Introduction

As a large developing country with an ambition to become one of the world's economic superpowers, China sees its future closely tied to its information technology industry, as well as to the deployment and use of IT, the Internet, and e-commerce. However, currently there is a great disparity between this vision for “informatization” and the reality of e-commerce diffusion and use. That disparity is rooted in aspects of China's environment and policy which shape the diffusion, use, and impacts of e-commerce.

China's economy has grown at an annual rate of more than 8% since 1995, but that growth has been accompanied by increasing inequality in income. There is also wide geographic inequality, with the eastern coastal regions around Beijing, Shanghai, and Guangdong having much higher incomes than the rest of the country. These regions, and especially their cities, have much better infrastructure and many more Internet users than the remote and economically poorer provinces elsewhere in the country. The larger enterprises, especially those located in these coastal regions, have larger IT budgets and better-trained staff than small and medium-sized enterprises, and are more capable of engaging in e-commerce, but tend to be conservative. The smaller, more entrepreneurial companies often lack the financial and human resources to engage in e-commerce.

This appendix describes the Global E-Commerce Survey (GEC Survey) – the primary data source used for the country case studies – including objectives, instrument construction, survey administration, sampling methodology, and response rates.

Objective

The GEC Survey was designed to benchmark the state of e-commerce diffusion in firms, industries, and countries, and to determine whether the Internet and e-commerce are making some more competitive than others. Specifically, the survey focuses on six areas: 1) globalization of firms and markets; 2) use of e-commerce technologies (EDI, Internet, extranet, etc.); 3) drivers for Internet use; 4) barriers to conducting business on the Internet; 5) prevalence of online sales and online service offerings; and 6) benefits from e-commerce use. Each country chapter uses the GEC Survey as the primary data source. Country cases may also employ secondary data sources, and authors were encouraged to supplement GEC data as needed.

Countries and time period

Data were collected via telephone interviews in ten economies: Brazil, China, Denmark, France, Germany, Japan, Mexico, Singapore, Taiwan, and the United States. Interviews were conducted during the period 18 February 2002 to 5 April 2002. A total of 2,139 companies were interviewed.

Instrument design

The questionnaire was designed by researchers at the University of California, Irvine and reviewed and critiqued by International Data Corporation's Global Research Organization and its global subsidiaries in the countries studied.

Introduction

Brazil presents an interesting case study of local factors influencing the adoption and impacts of e-commerce. Globalization is typically associated with the adoption of innovative technologies such as e-commerce that facilitate expansion into international markets and management of cross-border transactions. In the case of Brazil, however, its large size and considerable geographic distance from global production networks create a relatively inward-oriented economy. Other factors besides globalization have thus driven e-commerce. These include the need for financial efficiency driven by historically rampant inflation, as well as low GDP per capita typical of developing economies. Moreover, severely disproportionate wealth distribution impedes widespread adoption of certain forms of e-commerce. The overall result is the importance of local forces relative to global forces in driving e-commerce, the leadership of the financial sector in ecommerce adoption, and the innovation of large firms relative to small firms in the use of the technology.

• Less international orientation. On average, firms in Brazil are less internationally oriented than those in other economies. Only 4% of firms in the sample have establishments abroad, versus 24% in the global sample. Sales from abroad are less than a third of firms in other economies (4% versus 12%), and procurement from foreign firms is less than one half (10% versus 20%).

• Local forces key. Local forces are more important than global forces in driving e-commerce diffusion. Reasons include Brazil's inward orientation, its large domestic economy, and its unique economic history and government policies.

Introduction

Germany not only has a long history of being a leading innovator in several areas, but has also been a fast follower in adopting innovations, including information technologies. German firms generally have embraced and implemented IT solutions only after they have proved successful in other countries, but once proven, there is widespread adoption across large and small firms, and new technologies are integrated with existing technologies to obtain maximum benefits. This is somewhat analogous of many firms' adoption strategy for new information and communications technologies. These firms are unwilling to be the guinea pigs for brand-new, often cutting-edge or bleeding-edge ICT which is often unproven, “buggy,” unstable, and not perfected in many ways. Instead, fast-follower firms wait until right after early adopters have started the diffusion and just before “critical mass” has been achieved.

