A LONGITUDINAL STUDY OF THE CAUSES OF TECHNOLOGY ADOPTION AND ITS EFFECT UPON NEW VENTURE GROWTH J. Robert Baum, University of Maryland ABSTRACT

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. INTRODUCTION

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 new venture

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. THEORY AND HYPOTHESES 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 (Child, 1972). 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

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.

Formal technology strategies are composed of component plans of action. Not all

components of technology strategies have demonstrated positive relations with new venture performance. Those that have consistent positive relations with new venture performance include: (1) adopt existing process technologies to improve product quality or reduce costs, (2) develop radical breakthrough products, (3) redesign existing products, and (4) adopt technology aggressively (McCann, 1991; Zahra & Bogner, 1999). Technology strategy components that have little, or negative, effect upon venture performance are: (1) conduct R & D, (2) use outside technology development, and (3) protect intellectual property. Whatever the direct effects of technology strategy upon venture performance, I follow those strategic management researchers who assume that technology strategies are followed by action and positive performance (Zahra & Covin, 1993, Bantel, 1998; Zummoto, 1988). 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.

Cost Savings

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. Indeed, it has been shown that substantial inventory, order entry, order tracking, and customer service savings have been achieved through the use of information technologies and that these have improved performance (King, 1994). Other research has shown that internet-based auction sites have produced substantial supply cost savings in multiple industries, and these real cost savings are becoming widely recognized in the business community (Kalakota & Whinston, 1999). Furthermore, expected 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.

Customer Attraction 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. For example, internet commerce, e-mail, POS terminals, and improved communication and presentation tools have produced a set of consumers who do not tolerate sales, fulfillment, or service that is much less than perfect, free, and fast. Simultaneously, “old economy” retailers report increasing customer impatience that is only satisfied with applied technology (Julien & Raymond, 1994). Thus, while new marketing and information technologies have created ideal products/services in many market niches, they have raised expectations about quality and service even in niches where the product is not the technology itself (Bentley, 1990; Ghosh, 1998). 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).

New venture strategic decision-makers must be especially responsive to revenue-

generating customer attraction to adopted technology because new ventures are usually without slack resources (Acs & Audretsch, 1990). Furthermore, technology adoption affords new venture entrepreneur/CEOs an opportunity to differentiate themselves from established competitors (Julien & Raymond, 1994). 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.

Competitive Threat

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.

New ventures are not initially among the largest competitors in an industry or market,

and this condition has been presented as an advantage and a threat. Some suggest that new firms have an advantage in concentrated markets because oligopolists are slow to innovate (Acs & Audretsch, 1990). Other researchers note that not all new ventures fear or face direct competition because they intend to serve a small product or geographic market niche and may not even be aware of threats (Julien & Raymond, 1994). Researchers also note that entrepreneur/CEOs have been able to distinguish their companies with advanced products/services for which they can achieve higher prices than those obtained by the existing oligopolists (Gatignon & Robertson, 1989; Scherer & Ross, 1990). In contrast, other researchers suggest that industry concentration is threatening because top competitors have large resource pools and may cooperate against industry/market entrants. Whether concentration offers advantage or threat, it inspires adoption of technology (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.

Financial resources

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 Types

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.

Product design technologies are offered for adoption with required investments that

are low (One basic CAD drafting station can be purchased for less than $10,000.) to high (The latest CAD animation software used in movie production may require an investment of over $1,000,000.). Most material and assembly technologies have not increased the size of investment in product design technologies. Indeed, past users of product design technologies report cost savings (McCann, 1991). Customers may not know that product design has been affected by newly adopted product design technology.

Manufacturing Technology

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).

Investment in up-to-date manufacturing technologies is usually expensive compared

with investment in design technologies. For example, a single CAD station that may only cost $10,000 can control a single CAM station that may cost over $200,000 for machinery, training, and tooling. However, adopted manufacturing technology generally reduces variable production cost (while increasing fixed cost). Customers may not be able to recognize that product or service change has been caused by adopted manufacturing technology, although they may appreciate the improvements.

