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42
Small Manufacturing Enterprises and the National Information
Infrastructure
Statement of the Problem
The vision for the future is that an emerging national information infrastructure (NII) and its defense counterpart (DII) will equip U.S. industry to be second to none in the global economy. The NII will enable the U.S. industrial base to become more agile and to operate as a highly competitive, flexible, just-in-time, manufacture-on-demand system that facilitates free competition and specialization among manufacturers and suppliers. All firms, regardless of size, will have ready access to product requirements and specifications and will be able to compete fairly with other firms. Moreover, the NII with the DII will encourage commercial suppliers to respond to defense needs, enabling dual use designs and strengthening the flexibility of the nation's defense infrastructure.
The reality is that many existing small firms are ill equipped to participate in this vision. Moreover, there is concern that the learning cycle for small manufacturing enterprises (referred to here as SMEs) to implement information technology is too long and costly for them to effectively make the transition to the NII environment. The solution to the problem is not simply one of assuring that every SME can purchase and install a new information system. Instead, the solution requires an understanding of how a complex combination of structural, technical, managerial, and economic factors affect the diffusion of information technology in the manufacturing sector, especially among SMEs. From the viewpoint of our national economy, the problem is that this complex set of factors impedes the effective implementation of information technology in SMEs and puts at risk a significant component of the nation's manufacturing base, a component that is responsible for up to nearly 40 percent of the nation's manufacturing employment. Developing nations may "leapfrog" over the United States and other advanced nations if our established enterprises are unable to change quickly enough. The purpose of this paper is to help understand this set of factors and to explore how best to manage the risk associated with a slow rate of diffusion of information technology in SMEs.
Overview
The "Background" section provides a synopsis of the different views on the role of SMEs in the nation's economy and manufacturing infrastructure. This section also summarizes the different frameworks within which we can understand the economic, behavioral, structural, and technical issues associated with how SMEs may participate in the benefits of the NII.
The "Analysis" section of the paper provides more detail on the frameworks outlined in the Background section and examines the prospects for SMEs to become full participants in the NII. This section synthesizes adoption and diffusion studies and research on the implementation of new information technologies. The emerging framework is that of organizational learning at the level of the firm and the concept of the supply chain (or value chain). The learning framework enables us to make sense of the range of factors associated with
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technology adoption, and the value chain framework illustrates why action by an individual firm is inadequate for that firm to realize the benefits of an NII.
The final section of the paper discusses opportunities for national policy to alleviate the problem of SME's participation in the NII. The paper concludes that a coordinated collaboration among private, university, and government resources offers the best way to assist U.S. SMEs in making the transition to electronic commerce and the benefits of the NII.
Background
SMEs and the Economy
Small manufacturing enterprises (SMEs) are responsible for an estimated 28 to 40 percent of the employment in the manufacturing sector.1 Moreover, there is evidence that SMEs are more effective at job creation2 and job replacement,3 more innovative in the development of products and process improvements,4 and more flexible and thus more competitive in terms of the ability to produce small quantities. All these factors may explain the shift to a smaller average plant size.5 The claims to new job creation are open to question—SMEs also exhibit high failure rates, and thus new jobs may not be long-lived.6 However, others point out that small firms will continue to add jobs because much growth will take place in industries in which small businesses have relative advantages.7
There is no question that SMEs are a crucial element in the nation's manufacturing base. If one believes, as many do, that manufacturing must continue to be a foundation for U.S. economic competitiveness,8 then SMEs will continue to be a crucial part of this competitiveness.9 The role for small firms appears to be increasing; there is evidence of a trend toward more of the total production coming from smaller manufacturers.10 However, the United States is lagging behind Europe and Japan, where small firms account for 45 to 60 percent of manufacturing employment.11
SMEs and the NII
Neither the global competitiveness of U.S. industry nor the future role of SMEs is assured. The NII vision of preserving the tradition of free market competition both among manufacturing suppliers and among international companies is consistent with what Porter12 suggests are the conditions for global competitiveness: demanding customers who can choose from an intensely competitive local network of suppliers.
The NII and DII are expected to enable this competition and the development of dual use processes and designs. Large manufacturers (including Department of Defense purchasers and contractors) can make their specifications available online, eliminating distribution delays and increasing the scope of distribution. In one vision currently being articulated by the U.S. Department of Defense (DOD), the NII and DII enable the creation of an Integrated Data Environment (IDE) in which information itself (e.g., designs, production methods) becomes a commodity and is traded. With information available on both specifications and designs, firms can work only on those opportunities for which they are most capable, reducing the risk and costs of bidding on marginal opportunities.
