 .947 23
Total .000 1.000 183

 

Table 56 Analysis of Covariance of Business Competition Goal by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 24.677 7 3.525 3.921 .001 .136

Intercept .21 1 1 .21 1 .235 .629 .001

Market Size (log) . .000 1 .000 .091 .763 .001

Income (log) .000 1 .000 .032 .858 .000

Competition 23.004 1 23.004 25.589 .000 .128

Venture Origin 4.208 4 1.052 1.170 .326 .026
Error 157.323 175 .899

 

RQ3b: How does variation in venture origin affect ﬁrm's i) quantity of
human resources, ii) quality of human resources, iii) organizational
resources, and iv) innovation resources?

Quantity of Human Resources

Untransformed data of the quantity of human resources showed gross
violations of homogeneity mainly due to the nonnomality of variance as checked at

the data cleaning stage. AN COVA of the quantity of human resources, nevertheless,

99

was performed using the untransformed data because of the difﬁculty in interpreting
means from the transformed data. Instead, more stringent eta squared level of .06
cut-off point was applied to the interpretation of the result. With more conservative
criteria, the quantity of human resources varied in venture origin deﬁned by ﬁve
media groups (112 = .252). Table 57 reports the result of the ANCOVA. In
particular, the Internet venture and daily newspaper groups employed more people
dedicated to the WLIS than the local television, radio, and weekly newspaper

groups, as displayed in Table 57-1.

Table 57 Analysis of Covariance of Quantity of Human Resources by venture origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 2757.049 7 393.864 12.854 .000 .340

Intercept 32.483 1 32.483 1 .060 .305 .006

Market Size (log) 287.289 1 287.289 9.376 .003 .051

Income (log) 5.254 1 5.254 .171 .679 .001

Competition 157.400 1 157.400 5.137 .025 .029

Venture Origin 1804.384 4 451.096 14.722 .000 .252
Error 5362.236 175 30.641

 

Table 57-1 Mean Quantity of Human Resources for Five Categories of Venture Origin

 

 

Origin M SD.
TV 2.600 1 .569 4
Daily 10.121 8.879 4
Radio 3.295 3.845 4
I-Venture 10.564 8.136 2
Weekly 4.113 4.916 2
Total 5.658 6.679 18

 

Quality of Hum_an Resources

 

No difference in the quality of human resources was found, as the eta
squared of venture origin equaled .028 (see Table 58). Venture origin does not

seem to be associated with the quality of human resources.

100

Table 58 Analysis of Covariance of Quality of Human Resources by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 362.624 7 51.803 2.834 .008 .102

Intercept 20.801 1 20.801 1.138 .288 .006

Market Size (log) 174.395 1 174.395 9.542 .002 .052

Income (log) 87.531 1 87.531 4.789 .030 .027

Competition 153.885 1 153.665 8.408 .004 .046

Venture Origin 90.889 4 22.667 1.240 .296 .028
Error 3198.423 175 18.277

 

Organizational Coordination

Venture origin was different in the level of organizational coordination (112 =
.072). As displayed in Table 59-1, the Internet venture group has a higher level of
organizational coordination than the daily newspaper group. There was no violation
of homogeneity of variance on this ANCOVA. Table 59 reports the results of

AN COVA of organizational coordination by venture origin.

Table 59 Analysis of Covariance of Organizational Coordination by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 357.185 7 51.026 2.688 .011 .097

Intercept 29.012 1 29.012 1.528 .218 .009

Market Size (log) 8.803 1 8.803 .464 .497 .003

Income (log) 3.654 1 3.654 .192 .661 .001

Competition 118.465 1 118.465 6.241 .013 .034

Venture Origin 256.608 4 64.152 3.379 .011 .072
Error 3322.002 175 18.983

 

Table 59-1 Mean Organizational Coordination for Five Categories of Venture Origin

 

 

Origin M SD. N
TV 20.809 4.184 47
Daily 18.442 4.295 43
Radio 20.302 4.423 49
I-Venture 21.907 3.778 21
Weekly 20.565 5.476 23
Total 20.212 4.496 183

 

101

Innovation Resources

As summarized in Table 60, innovation resources varied with venture origin
with an eta squared of .091. The Internet venture and television groups showed a
higher level of innovation resources than the daily newspaper group (see Table 60-

l). The assumption of homogeneity of variance was not violated on this ANCOVA.

Table 60 Analysis of Covariance of Innovation Resources by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 861.841 7 123.120 3.963 .000 .137

Intercept 7.625 1 7.625 .245 .621 .001

Market Size (log) 10.170 1 10.170 .327 .568 .002

Income (log) 3.007 1 3.007 .097 .756 .001

Competition 437.736 1 437.736 14.088 .000 .075

Venture Origin 542.793 4 135.698 4.367 .002 .091
Error 5437.464 175 31.071

 

Table 60—1 Mean Innovation Resources for Five Categories of Venture Origin

 

 

Origin M SD. N
TV 32.857 5.705 47
Daily 28.975 5.384 43
Radio 31.892 5.980 49
l-Venture 33.381 5.104 21
Weekly 31.182 8.580 23
Total 31.485 5.883 183

 

RQ30: How does variation in venture origin affect ﬁrm’s i) product quality,
ii) market growth performance, iii) ﬁnancial growth performance and iv)
market share?

Product Quality

Five groups were different in the level of product quality (in2 = .076). As
reported in Table 61-1, the Internet ventures were evaluated the highest in product

quality (M = 64.71, SD. = 8.98). The radio group (M = 60.49, SD. = 14.63) was

102

ranked the second highest in product quality among the ﬁve categories of venture
origin. Table 61 summarized the result of the ANCOVA of product quality by

venture origin.

Table 61 Analysis of Covariance of Product Quality by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 3597.427 7 513.918 3.388 .002 .1 19

Intercept 23.629 1 23.629 .156 .694 .001

Market Size (log) 534.858 1 534.858 3.526 .062 .020

Income (log) 249.283 1 249.283 1.643 .202 .009

Competition 1501.323 1 1501.323 9.897 .002 .054

Venture Origin 2169.155 4 542.289 3.575 .008 .076
Error 26545.954 175 151.691

 

Table 61-1 Mean Product Quality by Five Categories of Venture Origin

 

 

Origin M SD. N
TV 55.938 13.707 47
Daily 58.349 10.179 43
Radio 80.490 14.830 49
l-Venture 84.714 8.979 21
Weekly 54.522 12.783 23
Total 58.552 12.870 183

 

Market Growth Performance

Market growth performance varied in venture origin deﬁned by ﬁve media
groups (112 = .090). Venture origin accounted for about 9% of variance in market
growth performance. Table 62 reports the result of ANCOVA of market growth
performance. The Internet venture group (M = 132.52, SD. = 39.98) was ranked
ﬁrst in market growth performance and the daily newspaper group (M = 111.02,

SD. = 29.80) was the second highest, as displayed in Table 62-1.

103

Table 62 Analysis of Covariance of Market Growth Performance by Venture Origin

 

 

Source Adjusted SS dt MS F Sig. Eta
Squared
Conected Model 61 150.587 7 8735.798 7.022 .000 .219
Intercept 3695.139 1 3695.139 2.970 .087 .017
Market Size (log) 1159.019 1 1159.019 .932 .336 .005
Income (log) 2938.475 1 2938.475 2.362 .126 .013
Competition 25500.196 1 25500.196 20.499 .000 .105
Venture Origin 21506.337 4 5376.584 4.322 .002 .090
Error 217697.043 175 1243.983

 

Table 62-1 Mean Market Growth Performance by Five Categories of Venture Origin

 

 

Origin M 8.0 N
TV 93.065 34.48 47
Daily 1 1 1 .023 29.79 43
Radio 97.979 44.51 49
l-Venture 132.524 39.98 21
Weekly 89.605 36.1 1 23
Total 102.694 39.14 183

 

Financial Growth Performance

 

Five groups were different in ﬁnancial growth performance. The strength of
association for the effect, however, was relatively weak, with 112 = .043. As
displayed in Table 63-1, the Internet ventures (M = .8524, SI). = 39.76) held
higher level of ﬁnancial growth performance than any other group. There was no
violation of homogeneity of variance on this ANCOVA. Table 63 reports the results

of ANCOVA of ﬁnancial growth performance by venture origin.

