J. Robert Baum, University of Maryland
This six-year study of the causes of technology adoption and its effect upon new venture growth explains anomalous past findings that new non-technology based ventures have been slow to adopt technology compared with established businesses. In contrast, I found that new ventures invest early in product design technology and low cost marketing technologies; however, they hold off adoption of production and management information technologies. Thus, timing of adoption depends upon technology type. Similarly, the causes of adoption depend upon technology type. Adoption of all technologies were motivated by technology strategy and competitive threat; however, expected cost savings, customer attraction, and financial resources had varying effects. Adoption of all types of technology caused new venture growth.
Adoption of new product/process technology contributes to business success (King, 1994; Utterback, 1994; Williamson, 1985). Indeed, current dominance of the U.S. economy in world markets is ascribed to applied technology benefits for product design, manufacturing process, and management information systems (Council on Competitiveness, 1991). However, new non-technology-based ventures have low rates of technology adoption, even lower than established “old economy” businesses (Barker, 1995; Gupta & Wilemon, 1990; Julien, 1995; Sleeth, Pearce, & George, 1995). This is surprising because sociologists and economists predict that new ventures will be more innovative than established firms (Acs & Audretsch, 1990; Burns and Stalker, 1961; Schumpeter, 1934).
There are many studies of the causes of technology adoption (diffusion) in the literatures of economics, management science, strategic management, and organizational behavior, including those that point to performance gains from having a technology strategy. However, these studies focus on established large firms (Collins, Hage, & Hull, 1988; Dewar & Dutton, 1986; Gatignon & Robertson, 1989; Khan & Manopichetwattana (1989); Katz & Shapiro, 1986; Kimberly & Evanisko, 1981; Zahra & Covin, 1993).
In contrast, there are few empirical studies about the causes of newventure technology adoption and its effect upon new venture performance. Shane (Forthcoming) found a relation between technology characteristics (regimes) and new venture formation. Other researchers have found positive relationships between technology strategies and new venture performance in high tech ventures (McCann, 1991; Zahra, 1996; Zahra and Bogner, 1999).
The overarching purpose of this study is to find out why new ventures have been slow to adopt technology. Thus, it focuses on finding the causes of technology adoption. However, the research questions begin with a challenge to the intuition that technology adoption contributes to new venture performance. If it does not, it may be that founders have been slow to adopt technology because they have knowledge that adoption has yielded little benefit. If there is benefit in adopted technology for new ventures, then the answer to the slow adoption anomaly may lie in analysis of multiple causes of multiple types of technology adoption.
Toward this end, the research questions are: (1) Does technology adoption contribute to new venture performance? (2) What causes technology adoption? And (3), Are causes consistent across technology types? In pursuit of answers, I propose a theory of internal and external forces that affect strategic decision-making and action about technology adoption (See Figure 1). Hypotheses are tested with responses from 201 entrepreneur/CEOs of new manufacturing ventures.
This study is the first that goes beyond technology strategy to strategic action. It extends research about technology and new ventures by studying multiple technology types and performance and by focusing on adopted technologies in non-technology-based manufacturing firms. The architectural woodwork industry is the setting. In the industry, entrepreneur/CEOs have choices about the timing and intensity of technology adoption. I chose venture growth as the output concept of interest rather than other types of performance, because entrepreneurship researchers point to growth as the crucial indicator of venture success (Covin & Slevin, 1997; Low & MacMillan, 1988).
The contribution of the study is that results should help academics and practitioners evaluate five causes of technology adoption: (1) technology strategy, (2) expected cost savings, (3) customer attraction, (4) competitive threat, and (5) financial resources. The study analyzes venture performance related to: (1) product design, (2) manufacturing, (3) marketing, and (4) management information systems (MIS) technology. Thus, this project continues the search for competitive advantages that enable entrepreneurs to grow and manage their companies. The usefulness and validity of the study is supported by: (1) its six-year longitudinal design (1993 to 1999), (2) verification of CEO reports of independent variables with subordinate reports, and (3) verification of CEO reports of financial accounting performance with Dun and Bradstreet.