For SMEs to participate, they must have access to the NII and they must be able to use computer technology to integrate their business and technical functions. They must understand and use electronic commerce technologies (ECTs). Currently, small businesses are not utilizing computers to the degree necessary to fully participate. A recent survey commissioned by IBM indicated that while 82 percent of small businesses (not just manufacturers) had desktop computers, only 37 percent had local or wide area networks.13 In a stage model of information technology maturity,14 almost two-thirds of these respondents would fall into the first, most elementary, stage of maturity.
SMEs are becoming aware of the need to adopt some form of electronic communications. With increasing frequency, prime contractors and large firms have demanded that their suppliers have electronic
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capabilities. As one would expect, this has heightened interest in electronic commerce capabilities among small-and medium-sized businesses. The interest is likely to escalate. One software vendor executive, explaining that his company was trying to respond to customers' needs for advice and consultation about electronic commerce, put it this way, "Our executives have been around long enough to tell the difference between a ripple and a wave. This one is a wave."15
Engineers from the Cleveland Electronic Commerce Resource Center (Cleveland ECRC) report similar interest but observe that some small firms are satisfied with a "rip and read" solution: they link into a bulletin board or value-added network with a personal computer but use the computer as an expensive fax machine. They "rip off the printed specifications," then read them and insert them into their manual system.16 This approach works for written specs and to some degree for drawings, but it clearly is limiting. More advanced firms install a computer aided design (CAD) system to enable them to accept design data in digital formats. Often, they too have a manual internal system and do not attempt to use the digitally stored format.
Compounding the technical problem is the lack of a single standard that is widely accepted; Chrysler, Ford, and GM use different, incompatible CAD systems. For most SMEs, the cost of implementing multiple standards is too high, and they either choose a single customer's standard or opt for another market. In either case, the situation does not lead to increased competition and to the increased competitiveness of SMEs. A single standard would help. Standards such as PDES/STEP are being developed, but agreements and adoption take time, and such standards address primarily the technical issues of data sharing.
Organizational (i.e., managerial and cultural) issues are equal to, if not greater than, technical capabilities in importance. In their discussion of agile manufacturing, Goldman and Nagel17 share the vision of integration of virtual enterprises through the use of information technology, including standards and "broad-band communications channels."18 They acknowledge the need for flexible production machinery but point out the need for organizational innovations as well. The agile system they envision requires flexible production workers and managers, not just technology. Getting the integration of technology and people into a new, responsive system is a challenge. They conclude, "An understanding of the nature of managerial decision-making is more important than ever before."19
Other researchers agree with Goldman and Nagel that the managerial, organizational, and cultural issues are at least equal in importance to the technical challenges of tapping into the benefits of the NII. In a field study of five large firms that were judged to be implementing integrated information systems successfully, a study team found six shared characteristics among the firms, and only one (the goal of capturing all data in electronic form at its origin) was technical.20 The other five characteristics (vision and clear strategy, vocabulary/language incorporating metrics shared by technical and business staff members, customer focus, and a sense of urgency) were organizational factors.
Factors Affecting SMEs' Adoption of Technology
One approach to understanding SMEs' use of information technology would be to view ECT as a technology that will be diffused throughout manufacturing. This diffusion approach21 uses the familiar S-curve to identify the percent of SMEs that have adopted ECTs over time. Factors associated with an individual firm's propensity to adopt technology might suggest strategies for working with innovators, early adopters, and so on.22 Implications of this type of model for policy are discussed further in the final section.
Another useful approach, the one taken for the remainder of this paper, is to seek an understanding of the decision-making process within the SME. From this viewpoint, we may gain some insight into the economic, technical, structural, and other barriers to adoption as seen by the SME.
The stage model suggested by Venkatraman23 of firms' use of information technology (Figure 1 shows an adaptation of this model) is used as a basis for identifying the gap between the "as is" state and the "desired" (or ''to be") state of SME capabilities.
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Figure 1
Stage model of information systems use. SOURCE: Adapted from
Venkatraman, N., 1994, "IT-enabled Business
Transformation: From Automation to Business Scope Redefinition,"
Sloan Management Review, Winter, pp. 73–87.
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The data from the IBM survey of small businesses24 indicate that almost two-thirds of the survey respondents are in the first stage of maturity in applying information technology. Virtually none of them have progressed beyond the second stage, and there is no assurance that they will go beyond this stage. For SMEs to benefit from the NII, they must be at level 3 or above, developing capabilities for network/supply chain integration. Although the IBM survey was not limited to manufacturing firms, our experience with SMEs leads us to speculate that small service firms and those in the retailing and trade sectors may use computers even more than manufacturers, lowering even further the estimate of how many SMEs have moved beyond the first stage of computer use.
