Developing business policy to enhance rural small business competitiveness.

Shore, JoAnna B. ; Henderson, Dale A. ; Childers, J. Stephen 等

INTRODUCTION

Small businesses play a vital role in our economy, creating most of the new net jobs and accounting for almost half of the non-farm private sector jobs. Further, small businesses produce 13 times as many patents per employee than do large businesses, and employ 40 percent of all high tech workers (SBA, 2009). Unfortunately, many of our small businesses are sometimes at a competitive disadvantage to larger businesses. One example of this competitive disadvantage is the lack of broadband Internet connection for rural populations, sometimes referred to as the "digital divide." In regards to our nation's rural small businesses, they appear to be trapped in the "digital divide," often without adequate access and unable to compete in one of the fastest growing segments of our economy: e-commerce (Snowe, 2007). The Appalachian region, in particular, has been cited as one of four underserved target areas for broadband support (Federal Communications Commission, 2006).

The Internet is an increasingly important part of the U.S. economy. A lack of broadband accessibility to the Internet places firms at a disadvantage relative to other firms, as the Internet has become a necessary component of business activity. We argue that unless our rural small businesses acquire and use broadband technologies, they will be at a severe disadvantage in our new economy. In turn, this digital divide will have disastrous effect on the survival rate of our nation's rural small businesses; and thus, our nation's economy. To present our argument and findings, we will first detail the importance of small businesses to the U.S. economy. From this, we point out the growing importance of e-commerce to small business owners and, more crucial, the growing need for broadband support. We then explain how differential treatment causes harm to our nation's rural small businesses, and highlight the movement in Washington, D. C. to correct this malady. Of course, no technology is useful if it is not accepted. To that end, we surveyed Appalachian small business owners regarding their acceptance of this technology. Lastly, we discuss our conclusions and the implications of our findings.

RURAL SMALL BUSINESSES AND THE DIGITAL DIVIDE

Small businesses are an integral part of the U.S. economy. They constitute 99.7 percent of all employer firms, employ over half of all private sector jobs, and generate more than 50 percent of the U.S. non-farm gross domestic product (Small Business Administration, 2009). An often overlooked aspect of small businesses is the cultural impact they create. The number one reason individuals start small businesses is to obtain independence, or to be one's own boss (Virarelli, 1991). As a nation founded on personal freedoms, small businesses and the U.S. seem to go hand-in-hand. These findings suggest that the success and continued contributions of our nation's small businesses are critical to the long term viability of the U.S. economy.

Due to technological changes, our nation's small businesses are experiencing an increase in both opportunities and challenges. In particular, the rise of e-commerce as an inexpensive mechanism from which to improve operations and provide customer service has proven to be an important opportunity for small business owners. As such, more and more practitioner oriented articles are advocating the use of e-commerce for small businesses (Lohr, 2006; Ossinger, 2006). The impetus for these calls are the many business functions that can be accomplished more economically via e-commerce; such as on-line advertising, email marketing campaigns, and back-office support programs. Using broadband technologies, small businesses can vie for businesses and consumers previously available only to large corporations. Thus, e-commerce may lead to higher growth and wealth creation for small businesses as they are able to economically reach larger markets (Lohr, 2006).

Due to the many opportunities created through the Internet and e-commerce, many small business owners are integrating e-commerce activities into their operations. A 2007 poll conducted by the National Small Business Association found that 74 percent of small business owners are "highly reliant" on the Internet to conduct their operations. This includes Internet banking, financial exchanges, and e-commerce activities. Seventy-eight percent of the polled firms indicated that they had increased the amount of business they conducted via the Internet in the past year (National Small Business Association, 2007a). Similarly, over the past decade, the number of firms having their own website has doubled, to 60 percent (National Small Business Association, 2007b), with many small businesses becoming more reliant on and engaged in e-commerce. This leads us to our first proposition:

P1: E-Commerce, and its vehicle, the Internet, is becoming an integral tool for business success.