Table 63 Analysis of Covariance of Financial Growth Performance by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Conected Model 31418.055 7 4488.294 3.191 .003 .113

Intercept 412.188 1 412.188 .293 .589 .002

Market Size (log) 2726.135 1 2726.135 1.938 .166 .011

Income (log) 600.247 1 600.247 .427 .514 .002

Competition 13054.579 1 13054.579 9.282 .003 .050

Venture Origin 10941 .446 4 2735.362 1.945 .105 .043
Error 246118.723 175 1406.393

 

104

Table 63-1 Mean Financial Growth Performance for Five Categories of Venture Origin

 

 

Origin M 8.0 N
TV 60.085 37.91 47
Daily 59.714 39.77 43
Radio 62.722 39.1 1 49
l-Venture 85.238 39.75 21
Weekly 50.826 33.92 23
Total 62.427 39.05 183

 

Market Share

Even if untransformed data of market share showed gross violations of
homogeneity, the ANCOVA of market share was performed using the
untransformed data because of the difﬁculty in interpreting means from the
transformed data. Instead, a more stringent eta squared level of .06 cut-off point
was applied to the interpretation of the result. Market share varied in venture origin
deﬁned by ﬁve media groups (112 = .223). Variation in venture origin accounted
for about 22.3 % of variance in market share. Table 64 reports the result of
ANCOVA of market share by venture origin. As can be seen in Table 64-1, the
daily newspaper group showed market share index of 5.47%, Internet venture group
5.42%, television station group 1.40%, weekly newspaper group 1.29%, and radio

station group .60% for their Web-based local information service.

Table 64 Analysis of Covariance of Market Share by Venture Origin

 

 

Source Adjusted df MS F Sig. Eta

SS Squared

Corrected Model 821 .251 7 1 17.322 8.704 .000 .296

Intercept 10.733 1 10.733 .796 .374 .005

Market Size (log) 101.916 1 101.916 7.561 .007 .050

Income (log) 27.930 1 27.930 2.072 .152 .014

Competition 17.360 1 17.360 1 .288 .258 .009

Origin 561.015 4 140.254 10.406 .000 .223
Error 1954.388 145 13.479

 

105

Table 64-1 Mean Market Share for Five Categories of Venture Origin

 

 

Origin M 8.0. N
TV 1 .404 1 .708 39
Daily 5.469 5.276 39
Radio .604 .676 41
I-Venture 5.420 6.892 1 8
Weekly 1 .289 2.192 16
Total 2.686 4.273 153

 

Additional Analysis: Intangible Resources

The present research also tested the effects of tangible and intangible
resources on ﬁrm performance even if the hypotheses were not addressed at the
research design stage. Hall (1993) explained variation in ﬁrm performance by
proposing a broad set of organization factors that are intangible. A ﬁrm’s unique
resources must be difﬁcult to replicate, because they are either tacit or socially
complex. Such resources are invisible assets based on learning by doing that are
accumulated through experience and reﬁned by practice. Grant (1991) also argued
that ﬁnancial balance sheets are inadequate in identifying and appraising a ﬁrm's
resources, because they disregard intangible resources and people-based skills. The
intangible resources such as the quality of human resources, organizational
coordination, and innovation resources are probably more strategically important
resources of the ﬁrm than tangible resources

For the testing of the relationship, tangible resources were operationalized by
the quantity of human resources. Intangible resources were deﬁned by three types
of resources: the quality of human resources, organizational coordination, and

innovation resources. For the intangible resources measure, the standardized scores

106

of three resources variables were combined with equal weighting. The standardized
item alpha for the intangible resources scale was .78.

No evidence of multicollinearity among the independent variables for the
hypotheses was found. All variables entered the equation without violating the
default value for tolerance. The highest correlation among the independent
variables was .503. None of cases had the Mahalanobis distance score in excess of
18.04. This shows no multivariate outliers in the solution for the hypothesis 9.
Pearson Product Correlations between the independent variables are displayed in

Table 65.

Table 65 Pearson Product Moment Correlations of Independent Variables for Intangible
Resources Hypotheses

 

 

Correlations

Market size Income Comp Service Tangible Intangible

(log) (log) age (log) resources

Market size (log) 1.000 .503* .005 -.146 .192* -.067
Income (log) .503* 1.000 -.046 .085 .079 -.006
Competition .005 -.046 1 .000 .122 .330‘ 223*
Service age -.146 .085 .122 1.000 .168 .099
Tangible resources (log) .192* .079 .330* .168 1.000 .133
Intangible resources -.067 -.006 .223" .099 .133 1.000

 

* Correlation is signiﬁcant at the 0.01 level (2-tailed).

H12a: The eﬂ‘ects of ﬁrm’s intangible resources on product quality will be
stronger than those of tangible resources.

The semipartial correlation of intangible resources was .401(B = .414) while
the tangible resources equaled only .103 (B = .113). The difference between two
semipartial correlations explains that the strength of association with product
quality is found to be different on each resources variable. Therefore, the

hypothesis was strongly supported. A ﬁrm’s intangible resources account for more

107

15 % variance in product quality than the tangible resources, which did not
contribute to the regression. The association of intangible resources was stronger
than the association of tangible resources with product quality. Information from

this analysis is summarized in Table 66.

Table 66 Multiple Regression of Tangible and Intangible Resources on Product Quality

 

 

Coefﬁcients

B S. E B t Sig. r sr. sri2
(Constant) -10.268 73.948 -.139 .890
Market Size (log) -1.452 2.695 -.043 -.539 .591 -.043 -.035 .001
Income (log) 800016.962 .037 .472 .638 .025 .031 .001
Competition .159 .175 .065 .907 .365 .203 .060 .004
Service age .000 .049 .084 1.207 .229 .161 .079 .006
Tangible resources (log) 3.881 2.483 .113 1.563.120 .198 .103 .011
Intangible resources 1.983 .325 .414 6.099.000 .455 .401 .161

 

Model Summary
R R1 Adjusted R2 S. E of the Estimate
.490 .240 .214 . 11.4080

 

 

H12b: The effects of ﬁrm’s intangible resources on market grth performance
will be stronger than those of tangible resources.

The hypothesis was supported. Both types of resources were associated with
market growth performance. However, the semipartial correlation of intangible
resources (B = .343, sri = .332) was higher than the semipartial correlation of
tangible resources ([3 = .288, sri = .261). Intangible resources account for more 4%
variance in market growth performance than tangible resources. Table 67 reports
the result of multiple regression analysis of market growth performance on two

types of resources.

108

Table 67 Multiple Regression of Tangible and Intangible Resources on Market Growth
Performance

 

 

 

 

 

Coefficients
B S. E B t Sig. r 5r. sri2

(Constant) 406.968 209.428 1.943 .054
Market Size (log) 10.433 7.632 .102 1.367 .173 .053 .084 .007
Income (log) 403.448 48.038 -.156 -2.153 .033 -.090 -.132 .017
Competition 1.296 .496 .173 2.610.010 .355 .160 .026
Service age .000 .138 .022 .346 .730 .097 .021 .001
Tangible resources 30.006 7.031 .288 4.268 .000 .401 .261 .068
Intangible resources 4.990 .921 .343 5.418.000 .416 .332 .110
Model Summary

R R‘ Adjusted R2 S. E of the Estimate

.584 .341 .319 32.3086

 

H12c: The eﬁ‘ects of ﬁrm’s intangible resources on ﬁnancial grth performance
will be stronger than those of tangible resources.

The hypothesis was strongly supported. The semipartial correlation of
intangible resources (B = .349, sri = .338) was higher than the semipartial correlation
of tangible resources (B = .094, sr; = .085). A ﬁrm’s intangible resources account
for more 10% variance in ﬁnancial growth performance than tangible resources.
Even the tangible resources were not related to ﬁnancial growth performance. The
association of intangible resources with ﬁnancial growth performance was far
stronger than the association of tangible resources. Table 68 reports the result of
multiple regression analysis of ﬁnancial growth performance on two types of

resources.

