Associations between e-business models and their business performances.

Kim, Dae Ryong ; Shin, Hoe-Kyun ; Kim, Jong-Chun 等

ABSTRACT

The right selection of e-business model is one of the most critical factors of e-business success and should be chosen carefully. However, little is understood about the association between e-business models and business performance. This study develops e-business models with different strategic positions in the value chain that accommodate unique demands in the e-business environment, then examines the association between e-business models and performance measures.

Five e-business models are developed from 6 strategic factors that are, in turns, derived from 22 strategic variables introduced by Hambrick (1983). Model 1 is an e-business model with the strategic emphases on 'comparative advantage' and 'concentration,' model 2 is that with expansion and low price, model 3 is that with expansion and product improvement, model 4 is that with 'comparative advantage' and process, and model 5 is that with the strategic emphasis on product improvement. These 5 e-business models are compared with their corresponding performance measures using Duncan grouping method.

This paper found that e-business models with dual core strategies outperform e-business model with single core strategy. Among these e-business models with dual core business strategies, the model with the strategic emphases on 'comparative advantage' and 'process' performs best, which is followed by the model with 'expansion' and 'product improvement.'

INTRODUCTION

E-business is the conduct of business on the Internet although it is defined with diverse terminologies, such as Internet business, Internet commerce, or extension of e-commerce (Kalakota & Whinston, 1996; OECD, 1997; Rayport & Sviokla, 1994; Yoo, Choudhary & Mukhopadhyay, 2003). E-business is different from conventional off-line based business in many ways. Not only the buying and selling of goods and services but also the servicing of customers and the collaboration with business partners are done on the Internet in an e-business. Information is accessed and absorbed more easily on the Internet than off-lines. Information is also arranged and priced in different ways on the Internet. Thus, the value generating cycle of a company (called the value chains hereafter) can be managed differently on the Internet. In other words, three elements of the value chains such as content (what a company offers), context (how to offer the content), and infrastructure (what enables the transaction to occur) can be disaggregated and managed differently in an e-business. These differences of an e-business relative to a conventional business create plenty of new opportunities for an e-business that may require different business strategies (Useem, 2000). Hence, as Rayport and Sviokla (1994) suggested, it is necessary to develop a new business model with different strategy portfolios to seize these opportunities for a business success. The new business model for an e-business should consist of new coherent business strategies that incorporate the different business environment on the Internet, for a strategy is a carefully devised plan of actions to achieve goals of a company (Jutla et al., 1999; Kenneth et al., 1998; Timmers, 1998). The right selection of e-business model is one of the most critical factors of the e-business success and should be chosen carefully. However, it has been difficult to apply e-business models developed so far to the practices, because little is understood about the association between e-business models and their business performance (Hermanek et al., 2001; Lee & Choi, 2000).

The purpose of this study is to develop e-business models with different strategic positions in the value chain that accommodate unique demands in the e-business environment and then examine the association between e-business models and performance measures.

RESEARCH PROCEDURE

Strategic variables for the business success introduced by Robinson and Pearce (1988) are analyzed using the factor loading method to develop 6 so-called critical success strategic factors with Eigen-values higher than 1. The 6 critical success strategic factors are 'comparative advantage,' 'expansion,' process, 'concentration,' 'low price,' and 'product improvement.' Using the cluster analysis, 5 e-business models with various emphases on the critical success strategic factors were developed. The cluster analysis that is also used in many previous researches (Galbraith & Schendel, 1983; Dess & Davis, 1984) derives the business models in such a way that the distance among those 5 e-business models is maximum in terms of the 6 critical success strategic factors. The five e-business models are as follows: Model 1 is an e-business model with the strategic emphases on 'comparative advantage' and 'concentration.' Model 2 is an e-business model with the strategic emphases on expansion and low price. Model 3 is an e-business model with the strategic emphases on expansion and product improvement. Model 4 is an e-business model with the strategic emphases on 'comparative advantage' and process. And model 5 is an e-business model with the strategic emphasis on product improvement. Then, the association between the 5 e-business models and 4 performance measures are investigated using the Duncan grouping method. As business performance measures, rate of return on sales (ROS), rate of return on total assets (ROA), sales growth rate (SGR), and rate of return on equity (ROE) are used in this study.