This stage model is descriptive, and it only indirectly suggests how an organization moves from one stage to another. Our concern is to understand how the organization, particularly an SME, progresses from the applications of isolated systems to network and supply chain integration and, more importantly, how this process can be accelerated. The relevant fields of research are those of technology policy, innovation adoption, the decision-making process within the firm, and the emerging field of inquiry on organizational learning.
The concept of organizational learning,25 particularly the use of the human experiential learning model proposed by David Kolb26 and recently applied to organizations,27 provides a useful framework to interpret the findings from the other fields. This model, shown in Figure 2, illustrates the different modes by which an individual (organization) learns. Learning takes place in two dimensions: in the concrete-abstract dimension (shown vertically as a continuum) and in the active-reflective dimension (shown horizontally). Individuals (and organizations) have different preferences for learning and processing information in these dimensions.28 Some prefer more concrete and active learning (e.g., entrepreneurs); others prefer more abstract and reflective learning (e.g., professors).
Figure 2
Experiential learning cycle. SOURCE: Reprinted from Kolb, David,
1984,
Experiential Learning: Experience as the Source of Learning
and
Development, prentice-Hall, Englewood Cliffs, N.J.
The learning cycle model suggests that only when the organization goes through all four modes is learning complete. For example, a firm may introduce a new process for a customer or product line (active experimentation), collect sales and quality data over time (concrete experience), interpret these data and compare with prior experience (reflective observation), and develop a projection of sales and costs of quality if the new process were applied to all their product lines or to all their customers (abstract conceptualization). Based on the model, the firm may choose to switch its other products to the new process, again moving to active experimentation and
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restarting the cycle. By passing through each of the learning modes, the firm generates new knowledge. The firm learns, and the learning is not limited to the simple aggregation of additional data or to thinking about a new idea—the cycle is complete.
Using the concept of the learning cycle, we can frame our concern as that of understanding the predominant learning modes of SMEs and of understanding how SMEs can incorporate all learning modes in their progress toward the higher stages of information technology maturity. For this understanding, we can draw on several areas of research about how organizations adopt technology. In each of the relevant areas, it is evident that one must use caution in applying concepts derived from the large organizational context to the SME.29 However, some studies have focused specifically on the decisionmaking and policy formulation in the small firm, and these studies are particularly helpful in our efforts to understand how to accelerate learning and ECT adoption in SMEs.
Analysis
The first three subheads below outline relevant concepts from three distinctive but overlapping areas of inquiry. Each has its own literature base and each offers some insight into how firms, and SMEs in particular, may implement and use information technologies. The fourth subhead outlines the structural issues that may initially inhibit SMEs' effective participation in the NII. The section concludes with a synthesis of ideas about how SMEs may approach the adoption of electronic commerce technologies and realize the benefits from the NII.
Diffusion of Technology
The diffusion literature30 characterizes the industry adoption of new products by an S-shaped curve. The curve reflects exponential growth with a rate that depends on the size of the remaining market. The diffusion model has been used with some success in technology forecasting. With good data on when a low level of adoption has been achieved (e.g., 5 percent), the model is effective in identifying the dates by which a specific level of industry penetration (e.g., 50 percent) will occur.
The S-curve model is often used to identify firms according to when (early or late) they make the decision to adopt the technology. The classifications may indicate different organizational characteristics. A modification of this conceptual model31 classifies the "buyer profiles" as being one of five types: innovators, early adopters, early majority, late majority, and laggards.
Recent research32 tested the idea that psychological characteristics (e.g., attraction to technology, risktaking) rather than economic variables might be used to discern buyer profiles. The study found that the benefit-cost variables were better predictors. Although one could argue with how the variables were operationalized and with the limits of the study (focus groups on a single product), the researchers' conclusion has face validity: Companies that pioneer new producs must focus on the benefits desired by purchasers. Even the early adopters, who are less price sensitive, seek benefits that meet their needs better than current technologies. What is not discussed in the study is the changing nature of the benefits and costs with changes in the organizational characteristics and with changes in risk as the technology matures.
Kelley and Brooks33 also showed the predictive power of economic incentives in the diffusion of process innovations. Not surprisingly, firms with high wage rates were more likely to adopt labor-saving technologies than were firms with low wage rates. The key is to note that the benefits and costs are established by the firms's perceptions; these perceptions are affected by the organizational values and the firm's particular situation.