However, not all small business owners seem interested or able to engage in e-commerce. A 2005 poll conducted by the National Federation of Independent Businesses found that 16 percent of those small business owners surveyed indicated that they try to avoid technology (National Federation of Independent Businesses, 2005). A slightly different type of story seems to be occurring in our rural areas. Our nation's rural areas tend to be less affluent and have faced a century of employment erosion due to technology and employment migration (Johnson, 2001; Rowe, 2003). Further, because of their location, rural areas tend to be more expensive to serve (Rowe, 2003). One implication of this added expense is a lack of investment in broadband connection capability for rural populations: sometimes referred to as the "digital divide" (Snowe, 2007). The consequence may be that rural areas may have a more difficult time supporting small businesses (Pociask, 2005).

Access to broadband Internet connections provides substantial benefits: economic productivity, output, increased market access, and jobs (Federal Communications Commission, 2006; Pociask, 2005). For small business owners, the lack of broadband support has caused them to be at a disadvantage to their more asset-capable urban competitors: both large and small. Instead of e-commerce leveling the playing field with big business (Grandon & Peterson, 2004), a lack of access to broadband e-commerce has forced rural firms to become more reliant on the services of asset-capable firms. These actions have created more concentrated industries, giving more power to other businesses (Pfeffer & Salancik, 1978), thereby making it more difficult for rural small businesses to compete effectively (Porter, 1985).

For small rural firms to be more competitive, and thus maximize their power, they must have the tools to compete (Pfeffer, 1982). From a resource dependence perspective, power cannot be realized as these small businesses do not possess the same resources as their external counterparts. As e-commerce has become more engrained into our society, it has become an essential tool to conduct business. Further, by its very nature, e-commerce can tear down barriers between rural and urban areas and allow rural small business owners to compete more effectively against their larger counterparts (Grandon & Peterson, 2004, Pociask, 2005). By granting access and allowing rural small businesses to acquire these resources, they can become less dependent on local communities for support and make headway into markets located in distant geographic areas which were previously unreachable.

While there is universal agreement that broadband holds the promise of technological innovation and better communications, fulfilling this charge (improved broadband for small businesses) is imperative if small businesses, particularly those in rural areas, are to have affordable access to the information superhighway and compete successfully in the global marketplace ... what is becoming equally visible is the so-called 'digital divide' between those who have tremendous access and those that do not.

Senator Olympia J. Snowe (ME). October 2, 2007

In turn, better connectivity can provide an economic stimulus to poorer, underserved regions. In fact, the Internet has in some cases reduced the importance of proximity. Hence, a once disruptive force on rural America, technology, can be the force that helps save rural America (Johnson, 2001). Building on these points, we posit:

P2: Affordable access to broadband technologies is crucial for rural economic well-being.

Organizations were once thought to be closed systems (Scott, 2003). While this made studying and analyzing firms easier, as we needed only to study transformation of inputs into outputs (c.u. Taylor, 1914), today it is generally recognized that firms operate within an open environment, and adjust their strategies and structure in reflection of this fact (Chandler, 1977; Covin & Slevin, 1989; Lawrence & Lorsch, 1967). External factors, such as those conditions that deter growth or development, may stifle entrepreneurship activity (Gnyawali & Fogel, 1994). With industries becoming more concentrated due to unequal power distribution (Pfeffer & Salancik, 1978), business opportunities in rural America decrease (Buzzell & Gale, 1987; Biggadike, 1979). Likewise, Gnyawali and Fogel (1994) argue that governments should adopt policies and procedures that increase opportunities for potential entrepreneurs. More specifically, the authors suggest that governments can effectively encourage entrepreneurial development through programs, protections, and minimization of entry barriers (Gnyawali & Fogel, 1994). Hence, government influence has been found by Bruno and Tyebjee (1982) to influence entrepreneurial activities.

The power of these influences is well known to many politicians. Senator Snowe of Maine recommends a market-based approach to increase broadband support to small, rural businesses (2007). Pociask (2005) cites several studies and concludes that broadband investment would have a multiplier effect above and beyond the cost of the needed investment. One such investment project currently in place is that of the Federal Communications Commission's "Lands of Opportunity" program. A key goal for the program is to encourage e-commerce in rural areas. To accomplish this, the program has identified four target areas that are currently underserved by broadband access: such as Appalachia (Federal Communications Commission, 2006), to create jobs and provide access to larger markets for rural small business owners.