We found that business models with dual strategic emphases (model 4, 3, 2, & 1) outperform a business model with a single strategic emphasis (model 5) in terms of all four-performance measures, which is consistent with Robinson and Pearce's findings with manufacturing companies 1988). Research procedures taken in this study are described in Figure 1.

[FIGURE 1 OMITTED]

METHODOLOGY

Instrument Administration

We developed a 35-item questionnaire including 22 items for the strategic variables adopted by Robinson and Pearce (1988), 4 items for the performance variables, and 9 demographic and company profile items. As a pilot-test, to improve the validity of the survey instrument, the instrument was reviewed by 12 Information Systems professionals and revised according to their recommendations until there are no further substantive recommendations from the reviews. Then, the revised instrument was pre-tested by 49 executive MBA students. Five demographic and four company profile variables were also measured in the instrument. Size was measured by number of employees, while industry was identified by categorical scale.

Data Collection

Data were collected via a survey questionnaire through on/off-line at the same time. The survey method was adopted to maximize generalizability of the test result by obtaining a statistically testable representation of the various categories of variables. In order to maximize the response rate, the survey questionnaire was carefully designed and pilot-tested. The cover letter was carefully worded and addressed to respondents by name. For those undelivered survey packages, we called those subject firms to obtain correct names or addresses and resent the packages. For those undelivered e-mails, we checked websites of those subject firms and called them later when we could not find the right e-mail addresses to confirm the addresses of respondents. We also mailed/e-mailed confirmation/remind letters four weeks after the first mailing according to Sudman and Bradburn's (1982) recommendation.

Five hundred survey questionnaires were e-mailed and 210 were mailed to those subject firms in the list of 2001 Annual Membership Directory of the 'Association of Internet Enterprise' in Korea. A total of 130 responses were received representing a response rate of about 18.3%. 127 questionnaires were used for analysis after 3 survey questionnaires were discarded for incompleteness.

Subject Characteristics

The collected data show that manufacturing firms practicing e-business make the most and service firms practicing e-business make the second most sample firms. A total of 83 sample firms are manufacturing firms, 65.35% of the total sample. The age of sample firms is widely dispersed. 94 out of the 127 sample firms have been in the e-business for more than 3 years (74.02%), and 20 for between 1 and 2 years. Regarding e-business area, 59 sample firms are involved in B2B e-commerce, 47 firms are in B2C, 16 firms are in B2B2C, and 10 firms are in B2G e-commerce. Demographic data show that 89.89% of the respondents are male and the largest group of employees is in between 30 to 40 years old (57.48%) with average age 31. A total of 94 of the 127 respondents (73.99%) have undergraduate education or higher, which implies firms practicing e-business require more educated people to run e-business with computers. Average work experience of the respondents is 7.7 years. Table 1 shows the profile of the respondents and responding companies.

ANALYSIS AND RESULTS

Reliability and Validity of Strategic Variables

Content validity of the survey instruments was established through the adoption of standard instruments, suggestions in the literature, and pre-testing with professionals in the IS field Construct validity (Kerlinger, 1986) was evaluated by discriminant validity that is the degree to which a construct differs from other constructs and is usually verified through factor analysis, shown in the Table 2. Bold numbers in the Table 2 show strategic variables with factor loading over 0.5.

From the factor analysis, 6 strategic factors (Comparative advantage, Expansion, Process, Concentration, Low Price, and Product Improvement) with Eigen-value greater than 1 were selected. Since 3 strategic variables such as 'Promoting Advertisement for E-commerce,' 'Product Specialization,' and 'Targeting High Price Market' did not exhibit high discriminant validity (loadings < 0.5), only 19 strategic variables out of the initial 22 were loaded to 6 strategic factors.