As noted by Schroeder,34 the survival of an SME is linked to the adoption of technology as a regular part of doing business. If it is in the nation's interest for SMEs to thrive, then the diffusion issue is how to accelerate the adoption of information technologies among SMEs. The diffusion model may be a useful metric by which we can track and predict adoption rates as early data become available. However, the diffusion model does not help explain how firm-level decisions are made. Concepts that examine how the individual firm makes a technology adoption decision may be more informative in the early development of the NII.
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SME Decisionmaking
The literatures relevant to an SME's decisions on technology adoption are those on corporate strategy, technology strategy, technology policy, information system implementation and planning, strategic information systems, and investment decisionmaking at the level of the firm. These areas of study are rich in topics that are relevant to technology adoption, but the focus on SMEs and their adoption of technical innovations reduces the scope considerably.
SMEs differ from large companies in how they develop their corporate strategies and their technology policies. Large companies typically have well-defined processes for developing and implementing strategies through a corporate planning process. Small firms often use less structured approaches; strategies and policies may not be formulated but may "emerge" from a set of actions and experiments.35
In an SME, the chief executive officer (CEO) often is one—or perhaps the—owner of the firm. In these firms, the CEO's viewpoint is a critical contributor to strategy and policy. A recent study of SMEs36 showed that implemented technology policies (not just written policies) in SMEs are strongly influenced by how the CEO perceives the world. Even though all the firms in the study were immersed in the same industrial setting in the same Canadian province, the CEOs differed in their view of how hostile and how dynamic their environment was. The firms' propensity to invest in new technology was strongly related to these views. The basis for decisions is not an objective reality but rather a socially constructed reality37 as reflected in the viewpoint of the CEO.
The social construction of the adoption decision by a firm has other participants as well. For the SME, a strong influence is the supplier, who may be a major source of information.38
The innovativeness of an SME is related to the firm's outward orientation (e.g., customer focus) and the participation of the firm's functional groups in the decision.39 There is evidence40 that the SME learns with increasing technological capabilities so that, over time, its decisionmaking places more weight on factors that are more closely related to the true potential of the technology.
SME Learning
Arrow41 noted that firms learn through experience. This learning normally is considered to be related to process improvements and is the foundation for the concept of reduced costs over time because of "the learning curve." More advanced technologies may have greater productive potential, but the firm has less expertise in implementing such technologies. Knowing it has less expertise, the firm expects greater costs. The firm thus faces a trade-off in its choices of technologies to adopt.42
The capacity for learning affects the rate of adoption of new technology. Firms that have existing technological capabilities have higher "absorptive capacity"43 for new technology; they are able to learn more quickly.44
A firm's installed technology also affects the extent and magnitude of benefits the firm experiences from installing new systems. Firms that have more existing technological capabilities—for example, firms that have implemented information technologies in both the administrative and engineering/production operations—enjoy benefits that are greater than the sum of the benefits from individual systems. There is synergy and, because of the added benefits and increased capacity for learning, the "rich get richer" and vice versa. This appears to be the case both for large firms45 and for SMEs.46
When a technology is new to an industry—before its technical and economic superiority has been widely accepted—the learning capacity of a small firm is related to the firm's linkages with other firms and other industrial organizations. These external linkages, many of which provide informal but trusted conduits for sharing of technical know-how, appear to lower the cost of learning for the firm. Kelley and Brooks put it this way: "Small firms' propensity to adopt a process innovation is particularly enhanced by the nature of linkages to external resources for learning about technological development.… Where linkages to such external learning opportunities are particularly well-developed we would expect to find a more rapid rate of diffusion of productivity-enhancing process innovations to small firms."47
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Organizational learning may be "single-loop" or "double-loop."48 In single-loop learning, the organization improves its efficiency, becoming ever better at dealing with a prescribed problem or environmental situation. The lowering of costs because of the ''learning curve" is an example of single-loop learning. Double-loop learning, by contrast, is characterized by a shift in viewpoint and a modification of basic premises. Double-loop learning requires unlearning prior assumptions and standard operating procedures; it involves developing new paradigms, new frames of reference, and new interpretive schemes. Single-loop learning reduces variability; double-loop learning increases variability in search of more relevant objectives or more effective strategies.