109

Table 68 Multiple Regression of Tangible and Intangible Resources on Financial Growth

 

 

Performance
Coefficients

B S. E B t Sig. r 3r. sriz
(Constant) 117.232 230.234 .509 .611
Market Size (log) 16.826 8.390 .165 2.005.046 .127 .135 .018
Income (log) -53.378 52.811 -.081 -1.011 .314 -.003 -.068 .005
Competition .931 .546 .125 1.706.090 .232 .115 .013
Service age -.110 .152 -.052 -.725 .469 -.017 -.049 .002
Tangible resources 9.748 7.730 .094 1.261 .209 .198 .085 .007
Intangible resources 5.070 1.012 .349 5.008.000 .374 .338 .114

 

Model Summary

R R‘ Adjusted R‘ S. E of the Estimate

 

.447 .200 .173

35.5183

110

Chapter V

SUMMARY AND DISCUSSION

Financial Commitment

The ﬁrst purpose of the present research was to assess the relationship
between competition intensity and a ﬁrm’s ﬁnancial commitment to the Web-based
local information service (WLIS). This reﬂected an important theoretical issue
about whether the ﬁnancial commitment theory is applicable to the monopolistic
competitive market of WLIS, since the theory was based on oligopoly markets of
daily newspapers and local television stations. Three aspects of ﬁnancial
commitment were hypothesized to examine the claim of the ﬁnancial commitment
theory: the quantity of human resources, the quality of human resources, and large-
scale strategy.

First, a ﬁrm’s competition intensity was positively related to a larger quantity of
human resources controlling for market size, income, and service age. As WLIS ﬁrms
perceive stronger competitive pressure, they tend to employ more people. This is
consistent with the results of previous research for newspapers and television stations.

The WLIS provides a more comprehensive breadth of product line including
banner advertising, premium archives, Web page services, and electronic commerce
merchandize which traditional media do not or cannot provide. With a variety of
products in WLIS, marketing and sales promotion areas seem to become more essential
to achieve higher performance for the ﬁrms. This result is consistent with the theory of

monopolistic competition (Chamberlin, 1933). In the monopolistic competitive market,

111

advertising is considered an important method by which a ﬁrm could help diﬂ‘erentiate its
product from the products of other ﬁrms (Ekelund, Jr. & Saurman, 1988). Firms entering
newly developing markets must diﬂ‘erentiate themselves to survive. Survival is
accomplished through product differentiation and advertising (Lacy, 2000).

Another interesting point is that competition intensity affects the size of the
design staﬂ‘ more than the size of the editorial and technical staﬂ‘s. This result is
consistent with newspaper research. Kenny and Lacy (1987) found that intense
competition results in spending more money on the visual aspects of a newspaper. The
visual aspects of WLIS are also perceived to be very critical to WLIS product
competition by top management.

Second, a ﬁrm’s competition intensity was expected to have a strong impact on
the quality of human resources since the higher quality of human resources also presents
another dimension of ﬁnancial commitment. It was assumed ﬁrms should allocate more
money for higher quality people in order to compete with other products. However, there
seems to be no strong relationship between competition intensity and the quality of
human resources. Considering the conservative criterion of the present research for
hypothesis supporting, caution is necessary in rejecting the relationship since the result
showed moderate positive eﬂ‘ect of competition intensity (er = .173, B = .175). This
moderate effect may reﬂect a limited factor market of human resources in the WLIS.
Even if a competitive environment impacts a ﬁrm’s desire to employ higher quality staﬂ‘,
quality persons might not be available on the market.

The third hypothesis of ﬁnancial commitment was also supported. A ﬁrm’s

competition intensity was positively associated with larger scale conduct in advertising,

112

customer service, specialty products, and large-scale entry into market. As a ﬁrm’s
competition becomes stronger, WLIS ﬁrms are likely to conduct larger scale strategies.
The results of three hypotheses conﬁrm the claim of ﬁnancial commitment theory in
developing monopolistic competitive markets. Firms in mature oligopoly markets have
abnormal proﬁts to invest in product diﬂ‘erentiation, while WLIS ﬁrms are not making
abnormal proﬁts. WLIS ﬁrms, however, can attract investment capital that gives them

money for ﬁnancial commitments (Lacy, 2000).

Competition and Performance

The second purpose of the competition hypotheses was to evaluate how
competitive industry conditions impact ﬁrm performance and product quality.
These hypotheses were intended to test the classical connection between market
structure and ﬁrm performance derived from the industrial organization model.

First, intensely competitive WLISs were expected to have higher quality
ratings for their products in terms of editorial, aesthetic, and functional quality.
However, the result showed only moderate effects for the relationship (St; = .188, B
= .190). There are several possible explanations for the moderately positive effects.
First, the measure of product quality may not be reﬂected in all scopes of product
differentiation even if higher quality of products means differentiating the product
from other competing products. Second, product quality may be more a function of
the ﬁrm’s resources and capabilities to exploit any given market than the ﬁrm’s
competition intensity.

Second, a ﬁrm’s competition intensity was positively related to market

growth performance including advertising sales, e-commerce sales, brand

113

identiﬁcation, and market share, controlling for market size, income, and service
age. As a ﬁrm’s competition intensity becomes stronger, the ﬁrm’s market growth
performance is likely to be better. This result suggests that industry conditions
considerably impact a ﬁrm’s market performance

Finally, the hypothesis of ﬁnancial growth performance was also supported.
A ﬁrm’s competition intensity has a signiﬁcant effect on ﬁnancial growth
performance in return on investment, return on equity, and net proﬁt margin. The
explanation is that the current WLIS market did not arrived at the equilibrium of
market demand and supply because it is still underdeveloped. Competition intensity
does not have a negative effect on a ﬁrm’s ﬁnancial growth performance even in the
short run. This situation was also found in the effects of competition intensity on
market share. A ﬁrrn’s competition intensity was also positively related to the
market share because of the unique feature of an underdeveloped market. In other
words, competition affects the increase of absolute WLIS market size. Figure 3
describes a model of competition intensity on the relationship with ﬁnancial
commitment, product quality, and ﬁrm performance based on the results of

competition intensity hypotheses.

114

Figure 3

Model of Competition Intensity on Relationship with Financial Commitment, Product
Quality, and Firm Performance Controlling for Market Size, Market Income, and Service
Age

 

 

Semipartial Correlation Coefficients

 

 

 

 

 

 

 

 

Financial Commitment
.30 ’
Quantity of Human Resources
.17 , Quality of Human Resources
COMPETITION
.29 ’ Large-scale Entry Strategy
INTENSITY
.19
P Product Quality
Firm Performance
.33 I Market Growth Performance
.23 + Financial Growth Performance
.21 + Market Share

 

 

 

 

115

 

Firm Resources

The purpose of ﬁrm resource-based hypotheses was to provide evidence that
valuable and unique ﬁrm resources and capabilities provide the key sources of
competitive advantage. Four categories of ﬁrm resources were examined to verify
the resource—based theory: quantity of human resources, quality of human
resources, organizational coordination, and innovation resources.

Human Resource_s_

First, the quantity of human resources was not related to perceived quality of
product controlling for ﬁrm’s competition intensity, market size, income, and
service age. The ﬁnding of this research suggests that staff size has no direct
impact on product quality in the WLIS industry. Hiring more people does not
guarantee a higher quality of WLIS product.

The quantity of human resources had a strong impact on WLIS market
growth performance and market share. The quantity of human resources was related
to higher level of market growth performance and market share. However, the
quantity of human resources was not associated with ﬁnancial grth performance.
This indicates that the WLIS ventures still do not recover their sunk costs for hiring
employees.

On the other hand, the quality of human resources was strongly related to
higher quality of product. Product quality in WLIS seems to be more a function of
staff quality than the number of staff. Speciﬁcally, the quality of design staff was a
more important contributor to higher quality of product than was editorial and

technical staff. With a smart design, the capability to motivate customers to

116

investigate WLIS contents is considered to be the most decisive source of product
quality by managers in the industry.

The quality of human resources was also positively related to higher level of
market growth performance controlling for ﬁrm’s competition intensity, market
size, income, and service age. In particular, the quality of editorial staff was found
to be the most important contributor to market growth performance. In other words,
resources to contribute to the breadth and depth of information are more related to
the higher level of market growth performance.

As for ﬁnancial growth performance, the quality of human resources had a
positive impact. As the quality of human resources becomes higher, ﬁnancial
growth performance is likely to be better even when the ﬁrms’ competition intensity
and market size are equal. It is important that human quality resources were deﬁned
in ﬁnancial terms. Speciﬁcally, both quality of design and marketing staff had a
stronger impact on ﬁnancial growth performance while the quality of editorial and
technical staff were not related to ﬁnancial performance. For ﬁnancial performance,
the quality of marketing staff should be emphasized because WLIS is more
business-oriented than traditional media.

The results of human resources hypotheses support the notion of Brush and
Chaganti (1991), suggesting that the quantity of resources is less important than the
combination or quality of resources relative to the opportunity for competitive
advantages in a start-up company. Figure 4 summarizes the results of the human

resources hypotheses.