Two important factors driving adoption of IT in Germany are the international orientation of the country's economy and the dynamism of its small and medium-sized enterprises (SMEs), the so-called Mittelstand. Large multinational firms use technologies such as EDI very heavily to coordinate regional and global operations and to compete in a high-wage environment. However, Germany stands out among other countries in that its SMEs use many of these technologies to an equal, and sometimes greater, extent than large firms. As suppliers to large multinationals and as international competitors in their own right, German SMEs have had to be innovative and flexible to survive.

The SGI Power C compiler (PCA) does not allow more threads than processors (cf. the document “Multiprocessing C Compiler Directives”). In this sense, programs execute like the fork() programming model.

The keyword critical corresponds most closely with mutex in that only one thread at a time can execute this code and all threads execute it. The keyword synchronize corresponds most closely with barrier in that all threads must arrive at this point before any thread can go on.

There are also additional directives. The directive one processor means that the first thread to reach this code executes it meanwhile other threads wait. After execution by the first thread, the code is skipped by subsequent threads. There is an enter gate and corresponding exit gate directive. Threads must wait at the exit gate until all threads have passed the matching enter gate.

Loops to run in parallel must be marked with the pfor directive. It takes the argument iterate (start index; number of times through the loop; increment/decrement amount).

A reduction variable is local to each thread and their contributions must be added in a critical section.

The need for speed. Since the beginning of the era of the modern digital computer in the early 1940s, computing power has increased at an exponential rate (see Fig. 1). Such an exponential growth is predicted by the well-known “Moore's Law,” first advanced in 1965 by Gordon Moore of Intel, asserting that the number of transistors per inch on integrated circuits will double every 18 months. Clearly there has been a great need for ever more computation. This need continues today unabated. The calculations performed by those original computers were in the fields of ballistics, nuclear fission, and cryptography. And, today these fields, in the form of computational fluid dynamics, advanced simulation for nuclear testing, and cryptography, are among computing's Grand Challenges.

In 1991, the U.S. Congress passed the High Performance Computing Act, which authorized The Federal High Performance Computing and Communications (HPCC) Program. A class of problems developed in conjunction with the HPCC Program was designated “Grand Challenge Problems” by Dr. Ken Wilson of Cornell University. These problems were characterized as “fundamental problems in science and engineering that have broad economic or scientific impact and whose solution can be advanced by applying high performance computing techniques and resources.” Since then various scientific and engineering committees and governmental agencies have added problems to the original list. As a result, today there are many Grand Challenge problems in engineering, mathematics, and all the fundamental sciences. The ambitious goals of recent Grand Challenge efforts strive to

• build more energy-efficient cars and airplanes,

• design better drugs,

• forecast weather and predict global climate change,

• improve environmental modeling,

• […]

Numerical computations are a fundamental tool for engineers and scientists. The current practice of science and engineering demands that nontrivial computations be performed with both great speed and great accuracy. More and more, one finds that scientific insight and technologial breakthroughs are preceded by intense computational efforts such as modeling and simulation. It is clear that computing is, and will continue to be, central to the further development of science and technology.

As market forces and technological breakthroughs lowered the cost of computational power by several orders of magintude, there was a natural migration from large-scale mainframes to powerful desktop workstations. Vector processing and parallelism became possible, and this parallelism gave rise to a new collection of algorithms. Parallel architectures matured, in part driven by the demand created by the algorithms. Large computational codes were modified to take advantage of these parallel supercomputers. Of course, the term supercomputer has referred, at various times, to radically different parallel architectures. This includes vector processors, various shared memory architectures, distributed memory clusters, and even computational grids. Although the landscape of scientific computing changes frequently, there is one constant; namely, that there will always be a demand in the research community for high-performance computing.

When computations are first introduced in beginning courses, they are often straightforward “vanilla” computations, which are well understood and easily done using standard techniques and/or commercial software packages on desktop computers. However, sooner or later, a working scientist or engineer will be faced with a problem that requires advanced techniques, more specialized software (perhaps coded from scratch), and/or more powerful hardware.