KEVIN ZHU, KENNETH L. KRAEMER, SEAN XU, AND JASON DEDRICK Reporter: Yu-Hsien Li

1. Information Technology Payoff inE-Business Environments:An International Perspective on Value Creation of E-Business in the Financial Services Industry KEVIN ZHU, KENNETH L. KRAEMER, SEAN XU, AND JASON DEDRICK Reporter: Yu-Hsien Li

2. Outline • Abstract • Introduction • Theoretical foundations • The Research Model and Hypotheses • Research Methodology • Empirical Analysis • Discussion

3. Abstract • Grounded in the technology-organization-environment (TOE) framework. • Develop a research model for assessing the value of e-business at the firm level. • Survey data from 612 firms across 10 countries in the financial service industry. • Six hypotheses and factors. • Five findings.

4. Introduction • Researchers and practitioners are struggling to determine whether e-business delivers value to firm performance, and if so , what factors contribute to e-business value. • Much literature still relies on case studies and anecdotes, with few empirical data to measure Internet-based initiatives or gauge the scale of their impact on firm performance.

5. Introduction (cont.) • What is missing in the existing literature is: • A solid theoretical framework for identifying factors that shape e-business value. • A research model for studying the relationship of these factors to e-business value. • Empirical assessments based on a broad data set instead of a few isolated cases.

6. Introduction (cont.) • This literature's key research questions: • What theory can be used to study e-business value? • What factors can be identified within this theoretical framework? • How would the patterns of e-business value creation vary across different economic/organizational environment?

7. Introduction (cont.) • Data analysis was performed by structural equation modeling (SEM). • This study has incorporated some suggestions which Kohi and Devaraj and Dedrick et al. have pointed out: • Gathering data from primary sources. • Increasing sample size for increased statistical power. • Capturing the actual usage of IT. • Developing process-oriented dependent variables.

8. Theoretical foundations

9. The Technology-Organization-Environment Framework • Identifies three aspects of firm's context that influence the process by which it adopts and implements a technological innovation: • Technological context • Organizational context • Environmental context • These three groups of contextual factors influence a firm's intent to adopt an innovation.

10. The Technology-Organization-Environment Framework (cont.) • Iacovou et al. develop a model formulating three aspects of EDI adoption: • Technological factors • Organizational factors • Environmental factors • EDI -- an antecedent of Internet-based e-business.

11. E-Business and the Financial Service Industry • Differs in important ways from industries such as manufacturing or retailing. • Its use of IT and e-business technologies reflects those differences. • In manufacturing and retailing: • IT is used mainly to coordinate the processing and movement of physical goods. • To manage supporting functions such as human resources, accounting and sales and marketing...

12. E-Business and the Financial Service Industry (cont.) • In financial services industry: • No inherently physical goods. • Cash, checks, etc. are just forms of information that can be represented digitally. • IT is used directly to store, process, and transport these "goods" in which the industry trade. • Financial service industry is the largest user of IT (U.S. firms in this industry average spend 8% of their revenues on IT, 2% in retail and 3% in manufacturing).

13. E-Business and the Financial Service Industry (cont.) • E-business technologies have the potential to add significant value. • Web-based applications to improve customer service. • e.g. Loan applications, bills paid... etc. • Innovations • Web-based, graphical interfaces to improve the user friendliness. • XML-based standards (makes EDI connections more flexible).

14. The Research Model and Hypotheses

15. A Research Model for E-Business Value • Independent Variables: • Technology infrastructure and competence (technology readiness). • Firm size. • Financial resource. • Competition intensity. • Global scope. • Regulatory environment.

16. A Research Model for E-Business Value (cont.) • Three dimensions of e-business value which are grounded in the value chain analysis of Porter: • Impact on commerce with customers. • Impact on internal operation efficiency. • Impact on coordination with business partners.

17. A Research Model for E-Business Value (cont.)

18. Hypotheses • Technological context • Technology readiness • This construct incorporates three dimensions: • Technologies in use. (intranet, extranet, EDI...etc.) • Web site functionality at front end. • Back-office integration within and beyond the firm's boundary. • H1: Technology readiness is positively associated with e-business value.

19. Hypotheses (cont.) • Organizational context • Firm size • The number of employees in the firm. • H2: Firm size is negatively associated with e-business value. • Global scope • Geographical extent of a firm's operations in the global market. • H3: Global scope is positively associated with e-business value

20. Hypotheses (cont.) • Financial resources • Financial resources are important factor for technology implementation. • Measured by annual IT spending and Web-based spending as percentage of total revenue. • H4: Financial resources are positively associated with e-business value.

21. Hypotheses (cont.) • Environment context • Competition intensity • The degree that the company is affected by competitors in the market. • Based on Porter's concept of five competitive forces. • H5: Competition intensity is positively associated with e-business value.