Marketing Technology

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.

[[FOR NEXT STUDY … The causes and performance effects of website use were

studied as well. This technology should be particularly useful for small businesses and should support entrepreneurship because it can be adopted with little investment (websites can be created and established for less than $1000), however sophisticated interactive websites may cost over $500,000 and require expensive maintenance (Ghosh, 1998). Initial website applications are online advertisement, description of the company, and contact information. Second stage applications involve interactive capabilities which enable query-response and online order placement. The general purpose of website use is for advertisement and order entry. Although website marketing offers cheap access to broad markets, no salesperson costs, and 24 hour availability, users note that some retail customers prefer to “touch the goods, get personal attention, and take it home now”. Nevertheless, researchers have pointed to the reputation value that having a website affords as well as management expectations of broadened markets, and reduced costs (Ghosh, 1998). Established companies have experienced resistance from internal marketing employees to

adoption of interactive website marketing. If websites offer important competitive marketing advantages, it may be that lack of entrenched internal stakeholders is an important asset of newness. ]]

Marketing has been aided by marketing management information technology and

general management information technology. These computer-based information systems can provide fast complete reporting and analysis of product line sales and performance according to an array of categories (by region, by salesperson, by production source, etc.). Furthermore they assist analysis of pricing-purchase behavior, response to promotion, and performance of distribution channels. The causes and effects of computer-based marketing information technologies are studied as part of management information technology which follows this section.

Excepting internal marketing information systems, adoption of marketing

technologies is highly visible to customers. One study of adoption of a type of marketing technology found users reporting reduced costs and improved quality of the customer’s experience (Gatignon & Robertson, 1989). 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.

MIS technology is not visible to customers unless it is manifested in better

product/service quality or faster fulfillment. However, if publicized, MIS technology may affect the reputation of a company. Finally, MIS may impact operating costs (Raymond & Lorraine, 1992).

Controls

I included five controls to clarify the relations between proposed causes, technologies,

and venture growth. (1) A single industry was studied to avoid confounding by industry type and industry-specific environmental conditions (Brush & VanderWerf, 1992). (2) Venture age was controlled because age has related with venture performance in many entrepreneurship studies (Low & MacMillan, 1988), and technoeconomic theorists predict that, as products and processes age, innovation declines (Dutton & Thomas, 1984; Utterback & Abernathy, 1975). (3) Venture size was controlled because hundreds of studies have found that size can systematically influence organizational practices (Pugh, Hickson, Hinings, & Turner, 1968). (4) Entrepreneur/CEO personal technical and industry specific

competencies were controlled, because studies have pointed to their effect upon venture growth (Dewar & Dutton, 1986) (While “competencies” is a personal-level concept in this study, it is also a firm-level concept used by strategic management and economics researchers. In those fields, it represents tangible or intangible bundles of firm resources, including organizational knowledge, skills, and abilities, as well as financial and “real” resources.). Similarly, (5) entrepreneur/CEO motivation was controlled because empirical research has shown significant relations with venture growth (Baum, Locke, and Smith, Forthcoming). METHODOLOGY Field Study Participants, Pilot Study, and Questionnaire Firms that manufacture architectural woodwork in the United States were studied because the first author is a member of the industry and has: (1) access to membership information, (2) industry knowledge to help construct measurement items for the survey, and (3) links with industry leaders who could encourage participation. Industry firms manufacture and install wood products (doors, windows, stairs, cabinets, and trim) for high-end residential, commercial, and industrial buildings (part of SIC 2431). Products are sold to general contractors, interior designers, or directly to end users. Markets are geographically limited due to job site installation and service distance constraints. Typical firms employ highly skilled woodworkers, production oriented high-tech machinery operators, carpentry installers, managers, and salespersons. Technology has dramatically affected the design, manufacture, and marketing of products; however, many companies have resisted change and continue to operate as they had more than 10 years ago.