Because prior procedures and paradigms have a history of success, organizations have difficulty engaging in double-loop learning; they actively resist.49 However, dynamic and turbulent environments demand that firms exhibit more variability in order to meet changing needs. One approach to stimulating variability—and possibly double-loop learning—is organizational restructuring. Restructuring (changing the top management team and/or the CEO) is especially effective when combined with a change in strategy (e.g., new products or markets).50
SMEs, especially the smaller ones, are less likely to adopt a restructuring approach. A turbulent environment sometimes stimulates an SME owner to sell or merge with a larger firm. Often, however, the SME that cannot adapt quickly enough to environmental changes simply ceases to exist. The latter outcome contributes to the statistics used by those who argue that SMEs provide unstable employment, even if they do create a significant portion of new jobs.
The learning model in Figure 2 provides a framework that helps synthesize these issues. Since complete learning means that the organization engages in each of the modes, an enterprise may engage in formal or informal collaboration with external organizations to learn. For example, the motivation for close alliances between suppliers and manufacturers51 is partially explained by the benefits of learning, and the higher rate of innovation adoption because of external contacts52 may be due to the expanded learning modes made possible by these contacts.
An alternative to restructuring or going out of business is to establish and maintain external relationships that enable learning. Such organizations, which "bridge"53 sources of knowledge about new technologies (e.g., universities) and the SMEs (as potential users), have been stimulated by federal- and state-level programs that have set up technology transfer centers and assistance networks. Ohio's Thomas Edison Technology Centers, the federally funded Manufacturing Technology Centers (MTCs), and, most appropriately, the federally funded Electronic Commerce Resource Centers (ECRCs) are examples of such bridging organizations.
The value of such organizations was set forth over a decade ago by Trist,54 who noted that complex societies and rapidly changing environments give rise to "meta problems" that a single organization is unable to solve. The solution is the development of "referent organizations" that mediate the interorganizational collaboration required in the organizational domain of interest.
Although detailed studies of the effectiveness of MTCs and ECRCs are premature given their recent formation, the political judgment seems to be that they are effective.55 Studies of Ohio's Thomas Edison Technology Centers generally have praised their value and effectiveness.56 One of the challenges noted is that of "relationship-building."57 There is the explicit acknowledgment that the relationships and the process of technology solving are equal to, if not greater than, the importance of developing the technology itself. These evaluations appear to support the concept that the bridging, or referent, organizations contribute to learning, and that at least part of the new knowledge created is not migratory knowledge but is embedded in the relationships that are established and maintained.58 Implicit in the Mt. Auburn report is the notion that the relationship-building role of these organizations is underdeveloped.
The Structural Issue: Of What Benefit Are a Few Telephones?
The current status of electronic commerce technology may be similar to that of the early telephone. Imagine being given the opportunity of purchasing the third or fourth (or even the fiftieth) telephone: unless you are assured that the other people (organizations) with whom you want to talk (trade/communicate) are equipped with compatible technology, the benefits are nil. Unless the advanced technology has its own appeal, a prudent business decision is to "wait and see"—wait until there is a critical mass of manufacturers and suppliers with
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whom you can beneficially communicate. Except for the innovators and early adopters, most of the potential SME users of ECTs—if they are aware at all of the NII and its electronic commerce benefits—are likely to think of these as something that may be possible in the future.
One approach to the structural barrier to the diffusion of ECTs is to think of the SMEs in clusters59 that share a characteristic or interest. Geographic clusters exhibit their own rate of technology diffusion that can be enhanced by bridging and referent organizations in those regions.60 Other clusters that share other interests (e.g., those firms in a supply chain) may be distributed geographically.
For industries in which the technology is relatively stable (e.g., automobile manufacturing) compared with the dynamism of emerging technologies (e.g., biotechnology), the shared interests of the supply chain may motivate groups of firms to adopt ECT more quickly. Although the relationships of suppliers to the manufacturers has become closer over the past several years, there still are no widely accepted technical standards, nor are there any established social mechanisms for engaging in collaborative efforts.
Summary: The Key Concepts
The literatures related to SME adoption of information technologies may be summarized in six key points:
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Implications for SMEs and the NII
The potential benefits of the NII to SMEs go much beyond simple manufacturing process improvements. If SMEs are to realize the full benefits of the NII, they must advance their level of information technology applications to levels 3 and 4 in the stage model shown in Figure 1.
Once at these levels, manufacturing costs may become lower—for example, firms can more readily specialize and develop core competencies in particular processes. Other benefits, however, contribute to the overall lower costs: shorter administrative lead times, improved risk management through better information about future demands, more flexible (agile) production, and so on. These benefits do not arise because a single firm or even a few firms adopt ECT; they will be realized only if a critical mass of firms in a value chain become interconnected.