Current legislation has begun to target broadband access. The momentum for improved broadband seems to be on the rise, as evidenced by HR 3919, S 1492 (Kroepsch, 2008); this bill is intended to analyze rural broadband service. Growing interest from our politicians may suggest that their constituents are also becoming aware of the need for better broadband support in rural areas. Considering the evidence of its effectiveness and its relative potential impact, our final proposition is as follows:

P3: U.S. government policy should support and increase programs that offer affordable access to broadband connectivity in rural America.

SMALL BUSINESS OWNERS' ACCEPTANCE OF BROADBAND TECHNOLOGY

Whether or not rural small business owners would take advantage of available broadband support is an unanswered question to this point. The intent is to predict the likelihood that this population would use and benefit from better broadband access initiatives. Why are we creating policy for our rural small businesses without asking them questions? How can we design proper interventions for rural small businesses when we have not taken time to better understand them? Additionally, how do we know we are spending our taxpayer money properly when we do not ask the right questions? We find this interesting because by addressing the questions asked in this paper, policy makers can be better assured that they will be creating more effective policy that would result from collaborating with the very population the government is attempting to serve. The inclusion of the end-user in this technology decision process will enhance the acceptance of the technology (Whitten, Bentley, & Dittman, 2001). System users must be included early enough to buy into the value of the system. If the end users are ignored at this critical point the acceptance of the technology may be jeopardized. Therefore, the answers to these questions means saving the government money and helping develop more effective interventions and implementation.

Here, we begin to ask important questions about an underserved population: Appalachian small business owners. Even in small business research this is a very unique and often difficult group from which to identify and collect data. The most pertinent research question to attempt to answer at this point is, "Will rural small businesses owners accept broadband if it is available?" To answer this research question, we seek out a model to apply which, once tested, will help us better understand rural small business owners. We utilize an adaptation of the Technological Acceptance Model (TAM) for that task.

TECHNOLOGY ACCEPTANCE MODEL

Since the introduction of the original Technology Acceptance Model (TAM) (Davis, 1986; Davis, 1989; Davis et al., 1992) it has become one of the most widely recognized and tested concepts in management of information systems literature and is often heralded as the best predictor of technology adoption (Davis, 1993; Hendrickson & Latta, 1996; Mathieson, 1991; Moore & Benbasat, 1991). The Technology Acceptance Model was developed by Davis (1986) to assist in explaining computer usage and the behavioral intentions attached to adoption or rejection of any given hardware or software. The theoretical foundation of the Technology Acceptance Model was an amalgamation of Fishbein and Ajzen's (1975) Theory of Reasoned Action (TRA) in its original state and Ajzen's (1985) Theory of Planned Behavior (TPB) after a number of modifications. From this model, we may conclude that intentions to use a technology have a strong positive relationship with actual future usage.

Researchers contend that in the TAM, behavioral intentions to use technology are primarily the result of a rational analysis of its desirable outcomes, namely perceived usefulness (PU; i.e. to what extent does the user believe the technology or application will enhance their job performance) and perceived ease of use (EU; i.e. to what extent does the user believe the technology or application will be free of effort) (Agarwal & Karahanna, 2000; Gefen & Straub, 1997; Gefen & Straub, 2000; Koufaris, 2002; and Wu & Farn, 1999). Igabaria, Zinatelli, Cragg, and Cavaye (1997) found that the perceived ease of use, perceived usefulness, and system usage constructs were dependable and relevant to small firms. The authors also found that exogenous variables such as management support and external support influence both perceived ease of use and perceived usefulness. Further, the importance of external support lends credence to our earlier proposition that government influence would be a positive factor for broadband deployment.

In most cases, the literature on TAM focuses on explaining the acceptance of information technology from the individual's standpoint (Davis, Bagozzi, & Warshaw, 1989; Hu, Chau, & Sheng, 1999; Hubona & Geitz, 1997; Mead & Fisk, 1998; Taylor & Todd, 1995; Venkatesh & Morris, 2000). Attitude toward use has usually been conceived as a construct based on a subject's belief perceptions and evaluations of the consequences of engaging in some behavior (Hubona & Geitz, 1997). This individual frame of reference fits nicely with small business research in as much as small business decision making is highly centralized in the owner/operator. Similarly, Barnard (1938) argued that it is top management's responsibility to match distinctive competence with business opportunities.