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. Participation in the industry-wide study was encouraged by trade association officers at industry conventions and in trade journal advertisements that identified benefits for the industry.

I employed pilot testing with 16 of the industry’s entrepreneur/CEOs to develop test

measures for a questionnaire. In 1993, after 2 written requests emphasizing the importance of participation, 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. The CEO questionnaire contained measures of the 14 concepts studied here. The EP version and the CEO version were identical except that references in the CEO version to “you” and “your company” were changed to “the CEO” and “the company” for the EP version. Only the CEO version collected year-end sales and employment data, and self-efficacy, a measure of motivation, was collected only from CEOs because self-efficacy is privately held. I received 414 CEO responses (49%) and 189 EP responses after follow-up reminders and phone calls.

A second questionnaire was mailed in 1999 to the 1993 CEO participants. It was

similar to the 1993 questionnaire except that it collected 1998 performance data and included

a matrix that permitted respondents to: (1) check the year of adoption of various technology items and (2) indicate changed views about responses to technology statements given in 1993. The 1999 survey included several questions (i.e., about website adoption, etc. that did not appear in the 1993 survey and which are not part of this study.).

Two hundred-one (201) responses from entrepreneur/CEOs were used in this study

(24% of the population). I disqualified responses from: (1) single employee firms, (2) CEOs who were not active owner/managers, (3) CEOs who had not founded or purchased their businesses 3 to 8 years before 1993 (Galbraith & DeNoble, 1992; McDougall et al., 1992; Shrader & Simon, 1997), and (4) those who supplied incomplete data. Forty-two (42) firms had closed. Forty-five (45) of the 1993 respondents are still active and their data has not been tabulated. Responses from 88 new ventures founded after 1992 are not included. The average qualified entrepreneur/CEO respondent had 15 employees and $1.6 M sales in 1992. The averages from the 1998 data are: 27 employees and $3.7 M sales.

Eighty-three (83) responding EPs (10% of the population - 41% of the net qualified

entrepreneur/CEO sample) had worked with matching qualified entrepreneur/CEOs for two or more years and had submitted complete data. The typical EP managed sales or production; all EPs reported directly to the entrepreneur/CEO. The average EP had 7 years experience in the industry and had worked for the entrepreneur/CEO for 2 years in 1992.

To test whether the 414 1993 respondents were representative of the population of 849

companies and to determine whether there was significant statistical bias, I performed a z-test of the mean number of employees and mean sales volume of the respondents and the population. The tests showed that the difference was not significant between the: (1) mean number of employees (z = .32; p < .38), or (2) mean sales volume (z = 1.0; p < .16). Many respondents (71) were not included in this study because in 1992 their ventures were only 1 or 2 years old (21) or more than 8 years old (50). Since the average age of the respondent firms exceeded 8 years, the sample firms that I studied are most likely smaller and younger than the average industry firm; however, my sample should represent the firms that would remain after a similar truncation of the population. Measures

Table 1 shows the 29 measurement model concepts: venture growth, 4 technology types, 5 predictor concepts for each technology type, and 4 controls. Table 1 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). Venture Growth

Venture growth was measured with two items: (1) the % change in annual sales from 1992 to 1998, and (2) the % change in year-end employment from 1992 to 1998. I chose venture growth as the venture performance indicator because growth represents the most important social and economic consequence of successful entrepreneurship (Low &

MacMillan, 1988). Objective measures were used (actual sales dollars) rather than subjective measures (e.g. respondents’ satisfaction with growth or respondents’ reports about growth relative to competitors’), because objective measures are more fine-grained (Chandler & Hanks, 1993). Furthermore, I believed that participants would report actual accounting data because: (1) Many industry respondents had regularly supplied performance data to a confidential third-party data service as part of the industry’s annual “cost of doing business” study. (2) I was able to promise confidentiality for this study because I used the industry’s third-party data service. And (3) Many industry members expressed interest in the results. Nevertheless, Rowe, Morrow, and Finch (1995) and Slevin and Covin (1995) have shown that objective and subjective measures point to the same unidimensional concept.