The NII is the key element in this interconnection; it is the communications backbone. Even with the backbone, interconnections are not assured. The problem is not merely one of enabling individual firms to adopt ECT; it is one of enabling groups of firms to adopt ECT. This framing of the problem is more than just a change in scale (from one to many); it is a major change in scope and may add significantly to the complexity of the solution. As a minimum, it changes how we approach accelerated learning and adoption of ECT in SMEs.
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The SME technology adoption process, as studied by most researchers and as understood today in the United States, presumes independence among the adopters. However, interdependence, not independence, is necessary if the full economic benefits of the NII are to be realized.
This requires cultural changes in SMEs, and the rate at which SMEs change their cultures can be expected to dominate the rate of diffusion of the technology itself (including ECTs) among SMEs. Firms that traditionally have viewed the world through lenses of competition as a zero-sum game now must view competition as a positive sum game: competition as a means of benchmarking and improving one's own performance (e.g., as in organized sports, such as the Olympics). In such a view, technological advances by other firms provide a learning opportunity for their own firm.
Recommendations
A Strategy for Setting Priorities for NII Services to SMEs
SMEs, perhaps more than larger firms, have fewer options for second-order learning. For most SMEs, moving to the higher levels of information technology maturity—those levels required for electronic commerce and for realizing the greatest benefits from the NII—will be possible only by evolutionary change. The services available over the NII are expected to be offered by a mix of private, not-for-profit, and government providers. To enable SMEs to benefit from the NII, these providers, to the extent possible, should:
1. Give early priority to encouraging and establishing high-value, low-cost services that SMEs can use as individual firms.
Rationale: Most SMEs are at the lowest level of information technology maturity. They will perceive the highest costs (including learning) to be for services that require additional technology and integration. If individual firms can learn to use networks to obtain valued information from read-only services or for simple firm-firm communication (e.g., e-mail), the cultural change is evolutionary and the perceived subsequent costs of moving to more integrated levels will be lower.
2. Match the services offered to the information technology maturity level of the early adopters (e.g., the most advanced 10 percent) of SMEs.
Rationale: The perceived costs of moving more than one level make the benefits of adopting a new technology seem "out of reach"; setting the most advanced services just above the capabilities of the early majority balances the need for SMEs to see the possibilities of greater additional benefits with affordable costs of organizational change.
A Strategy for Public-Private-University Partnerships
Much of the technology will be developed and made available from the private sector. Moreover, the federal government is expected to continue to help establish and encourage the widespread acceptance of international standards for ECT. As established by the summary of research in this paper, the rate at which SMEs adopt ECTs (and benefit from the NII and DII) is dominated by organizational issues rather than purely technical factors. Consequently, the following paragraphs outline high-leverage opportunities for the federal government to improve the capabilities of existing public and partnership programs to address these issues.
In particular, DOD and the Department of Commerce programs such as the Manufacturing Technology Centers (MTCs), Electronic Commerce Resource Centers (ECRCs), and Manufacturing Learning Centers (MLCs) provide an appropriate infrastructure for accelerating the changes required in SMEs. These programs comprise geographically distributed networks of centers through which SMEs can receive assistance. From all appearances, these programs are performing their perceived missions successfully and satisfying their constituents. However, there are opportunities to expand these perceived missions and to accelerate the learning
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and development of SMEs as participants in the NII. The following is a recommended strategy for expanding these roles:
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Rationale: These centers should be the leaders in proposing to expand their missions, but they first need to understand the potential roles. Although many of the centers have universities as partners, the partnership may be limited to technological topics. Research results on technology adoption and the management of technology, especially in SMEs, may not be a normal part of the learning environment for the center staffs.
Possible outcomes: Expanded roles would emerge, for example, if these programs viewed their centers as referent organizations, operating in a particular interorganizational domain. In addition to the emphasis on technology awareness and technical training that the centers provide to SMEs, the centers could identify opportunities for SME learning that goes beyond the migratory knowledge that an SME can acquire through normal classroom environments or even short-term consulting arrangements. In particular, the center's mission might include convening, facilitating, and maintaining interorganizational consortia that are focused on particular issues. (The ECRCs are doing this to an extent now with regional interest groups.)
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Rationale: Research universities (business schools and universities with management of technology programs) can assist the centers in understanding SME adoption of technology and benefit from the centers' experiences. The centers could establish and support ECT facilities in community colleges and public libraries, enabling SMEs to have access to bid information and other emerging NII services until they are prepared to invest in their own facilities.
References and Notes
1. Acs, Z. 1992. "Small Business Economics: A Global Perspective," Challenge 35(6):38–44; U.S. Small Business Administration. 1993. The Annual Report on Small Business and Competition, U.S. Small Business Administration, p. 49.