22. Hypotheses (cont.) • Regulatory environment • Government regulation could affect innovation diffusion. • Designed four items to measure it: • E-business usage incentives provided by the government. • Requirements for government procurement. • Legal protection of consumers' Internet purchases. • Supportive business and tax laws for e-business. • H6: A supportive regulatory environment is positively associated with e-business value.

23. Research Methodology

24. Data and sample • Designed a questionnaire and conducted a multicountry survey. • Survey was executed by IDC (International Data Corporation). • Survey was conducted in US and nine other countries (Brazil, China, Denmark, France, Germany, Japan, Mexico, Singapore, and Taiwan). • period: February-April 2002

25. Data and sample (cont.)

26. Operationalization of constructs • Measurement items were developed on the basis of a comprehensive review of literature as well as expert opinion.

27. Dependent variable • Three dimensions of e-business value which are grounded in the value chain analysis of Porter: • Impact on commerce with customers. • Impact on internal operation efficiency. • Impact on coordination with business partners.

28. Dependent variable (cont.)

29. Instrument Validation • Used MLE to assess the four items: • Construct Reliability • Convergent Validity • Discriminant Validity • Validity of the Second-Order Construct • Construct Reliability • Most have a composite reliability over the cutoff of 0.70, as suggested by Straub. • Three have a reliability close to 0.70 (0.67 for TR, 0.65 for GS and RE)

30. Instrument Validation (cont.) • Convergent Validity • All are significant. • Discriminant Validity • Testing whether the correlations between any tow constructs are significantly different from unity. • All paired comparisons are highly significant.

31. Instrument Validation (cont.)

32. Instrument Validation (cont.) • Validity of the Second-Order Construct • The paths from the second-order construct to the three first-order factors are significant. • The model has a very high T-ratio of 0.98, the relationship among first-order constructs is sufficiently captured by the second-order construct.

33. Empirical Analysis

34. Split the whole sample into tow groups to test • IS manager (CIO, CTO, IS director, IS planner...etc.) • Non-IS manager (CEO, managing director, business operation manager....etc.) • The tow groups do not differ significantly -- pool the tow groups together for hypotheses testing

35. Analysis of the Full Sample • The model has normed x2 of 3.377, indicating a good model fit. • All hypotheses, except H5, are supported

36. Analysis of the Full Sample (cont.)

37. Sample Split: Developed Versus Developing Countries • Developing countries and newly industrialized countries: • Brazil, China, Mexico, Singapore, and Taiwan, N=283 • Normed x2 = 2.340, NFI=0.958, RFI=0.945, IFI=0.976, TLI=0.967, CFI=0.976, RMSEA=0.069 (acceptable). • Three of six TOE factors are significant (H1, H4, H6 are supported).

38. Sample Split: Developed Versus Developing Countries (cont.) • Developed countries • Denmark, France, Germany, Japan, and US, N=329 • Normed x2 = 2.289, NFI=0.957, RFI=0.944, IFI=0.975, TLI=0.968, CFI=0.975, RMSEA=0.056 (acceptable). • Three of six TOE factors are significant (H1, H2, H4 are supported).

39. Sample Split: Developed Versus Developing Countries (cont.)

40. The Summary of Hypotheses Testing

41. The Summary of Hypotheses Testing (cont.) • Global scope is a significant factor in full sample but a insignificant factor in each subsample. • Competition intensity is not significant in the full sample and each subsample.

42. Discussion

43. Major Findings and Interpretations • Finding 1: Within the TOE framework, technology readiness emerges as the strongest factor for e-business value, while financial resources, global scope, and regulatory environment also significantly contribute to e-business value.

44. Major Findings and Interpretations (cont.) • Finding 2: Large firms are less likely to realize the impact of e-business on their performance than small firms, which seems to suggest that structural inertia associated with large firms may retard e-business value creation.

45. Major Findings and Interpretations (cont.) • Finding 3: Competitive pressure often drives firms to adopt e-business, but e-business value is associated more with technological integration and organizational resources than with external competition.

46. Major Findings and Interpretations (cont.) • Finding 4: While financial resources are an important factor in developing countries, technological capabilities become far more important in developed countries. This seems to suggest that, as firms move into deeper stages of e-business transformation, the key determinant for e-business value shifts from monetary spending to real capabilities.

47. Major Findings and Interpretations (cont.) • Finding 5: The importance of firm size and regulatory environment differs across developed versus developing countries. In developing countries, problems with structural inertia associated with size tend to be offset by the resource advantages associated with large firms. Also, in developing countries, government regulation plays a more significant role than in developed countries.

48. Managerial Implications • Offer a useful framework for managers. • Offer some suggestions for managers. • Financial firms should improve internal technology capability. • Offer implications for policy-makers.

49. Limitations and Future Research • Limitations: • Can only show association, not causality. • Just focuses on one industry. • Measurement instruments are not "set i stone.“ • Future Research: • To refine measurement instrument. • To enhance the database over time to pave the way for a longitudinal study. • To expand study into other industry sectors.

50. Thank You.