To examine the internal coherence amongst determinants of each strategic factor, the Cronbach's alpha coefficient was measured. Coefficients of all 6 strategic factors are larger than 0.5252, indicating that internal coherence among determinants is good (Nunnally, 1978). The results from the reliability and validity analysis of the strategy variables are presented in Table 2. Each strategic factor identified by factor analysis has its own strategic behavior. These different behaviors are described in Table 3.

Cluster Analysis

Using the cluster analysis introduced by Hambrick (1983), 5 e-business models with various emphases on strategic factors were developed. This cluster analysis used in many previous researches (Dess & Davis 1984; Galbraith & Schendel, 1983; Hambrick & Schecter, 1983) derives the business models in such a way that the distances among those 5 e-business models are maximums in terms of the 6 strategic factors. Although each strategic factor has its own portfolio of strategic variables, these factors could be grouped together to form a business model according to Hambrick (1983). Thus, these five e-business models were extracted from 6 strategic factors. These models are different business models that take different strategies to compete with other companies in the e-business industry. The result of cluster analysis shows that 4 models (model 1, 2, 3, & 4) take multiple core strategies, while model 5 takes single core strategy, product improvement. Summary results of the cluster analysis are presented in Table 4.

Table 5 describes strategic behaviors of each model (cluster) in details. Each model behaves differently for competition. Four of them take multiple core strategies to be in better position in e-business industry. Only one of them focuses on single core strategy to compete with other companies, but this model might have limitation in adaptability to business environment changes.

Variance Analysis and Duncan Grouping Test

Since e-business models are developed and performance measures are measured, the relationship between e-business models and performance measures are investigated. First, correlation analysis has been conducted on performance measures to see if there is homogeneity amongst the four performance measures. As shown in Table 6, all correlation coefficients among the performance measures are higher than 0.7 (P < 0.0001), which means that the four performance measures are significantly related one another and can be used in variance analysis as variables.

This study conducted MANOVA tests to examine if e-business models affect their business performances. The results from this MANOVA presented in Table 7 show that F-value is 8.98 (P < 0.0001), which means that the e-business models affect the business performance.

Finally, the association between e-business models and performance measures is analyzed using Duncan grouping method where each business model is given a letter grade of A, B, and C for its performance in terms of four different performance measures. As shown in Table 8, Model 4 with the strategic emphases on 'comparative advantage' and 'concentration' has the highest performance mean and hence grade of A in all four performance measures. Model 3 with the strategic emphases on expansion and product improvement has the second highest performance mean in all four performance measures but earns 3 A's with 1 B. Model 2 with the strategic emphases on expansion and low price has the median performance mean but earns only 2 A's with 2 B's. Model 1 with the strategic emphases on 'comparative advantage' and 'concentration' has the second lowest performance mean and earns 2 B's, 1 A, & 1 C. Model 5 with the strategic emphasis on product improvement has the lowest performance mean and earns 2 B's with 2 C's.

The result of Duncan grouping method indicates that e-business models with dual core strategies (model 4, 3, 2, and 1) outperform e-business model with a single core strategy (model 5). Among these e-business models with dual core business strategies, the model with the strategic emphases on 'comparative advantage' and process (model 4) performs best, which is followed by the model with expansion and product improvement (model 3), the model with expansion and low price (model 2), and the model with 'comparative advantage' and 'concentration' (model 1).

CONCLUSIONS AND IMPLECATIONS

The purpose of this study is to develop e-business models with different strategic positions in the value chain that accommodate unique demands in the e-business environment and then examine the association between e-business models and performance measures. To accomplish these objectives, 5 e-business models are developed from 6 strategic factors that are, in turns, derived from 22 strategic variables introduced by Hambrick (1983). Then, these 5 e-business models with different core strategies are compared with their corresponding performance measures using Duncan grouping method.