Despite follow-up efforts, 24 of the entrepreneur/CEOs who qualified for the sample

in all other respects had incomplete 1992 data. Seventeen (17) of these cases were completed with 1992 data supplied by Dun and Bradstreet, Inc. (1993), and the 7 remaining cases were omitted from the sample. Similarly, follow-up phone calls and Dun and Bradstreet, Inc. (2000) reports were used to get 1998 performance data for 12 firms.

The accuracy of the raw performance data was evaluated by checking the agreement

of a random sample of 25 of the firms with Dun and Bradstreet, Inc. (1993) reports about 1992 performance. Results of the correlation and t-tests of 21 of these cases, for which Dun and Bradstreet, Inc. (1993) reports were available, reveal high correlation between the respondents and Dun and Bradstreet’ reported sales (r = .94, p < .001). The difference between the means was not significant (t = .96, p < .36).

The average sales and employment growth rate for the sample companies was 13%

(The compounding rate was calculated for 1992 to 1998). These results are consistent with industry reports that industry recovery began in 1992 and that growth was “strong” and above construction industry growth rates (8%) for the same 6-year period studied. Causes of Adoption

Each item that measured a cause of adoption was worded to relate specifically to the technology type studied. Technology strategy was measured with Likert response format (LRF) statements such as: “My strategy is to try new manufacturing technologies ahead of my competitors.”, and “My strategy is to be on the cutting edge of product design technology.”. “Cost savings” was measured with LRF statements such as “Digitized templates save drafting time and expense.” “Customer attraction” was measured with LRF statements such as, “Customers want products that are marketed by salespeople who have high tech communication and computing devices.”. “Competitive threat” was measured by the statement, “Circle the % of your market that is served by the 3 largest companies.” (Note that competitors are limited by installation and service obligations to regional markets.). “Financial resources” was measured with LRF statements such as “We have enough money to invest in product design technology”. Technology Adoption Types

Respondents were asked to circle examples of each type of technology studied if they

had adopted the technology. In the 1998 survey, they were also asked to indicate the year adopted. A typical item on the list for “product design technology” was “In house CAD shop drawing capability”. A typical item for “manufacturing technology” was “In house CAM routing/boring capability”. Marketing technology adoption was measured in part with the item: “Laptop computers for field salespersons”. One of the items that measured management information system technology was “Computer-based production scheduling/tracking system”. Controls

Size was measured with the number of employees at the end 1992. Log transformation was not used because the size range was not large and the distribution was not highly skewed. Age was measured as the number of years from founding to 1992. Competency (technical and industry) was measured with 4 self-assessment items about the entrepreneur/CEO’s expertise with architectural woodwork industry technical skills and customer and supplier exchange practices. I offered the following scale: 4 = expert, 3 = high, 2 = moderate, 1 = low, 0 = none), and the instructions were “Assess the skill levels that you have for (1) project management, (2) cabinet assembly, (3) contract negotiation, and (4) finding and hiring experienced project managers.” Motivation (goals and self-efficacy) was measured with 1993 entrepreneur/CEO reports about their % growth goals for sales and number of employees for the next 3 years. Also, entrepreneur/CEO self-efficacy was measured with 2 self-assessment-scale questions about sales and employment growth: “Thinking about your skills, write a number from the confidence scale (0 = no confidence at all to 7 =complete confidence) to show how sure you are that you can beat the “% change in sales” shown. The respondents recorded their confidence beside rates of change from -25% to +100% or more. RESULTS

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. LISREL is particularly useful for analysis of mediation models that have multiple measures of multiple concepts like the one employed here. Univariate homogeneity testing (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). Thus, I found that CEO’s with EPs were not rationalizing their own performance as they made their reports, and that this validity extended to the full entrepreneur/CEO data set (n = 201).