117

Figure 4

Model of Human Resources on Relationship with Product Quality and Firm Performance
Controlling Competition Intensity, Market Size, Market Income, and Service Age

 

 

Semipartial Correlation Coefficients

Quantity of
Human Resources

Product Quality

Market Growth Performance

Financial Growth Performance

 

Quality of
Human Resources

Market Share

 

118

 

Organizational Coordination and Innovation Resources

 

All three hypotheses to test the effects of organizational coordination were
supported. Organizational coordination was deﬁned as teamwork within the
business unit, coordination between departments, and organizational ﬂexibility. A
ﬁrm’s organizational coordination had a strong impact on product quality and ﬁrm
performance such as market growth and ﬁnancial growth performance. WLIS
product activity is more than a simple assembly of design, editorial, and technical
resources. The ability to organize and synthesize the productive capacity of human
resources appears to be of key importance. The result supports that sustainable
competitive advantage involves complex patterns of coordination between people
and between people and other resources (Grant, 1991).

The hypotheses of innovation resources were intended to examine whether
innovation in combining or deploying resources can lead to a competitive
advantage, thus superior performance. The innovation resources were deﬁned as
innovative ideas, creative marketing, new service development, innovative staff, and
ﬁrm reputation for innovativeness. Innovation resources were positively and
strongly related to product quality, market growth performance, and ﬁnancial
growth performance. As expected in the hypothesis stage, the strength of
association with dependent variables was the highest of other resources examined
for the present research. Companies that competed based on innovative resources
were in the best position to take advantage of opportunities in the rapidly changing
WLIS industry. For superior performance, it is essential to have the capability to

create a new and innovative applications that others will want to imitate.

119

It is important to recognize the internal connection between organizational
coordination and innovation resources. Both ﬁrm resources are involved in creating
and exercising strategic ﬂexibility (Sanchez, 1995). Dynamic product markets such
as Web information service require frequent adjustments in product strategies and
coordinating the uses of product creation resources. Thus, organizational
coordination can provide capabilities to respond to various demands from a dynamic
competitive market. Without organizational coordination and cooperation, change
in product strategies could take a longer time and more effort. In other words, the
ﬂexibility reduces the difficulty of making an innovational shift in the organization.
The uncertainty of the WLIS market requires more of a ﬁrm’s strategic ﬂexibility to
exploit new market opportunities. According to the results of the hypothesis testing,
Figure 5 describes a model of organizational coordination and innovation resources
in relationship with product quality and ﬁrm performance.

In addition, the present research tested the effects of tangible and intangible
resources on ﬁrm performance. For the testing of the relationship, tangible
resources were operationalized by the quantity of human resources. Intangible
resources were deﬁned by three types of resources: the quality of human resources,
organizational coordination, and innovation resources. The effects of a ﬁrm’s
intangible resources on product quality were stronger than those of tangible
resources controlling for competition intensity. The results are consistent with the
argument of resource-based theory. Invisible assets based on learning by doing that
are accumulated through experience are more important for product quality in

WLIS. A ﬁrm’s intangible resources also had a stronger association with market

120

growth and ﬁnancial growth performance than tangible resources such as the
number of employees. Although a ﬁrm’s resource-based capabilities are shaped by
a combination of tangible and intangible resources, the higher levels of intangible
resources seem to determine the capabilities of WLIS ﬁrms to build sustainable

competitive advantages.

Figure 5

Model of Organizational Coordination and Innovation Resources on Relationship with
Product Quality and Firm Performance Controlling Competition Intensity, Market Size,
Market Income, and Service Age

 

 

Semipartial Correlation Coefficients

Organizational
Coordination .32
Product Quality
.28
Market Growth Performance
.36
Financial Growth Performance
.35
Innovation
Resources

 

121

 

Competitive Strategies

The present research assumed that the large-scale strategy would be more
appropriate to drive success in product quality and ﬁrm performance because WLIS
is an increasing return business. A ﬁrm’s large-scale strategy was positively related
to higher quality of product controlling for ﬁrm’s competition intensity, market size,
income, and service age. Product quality is a function of a ﬁrm’s large-scale
strategy including specialty services, advertising, customer services, and large-scale
entry. A ﬁrm’s large-scale strategy had a stronger impact on market and ﬁnancial
growth performance. The exception to these hypotheses was the lack of a
relationship between large-scale strategy and market share. Thus market share
seems to be more a function of service age rather than of large-scale strategy.

A ﬁrm’s earlier entry into market was not related to higher quality of
product, market growth performance, and ﬁnancial growth performance controlling
for market size, income, competition intensity, and strategy of scale. There seems
to be no pioneer advantage for product quality and ﬁrm growth performance. A
ﬁrm’s earlier entry into market was only positively related to market share.
However, growth performance is a more important measure for ﬁrm success in an
immature industry than current market share.

As Covin, Slevin, and Heeley (1999) noted, market pioneers are often
outperformed by later market entrants. Kerin, Varadarajan, and Peterson (1992)
stated that the overall magnitude of positional advantages accruing to the ﬁrst
mover depends on the comprehensive competitive strategies employed by the

pioneer and followers. Thus, the tactics associated with early entry and later entry

122

can have a strong impact on the ultimate effectiveness of the market entry order
decision. As discussed earlier, large-scale strategy attenuated the overall magnitude
of the ﬁrst mover’s competitive advantage. In the WLIS industry, without effective
strategies such as large-scale strategy and intangible resource commitment, ﬁrst
mover advantage has no effects on product quality and ﬁrm growth performance.

Figure 6 summarizes the testing results of the competitive strategy hypotheses.

Figure 6

Model of Large-scale and Eariy Entry Strategy on Relationship with Product Quality and
Firm Performance Controlling Competition Intensity, Market Size, Market Income, and
Service Age

 

 

Semipartial Correlation Coefﬁcients

 

 

Large-Scale .21
Strategy P Product Quality
.33
.26
Market Growth Performance
Financial Growth Performance
Early .27 ’ Market Share
Entry Strategy

 

123

 

Product Quality

Perceived product quality was positively related to higher level of market
and ﬁnancial growth performance. The ﬁnding that quality enhances ﬁrm
performance is consistent with the premise that customers are drawn to quality
products. With loyal customers, WLIS ﬁrms have some market power to charge
more than market price for their premium services.

It is interesting that product quality was not related to higher level of market
share. In a mature industry, a ﬁrm’s product quality directly impacts its relative
market share (Kroll, Wright, & Heiens, 1999). The increasing demand for high-
quality products may result in a larger market share. The WLIS, however,
subscribes to an immature industry. In an infant industry such as WLIS, product
quality does not seem to be directly related to market share. A considerable time

frame may be needed in order for product quality to materialize market share.

Resource and Competition

The ﬁrst research question was intended to compare the relative strengths of
the effects on performance and product quality for two different levels of predictors,
market and ﬁrm resources. The results suggest that the effects of a ﬁrm’s intangible
resources are more important than market effects on product quality. The difference
in the strength of association was substantial. The results are consistent with
previous research about ﬁrm-speciﬁc effects and market effects on ﬁrm
performance (McGahan & Porter, 1997; Roquebert, Phillips, & Westfall, 1996;

Rumelt, 1991).

124

For market growth performance, the effects of a ﬁrm’s quantity of human
resources and innovation resources were stronger than the effects of competition
intensity and market size. However, human resources quality and organizational
coordination showed no stronger effects compared with the effects of competition
intensity. The results suggest that ﬁrm resources can somewhat better explain the
mechanism of market growth performance than can industry conditions such as
competition intensity and market size. Market-level effects that promote
homogeneity among ﬁrms coexist with ﬁrm-level effects that generate heterogeneity
(Mauri & Michaels, 1998). The results provide evidence of the complementarity
between resource-based theory and industrial organization, because the degree of
difference was not substantial. In other words, both effects of market and ﬁrm
should be examined to explain the mechanism of market growth performance.

The effects of a ﬁrm’s intangible resources were stronger than the effects of
industry conditions on ﬁnancial growth performance. Especially, innovational
resources had substantially stronger impacts on ﬁnancial grth performance than
competition intensity. The ﬁndings support that a ﬁrm’s resource effects are more
important than market competition effects on product quality and ﬁrm performance,
even if a complementarity between resources and market competition effects was
found on market performance. Moreover, market effects actually work through a
manager’s perceived frame of a competitive market. Porter (1980) argued that a
viable competitive strategy is dependent on the manager’s understanding of a

competitive market.