Technology Adoption and Venture Growth
Technology adoption is the application of new science to improve products or processes. Two competing perspectives about technology adoption (the structural and technoeconomic perspectives) agree that product/process innovation improves business performance (Burns and Stalker, 1961; Hull and Hage, 1982). Indeed, Zahra and Covin (1993) found that established manufacturing companies with plans to adopt technology have better returns on sales than those without plans. Similarly, Zahra (1996) found that several new venture technology strategies are associated with venture growth and profitability. Thus, assuming that action mediates the strategy-performance relation, and that improved products/processes improve venture competitiveness, I expect venture growth to follow adoption of technology in new ventures:
Hypothesis 1: Technology adoption causes new venture growth.
Causes of Technology Adoption
The choice to adopt technology is a strategic choice that is important for business performance. The choice process may be formal or informal, individual or team-based (Schwenk, 1988). Whatever the format and structure, internal and external forces impact the outcome and subsequent action (Hamel & Prahalad, 1989). A review of management science, marketing, economics, organizational behavior, and strategic management revealed five factors that researchers believe are important in technology adoption decisions. The factors are: (1) technology strategy, (2) expected cost savings, (3) customer attraction, (4) competitive threat, and (5) financial resources (Gatignon & Robertson, 1989; Utterback & Abernathy, 1975; Zahra & Covin, 1993).
Technology strategy is a company’s plan of action for acquiring, developing, and exploiting technological resources. Although technology adoption may occur in businesses without a formal technology strategy (Baum, Locke, & Smith, forthcoming), Gatignon and Robertson (1989) found that having a formal technology strategy improves business performance, and Zahra (1996) and Zahra and Bogner (1999) extended this positive finding to the new venture case. Since entrepreneurs expect their strategies to work and their strategic intentions are followed by action (Bird, 1992), I hypothesize:
Hypothesis 2: New venture entrepreneur/CEOs who have a technology strategy will adopt more technology than those who do not have a technology strategy.
The most frequently evaluated characteristic of strategic decision options is financial cost (Schwenk, 1988). Indeed, expected cost savings may be the most objective dimension in strategic choices and the most easily related with expected profits. Cost savings provide a powerful inside-the-firm motivation for change because they may permit price reductions that produce competitive advantage. Research has shown that cost savings are a motivator of technology adoption (McCann, 1991):
Hypothesis 3: New venture entrepreneur/CEOs who expect cost savings from technological innovation will adopt more technology than those who do not expect cost savings.
Perceived customer attraction may be as important as expected cost savings in motivating technology adoption. Customers are attracted when new technology fills a product or service void, or technologies may simply improve the quality or service of existing products/services (Utterbach & Abernathy, 1975). Also, visible adopted technology may be attractive because it reveals or suggests producer sophistication, legitimacy, or modernity. Technology adoption affords new venture entrepreneur/CEOs an opportunity to differentiate themselves from established competitors (Julien & Raymond, 1994). Whatever the dynamic, customers effectively control the patterns of resource allocation in well-run companies (Christensen, 1997), and customer attraction is essential for sales and, therefore, profits (Gatignon & Robertson, 1989). Thus, I hypothesize:
Hypothesis 4: New venture entrepreneur/CEOs who expect customer attraction to adopted technology will adopt more technology than those who do not expect customer attraction.
Perceived competitive threat may motivate strategic decision-makers to adopt technology, because they fear competitors will achieve cost or marketing advantages through technology. Decision-makers may even attempt to preempt a competitor’s reputed technological advantage by making similar, early, adoptions of technology (Gatignon & Robertson; 1989; Utterback & Abernathy, 1975). Researchers suggest that competitive threat is most intense in markets where a few companies dominate and cooperation is not present (Hofer and Schendel, 1978); thus, industry concentration has associated with intense competitive threat (Dess & Beard, 1984; Katz & Shapiro, 1986; Sheppard, 1985. Industry concentration may be threatening because top competitors have large resource pools and may cooperate against industry/market entrants (Blili & Raymond, 1993). I am persuaded that most new venture entrepreneur/CEOs sense and fear competitive threat and look to technological advantages for competitive advantage. Thus, I hypothesize:
Hypothesis 5: New venture entrepreneur/CEOs who compete in concentrated markets will adopt more technology than those who do not compete in concentrated markets.