2. U.S. Small Business Administration, 1993, op. cit.
3. Long, Andrea L. 1984. "Net Job Generation by Size of Firm: A Demographic Analysis of Employer Event-Histories," a report to the U.S. Small Business Administration Office of Advocacy, July 12.
4. Acs, 1992, op. cit.; and Acs, Z. 1994. "Where New Things Come From," INC 16(5):29.
5. Carlsson, Bo, and David B. Audretschlogy. n.d. "Plant Size in U.S. Manufacturing and Metalworking Industries," International Journal of Industrial Organization 12(3):359–372.
6. Davis, Steward, John Haltiwanger, and Scott Schuh. 1994. "Small Business and Job Creation: Dissecting the Myth and Reassessing the Facts," Business Economics, July 29(3):13–21.
7. Asquith, David, and J. Fred Weston. 1994. "Small Business Growth Patterns and Jobs," Business Economics 29(3):31–34.
8. Wheelwright, Steven C., and Robert H. Hayes. 1985. "Competing Through Manufacturing," Harvard Business Review 63:99–109; and Wheelwright, Steven C. 1985. "Restoring the Competitive Edge in U.S. Manufacturing," Calif. Management Review 27(3):26–42.
9. United States Congress. 1992. Small Business Manufacturing and Work Force Capability, hearing before the Subcommittee on Technology and Competitiveness of the Committee on Science, Space, and Technology, U.S. House of Representatives, 102d Congress, second session, Washington, March 9.
10. Carlsson, B. 1994. "Flexible Technology and Plant Size in U.S. Manufacturing and Metalworking Industries," International Journal of Industrial Organization 76(2):359–372.
11. Acs, 1992, op. cit.
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12. Porter, Michael. 1990. "Competitive Advantage of Nations," Harvard Business Review, March–April, pp. 73–79.
13. Mangelsdorf, Martha E. 1994a. "Technology Management in Growth Companies," INC 16(14):140.
14. Nolan R. 1979. "Managing the Crises in Data Processing," Harvard Business Review 57(2):115–126; Venkatraman, N. 1994. "IT-Enabled Business Transformation: From Automation to Business Scope Redefinition," Sloan Management Review, Winter, pp. 73–87.
15. Personal communication with Henry Nelson, Harris Data, April 1995.
16. Personal communication with Mike Kolbe, Cleveland ECRC, March 1995.
17. Goldman, Steven L., and Roger N. Nagel. 1993. "Management, Technology, and Agility: The Emergence of a New Era in Manufacturing," Interscience Enterprises Ltd. 8(1/2):18–37.
18. Ibid., p. 29.
19. Ibid., p. 36.
20. Mason, Robert M., A. Thomson, and H. Nelson. 1993. "Implementation of Integrated Information Systems from a Business Perspective," final report on a study performed for the CALS Program Office, Air Force Materiel Command, WPAFB, Ohio 45431. See also Mason, R.M., and H. Nelson. 1993. "Implementation of Integrated Information Systems: Comparisons of Field Studies and the Literature," Proceedings of the 27th Hawaii International Conference on System Sciences. Computer Society Press, Los Alamitos, Calif., pp. 987–997.
21. Rogers, E. 1983. Diffusion of Innovations. Free Press, New York.
22. Ibid.
23. Venkatraman, op. cit.
24. Mangelsdorf, 1994a, and Mangelsdorf, Martha E. 1994b. "Small-Company Technology Use," INC 16(12):141.
25. Senge, Peter M. 1990. "The Leader's New Work: Building Learning Organizations," Sloan Management Review, Fall, pp. 7–23.
26. Kolb, David. 1984. Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall, Englewood Cliffs, New Jersey.
27. Dixon, Nancy. 1994. The Organizational Learning Cycle. McGraw-Hill, London.
28. Kolb, op. cit.
29. Romano, Claudio A. 1990. "Identifying Factors Which Influence Product Innovation: A Case Study Approach," Journal of Management Studies 27(1):76.
30. Rogers, op. cit.
31. Moore, G. 1991. Crossing the Chasm. Harper Business, New York.
32. Taylor, James R., Eric G. Moore, and Edwin J. Amonsen. 1994. "Profiling Technology Diffusion Categories: Empirical Tests of Two Models," Journal of Business Research 31(1):155–162.
33. Kelley, Maryellen R., and Harvey Brooks. 1991. "External Learning Opportunities and the Diffusion of Process Innovations to Small Firms," Technological Forecasting and Social Change 39:103–125.