125

Organizational Goals

The present research identiﬁed three organizational goals in the WLIS:
service quality, proﬁtability, and business competition. The unique dimension of
organizational goals in the WLIS is business competition. The business competition
goal was characterized by beating the competition, protecting the franchise,
maintaining the ﬁrm’s position, and attaining business leadership. The business
competition goal reﬂects the facts that the WLIS ﬁrms are concerned not only with
the present proﬁtability, but also with their future positions to survive in an
emerging market.

First, the service quality goal was related to the higher level of intangible
resources and large-scale strategy. The goal seems to strongly affect ﬁnancial
commitments to achieve a higher quality of service. As a result, the service quality
goal had a positive impact on product quality. The interesting point is that the
service quality goal is not directly related to ﬁrm performance. Without business
orientation, a simple service quality goal does not seem to affect higher market and
ﬁnancial performance.

Second, the business competition goal was associated with the higher level of
quality of human resources and large-scale strategy. However, unlike the service
quality goal, the organizational goal of business competition had a strong impact on
market growth performance. Thus, there seems to be interaction effects between the
business competition goal and ﬁnancial commitment on market performance. In
other words, when a ﬁrm has a stronger goal for business competition, its ﬁnancial

commitments may materialize market performance in the WLIS industry. The

126

pursuit of the business competition goal is more important in the WLIS industry
than in the traditional media industry.

Finally, the proﬁtability goal was not related to resource commitments, but
the goal was related to higher level of ﬁnancial growth performance. This is a very
interesting phenomenon in that ﬁrms with a strong proﬁtability goal can achieve
high ﬁnancial performance without valuable resources. Porter (1980) proposed two
kinds of strategies for competitive advantages: cost-leadership and differentiation.
The resource commitments are related to competitive advantage of differentiation in
product and service.

On the other hand, having a low-cost position can yield the ﬁrm above-
average returns in its industry. Cost-leadership is often pursued as a strategy by
ﬁrms without resources (Chandler & Hanks, 1994). For example, in the initial stage
of WLIS market growth, even if a ﬁrm did not provide a higher quality of content
and services, the ﬁrm could achieve above-average return with minimum costs by
providing only classiﬁeds. This may be a viable short-term strategy for a small
start-up ﬁrm due to the current market condition, in which an average WLIS ﬁrm
has no success in ﬁnancial performance. Figure 7 illustrates a model of
organizational goals in relationship to ﬁrm resources, product quality, and ﬁrm

performance.

127

Figure 7

Model of Organizational Goal on Relationship with Firm Resources, Strategies, Product
Quality, and Firm Performance Controlling Market Size, Market Income, and Service
Age

 

 

Semipartial Correlation Coefﬁcients

Goal 1

.22

Service Quality 4 Quality of Human Resources
.37

\ Organizaticmal Coordination

 

Innovational Resources

Goal 2
Proﬁtability Large-scale Entry Strategy
.27
Product Quality

.23
[A Market Growth Performance
Goal 3

Business Competition Financial Growth Performance

 

128

 

Venture Origin

A ﬁrm’s service quality goal varied with venture origin deﬁned by daily,
weekly newspapers; local television stations; radio stations; and Internet ventures.
Internet ventures pursued higher service goals than any other service. Venture
origin also affected the pursuit of the proﬁtability goal. Internet venture and radio
groups had a higher proﬁtability goal than the newspaper group.

The research question also examined the impact of venture origin on resource
commitments. Internet venture and daily newspaper groups had more dedicated
staffs than TV, radio, and weekly newspaper groups even if there was no difference
in quality of human resources. For organizational coordination, the Internet venture
group had a higher level than the daily newspaper group. Internet venture and
television groups also showed a higher level of innovation resources than the daily
newspaper groups. Thus the Internet venture group was found to have more human
resources and higher organizational and innovational resources than other origin
groups. In particular, although the daily newspaper group was found to have the
same level of human resources as the Internet venture group, it has the lowest level
of organizational coordination and innovation resources among the ﬁve groups.

The next research question attempted to identify how variation in venture
origin affects a ﬁrm’s product quality and performance. Five groups were different
in product quality. Internet venture and radio groups had a higher quality of product
than other groups. For market growth performance, Internet venture was ranked
ﬁrst and daily newspaper was the second highest. Market share index varied in

venture origin. Daily newspaper held a market share of 5.47%, Internet venture

129

5.42%, local television station 1.40%, weekly newspaper 1.29%, and radio station
.60%. Considering the market share and market growth performance, the current
WLIS market seems to be characterized by strong competition between daily
newspapers and Internet ventures.

On the other hand, ﬁnancial growth performance was different according to
venture origin. Internet venture had higher level of ﬁnancial growth performance
than any other group. Thus, although daily newspaper has a higher market share
than other groups, only Internet venture achieves ﬁnancial success in the WLIS
industry. There are several reasons for the superior performance of the Internet
venture group. First, Internet ventures including independent Internet ﬁrms, media
subsidiaries, and joint ventures might have greater managerial and strategic
autonomy to exploit resources leading to superior performance. In other words,
they seem to have operational and market ﬂexibility to react to strategy by
competitors and changing environments. The newspaper group may have low
ﬂexibility because resources are shared with the parent service. Second, in the
Internet venture group, WLIS is deﬁned as an independent business unit with
distinctive organizational goals. Thus the Internet venture group does not have
supplemental goals to directly protect or help a parent service. The priority of

business goal pursuit seems to affect ﬁrm performance.

Synthetic Model and Implications for the WLIS industry
Eight factors were found to be important contributors to higher level of

market growth performance: competition intensity, four ﬁrm resource variables,

130

large-scale strategy, business competition goal, and product quality. The model of
market growth performance in the WLIS is summarized in Figure 8. As can be seen
in Figure 9, seven factors were found to affect higher level of ﬁnancial growth
performance: competition intensity, three ﬁrm resource variables, large-scale
strategy, proﬁtability goal, and product quality. Six factors were important
contributors to higher quality of product: competition intensity, three ﬁrm resource
variables, large-scale strategy, and service quality goal. The model of product
quality is showed in Figure 10.

Competitive market condition impacts both market growth and ﬁnancial
growth performance. A ﬁrm’s human resource quality, organizational coordination,
and innovation resources affect the level of both performances. However, the
quantity of human resources impacts only market growth performance.
Strategically, large-scale strategy was positively related to higher level of market
and ﬁnancial growth performance. However, earlier entry strategy does not affect
the level of either performances. Finally, as a ﬁrm strongly pursues the business

competition goal, its market growth performance tends to be higher.

131

Figure 8

Model of Market Growth Performance in the Web-based Local Information Service

 

 

Semipartial Correlation Coefﬁcients

Competition lntensi

 

 

.33
Quantity of Human Resources .31
. Market
Quallty of Human Resources £8 Growth
.28 Performance

Organizational Coordinatio

 

 

Innovation Resources

Large-scale Strateg

Product Quality

 

Business Competition Goal

 

132

 

Figure 9

Model of Financial Growth Performance in the Web-based Local Information Service

 

 

Semipartial Con'elation Coefﬁcients

Competition Intensity

 

 

 

 

 

 

    
  

.23
Quality of Human Resource .26
26 Financial
Organizational Coordination Tr Growth
Performance
.35
Innovation Resources
.26

Large-scale Strategy

Product Quality

Proﬁtability Goal

 

133

 

Figure 10

Model of Product Quality in the Web-based Local lnforrnation Service

 

 

Semipartial Correlation Coefficients

Competition Intensity

  

Quality of Human Resource

 

 

.32

 

 

 

 

Organizational Coordination * [gaging
Innovation Resources

.21
Large-scale Strategy .24

Service Quality Goal

 

134

 

There are several implications and suggestions for the WLIS industry in
these results.

1. Competitive market condition positively affects the level of market growth
performance. Even the competitive market was related to a higher level of ﬁnancial
growth performance. Competition is currently acting to increase absolute market
size. Even if there are more than twenty WLISs in a metropolitan market, the
market is still underdeveloped. This indicates that many opportunities are open to
both entrant and incumbent ﬁrms to exploit the market even in intensively
competitive conditions.