This study found that e-business models with dual core strategies outperform e-business model with one core strategy. Among those e-business models with dual core business strategies, model 4 with 'comparative advantage' and 'concentration' as core strategies performs best, which is followed by model 3, model 2, and model 1 in the order of the performance. According to the results of this study, companies should pay attention to the strategies such as 'comparative advantage' and 'concentration' to compete very best with other companies. The strategies that these companies should involve are product diversification, product and service development, skilled human resource arrangement, competitive pricing, low cost commitment, advertisement promotion, low inventory level, and limited product supply to a certain market segment.

The findings of the study have interesting implications for practice. E-business companies that want to compete with other e-business companies should focus on multiple core strategies rather than a single strategy. When they select one of e-business models with a strategic consideration, they should check where they put their emphases. The results of this study may be one of the guidelines in practice when companies choose their strategic e-business model.

Since this research is an empirical study using large sample and validated instruments, the results can be generalized with high degree of confidence. The results of this study have meaningful implications for the development of e-business model, in general. However, due to a relatively short history of e-business industry, a thorough investigation into theoretical and empirical background of e-business strategies could not be performed. It also should be noted that the analysis was based on an 18.3% response rate. Although non-response bias was estimated, it should be recognized that the potential of sample frame error exists. Also, the scope of this study was restricted to the demographic variables of industry and business type.

REFERENCES

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Rayport, J.F. & J.J. Sviokla (November-December 1994). Marketing in the Marketspace, Harvard Business Review, 17-34.

Robinson, R.B. Jr. & J.A. Pearce II (1988). Planned Patterns of Strategic Behavior and Their Relationship to Business-Unit Performance, Strategic Management Journal, 9, 43-60.

Sudman, E. & N. Bradburn, (1982). Asking Questions: A Practical Guide to Questionnaire Design, San Francisco, CA: Jossey-Base Publishers.

Timmers, P. (1998). Business Models for Electronic Markets, Electronic Markets, 8(2), 3-8.

Useem, J. (2000). Lessons From the Dot-Com Crash, Fortune Magazine, 11(6), 46- 79.

Yoo, B., V. Choudhary & T. Mukhopadhyay (2003). A Model of Neutral B2B Intermediaries, Journal of Management Information Systems, 19(3), 43-68.

Dae Ryong Kim, Delaware State University

Hoe-Kyun Shin, Kumoh National Institute of Technology

Jong-Chun Kim, Kumoh National Institute of Technology

Sehwan Yoo, University of Maryland Eastern Shore

Jongdae Jin, William Paterson University

Table 1: Sample Descriptions

 Frequency Percent
(a) Sex
 Male 113 88.89
 Female 14 11.11
Total 127 100

(b) Age
 Less than 30 30 23.62
 30 to below 40 73 57.48
 40 and above 24 18.90
 Total 127 100

(c) Education
 High School 12 9.45
 Community College 21 16.54
 Undergraduate 77 60.63
 Graduate School 17 13.39
Total 127 100

(d) Rank
 Clerk 38 29.92
 Supervisor 40 31.50
 Manager 20 15.75
 Director 11 8.66
 Executive 18 14.17
Total 127 100

(e) Years on the Job
 Less than 3 41 32.28
 3 to below 6 28 22.05
 6 to below 9 20 15.75
 9 to below 12 16 12.60
 12 and above 22 17.32
Total 127 100

(f) Industry
 Manufacturing 83 65.36
 Service 23 18.11
 Telecommunication 5 3.94
 Distribution 2 1.57
 Others 9 7.09
 Unanswered 5 3.94
Total 127 100

(g) Years of Company
 Less than 1 8 6.30
 1 to below 2 20 15.75
 2 to below 3 5 3.94
 3 and above 94 74.02
Total 127 100