I used the following indices to guide conclusions about the measurement model and to

indicate the fit of data to hypotheses: (1) The X2 probability should be larger than <.05; however, when n is large, as it is in this study, significant X2 are typical. Thus, I used the

X2 difference between the independence model and the LISREL solution model as an indicator of the model’s explanatory power (Medsker, Williams & Holahan, 1994). (2) The goodness-of-fit index (GFI) and adjusted goodness-of-fit index (AGFI) should be near or better than .90. (3) The root mean square residual (RMR) should be less than .100. And (4) the root mean square error of approximation (RMSEA) should be less than .080 (Bollen & Long, 1993; Breckler, 1990; Joreskog & Sorbom, 1999; Wheaton, 1987).

As shown in Table 1, 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 checked with LISREL confirmatory factor analysis by linking a common latent variable with all of the measures. The resultant coefficient, LAMBDA = .11 (t = .30, p <.05), indicated that common variance was less than 2%. Thus, there appears to be no threat that relationships were inflated because one person provided information for all of the concepts. 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 2 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 3 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.

There is no way to make valid comparisons about the amount of technology adoption

across technology types. However, note that all of the 1999 respondents reported adopting at least one of the technology measurement items, and more than half of the respondents had adopted more than half of the measurement items of each of the four 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.

LIMITATIONS 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. For example, existence of personal computer technology competency may be an important resource not considered, and certain personal predispositions that affect strategic decision-making may be important (Baum & Wally, 1994). Organization structure may be an important determinant of technology adoption not included (Khan & Manopichetwattana, 1989; Miller, Glick, Yang, & Huber, 1991), and researchers have pointed to the importance of environmental forces beyond industry (Tushman & Anderson, 1986; Dess & Beard 1984; Zahra 1996). 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.

[[The 1999 survey collected information about website utilization; but the 1993 survey did not. The purpose and structure of this paper was not served by analysis of the website utilization data; however, it may be interesting that 71% of the respondents studied here access e-mail daily, 40% look for sources of supply, 31% have a promotional web page (most were created in 1997 and 1998), and 6 of the 201 respondents have established interactive websites that accept order entry.]] later Other limitations are that this study did not deal with two definitional issues that confound entrepreneurship research: (1) “Who is an entrepreneur?”, and (2) “Which firms are entrepreneurial?”. I simply used a sample of young small businesses that were run by active owner/managers, regardless of their mode of entry, to study the causes of technology adoption. Finally, I modeled the causes and effects of technology adoption with linear equations rather than multiple-order equations because structural equation modeling is not well suited to testing non-linear equations 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. REFERENCES Acs, Z.J., & D.B. Audretsch. (1990) Innovation and Small Firms. Cambridge, MA: MIT Press. Bantel, K.A. (1998) “Technology-based, “adolescent” firm configurations: Strategy identification, context, and performance.” Journal of Business Venturing 13: 205-230. Barker, P. (1995) “Fear, Resistance, Holding Back Electronic Commerce.” Computing Canada 21 (13): 36. Baum, J.R., & S. Wally (1994) “Personal and Structural Determinants of the Pace of Strategic Decision Making.” Academy of Management Journal 37(4): 932-956. Baum, J.R., E.A. Locke, & K.G. Smith. (Forthcoming) “A Multi-dimensional Model of Venture Growth.” Academy of Management Journal 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.

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Cost Savings H3 Customer Attraction H4 Technology Adoption H1 Venture Growth Competitive Threat--- H5 Financial Resources H6

Controls: Controls:

Size Competency Age Motivation

Table 1 Measurement Model

CONCEPT #ITEMS FORMAT* CR** RESEARCH SOURCE _______________________________________________________________________________ VENTURE GROWTH Sales & Employment 2 1.0 –(1998/1992) .96 Low & MacMillan (1988) PRODUCT DESIGN TECHNOLOGY Product Design Adoption 5 FC .85 McCann (1991)