34. Schroeder, Dean M. 1989. "New Technology and the Small Manufacturer: Panacea or Plague?," Journal of Small Business Management 27(3):1–10.
35. Mintzberg, Henry, and James A. Waters. 1985. "Of Strategies, Deliberate and Emergent," Strategic Management Journal 6:257–272.
36. Lefebvre, Louis A., R.M. Mason, and E. Lefebvre. n.d. "The Influence Prism in SMEs: The Power of CEOs' Perceptions on Technology Policy and Its Organizational Impacts," submitted and under revision for Management Science.
37. Berger, Peter L., and Thomas Luckman. 1966. The Social Construction of Reality. Doubleday, Garden City, New York.
38. Preece, David A. 1991. "The Whys and Wherefores of New Technology Adoption," Management Decision 29(1):53–58.
39. Lefebvre, L.A., J. Harvey, and E. Lefebvre. 1991. "Technological Experience and the Technology Adoption Decisions of Small Manufacturing Firms," R&D Management 21:241–249.
40. Ibid.
41. Arrow, K. 1962. "The Economic Implications of Learning by Doing," Review of Economic Studies 29:155–173.
42. Parente, Stephen L. 1994. "Technology Adoption, Learning-by-Doing, and Economic Growth," Journal of Economic Theory 63:346–369.
43. Cohen, Wesley M. 1990. "Absorptive Capacity: A New Perspective on Learning and Innovation," Administrative Science Quarterly 35(1):128–152.
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44. Kelley and Brooks, op. cit.
45. Virany, Beverly, Michael L. Tushman, and Elaine Romanelli. 1992. "Executive Succession and Organization Outcomes in Turbulent Environments: An Organizational Learning Approach," Organization Science 3(1):72–91.
46. Lefebvre, E., Louis A. Lefebvre, and Marie-Josee Roy. 1995. "Technological Penetration and Cumulative Benefits in SMEs," Proceedings of the 28th Hawaii International Conference on Systems Sciences, Vol. III. Computer Society Press, Los Alamitos, Calif., pp. 533–541.
47. Kelley and Brooks, op. cit.
48. Argyris, C., and D. Schon. 1978. Organizational Learning: A Theory of Action Approach. Addison Wesley, Reading, Mass.
49. Argyris, Chris. 1977. "Double Loop Learning in Organizations," Harvard Business Review, September/October, pp. 115–125.
50. Virany et al., op. cit.
51. Helper, Susan. 1991. "Strategy and Irreversibility in Supplier Relations: The Case of the U.S. Automotive Industry," Business History Review 65 (Winter):781–824; and Helper, Susan, and David Hochfelder. 1992. "Japanese Style Supplier Relationships in the U.S. Automobile Industry: 1895–1920," chapter in forthcoming book edited by June Akudo for Oxford University Press.
52. Kelley and Brooks, op. cit.
53. Westley, Frances, and Harrie Vredenburg. 1991. "Strategic Bridging: The Collaboration Between Environmentalists and Business in the Marketing of Green Products," Journal of Applied Behavioral Science 27(1):65–90.
54. Trist, Eric. 1983. "Referent Organizations and the Development of Inter-organizational Domains," Human Relations 36(1):269–284.
55. A "back-of-the-envelope" calculation shows why. If we posit that SMEs employ approximately 6.9 million people and that two-thirds of them are employed at SMEs that are incapable of engaging in electronic commerce today (as noted by the survey cited above), we may assume that these employees are "at risk" of being dislocated or losing their jobs if their firm becomes noncompetitive. If we further posit that the ECRCs and MTCs enable only 10 percent of the employees who otherwise would be dislocated to keep working, and that each one earns at least $20,000 annually, then the potential savings from unemployment payments alone (one-half of the annual salary) approaches $4.6 billion—making the DOD and DOC programs seem like a bargain.
56. Mt. Auburn Associates. 1992. "An Evaluation of Ohio's Thomas Edison Technology Centers"; and Commission on Engineering and Technical Systems, National Research Council (NRC). 1990. Ohio's Thomas Edison Centers: A 1990 Review. National Academy Press, Washington, D.C.
57. NRC, op. cit.
58. Badaracco, Jr., Joseph L. 1991. The Knowledge Link. Harvard Business School Press, Boston, Mass.
59. Porter, op. cit.; and Fogarty, Michael S., and Jar-Chi Lee. 1992. "Manufacturing Industry Clusters as a Logical Way to Structure Technology Deployment," Center for Regional Economic Issues Report, Case Western Reserve University, Cleveland, Ohio.
60. Fogarty, op. cit.