2. As WLIS ﬁrms perceive stronger competitive pressure, the ﬁrms tend to
employ more people. A WLIS ﬁrm seems to perceive that quality of peOple is more
important for Web information services. As the result showed a moderate
relationship between quality of human resources and competition intensity,
however, a ﬁrm’s desire to employ higher quality people does not exactly match
with real employment. The quality of human resources directly affects ﬁnancial
growth performance. Speciﬁcally, a higher quality of design and marketing staffs
was found to be very important in achieving higher ﬁnancial performance.

3. Even if quality of human resources affects ﬁrm performance, a ﬁrm’s
organizational coordination and innovation resources are more critical for ﬁrm
performance. A ﬁrm’s efforts should be focused on developing coordination
between design and editorial staff, teamwork within the business unit,
organizational ﬂexibility, innovative ideas and innovative staff, creative marketing,

and new services.

135

4. Mainstream theory has emphasized the ﬁrst mover advantage in Internet
business. However, there seems to be no pioneer advantage for ﬁrm performance in
the WLIS. Thus, no ﬁrm seems to achieve critical mass to realize economy of scale
and scope. Large-scale strategy is critical to achieve higher ﬁrm performance than
earlier entry. Without large-scale strategy and intangible resource commitment,
ﬁrst mover advantage has no effects on ﬁrm performance.

5. There are three main organizational goals in the WLIS ﬁrms: service
quality, proﬁtability, and business competition. A WLIS ﬁrm’s service quality goal
is not directly related to ﬁrm performance. Only the business competition goal
affects a higher level of market performance. If a ﬁrm has a strong goal for
business competition, its resources may materialize market performance. The
pursuit of the business competition goal is very important in the WLIS industry in
the long run. The proﬁtability goal seems to be currently pursued by small ﬁrms
with limited resources. This may be a viable short-term strategy for very small
ﬁrms.

6. The daily newspaper group holds an average market share of 39%; Internet
venture group 38%; local television station group 10%; weekly newspaper group
9%; and radio station group 4% in 81 metropolitan markets. Thus, the current WLIS
market is characterized by competition between daily newspapers and Internet
ventures. There are distinctive differences in resource commitment and ﬁrm
performance between the two groups. Even if daily newspaper has the same level of
human resources as Internet venture, the daily newspaper group has the lowest level

of organizational coordination and innovation resources. This difference of

136

resource commitment results in lower ﬁrm performance than the Internet venture
group. Daily newspapers should focus on developing organizational coordination
between task areas and an innovative organizational climate to achieve ﬁnancial

performance and compete with Internet ventures in the WLIS.

Limitations and Recommendation for Future research

This study contributes to literature on media economics and management.
The results of the present research are consistent with previous media economic
research on market structure and competition. It conﬁrms the claim of ﬁnancial
commitment theory even in a monopolistic competitive market. This research
elaborated the link between market structure and ﬁrm performance by analyzing the
effects of ﬁrms’ resource commitments, organizational goals and behavior. The
results also conﬁrm the argument of the resource-based theory focusing on ﬁrms’
uniqueness to explain the performance. The present research reveals how market-
level effects are mediated by ﬁrm-level conduct for ﬁrm performance and product
quality.

Nevertheless, the research has some inherent limitations. First, this research
employed perceptual measures of product quality and ﬁrm performance constructs.
There is a possibility that the results may be contaminated by the perceptual
measures of ﬁrm performance. For example, the respondent’s position and task
could affect the assessment of the ﬁrm’s resources, product quality, and
performance. In addition, the statistical results of the present research showed low

squared multiple correlations for individual predictor tests because measures with a

137

seven-point scale have a limited amount of variance. A future study could be
strengthened by using continuous industry data when the industry becomes more
mature. In addition, ﬁrm-level measures could be improved by aggregating multiple
responses in the same ﬁrm.

Second, the present research only investigated the ﬁrm’s internalized
resources including human resources, organizational, and innovation resources.
However, the resources available to a ﬁrm could include its relational resources
owned or controlled by other ﬁrms which the ﬁrm can access, and market resources
which the ﬁrm can obtain through market transactions (Sanchez, 1995). Further
research needs to develop the effects of relational resources and market resources
for sustainable competitive advantage.

Third, the nature of the relationship with the parent company may inﬂuence
the objectives with which the ﬁrm is managed, the resources available to it, and
determine some operations or functions that it shares with other units (Porter, 1980).
The relatedness in venturing refers to the degree to which a new venture shares
important resources with the parent ﬁrm. Future research needs to explore how the
relatedness in venturing affects ﬁrm resource commitments, competitive strategies,
and ﬁrm performance.

Fourth, the resource base for the present research focused on Ricardian rents
which are based on the possession of scarce and valuable resources. In venturing,
Schumpeterian rents may be derived from successful entrepreneurship (McGrath et
al., 1996). In a Schumpeterian view, ﬁrms develop resources that are unique for a

signiﬁcant period of time although the resources do not yield rents in the long run

138

(Bogner, Mahoney, & Thomas, 1998). In the WLIS business, like in other Internet
businesses, Schumpeterian entrepreneurship could have a strong impact on ﬁrm
performance in the short-run.

Finally, the results of the present research reﬂect the early stage of the WLIS
market. The relationships between market and ﬁrm variables may change over time.
As the WLIS industry matures, the market structure may be more concentrated with
a few dominant players. Therefore future research is needed to examine possible
changes in relationships between market and ﬁrm variables. The product content
may also change reﬂecting better knowledge of consumer demand and usage. This

suggests ongoing content analysis to describe the evolving WLIS product.

139

APPENDICES

140

APPENDIX A

 

1419' h

 

MISSING VALUE ANALYSIS
N Mean Std. Deviation Missing No. of Extremes
Count Percent Low
GL_1 183 6.3716 .9629 0 .0 14
GL_2 182 6.2088 1 .0301 1 .5 15
GL_3 183 6.1585 1.1054 0 .0 13
GL_4 183 5.8525 1 .2861 0 .0 4
GL_5 183 6.2186 1.0251 0 .0 11
GL_6 182 4.8956 1 .7225 1 .5 7
GL_7 183 6.2842 .9055 0 .0 10
GL_8 183 6.1475 1 .0509 0 .0 16
GL_9 183 5.5301 1.5994 0 .0 11
GL_10 183 5.8525 1 .3927 0 .0 7
GL_1 1 183 5.6940 1 .2728 0 .0 3
GL_12 183 6.0546 1.0931 0 .0 2
GL_13 183 5.6175 1.3852 0 .0 6
GL_14 183 5.7049 1 .4974 0 .0 6
GL_15 183 6.3825 1 .0088 0 .0 9
GL_16 182 6.3132 1.0594 1 .5 12
GL_1? 181 6.2541 1.0009 2 1.1 10
GL_18 182 4.0275 1.8311 1 .5 0
GL_19 183 6.0710 1.2316 0 .0 18
GL_20 182 5.5330 1 .4206 1 .5 7
GL_21 183 6.2678 1.1190 0 .0 16
GL_22 183 5.7158 1.3733 0 .0 6
RE_1 182 67.72% 41 .95% 1 .5 0
RE_2 182 61 .68% 38.05% 1 .5 0
RE_3 181 58.70% 40.27% 2 1 .1 0
RE_4 182 55.36% 41 .33% 1 .5 0
RE_5 182 60.30% 37.81% 1 .5 0
RE_6 181 58.29% 38.37% 2 1 .1 0
QOE1 181 5.6243 1.4307 2 1.1 5
QOE2 181 5.5028 1.5078 2 1.1 0
QOE3 182 5.3571 1 .4485 1 .5 6
QOE4 181 4.9558 1 .7056 2 1.1 6
OR1 179 5.2626 1 .3834 4 2.2 1
0R2 181 4.6906 1.5178 2 1.1 4
OR3 181 5.2818 1.4233 2 1.1 1
0R4 181 4.9890 1 .5705 2 1 .1 5
IR1 181 6.0110 1.0593 2 1.1 1
IR2 180 4.8667 1.5259 3 1.6 3
IR3 180 4.8111 1.4094 3 1.6 3
IR4 179 5.0559 1 .3099 4 2.2 1
IRS 179 5.0894 1 .4889 4 2.2 1
IR6 179 5.6480 1.2012 4 2.2 6
ST1 179 5.1285 1 .4689 4 2.2 2
ST2 181 4.0829 1 .7635 2 1 .1 0
ST3 181 5.2320 1 .6704 2 1 .1 0
ST4 180 5.4944 1 .3474 3 1 .6 7

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

 

141

 

High

 