While a centralized decision process ensures alignment of direction and command, it can sometimes come at a cost. In particular, due to bounded rationality (Simon, 1997), small business owners are sometimes overwhelmed by the many variables in need of attention. As a result, some areas of operations either get ignored or inadequate implementation. For the rural small business owner, this may mean that possible technological gains and the accompanying wealth of advantages will not be achieved if an owner feels that the use of the technology is too difficult or time consuming to pursue. Therefore, we posit that the business owner must perceive the technology to be easy to use. Stated formally in our first tested hypothesis, we predict:

H1: Perceived ease of use is positively related to intention to use.

However, just because a technology is easy to use, does not mean that people will use it. Technology deployed by rural small business owners can be viewed as a combination of resource availability and the owner's ability to use it in a way that creates an advantage (Grant, 1991). It appears that small business owners may need appropriate training and education to more fully engage broadband benefits. Hence, when small business owners perceive that e-commerce will be helpful to their firms' bottom lines, then one would expect an increase in the involvement between the user and the technology. So, while ease of use may be necessary for intended usage, it may not be sufficient without perceived benefits. This leads us to our second tested hypothesis:

H2: The perceived usefulness of technology intervenes, or mediates, the relationship between ease of use and intention to use.

METHODS

Because of the unique setting and sample of Appalachian firms, we opted to pilot test our survey instrument: a four-page survey instrument consisting of 35 items, based primarily upon selected sections of the Technology Acceptance Model (Shore, 2004). To test, we assembled a focus group to assess, evaluate, and offer feedback regarding the survey instrument. This focus group consisted of 13 information technology users, trainers, and practitioners from the local area who were not part of the follow-up study. We ensured that the focus group was representative of the broader target population (Gilner & Morgan, 2000). Specifically, this focus group provided insight into clarity of the instrument, the wording and education level required to navigate the instrument, the appropriateness of the survey format, and the length of time needed to complete the survey instrument (Fink, 1995). Several suggestions were incorporated into the pilot draft of the survey instrument.

This draft was then circulated to a pilot test group of approximately fifty participants randomly chosen from our full database by selecting every tenth name on the list until fifty names were collected. Twenty surveys from the pilot group were returned and declared usable as they were returned in a timely manner and had no missing values. While we understand that both the stability and confidence surrounding Cronbach alphas are at least partially affected by sample size, we were nonetheless encouraged by the feedback and Cronbach alpha scores for our scales, which were all significantly above the prescribed 0.70 (Nunnally, 1978).

The final data tested for this study was gathered through a self-reporting mail survey of small and medium sized enterprises found among ten counties spanning two Mid-Atlantic States recognized by state and federal governments as Appalachian counties (SBA, 2007). Our sample was drawn from Chamber of Commerce membership lists, telephone directories, and business directories within these ten counties. As firm size was not initially clear, surveys were sent to all business and firm size was controlled for post-hoc. Our survey was mailed out and achieved 9.4% response rate resulting in a sample of 188 small and medium sized Appalachian small business owners.

VARIABLES

INDEPENDENT VARIABLES

For each independent variable, respondents were asked to report their agreement based on a traditional seven-point Likert scale. Measures were based upon Davis's (1986) original survey and included:

(1) Perceived Ease of Use. This item measured perceptions regarding the ease or simplicity of use of internal technologies. Sample items include "I find websites easy to use" and "I find websites easy to use for information" The Cronbach alpha for this five question scale was 0.916.

(2) Perceived Usefulness of the Technology. This item measured perceptions regarding the usefulness and general efficacy of internal technologies. Sample items include "Doing business via websites would improve my company's performance" and "Using a website would make it easier to do business outside my present market area." The Cronbach alpha for this eight question scale was 0.928.

DEPENDENT VARIABLE--INTENTION TO USE.