(h) Type of e-business
 B2B 59 42.45
 B2C 47 33.81
 B2B2C 16 11.51
 B2G 10 7.19
 Others 7 5.04
Total 127 100

Table 2: Factor Analysis

Factors & Strategy Variables C A. Exp. Proc

Comparative Advantage (CA)
 Product Diversification 0.7371 0.0967 0.2102
 New Product/Service 0.6742 0.2985 0.1574
 Skilled Human Resource 0.6091 0.4871 -0.0300
 Competitive Pricing 0.5694 0.1141 0.2964
 Low Cost Focus 0.5399 -0.0183 0.2639

Expansion (Exp)
 Internet Marketing Technique -0.0251 0.8202 0.1199
 Reputation in E-business 0.2180 0.7664 0.0496
 Industry
 Distribution Channel 0.1897 0.6268 0.1641
 Establishing Brand Identity 0.1836 0.6084 0.1160
 Enhancing Customer Service 0.5030 0.5148 0.1912

Process (Proc)
 Process Innovation 0.0736 0.0440 0.8036
 Resource Utilization 0.1015 0.0460 0.7080
 Quality Control 0.1790 0.2205 0.5992
 Research on Business Process 0.3782 0.2325 0.5582

Concentration (Concent)
 Inventory Level Control -0.0310 0.1872 0.2147
 Geographic Market -0.0468 -0.2471 -0.1382
 Product Limit -0.2079 -0.0548 -0.1528

Low Price (LP)
 Targeting Low Price Market 0.1142 0.0960 0.0481

Product Improvement (PI)
 Product Improvement 0.2604 0.1889 0.2169
 Cronbach's alpha 0.7683 0.7852 0.6314
 Eigen-Value 3.1335 3.1300 2.6971
 Percent (%) Explained 14.2431 14.2272 12.2598

Factors & Strategy Variables Concent L P PI

Comparative Advantage (CA)
 Product Diversification -0.2295 -0.0384 0.1522
 New Product/Service -0.1901 0.0357 0.1978
 Skilled Human Resource -0.0118 -0.1462 0.1429
 Competitive Pricing 0.0275 0.5189 0.1753
 Low Cost Focus 0.1930 0.3108 -0.0420

Expansion (Exp)
 Internet Marketing Technique -0.0368 0.2125 0.0027
 Reputation in E-business 0.0462 0.1450 0.3077
 Industry
 Distribution Channel 0.0681 -0.0872 0.2012
 Establishing Brand Identity -0.0935 -0.1266 -0.1350
 Enhancing Customer Service -0.1768 -0.0834 -0.0579

Process (Proc)
 Process Innovation -0.0445 0.1438 -0.0807
 Resource Utilization 0.1140 0.0858 0.3112
 Quality Control -0.0420 -0.3252 0.0214
 Research on Business Process -0.0792 0.0123 0.1287

Concentration (Concent)
 Inventory Level Control 0.7318 -0.0635 0.0384
 Geographic Market 0.6704 -0.0240 0.1223
 Product Limit 0.6295 0.3287 -0.2496

Low Price (LP)
 Targeting Low Price Market 0.0287 0.8097 -0.0419

Product Improvement (PI)
 Product Improvement 0.0758 -0.1214 0.7402
 Cronbach's alpha 0.5252 1.0000 1.0000
 Eigen-Value 1.7134 1.6550 1.4243
 Percent (%) Explained 7.7883 7.5230 6.4742

Table 3: Behavior of Strategic Factors

Factor Interpretation

Comparative Focus on retaining comparative advantage on
Advantage diverse fields such as product, cost, price,
 and human resource

Expansion Focus on distribution channel and marketing
 effort to establish reputation within an
 e-business industry and to enhance customer
 service

Process Focus on business process by investing research
 on business process, innovating the process,
 utilizing material effectively, and applying
 strict quality control

Concentration Concentrate on a certain geographic area, a
 limited number of product, and inventory control