Technology Strategy 3 LRF .72 Zahra & Bogner (1999) Cost Savings 3 LRF .79 Kolakota &Whinston (1999) Customer Attraction 3 LRF .87 Julien & Raymond (1994) Competitive Threat 1 % IC 1.00 Gatignon&Robertson(1989) Financial Resources 3 LRF .86 Dowling & McGee (1994) MANUFACTURING TECHNOLOGY Manufacturing Adoption 6 FC .91 McCann (1991)

Technology Strategy 3 LRF .72 Zahra & Bogner (1999) Cost Savings 3 LRF .80 Kolakota &Whinston (1999) Customer Attraction 3 LRF .88 Julien & Raymond (1994) Competitive Threat 1 % IC 1.00 Gatignon&Robertson(1989) Financial Resources 3 LRF 87 Dowling & McGee (1994) MARKETING TECHNOLOGY Marketing Adoption 6 FC .90 Gatignon&Robertson(1989)

Technology Strategy 3 LRF .69 Zahra & Bogner (1999) Cost Savings 3 LRF .80 Kolakota &Whinston (1999) Customer Attraction 3 LRF .88 Julien & Raymond (1994)

Competitive Threat 1 % IC 1.00 Gatignon&Robertson(1989) Financial Resources 3 LRF .89 Dowling & McGee (1994)

MANAGEMENT INFORMATION TECHNOLOGY MIS Adoption 4 FC .62 Julien (1995)

Technology Strategy 3 LRF .71 Zahra & Bogner (1999) Cost Savings 3 LRF .77 Kolakota &Whinston (1999) Customer Attraction 3 LRF .81 Julien & Raymond (1994) Competitive Threat 1 % IC 1.00 Gatignon&Robertson(1989)

Financial Resources 3 LRF .86 Dowling & McGee (1994) CONTROLS Size 1 # Empl. 1.00 Pugh, et al. (1968) Age 1 # Years 1.00 Low & MacMillan (1988) Competency 4 E. Scales .71 Doutriaux & Simyar (1987) Motivation 2 % GG .82 Locke & Latham (1990)

2 SE. Scales .73 Bandura (1997) Notes: *FC = Forced Choices; LRF = Likert Response Format (Five point scale: strongly

disagree to strongly agree); % IC = % Industry Concentration; % GG = % Growth Goals; E. Scales = Expertise Scores; SE. Scales = Self Efficacy Scores.

**CR = Composite Reliability which is similar to ALPHA.

Table 2 Results: Structural Equation Fit Statistics TECHNOLOGY X2 c.m. d.f. X2 i.m. d.f. GFI AGFI RMR RMSEA Product Design 136 93 1262 124 .95 .92 .056 .072 Manufacturing 172 98 1280 127 .94 .91 .056 .070 Marketing 153 98 1280 127 .92 .88 .069 .088 MIS 139 89 1144 120 .93 .88 .064 .078 Notes: c.m = constrained model; i.m. = Independence model

Table 3 Results: Structural Equation Coefficients _______________________________________________________________________________ CAUSES COEF. TECHNOLOGY COEF. OUTCOME

Controls _______________________________________________________________________________ Technology Strategy .37* Product Design .42* Growth Cost Savings .06 Customer Attraction .47* Competitive Threat .58* Financial Resources .03

Size -.06

Age -.16*

Competency .14*

Motivation .33*

_______________________________________________________________________________ Technology Strategy .21* Manufacturing .38* Growth Cost Savings .41* Customer Attraction .47* Competitive Threat .56* Financial Resources .53*

Size .42*

Age .18*

Competency .16*

Motivation .35*

_______________________________________________________________________________ Technology Strategy .34* Marketing .19* Growth Cost Savings .02 Customer Attraction .11 Competitive Threat .33* Financial Resources .07

Size .02

Age -.13*

Competency .23*

Motivation .41*

_______________________________________________________________________________ Technology Strategy .62* MIS .16* Growth Cost Savings .17* Customer Attraction .10 Competitive Threat .46* Financial Resources .08

Size .55*

Age .29*

Competency .36*

Motivation .43*

_______________________________________________________________________________ Notes: * = p <.05