Given our prior argument that actual use can be estimated from behavioral intentions, our dependent variable was Intention to Use which we assessed via three items offered in a seven point Likert Scale. This Likert Scale ranged from "extremely frequently" to "extremely infrequently." The Cronbach alpha for this item was 0.930.

CONTROL VARIABLES

After a review of the literature, three control variables were utilized in this study. First, the type and nature of small businesses may affect its global and tactical orientations towards technology (Porter, 1980). Thus, we captured, identified, and controlled for type of business by creating dummy variables to indicate type as service, manufacturing, retail, wholesale, or technology. Second, there is considerable theoretical and empirical research suggesting that the age of a given firm or business affects both its technology strategy and day-to-day operations (Barnett, 1990; Hannan & Freeman, 1989). We controlled for temporal effects with the Business Longevity variable. Finally, recognizing both resource constraints and scale related competitive advantages that impact both the choice and use of technology (Chandler, 1990); we captured annual revenues as an additional control variable. Our model for testing is presented in Figure 1.

[FIGURE 1 OMITTED]

RESULTS

Table 1 presents the basic descriptive statistics and Pearson correlation coefficients for our variables under study. Of note, wholesale firms had a positive correlation with ease of use. Perhaps previous automation tools in this industry have created learning effects and a greater ease with Internet technologies. Interestingly, we found a negative relationship between revenues and intention to use. Perhaps the small rural business owner's most handicapped from a lack of access are already seeing declines in profitability, and are eager to try and level the playing field.

To test our hypotheses, we used Ordinary Least Squares (OLS) regression analysis. To evaluate the marginal contribution above and beyond the predictive power of the control variables, we pursued a step-wise approach (Pedhazur & Schmelkin, 1991). Related to issues surrounding multicollinearity of both the control and independent variables along with the modest sample size, we chose to examine the effects of each predictor variable in a separate regression model. Consequently and as suggested by Pedhazur and Schmelkin (1991), we adopted a conservative approach to test our hypotheses. Therefore, any explanatory contribution of the independent variables was only after the first three control variables were entered into the regression equation.

Table 2 presents the results of these analyses. To test Hypotheses 1, Perceived ease of use is positively related to intention to use, we regressed Intention to Use onto our variable Perceived Ease of Use. Our results indicate that Ease of Use is a highly significant factor in determining the Intention to Use Technology. Therefore, we find support for our first hypothesis. The results are presented in Model 2 on Table 2.

To test our second hypothesis, Perceived usefulness of a technology acts as a mediator in the relationship between perceived ease of use and intention to use a technology, we

followed Baron and Kenny's (1986) prescriptive account of mediation testing. For this mediation testing, we ran four independent regression analyses:

The independent variable should be significantly related to the dependent variable

The independent variable should be significantly related to the proposed mediating variable

The mediating variable should be significantly related to the dependent variable

The independently variable is not significantly different than 0 when the mediating variable is introduced as control in the relationship with the dependent variable

As a first step, and as performed in hypothesis 1, we determined if our independent variable, Ease of Use, was significantly related to our dependent variable, Intention to Use (Model 2--Table A2). We did find a highly significant relationship.

Next, we determined that our proposed mediating variable, Perceived Usefulness of Technology, was significantly related to our independent variable Ease of Use (Model 5--Table A2). For step three, we regressed the dependent variable, Intention to Use, onto the proposed mediating variable, Perceived Usefulness of Technology and found a highly significant relationship (Model 3--Table A2). For the final step, we regressed the dependent variable on both the independent variable and the proposed mediating variable (see Model 4--Table A2). When this model was tested, our independent variable, Ease of Use, dropped out of the equation and only Usefulness was significant with Intention to Use.

Having met the conditions set forth by Baron and Kenny (2006), we accept our second hypothesis and find that Perceived Usefulness of a Technology acts as a mediator in the relationship between Perceived Ease of Use and Intention to Use a technology. Our final model is presented in Figure 1.