With few exceptions, adoption of technology requires expenditures that consume free cash, untapped banker’s commitments, or untapped opportunities to raise debt or equity from private or public capital markets. The consumption of financial resources may be constrained by decision-makers’ perceptions of financial risk or by personal goals about acceptable levels of shared financial control (Gatignon & Robertson, 1989). Indeed, limited financial resources may limit growth (Porter, 1985).
Researchers have found that the interaction of investment requirements and financial resources are important considerations in strategic decisions about adoption of technology (Dowling and McGee, 1994; Khan & Manopichetwattana, 1989). Similarly, those who have studied new venture/small business investment in technology have pointed to the importance of financial resource limits for technology adoption (Julien 1995; McCann, 1991; McGrath, Venkatraman, & MacMillan, 1994). I expect new venture entrepreneur/CEOs to reflect these findings in their decisions about investment in technology. Thus, I hypothesize:
Hypothesis 6: Technology adoption will be affected by new venture entrepreneur/CEO’s evaluations of the sufficiency of financial resources.
Technology impacts all business activities from invention/creation to delivery and customer payment. Strategic management researchers, who have focused on technology strategies, have found varying relationships with performance across technology types (Bantel, 1998; Madique & Patch, 1988; Zahra & Covin, 1993). Following these studies, I focused on: (1) product design, (2) manufacturing, (3) marketing, and (4) management information systems (MIS) technologies.
Product Design Technology
Computer aided design (CAD) has impacted product/process design practices. CAD software and hardware configurations simplify drawing, storage, transfer, design change, and communication with manufacturing. This technology enables 3-D visualization of products, clear and fast electronic transmission of plans to internal and external decision-makers, and rapid modification to assist evaluation of downstream impact. Material and assembly technologies are a second form of product design technology that has simplified and lowered the cost of prototype production and broadened the array of appearance, function, and cost options available to designers. In short, product design technology has broadened options for the designer, and it has enabled higher rates of product change.
Manufacturing has been impacted by computer-based information and communication technology, CAM (computer-aided machining) technology, material technology, and transportation technology. Information and communication technology permitted “just in time” inventory controls and production scheduling. CAM has improved production line practices to yield consistent high quality production, and it has enabled the manufacture of customized products in shorter intervals at lower costs. Small manufacturing enterprises, such as those studied here, have been implementing computerized manufacturing technologies at an increasing rate (Acs & Audretsch, 1990; Julien & Raymond, 1994; Julien, 1995). This trend is probably in response to global competition and regional competitive threat (Zahra, 1996). Also, in established industries where dominant designs have developed, such as the industry studied here, investment focus has been shifted from design aids to cost-saving manufacturing aids (Utterback, 1994). Whatever the emphasis, product design and production process technologies are increasingly integrated to achieve economies and quality. It appears that both technologies are important for market success (McCann, 1991).
Marketing technologies studied here are: (1) high tech field communication tools, and (2) marketing information systems (Adoption of promotion-only and interactive websites was studied but not reported here because data was only collected in the 1998 survey.). High tech field communication tools are employed by field-based product/project managers. These technologies include laptop computers, mobile point-of-service terminals, cell-phone/beepers, and field promotion/demonstration software. The general purpose of these marketing aids is to improve the quality and efficiency of field sales and service by increasing the amount and timeliness of information that is available to the field salespersons/project managers for their customers. Adoption costs are low for this type of marketing technology in the industry studied; a field person can be outfitted with the latest technology for less than $10,000. The technologies are highly visible to customers. The causes and effects of computer-based marketing information technologies are studied as part of management information technology which follows this section.
Management Information Systems Technology
MIS technologies are those computer-based applications, internal networks, and external networks that enable collection and analysis of large amounts of financial, market, and organizational data to assist management. Applications include general office suite software as well as customized internal applications for collection and analysis of sales, cost, product, inventory, production, and distribution. The purpose of MIS systems is to provide management with real-time information to assist strategic and operational decision-making. MIS researchers point to advantages offered by MIS to businesses that are decentralized or which have complex communication, coordination, and control challenges (Raymond & Pere, 1992). Thus, it may be that simple new ventures do not benefit from MIS.
Controls (Abbreviated Version)
I included five controls to clarify the relations between proposed causes, technologies, and venture growth: (1) A single industry study, (2) Venture age, (3) Venture size, (4) Entrepreneur/CEO personal technical and industry specific competencies, and (5) entrepreneur/CEO motivation.