Low Price Focus on low price to defeat competitors in
 e-business market

Product Improvement Focus on continuous product improvement

Table 4: Cluster Analysis

Cluster Comparative
(Model) Advantage Expansion Process

1 (n=32) 0.42 * 0.09 0.19
2 (n=26) 0.15 0.98 * 0.31
3 (n=15) 0.37 0.77 -1.33
4 (n=20) 0.77 * -1.16 0.56 *
5 (n=33) -1.16 -0.50 -0.16

Cluster Low Product
(Model) Concent. Price Improvemt

1 (n=32) 0.57 * -0.25 -0.99
2 (n=26) 0.15 0.78 * 0.42
3 (n=15) -0.78 -0.40 0.72 *
4 (n=20) -0.69 0.12 0.20
5 (n=33) 0.13 -0.27 019 *

* Cluster means selected

Table 5: Strategic Behavior of Each Model

Cluster Strategy Description

1 Comparative This model focuses on comparative
 Advantage & advantage and concentration strategies.
 Concentration Companies utilizing this strategy involve
 product diversification, product and
 service development, skilled human
 resource arrangement, competitive pricing,
 low cost focus, advertisement, and low
 inventory level. They also are interested
 in providing a limited product to a
 limited market segment to focus on a market.

2 Expandability This model focuses on expandability and
 & Low Price low price. Companies utilizing this
 strategy invest in Internet marketing to
 establish name on e-business industry,
 try to set up powerful influence on
 distribution channel, and expand customer
 service. They also focus on low price
 market.

3 Expandability This model focuses on expandability and
 & Product product improvement. Companies utilizing
 Improvement this strategy rely on the expandability
 strategy and try to improve its product
 quality.

4 Comparative This model focuses on comparative
 Advantage advantage and business process. In
 & Process addition to the comparative advantage
 Focus strategy, companies utilizing this
 strategy invest in research on innovative
 business process, quality control process,
 and better utilization of material.

5 Product This model focuses only on product
 Improvement improvement. This strategy is simple and
 also powerful on the product innovation,
 but has limitations on environmental
 changes.

Table 6: Correlation Among Performance Measures

 Mean Std Dev ROS

Return on Sales 3.07874 1.10989 1.00000
 (ROS)
Return on Assets 3.09449 1.10865 0.85820 *
 (ROA)
Sales Growth 3.24409 1.12487 0.72189 *
 Rate (SGR)
Return on Equity 3.29921 1.11494 0.70554 *
 (ROE)

 ROA SGR ROE

Return on Sales
 (ROS)
Return on Assets 1.00000
 (ROA)
Sales Growth 0.73868 * 1.00000
 Rate (SGR)
Return on Equity 0.71533 * 0.80192 * 1.00000
 (ROE)

*: P < 0.0001

Table 7:
MANOVA: Overall Impact of E-Business Models on Business Performances

 Sum of Mean
Source DF Squares Square F Value Pr > F

Model 4 35.47894 8.86973 8.98 <.0001
Error 121 119.4496 0.98719
Corrected 125 154.9286
Total

Table 8: Duncan Grouping Analysis

Statistic Value F Value Num DF Den DF Pr > F

Wilks' 0.717 3.48 12 315.14 <.0001
Lambda

 Return on Equity Return on Sale
 (ROE) (ROS)

Cluster N Mean D/G * Mean D/G *

4 20 3.900 A 3.650 A
3 15 3.800 A 3.467 A
2 26 3.692 A 3.192 B
1 32 3.219 B 3.000 B
5 33 2.515 C 2.546 B

 Return on
 Assets Sales Growth
 (ROA) Rate (SGR)

Cluster Mean D/G * Mean D/G *

4 3.650 A 4.100 A
3 3.533 A 3.667 B
2 3.346 A 3.462 B
1 3.125 A 3.094 C
5 2.364 B 2.546 C

* Duncan Grouping