[FIGURE 1 OMITTED]

DISCUSSION AND IMPLICATIONS

In this research, we examined the consequences of the lack of broadband support available to small business owners in rural Appalachia. We concluded that rural small business owners must be given the tools they need to effectively compete in today's information society. Further, government, through the creation of economic incentives that offset the added expense of serving rural areas, is the ideal driver of such change. Of course, broadband support will not matter much if small business owners will not use it. A survey of rural Appalachia small business owners found that they would indeed embrace such technology as moderated by the overall usefulness of the technology. In other words, perceptions of ease of use of a technology would indeed increase the probability that rural small business owners would use new technology, but only if they perceived the technology to be useful.

This study makes several important contributions to both research literature and to future policy decisions. For decades, the Appalachia region of the United States has been described as under-researched and under-served (Federal Communications Commission, 2006; Pociask, 2005; Snowe, 2007). Our research works against this trend by informing both academics and policy decision-makers about the unique business and economic context that surrounds Appalachia. Although our research is exploratory and emerging, it appears that resources, alone, do not drive technology usage. This is important since many of the more recent technology policy decisions regarding Appalachia focus on either access or infrastructure (c.f. Federal Communications Commission, 2006). In particular, conventional policy is often crafted in a manner which suggests that by increasing computers, tying into optical fiber, and providing computing workshops, it is enough to spark technology usage and economic development in many rural parts of this nation (Rasiej & Sifry, 2007).

However, our results indicate that "policy selling" and careful attention to selling the benefits of this technology to these small business owners is equally, or maybe even more important than access and infrastructure. Specifically, small business owners and operators must be convinced and perceive that technology is easy to use and useful to create the best opportunity for actual usage and full business parity. Thus, significant government spending on optical fiber outlays may not garner the anticipated returns unless small business owners and operators see the ease and value associated with the technology.

Taken to its natural conclusion, this suggests the need for a marketing and public relations campaign to accompany hard investments such as the laying of optical fiber. Policy makers, from both elected officials and agency administrators, should understand the importance of shaping perceptions to reap the most out of agency and government technology spending. This enhances the effectiveness of tax leveraged dollars.

By building on our conceptual and empirical developments, future research could adopt a more fine-grained and nuanced approach to this phenomenon of technology use in Appalachia. For instance, it is conceivable that there is some path dependency to this phenomenon. In particular, perceived value could lead to usefulness, which, in turn, contributes to ease of use. Also, moderating variables could be more fully explored: this research paper only investigated main effects. It is plausible that firm size could moderate the relationship between perceptions and actual use. Specifically, bounded rationality may weigh heavily on the smallest of business owners causing them to value ease of use over other technology characteristics. Interestingly, this perception may coincide with a lack of a precious small business resource-time. As discretionary times shrinks, small business owners may overvalue simplicity and ease of use over other variables. Related, role conflict and role overload may also stress the importance of ease of use over other technology attributes.

Regardless, Appalachia provides a unique sample and an even richer setting encompassing variables that oft-overlooked in other samples (i.e., publicly traded firms) and regions. So, in addition to exploring the potential of moderating and mediating variables, qualitative research that stresses fewer cases (or a smaller sample), but more variables may add to the contextual richness of small business research in underserved areas such as Appalachia.

There are limitations with this study, which we highlight here. First, the setting for this study consists of small to mid Appalachian firms. A more robust context in which to draw conclusions regarding this particular sample is to include other small businesses and maybe even larger firms outside our limited boundary conditions for a comparative analysis. It could be that the hypotheses supported here apply to all firms-not just those found in Appalachia. For that reason, the issue of external validity and generalizability may be questioned. Second, this study, like many others, suffers from common method bias. We only use one method, a self-report instrument, to draw our conclusions. As it pertains to convergent validity, it would be interesting and important to distinguish if other methods would result in similar conclusions. Third, our study is cross-sectional as opposed to longitudinal. Without the time lag, the confidence we place on the basic inference of causality is suspect. Specifically, causality could be reversed or opposite than what we predict; actual use could actually cause or influence perceptions regarding value, ease, and usability. Alternatively, causality could be plausibly explained by a non-recursive model. For instance, just as the perceptions and intentions may influence small business technology use, small business technology use could simultaneously cause and reinforce perceptions and intentions. Use of structural equation modeling to test this type of non-recursive model could inform this issue (Bollen & Lennox, 1991).