N Mean Std. Deviation Missing No. of Extremes
Count Percent Low
ST5 175 3.7657 1.6213 8 4.4 0
ST6 178 4.1629 1 .7439 5 2.7 0
IPF1 182 5.9615 1.5710 1 .5 30
IPF2 182 4.5769 1 .9587 1 .5 0
IPF3 183 6.1421 1.2005 0 .0 16
IPF4 182 5.9121 1.4539 1 .5 7
IPF5 183 5.3169 1.7691 0 .0 0
IPF6 182 5.0934 1.8107 1 .5 0
IPF7 182 5.4396 1 .6468 1 .5 0
SPF 1 179 4.2961 1.9736 4 2.2 0
SPF2 176 3.0511 1.8586 7 3.8 0
SPF3 181 5.1602 1.4764 2 1.1 6
SPF4 179 4.8547 1 .5904 4 2.2 9
SPFS 179 3.8603 1 .8383 4 2.2 0
SPF6 178 3.6798 1 .8392 5 2.7 0
SPF7 178 3.7978 1 .8937 5 2.7 0
SQR1 182 5.3681 1.6086 1 .5 0
SQR2 182 5.5824 1 .5382 1 .5 9
SQR3 182 5.4780 1.5113 1 .5 0
SQR4 182 5.3242 1 .4752 1 .5 0
SQR5 182 4.7143 1.6134 1 .5 0
SQR6 182 5.1813 1.5142 1 .5 2
SQR7 182 5.0604 1.5562 1 .5 1
SQR8 182 5.1923 1 .4722 1 .5 1
SQR9 182 4.8516 1.6933 1 .5 5
SQR10 182 4.1429 1.9444 1 .5 0
SQR1 1 182 4.2088 2.0275 1 .5 0
SQR12 182 3.4945 2.0991 1 .5 0
COM1 182 4.9286 1 .7465 1 .5 10
COM2 182 4.8626 1 .6480 1 .5 8
COM3 182 5.3571 1 .5588 1 .5 0
COM4 182 4.8132 1.8171 1 .5 13
COM5 181 4.1989 1.9219 2 1.1 0
START 180 35.66 18.31 3 1.6 0
NOE1 177 2.7839 3.7346 6 3.3 0
NOE2 177 1.4319 1.9569 6 3.3 0
NOE3 177 1 .4090 2.0813 6 3.3 0
NOE4 177 1.8828 2.8719 6 3.3 0
ECH 175 4.41 1.67 8 4.4 0
USER 153 20051 .26 31292.75 30 16.4 0

gogioﬁoooooooooooooocooooooooooooooooooo

 

a Number of cases outside the range (01 - 1.5‘IQR, Q3 + 1.5*IQR).

142

APPENDIX B

 

 

MARKET CHARACTERISTICS
(Sorted by market siza)
Metro Population Income/hrs. Education Market Size“ Response
New York 8,712,127 70,507 75.60 6,882,580 3
Chicago 8,078,169 68,958 81.76 4,951 .91 8 3
Los Angeles 9,297,896 65,408 72.38 3,998,095 3
Boston 5,985,439 68,453 85.05 3.591 .263 2
Washington DC 4,794,945 73,138 89.14 3,188,638 4
Philadelphia 5,055,780 68,216 81.71 2,467,221 4
Detroit 4,424,187 68,676 80.86 2,433,303 3
Riverside, CA 3,185,622 50,963 77.32 2,229,935 1
Atlanta 3,926,961 57,619 84.20 2,159,829 3
Dallas 3,263,944 63,207 82.24 1 .840,864 7
Nassau NY 2,710,066 90,927 89.58 1 .761,543 1
Minneapolis 2,915,783 63,961 92.30 1 .370.418 3
Seattle 2,338,368 66,257 94.24 1 227,643 4
San Jose 1,632,382 81 .423 84.79 1,224,287 1
Baltimore 2,553,142 66,930 77.97 1,148,914 2
San Diego 2,740,077 61 .612 84.68 1,115,211 3
Cleveland 2,256,895 62,560 81.49 1,112,649 4
Portland 1 ,884,047 54,048 88.13 1 .079,559 8
Pittsburgh 2,388,733 59,265 82.41 1 .058,209 3
St. Louis 2,634,547 64,619 76.54 1,053,819 5
Tampa 2,300,477 51,844 81.33 1 ,035.215 2
Denver 2,026,581 56,902 91 .93 952.493 2
Phoenix 2,964,296 50,734 83.59 939.682 3
Norfolk, VA 1 .621 .878 51.723 85.00 932,580 2
Fort Worth 1,628,691 56,170 82.16 918,582 2
Milwaukee 1 ,492,229 64,370 86.55 895,337 1
San Francisco 1,681,221 87,424 85.32 840,611 1
New Haven. CT 1,643,928 86,336 84.37 821,964 2
Columbus 1 ,527,880 56,310 85.97 779,219 1
Austin 1,153,433 48,979 84.46 767,033 4
Salt Lake City 1,311,798 55,109 90.10 764,778 3
Cincinnati 1 .660,813 58,913 80.56 758,992 3
Las Vegas 1 ,424.325 46.945 81 .55 750,619 3
Sacramento. CA 1 .561 .669 56.105 85.85 702.751 1
Kansas City 1 ,752,413 58,444 88.61 700.965 2
Nashville 1,218,637 57,847 81.60 661.720 4
Oriando 1,531,503 51 .035 85.16 638.637 3

 

143

 

 

Metro Population Income/hrs. Education Market Size“ Response
New Orleans 1,356,430 56,088 72.51 623,958 1
Oklahoma City 1 .073.175 48.817 85.71 622,442 1
Fort Lauderdale 1,535,978 57,259 83.25 614,391 1
Buffalo 1 ,174.216 59.404 82.16 587.108 1
Indianapolis 1 .573.568 58.488 83.26 577.499 3
Tucson 847,607 44,609 83.40 550.945 1
Hartford. CT 1,125,446 72,230 84.28 545.841 3
San Antonio 1,600,968 51,758 75.43 536,324 2
Honolulu 898,398 78,194 84.45 516.579 2
Charlotte. NC 1,422,683 54,703 77.35 497,939 1
Greensboro. NC 1 208,425 53,814 76.1 1 482.570 2
Providence, RI 91 1 .074 61 .129 76.18 423.649 2
Scranton PA 633,805 52,566 77.69 411.973 1
Louisville 1 .024.323 56.577 81 .07 409.729 1
Greenville. SC 939.973 49.367 70.45 404.188 1
Jacksonville, FL 1 .0461 15 55.270 84.02 392.293 2
Dayton 970.362 57.053 83.67 388.145 1
Tacoma. WA 701 .157 51 .299 89.52 373.717 1
Fresno, CA 905,956 49,749 68.08 371 .442 4
Memphis. TN 1,143,631 59,250 80.52 362.531 3
Albany 884.888 63.864 86.01 353.955 2
Colorado Spring 525.360 47.209 90.01 351.991 2
Omaha 707,575 58,189 90.50 346,712 1
Wilmington DE 576.295 65.579 82.45 345.777 2
Rochester, NY 1 .106,032 65,110 85.07 331.810 1
El Paso 744.633 41 .221 66.31 279.237 1
Little Rock 578.451 52.639 83.62 267.823 5
Bakersﬁeld, CA 669.363 46.987 69.90 267.745 1
Mobile, AL 549.447 46.345 76.19 264,284 1
Columbia 508,984 56,957 83.24 259.582 2
Toledo 619,653 58,172 83.84 258,395 3
Harrisburg. PA 635.454 57.674 82.09 254.182 1
Albuquerque 730.069 51 .288 84.82 240.923 4
Allentown. PA 632.844 57.584 78.39 240,481 4
Akron. OH 699,621 55,544 84.57 230.875 1
Knoxville 693.503 49.626 78.29 221 .921 1
Tulsa 780,085 51 .121 85.61 214.523 2
Sarasota FL 564,807 61 .241 85.41 211,803 2
Fort Wayne 490.336 57.162 84.40 210.844 1
Baton Rouge. LA 594.891 55.169 82.14 208,212 1
Youngstown. OH 601.607 52.299 80.02 180,482 1
Springﬁeld. MA 595.436 56.447 80.44 178.631 1
Wichita KS 526.218 56.291 86.43 157,865 2
Charleston, SC 493,767 57,322 80.23 148.130 1

 

* Estimates based on responses of WLIS managers.