METHODOLOGY (ABBREVIATED VERSION)
Field Study Participants, Pilot Study, and Questionnaire
Firms that manufacture architectural woodwork in the United States were studied (part of SIC 2431). In 1993, the industry’s 849 CEOs were asked to return a response card if they were willing to participate, and they were asked to identify a subordinate employee with whom they worked directly. I employed pilot testing with 16 of the industry’s entrepreneur/CEOs to develop test measures for a questionnaire. In 1993, I mailed a questionnaire to each of the 442 CEOs who agreed to participate and sent an adapted version of the questionnaire separately to the 202 employee-participants (EP) whose CEO had also agreed to allow such EP participation (EP responses were used to verify CEO responses.). A second questionnaire was mailed in 1999 to the 1993 CEO participants. Tests verified the representativeness of the respondents. Disqualification of respondents who did not qualify as entrepreneurs, yielded two hundred-one (201) responses from entrepreneur/CEOs (24% of the population).
Measures and Controls
A measurement model table is available from the author. It shows the 29 measurement model concepts: venture growth, 4 technology types, 5 predictor concepts for each technology type, and 4 controls. The table also shows the number of measurement items, format, and LISREL 8.3 composite reliability (CR) for each concept. CR is conceptually similar to ALPHA (Cronbach, 1951); it should exceed .60 for exploratory model testing (DeVellis, 1991; Van de Ven & Ferry, 1980).
RESULTS (ABBREVIATED VERSION)
LISREL 8.3 and PRELIS 2 were used to: (1) impute missing data, (2) evaluate concept validity [reliability (including dual-source similarity), convergent, and discriminant validity], (3) perform confirmatory factor analysis to verify the validity of the proposed configuration of causal concepts, and (4) test the hypotheses. (PRELIS 2 HT) and multiple sample analysis (LISREL MSA) confirmed the similarity of the response distributions of the 83 entrepreneur/CEO-EP pairs, as well as the distributions of the entrepreneur/CEOs with EPs (n = 83 ) and without EPs (n = 118).
The measurement model table (available from the author) shows that the measurement model had 21 concepts with CR > .80, 7 concepts with CR between .70 and .79, and 2 concepts with CR between .60 and .69. All measure coefficients were significant (t > 2.0; p < .05); thus, convergent validity was established. Discriminant validity was verified by determining for each latent variable that the average variance extracted by the latent variable’s measures was larger than the latent variable’s shared variance with any other latent variable (Fornell & Larcker, 1981). Common source bias was not significant according to CFA analysis with a common latent variable. In summary, the measurement model exhibited reliable measurement of the latent variables, convergence of the measures of each concept, and divergence of the concepts.
Table 1 shows the “fit” results of the structural equation modeling. All 4 technology-type models have acceptable fit statistics. For example, the poorest fit among the 4 is for marketing technology: [X2 (98) = 153 which is significantly better than the independence model X2 (127) = 1280; GFI = .92; AGFI = .88; RMR = .069; RMSEA = .088], and 46% of the variance is explained.
Table 2 shows the structural equation coefficient results. All 4 technologies are significant causes of venture growth which confirms Hypothesis 1. The controls for entrepreneur/CEO industry and technical competency and motivation impacted venture growth positively across technologies. Technology strategy and competitive threat are predictors of technology adoption regardless of technology type. This confirms Hypotheses 2 and 5. (1) Cost savings affects manufacturing and MIS technology adoption, (2) customer attraction affects product design and manufacturing technology adoption, and (3) financial resources affects manufacturing technology adoption. Thus, Hypotheses 3, 4, and 6 are partially confirmed: Support is dependent upon technology type. The size and age controls also have varying impacts upon technology adoption: (1) Product design and marketing technologies are adopted by younger ventures. (2) Larger and older firms adopt more manufacturing and management information technologies.
DISCUSSION AND CONCLUSION
The most important finding of this study is that the causes and timing of technology adoption in new ventures are dependent upon technology type. Studies that point simply to findings that newer ventures are slow to adopt technology are misleading because newer manufacturing ventures are early adopters of product design and marketing technologies. However, new ventures do hold off adoption of manufacturing and MIS technologies until they grow larger, have sufficient resources, and expect internal and external benefits. Nevertheless, the new ventures studied were committed users of manufacturing and MIS technologies at the end of the six-year study period (More than half had adopted 50% or more of the manufacturing technology and MIS technology measurement items.).
