NC 1030: Concept Paper



NC 1030: Concept Paper

E-Commerce Technology and Family Firms

Prepared by: Linda S. Niehm and Margaret A. Fitzgerald

(Much of this information is adapted from two papers on internet use in small family firms by Niehm, L. S., Tyner, K. E., Fitzgerald, M. A., & Shelley, M. C.. One paper is currently in revision and the pother in process for submission).

Electronic commerce (e-commerce) increasingly has become a common mode of exchange among businesses and consumers alike. E-commerce technology may be defined as the use of computer and Internet-based applications that support the electronic exchange of goods and services at both the personal and business level (Jap & Mohr, 2002; Robeiro & Love, 2003). E-commerce scholars have acknowledged the computer and the Internet as technological applications that have changed the business landscape dramatically over the past few decades (Pratt, 2002), allowing for more efficient and productive means of business operation. While it may be assumed that businesses of all types are using technology to their benefit, small firm involvement is varied and tends to lag behind that of larger firms (O’Cass & Fenech, 2002). Technology pushes and pulls businesses of all sizes to adopt new strategies and modes of doing business. Little is known about how small family firms adopt and integrate e-commerce strategies to complement traditional business practices and to sustain and enhance their performance (Dinlersoz & Hernandez-Murillo, 2004).

Studies concerning large firm e-commerce activities are abundant in the trade press and scholarly journals. In contrast, there is limited information concerning the integration of e-commerce among small family firms. At present, only geographically limited studies have addressed small family business use of computer technology and the Internet, and the impact of technology and e-commerce on family firm performance (Muske, Stanforth, & Woods, 2004). Thus, a gap exists in understanding the small business sector, which is a major component of the U.S. economy.

Small Firms and Technology Use

Although standards vary by industry, small firms are identified most often as those with 500 or fewer employees. Aggregate numbers demonstrate that these businesses constitute 99.7% of all U.S. employer firms, a majority of which (61%) are retail- and service-related enterprises (U.S. Small Business Administration, 2004). Small family-owned firms comprise a bulk of the U.S. private sector employment and create the majority of new job growth (Poza, 2004). These firms will need to transform and reorganize as e-commerce becomes a preferred choice of consumers. To be competitive, small firms will need to integrate Internet applications that are complementary to their traditional store formats (Zeithaml, Parasuraman, & Malhotra, 2001). Both consumers and businesses have become more engaged in e-commerce due to the relative affordability of computers, ever-increasing simplicity of computer applications, rising availability and speed of the Internet, and the perceived advantage of using the Internet over other forms of communication and information technologies (Katz & Safranski, 2003). As consumers become more familiar with Internet shopping and continue to seek out value and efficiency in the virtual marketplace, small family-owned firms undoubtedly will experience competitive pressure to integrate e-commerce functions (Olson & Boyer, 2003).

Theoretical Frameworks

Researchers have used several theoretical bases to explain the diffusion and acceptance of technological innovations such as computers and the Internet. Two perspectives useful for understanding how new ideas, processes, and products diffuse within and across organizations

are Rogers’ (2003) Diffusion of Innovations framework and the Technology Acceptance Model (TAM) (Davis et al., 1989).

Diffusion of Innovations Framework

Rogers’ Diffusion of Innovations framework presents five stages of the innovation decision process: (1) knowledge of and access to the innovation, (2) persuasion of a favorable attitude toward the innovation, (3) decision to adopt, (4) implementation of the innovation, and (5) confirmation of the innovation. The Diffusion of Innovations framework posits that there will be an increased rate of diffusion and the decision to adopt an innovation if it is perceived to have a relative advantage, is compatible with existing values, needs, and experiences, is not overly complex, is able to be experimented with on a limited basis, and offers visible, positive results (Rogers, 2003). Innovations vary in the degree of behavioral change required for their adoption. Resistance to innovations may be overcome when the innovation is perceived to provide value, involve minimal consumer learning, involve relatively high certainty, and is viewed as high in social relevance, legitimacy, and adaptability (Scarborough & Zimmerer, 2003). In their meta-analysis of 75 diffusion articles, Tornatzky and Klein (1982) found that only relative advantage, compatability, and complexity were consistently related to the rate of innovation adoption. According to Rogers (2003), the Internet represents information technology diffusion as well as a forum for the introduction of other technology and communications. With each subsequent adoption, the Internet becomes more valuable to the mass population, making it distinct among innovations (McGrath & Zell, 2001). The value of Internet based applications for small family owned businesses is presently unknown.