144

APPENDIX C

SURVEY INVITATION E—MAIL LETTER

 

 

Dear Web Site Manager".

We are conducting research that can contribute to an understanding of how the Internet shapes a
new model for local information service. The enclosed survey is part of the academic research
project being conducted at Michigan State University. Your Web site has been specially selected
as a participant in this research. Your response is very important to its success. Your participation
is voluntary and your privacy will be protected.

We have tried to keep the survey Short so it will take only about 10 minutes to complete. The
success of this study depends on your willingness to share your thoughts. You indicate your
voluntary agreement to participate by completing and returning this questionnaire. Data will be
reported in aggregate and reports of research ﬁndings will not associate subjects with speciﬁc
responses or ﬁndings.

We will acknowledge your valuable response by providing you with a copy of the ﬁnal repart.
Please click through the following link now or enter the URL in your web browser to complete the

questionnaire and submit your response to us.

http://www.msu.edul~nohgheeyflssurveyhtm

Sincerely.

Thomas Baldwin, Distinguished Professor
Department of Telecommunication

Ghee Noh. Researcher
Mass Media Ph. D. Program

College of Communication Arts and Sciences
Michigan State University

East Lansing. MI 48824

517-333-6696

nohgheengilotmsuedu

If you have any questions or concerns about participants' rights as human subjects of research, please
contact to UCRIHS Chair. David E. Wright (517-355-2180).

 

145

 

APPENDIX D

FIRST FOLLOW UP E-MAIL

 

 

Dear Web Site Manager:

Two weeks ago you received a survey invitation letter on your local information service on the
Web. If you have already completed and submitted the questionnaire. thank you! If you have not
done so, then please take a few minutes now to complete it and submit it. We need your answers
in order to present a true picture of how the Internet shapes a new model for local information
service. We are sure you will ﬁnd the questionnaire stimulating and interesting to answer. Data
will be reported in aggregate and reports of research ﬁndings will not associate subjects with
speciﬁc responses or ﬁndings.

We will acknowledge your valuable response by providing you with a copy gf the ﬁnal Lama.
Please click through the following link now or enter the URL in your web browser to complete the
questionnaire and submit your response to us.

http:Ilwww.msu.edul~nohgheeylwi§§ur,htm

Sincerely,

Thomas Baldwin, Distinguished Professor
Department of Telecommunication

Ghee Noh. Researcher
Mass Media Ph. D. Program

College of Communication Arts and Sciences
Michigan State University

East Lansing MI 48824

517-333-6696

nohgheeyQpilotmsuedu

 

146

 

APPENDIX E

SECOND FOLLOW UP E-MAIL

 

 

Dear Web Site Manager.

Three weeks ago you received a survey invitation letter on your local entertainment and
information service on the Web. If you have already completed and submitted the questionnaire,
thank you! If you have not received the letter or have not yet had time to complete it. then please
take a few minutes now to complete it and submit it. Please be assured that your responses will
be anonymous and aggregated with other respondents.

As academic researchers. we rely on the cooperation of people like you in order to ﬁnd out more
about how the Intemet shapes a new model for local information service.

We will acknowledge your valuable response by providing you with a copy of 1119 ﬁnal mmd.
Please click through the following link now or enter the URL in your web browser to complete the
questionnaire and submit your response to us.

Questionnaire: http://www.msu.edul~nohgheeylissumy.htm

Sincerely.

Thomas Baldwin, Distinguished Professor
Department of Telecommunication

Ghee Noh. Researcher
Mass Media Ph. D. Program

College of Communication Arts and Sciences
Michigan State University

East Lansing MI 48824

517-333-6696

nongheeyggilotmsuedu

 

147

 

APPENDIX F

 

WIS SURVEY

 

 

e would like you to consider the ways that your business unit competes
'th other services in local information services on the Web. These
ervices include newspapers. local TV and radio stations. and city guide
Web sites. Your business unit may not be a whole organization but a

epartment or section involved in production. sales and promotion for Web-
based information service. Please respond to the following statements as
completely as possible.

1. The following statements represent organizational goals that a business unit may pursue.
Please indicate how much importance top management at your Web-based service places on
the following beliefs or values by selecting the number that best represents the importance.

Not at all important 1 2 3 4 5 6 7 Extremely important

the well
the information
to the user's needs

the needs of the

3
3
3
3
3
3
3

3
3
3
3
3
3
3

costs

3
3
3
3
3
3
3

ﬁrm and brand

3
3
3
3
3
3
3

revenue

transmission standards

the service and

the best

our ﬁrm's in the market
the best

33333333
33333333
33333333
33333333
33333333
33333333
33333333

 

148

information

leaders

our franchise in the market

of

 

2. Think about the relationship between your Web business unit and parent organization or
company. Roughly what proportion of your business resources are shared with the parent
company? Please circle the percentage that applies to your unit. For example. if you are
located entirely in the parent building you would respond 100%.

 

3. Compared to the top competitor in your city market. how do you evaluate the quality of
your employees working on the following areas? Please select the number that best
describes your Web business unit.

Much Worse 1 2 3 45 8 7 Much Better

  
   
     

  

[IIIZIEIEIEIEIIZI

149

4. To the best of your knowledge. how do you evaluate your organizational routines in the
following areas?

Very Poor 1 2 3 4 5 6 7 Excellent

 

 

oordination between editorial staff and design
Eaff

 

oordination between advertising staff and
roduction Staff
[Teamwork within our business unit
lexibility of organization structure in response
0 the si nals of market

5. The following statements deal with speciﬁc resources that a business unit may have. To
the best of your knowledge. please select the number that represents how much you agree
with the following statements. Our business unit:

 

 

 

 

 

 

 

 

 

 

Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree

to fresh ideas.
creative at the service.
in new service
a

innovative

 

6. Each of the following items represents different methods by which businesses may
compete. Please select the number that best describes the emphasis your business unit has
placed on the means of competition.

Never Emphasized 1 2 3 4 5 6 7 Always Emphasized

150

services

limited or markets

level of customer service

a narrow of services
on a

 

 

Now we w0uld like to ask you a few questions about how your unit conducts on business.
Please select the number that best describes your Web business unit.

7. How much importance does the top management at your organization place on the
following performance areas?

Not at all important 1 2 3 4 5 6 7 Extremely important

sales
sales
identiﬁcation

share

on investment

on

 

8. How successful was your Web service in the following performance areas during the last
12 months?

Not at all successful 1 2 3 4 5 6 7 Extremely successful

151

sales

e-commerce sales

brand identiﬁcation
market share

return on investment
return on

net

 

9. Compared to the top competitor in your city market. how would you rate your service
quality in the following areas?

Much Worse 1 2 3 4 5 6 7 Much Better

of the information
content
of the
of the information
effects

of

of

of customization

of transaction

333333333333
333333333333
333333333333
333333333333
333333333333

(2
(s
f‘
(a
(‘
f‘
(a
(a
F
f‘
F
F

board function

 

 

Finally. we would like to know a little about your business unit for Web-based information
service.

 

10. At your Web site. how much concern do you have for competition from other local Web
sites? Please select the number that best describes your approach.

152

Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree

 

11. Please provide the percentage of lntemet users in your city market. If you are not sure
of the percentage, provide your best estimate.

City Name

 

12. Please provide your Web site URL?

 

Http:ll j

 

13. At what date was you Web site ﬁrst operational as a service (after any trial periods)?

 

Month I Year 1

 

14. Please provide your full banner advertising rate on the main page per month.

 

15. Usually how many people visit your Web site a day?

 

16. In the following areas, how many full-time employees are employed by your Web site
unit? (Please estimate if exact number is not known. Consider two half-time staff as one full-
time staff)

153

 

Editorial staff: {

....................................
O

Desr n staff '
I

a”:.’.-.-.-.-.-.-.-n.-.-.-.-H. ....................

.'

- . i

e l . .

. .'.'.‘.’.'I.’.'.'.'.'.'.'.‘J'c‘.'.’I‘.‘.‘.'.‘.’.'Ir‘.'r'.’.'f.‘.'.".

Marketing staff: 1

....................................

 

 

 

 

 

17. How much has your Web business unit changed in total employment during the last two
years? Please select from one of the dropdowns that best describes your organization.

 

......

 

...............................................................................

19. If you have any comments about or additions to the information provided here, please
use the space below.

 

 

 

THANK YOU VERY MUCH FOR COMPLETING THIS FORM! Select Submit Survey now
to send your responses to us.

 

154

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155

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