This study of actual behavior confirms that technology adoption contributes to new venture performance. The six-year duration of the study adds credibility to the proposition that the relation is causal.
My findings about adoption support those strategic management and entrepreneurship researchers who have found significant correlations between technology strategy and performance. It makes sense that adoption would also relate with performance, because it is one-step closer to performance in the causal chain that spans strategic choice and results. The findings suggest that rational planning precedes action and success in new entrepreneurial ventures.
By studying multiple causes and multiple technologies, I offer a more complete picture of the technology adoption process and outcome. Indeed, this study offers a foundation for a more complete theory of technology adoption. This study’s (1) identification of some of the causes of technology adoption, (2) finding that causes are dependent upon technology type, and (3) finding that adoption leads to venture growth should help researchers craft more complex theories and empirical studies. This study also supports my view that the strategic choice perspective offers a useful platform for identifying the internal and external forces that impact technology adoption.
The picture that emerges is of an entrepreneur who plans to be “on the cutting edge” technologically but who weighs marketplace forces presented by competitors and customers within the limits of financial resources and opportunities. Indeed, the early adoption of product design and marketing technologies found here may be evidence of choices that have been made to distribute resources to generate demand before investing in supply.
According to this study, the causes of technology adoption depend upon technology type. For example, adoption of product design technologies appears to depend little on expected internal cost savings. Furthermore, respondents report less concern about financial resources when considering product design technology investment. Perhaps, this is because product design is a first step in producing and marketing a product/process, so that new ventures must adopt associated technological aids earlier than others. That is, without external acceptance of the product design, nothing else matters. Indeed, the prime motivators of product design technology adoption were the two external forces: expectations about customer attraction and perceptions of competitive threat.
In contrast, manufacturing technology adoption occurred later in the typical venture’s life and with large significant standardized coefficients for the internal causes associated with adoption (cost savings = .41 and financial resources = .53). The adoption hold back for manufacturing technologies may also be related to the high investment required for most manufacturing technologies in the industry studied (CAM and high-tech tooling is generally more expensive than product design, marketing, and information systems hardware and software for SIC 2431 manufacturing companies.).
Marketing technology adoption was accomplished early in the new ventures studied. I was surprised that customer attraction did not appear as a significant cause. One would think that marketing technology adoption (laptop computers with 3-D display software, cell phones, etc.) would be driven by perceptions that these technologies create a positive impression with customers. Competitive threat was a significant cause; thus, it may be that marketing technologies are adopted more as a defensive move. Perhaps customers expect these technologies rather than seek them out. In short, marketing technology adoption may be essential, not optional.
Management information system (MIS) technology adoption followed timing patterns that were similar to manufacturing technology adoption patterns: Adoption occurred later when the new ventures were larger. It may be that there is no significant benefit from these technologies until scale is achieved (For example, computer networks are of little value in firms with few employees.). It may be that founders delay adoption because adoption is organizationally disruptive. It may be that customers do not care about these internal practices. Whatever the cause of the delayed implementation, this study pointed to two motivators of MIS adoption: expected cost savings and competitive threat.
Analysis of a single industry provided control of industry effects and may have added richness and clarity; however entrepreneurship researchers have found that industry characteristics affect venture performance (Shrader & Simon, 1997; Shane, forthcoming). Thus, these results may not generalize to other industries.
Although I reviewed literature from multiple domains in search of likely causes of technology adoption decisions, I may have missed important personal, organizational, and environmental causes. Finally, venture growth may not be a sufficient indicator of venture performance. Indeed, researchers point to the importance of successful founding, founder satisfaction, profits, and other indicators of performance not studied.
In conclusion, the model of the causes of technology adoption presented in this paper offers a platform and framework to guide those who make strategic decisions about technology. The specific findings about the varying importance of technology strategy, expected cost savings, customer attraction, competitive threat, and financial resources in decisions about adoption of product design, manufacturing, marketing, and MIS technologies may help those who teach courses about technology, and researchers who are forming more complete theories about technology.