Several researchers have used Rogers’ (2003) Diffusion of Innovations framework for analyzing the adoption process of computers and the Internet in retailing, manufacturing, consumer behavior, and new product development. Results concur that the compatibility and relative advantage of the Internet has led to its quick diffusion in these applications. The Internet has been found to be compatible with and enhance the efficiency of many business support functions (Chen & Crowston, 2001; Hovav, Patnavakuni, & Schuff, 2004; Jurison, 2000). Dinlersoz and Hernandez-Murillo (2004) analyzed U.S. Census data regarding electronic business diffusion in the retail, service, and manufacturing industries. Results suggested that the decision to adopt Internet applications resulted from knowledge of and access to technology and the perceived ease of use and usefulness of the Internet.

Technology Acceptance Model (TAM)

Various theoretical models have been used to explain information technology acceptance and end-user behavior. The Technology Acceptance Model (TAM) (Davis, et al., 1989) accounts for user acceptance of information systems based on individual perceptions and intentions. The TAM asserts that perceived ease of use defined as “the degree to which a person believes that using a system would be free of effort” and usefulness defined as “the degree to which a person believes that using a particular system would enhance job performance” predicts information technology acceptance and adoption (Davis et al., 1989, p. 320). The TAM is also relevant in explaining the unique set of rural environmental variables that assist or impede e-commerce technology acceptance by small retailers (Davis et al., 1989).

The TAM has been applied in studies regarding technology acceptance and integration. Pikkarainen, Pikkarainen, Karjalouto, and Pahnila (2004) utilized the TAM to indicate that online-banking acceptance was influenced by perceived usefulness and knowledge concerning online banking services. Ma and Liu’s (2004) findings from a meta-analysis of the TAM results in 26 selected empirical studies suggested a strong correlation between usefulness, ease of use, and acceptance. Researchers concluded that the relationship between ease of use and usefulness cannot be ignored, and that ease of system use has a strong impact on the end-users' perception of overall usefulness.

Integrated Model of Diffusion of Innovations and the TAM

Dual theoretical approaches have been used in the marketing literature to identify technology adoption and acceptance processes in a variety of industry sectors. Chen, Gillenson, and Sherrel (2004) extended the TAM with elements of Rogers’ Diffusion of Innovations framework to identify variables critical to the success of e-retailing sites, finding that perceived trust significantly influenced the perceived usefulness of e-retailing sites. Neihm et al. (in revision) propose an integrated model of the Diffusion of Innovations and the TAM to assess e-commerce technology acceptance and integration by small, family-owned firms.

Focus on technology use and acceptance among family business operators is important to understanding the impact of technology diffusion and the relationship between community, family, and family business performance. Family firms are inextricably related to characteristics of the family, and in particular the chief operating officer and decisionmaker (Handler 1989, 1992). Emphasizing this point, Fenn (1999, p. 2) quotes a family firm CEO saying, “how you run your company is intimately related to how you run your life—it is virtually impossible to change one without changing the other.” This observation suggests that family business manager’s acceptance and use of e-commerce technology may have a significant impact on how effectively family firms access and use information, perceive and react to business needs and opportunities, and make strategic business decisions, which all in turn may impact overall business performance.

Integration of the Diffusion of Innovations framework and the TAM highlights the importance of context, information seeking, and decision activities in the implementation of technology applications. Diffusion is a more generalized approach, addressing the decision stages family firms experience in the technology integration process. In comparison, the TAM model includes external environmental variables, which are highly relevant to issues concerning technology integration. Both perspectives are necessary for understanding the processes surrounding family firms’ use of technology. An integrative diffusion-TAM model based on the 2000 NFBS data indicated that Internet and technology strategies accounts for nearly 40 percent of the impact of the Internet on performance for small family owned firms (Niehm, Tyner, Fitzgerald, Shelley, 2007). A second study under development by Niehm, Shelley, Fitzgerald, Tyner, and Muske (2007) also uses data from the 2000 NFBS to explain the impact of Internet technology and innovations strategies on family firm performance and goal attainment. This study is framed by the Sustainable Family Business Model.

Technology Questions in the NFBS

Questions on technology use are included in both the NFBS 1997 and 2000 data sets.