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JoAnna B. Shore, Frostburg State University

Dale A. Henderson, Radford University

J. Stephen Childers, Radford University

Table 1.
Descriptive Statistics for Variables Used in the Study (N=188) and
Pearson Correlation Coefficients (columns 1-11)

    Variable                   Mean    SD       (1)         (2)

1   Service Firm               0.45   0.50   1.00
2   Mfg Firm                   0.11   0.31   -0.31 ***   1.00
3   Retail Firm                0.20   0.19   -0.45 ***   -0.17 *
4   Wholesale Firm             0.37   0.19   -0.18 *     -0.07
5   Technology Firm            0.01   0.10   -0.09       -0.04
6   Other Firm                 0.20   0.40   -0.45 ***   -0.17 *
7   Business Longevity         5.03   1.86   -0.03       0.00
8   Revenues                   3.73   1.83   -0.09       0.23 ***
9   IV- Ease of Use            2.49   0.94   -0.02       0.00
10  MV Usefulness of Tech      2.76   1.15   0.05        -0.03
11  DV Intention to Use Tech   3.19   1.84   -0.01       0.01

    Variable                      (3)       (4)      (5)     (6)

1   Service Firm
2   Mfg Firm
3   Retail Firm                1.00
4   Wholesale Firm             -0.10       1.00
5   Technology Firm            -0.05       -0.02    1.00
6   Other Firm                 -0.25 ***   -0.10    -0.05   1.00
7   Business Longevity         0.09        -0.00    -0.11   -0.02
8   Revenues                   -0.02       0.03     0.04    -0.08
9   IV- Ease of Use            0.04        0.15 *   -0.04   -0.08
10  MV Usefulness of Tech      0.00        -0.05    -0.12   0.02
11  DV Intention to Use Tech   0.02        0.01     -0.12   0.01

    Variable                     (7)        (8)        (9)

1   Service Firm
2   Mfg Firm
3   Retail Firm
4   Wholesale Firm
5   Technology Firm
6   Other Firm
7   Business Longevity         1.00
8   Revenues                   0.26 ***   1.00
9   IV- Ease of Use            0.06       -0.07      1.00
10  MV Usefulness of Tech      0.04       -0.12      0.41 ***
11  DV Intention to Use Tech   0.04       -0.21 ***  0.27 ***

    Variable                     (10)     (11)

1   Service Firm
2   Mfg Firm
3   Retail Firm
4   Wholesale Firm
5   Technology Firm
6   Other Firm
7   Business Longevity
8   Revenues
9   IV- Ease of Use
10  MV Usefulness of Tech      1.00
11  DV Intention to Use Tech   0.70 ***   1.00

*** Correlation is significant at the 0.001 level (2-tailed test)

** Correlation is significant at the 0.01 level (2-tailed test)

* Correlation is significant at the 0.05 level (2-tailed test)

Table 2
Models 1-4: DV= Intention to Use Technology
Model 5: DV= Usefulness of Technology

Variable                      Model 1    Model 2    Model 3

Manufacturing Firm              0.38       0.33       0.46
Retail Firm                     0.10       0.06       0.17
Wholesale Firm                  0.17      -0.20       0.55
Technology Firm                -1.76      -1.65      -0.41
Other Firm                      0.04       0.10       0.08
Business Longevity              0.09       0.07       0.05
Revenues                      -0.25 **   -0.22 **   -0.16 **
Ease of Use                              0.49 ***
Usefulness of Technology                            1.09 ***
Intention to Use Technology

Variable                      Model 4    Model 5

Manufacturing Firm              0.47      -0.13
Retail Firm                     0.18      -0.11
Wholesale Firm                  0.62      -0.73
Technology Firm                -0.39      -1.13
Other Firm                      0.07       0.03
Business Longevity              0.05       0.01
Revenues                      -0.17 **    -0.05
Ease of Use                    -0.08     0.51 ***
Usefulness of Technology      1.11 ***
Intention to Use Technology

*** p < 0.001; ** p < 0.01; * p < .05 (all two-tailed tests). Service
Firm was used as our comparison group