CONTACT: J. Robert Baum, Smith School of Business, University of Maryland, College Park, Md. 20742; (T) 301-405-9308; (F) 301-403-4292; firstname.lastname@example.org
Acs, Z.J., & D.B. Audretsch. (1990) Innovation and Small Firms. Cambridge, MA: MIT Press.
Barker, P. (1995) “Fear, Resistance, Holding Back Electronic Commerce.” Computing Canada 21 (13): 36.
Bentley, K. (1990) “A Discussion of the Link Between One Organization’s Style and Structure and Its Connection with its Market.” Journal of Product Innovation Management 7 (1): 19–34.
Christensen, C.M. (1997) The Innovator’s Dilemma. Boston: Harvard Business School Press.
Dewar, R.D. & J.E. Dutton. (1986) “The Adoption of Radical and Incremental Innovations: An Empirical Analysis.” Management Science 32: 1422–1433.
Dowling, M.J. & J.E. McGee. (1994) “Business and Technology Strategies and New Venture Performance: A Study of the Telecommunications Equipment Industry.” Management Science 40 (12): 1663–1677.
Gatignon, H., & T.S. Robertson. (1989) “Technology Diffusion: An Empirical Test of Competitive Effects.” Journal of Marketing 53 (January): 35–49.
Julien, P.A. (1995) “New Technologies and Technological Information in Small Businesses.” Journal of Business Venturing 10: 459–475.
Khan, A.M., & V. Manopichetwattana. (1989) “Innovative and Non Innovative Small Firms: Type and Characteristics.” Management Science 35 (5), 597–606.
Kalakota, R., & A.B. Whinston. (1999) Electronic Commerce. Reading, MA: Addison-Wesley.
Kimberly, J.R., & M.J. Evanisko. (1981) “Organizational Innovation: The Influence of Individual, Organizational, and Contextual Factors on Hospital Adoption of Technological and Administrative Innovations.” Academy of Management Journal 24 (4): 689–713.
King, W.R. (1994) “Forecasting Productivity: The Impact of IT.” Information Systems Management 11(1): 68–70.
Maidique, M.A., & P. Patch. (1988) “Corporate Strategy and Technological Policy.” In M.L. Tushman and W.L. Moore (eds.), Readings in the Management of Innovation (2nd edition) pp 236–248. Cambridge, MA: Ballinger.
Miller, C.C., W.H. Glick, Y.D. Yang, & G.P. Huber. (1991) “Understanding Technology-structure Relationships: Theory Development and Meta-analytic Theory-testing.” Academy of Management Journal 34 (2): 370–399.
Raymond, L., & G. Pere. (1992) “Measurement of Information Technology Sophistication in Small Manufacturing Businesses.” Information Resources Management Journal 5 (2): 4–16.
Scherer, F.M., & D. Ross. (1990) Industrial Market Structure and Economic Performance. Boston: Little, Brown.
Sheppard, W. (1985) The Economics of Industrial Organization (2nd ed.). Englewood Cliffs, NJ: Prentice Hall.
Shrader, R.C., & M. Simon. (1997) “Corporate Versus Independent New Ventures: Resource, Strategy, and Performance Differences.” Journal of Business Venturing 12: 47–66.
Sleeth, R.G., C.G. Pearce, & G. George (1995) “Community Networking: Managing Phobic Behavior at the Individual and Community Levels.” Proceedings of the Second International Workshop on community Networking. IEEE communications Society with ACMSIGCOMM, 249–253.
Tushman, M.L., & P. Anderson. (1986) “Technological Discontinuities and Organizational Environments.” Administrative Science Quarterly 31: 439–465.
Utterback, J.M. (1994) Mastering the Dynamics of Innovation. Cambridge, MA: Harvard Business Press.
Zahra, S.A., & W.C. Bogner. (1999) “Technology Strategy and Software New Ventures’ Performance: Exploring the Moderating Effect of Competitive Environment.” Journal of Business Venturing 15: 135–173.
Zahra, S.A. & J.G. Covin. (1993) “Business Strategy, Technology Policy and Firm Performance.” Strategic Management Journal 14: 451–478.
© 2000 Babson College All Rights Reserved. Last Updated April 2001