NFBS 1997

B22: Are computers used in our business? (0=no/1=yes)

B23: Is the internet or the World Wide Web used in your business? (0=no/1=yes)

B24. Do you use it….

a. for company communication?

b. for company Web pages?

c. to operate a virtual store? (0=no/1=yes)

NFBS 2000

Computer (BZB4): How often are computers used in the day-to-day operations of your business? (1=never to 5=very often)

BZB5: To what extent does your business use computers for each of the following purposes? Please use a scale from 1 to 5, where 1 means not at all and 5 means a great deal (1=not at all to 5=a great deal)

Email (BZB5a): Sending or receiving e-mail

Sellinte (BZB5b): Selling products or services over the internet

CompDesi (BZB5c): Using the computer for other business purposes such as inventory, control, accounting, payroll, or ordering supplies

Internet (BZB6a): Overall has the internet affected your business….

1 not at all

2 a little

3 some

4 a great deal

Impact (BZB6b): Has this impact been largely negative or largely positive?

1 Largely negative

2 Largely positive

3 Neutral, some of each

References

Chen, H., and Crowston, K. (2001). Comparative diffusion of the telephone and the World Wide Web: An analysis of rates of adoption. Retrieved September 1, 2004 from .

Chen, L., Gillenson, M., and Sherrell, D.L. (2004). Consumer acceptance of virtual stores: A theoretical model and critical success factors for virtual stores. Database for Advances in Information Systems, 35(2), 8-32.

Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1002.

Dinlersoz, E.M., and Hernandez-Murillo, R. (2004). The diffusion of electronic business in the U.S. (Working Paper 2004-009A). The Federal Reserve Bank of St. Louis Research Division. [If this is a print publication we need to add the city (presumably St. Louis). If it is online, we need to add the URL.]

Handler, W.C. (1989). Methodological issues and considerations in studying family businesses. Family Business Review, 2(3), 257-276.

Hovav, A., Patnavakuni, R., and Schuff, D. (2004). A model of Internet standards adoption: The case of IPv6. Information Systems Journal, 14(3), 265.

Jap, S., and Mohr, J. (2002). Leveraging Internet technologies in B2B relationships. California Management Review, 44(4), 24-38.

Jurison, J. (2000). Perceived value and technology adoption across four end-user groups. Journal of End User Computing, 12(4), 21-29.

Katz, J.A., and Safranski, S. (2003). Standardization in the midst of innovation: Structural implications of the Internet for SMEs. Futures, 35(4), 323-340.

Ma, Q., and Liu, L. (2004). The technology acceptance model: A metal analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-74.

McGrath, C., and Zell, D. (2001). The future of innovation diffusion research and its implications for management: A conversation with Everett Rogers. Journal of Management Inquiry, 10(4), 386-393.

Muske, G., Stanforth, N., and Woods, M. (2004). Micro business use of technology and extension’s role. Journal of Extension, 42(1), 1-12.

Niehm, L.S., Tyner, K.E., Fitzgerald, M., & Shelley, M.C. (2007-in revision). E-commerce

technology diffusion and acceptance for family owned businesses. In revision with

Family Business Review.

Niehm, L.S., Shelley, M.C., Tyner, K.E., Fitzgerald, M., & Muske, G. (2007-in process). The

use of Internet technology and innovation strategies by small family firms and its impact

on performance. In process for submission to the Journal of Family and Economic

Issues.

O’Cass, A., and Fenech, T. (2002). Web retailing adoption: Exploring the nature of Internet users’ web retailing behavior. Journal of Retailing and Consumer Services, 10(2), 81-94.

Olson, J.R., and Boyer, K. (2003). Factors influencing the utilization of Internet purchasing in small organizations. Journal of Operations Management, 21(2), 225-245.

Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., and Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the Technology Acceptance Model. Internet Research, 14(3), 224-235.

Poza, E.J. (2004). Family business. Mason, OH: South-Western.

Pratt, J.H. (2002). E-: Strategies for small business success (SBAHQ-00-C-0004). Washington, DC: Small Business Administration, U.S. Department of Commerce.

Robeiro, F., and Love, P. (2003). Value creation through an e-business strategy: Implications for SMEs in construction. Construction Innovation, 3, 3-14.

Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.

Scarborough, N., and Zimmerer, T. (2003). Effective small business management: An entrepreneurial approach (7th ed.). Upper Saddle River, NJ: Prentice Hall.

Teo, T.S. (2001). Demographic and motivation variables associated with Internet usage activities. Internet Research, 11(2), 125-134.

Tornatzky, L.G., and Klein, K.J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 1-18.

U.S. Small Business Administration. (2004). Small business by the numbers. Retrieved September 1, 2004 from .

Zeithaml, V.A., Parasuraman, A., and Malholtra, A. (2001). Service quality delivery through websites: A critical review of extant knowledge. Journal of Marketing Science, 30(4), 362-375.

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