Adopter characteristics

Adopter characteristics can influence the decision to adopt. Five factors at the adopter level are investigated. These factors are farm size (Al-Qirim, 2007;

Harrison et al., 1997; Iacouvou et al. 1995; Palvia et al., 1994; Prekumar and Roberts, 1999; Thong, 1999; Thong and Yap, 1995), experience (Fink, 1998;

Triandis, 1971; Karahanna et al., 1999; Yap et al., 1992), age (Daberkow and McBride, 2003; Morris et al., 2005; Morris and Venkatesh, 2000; Raub, 1981), education (Cooper and Zmud, 1990; Chun, 2003; Koundouri et al., 2006; Nelson and Phelps, 1966; Wozinak, 1987), and social influences (Bandiera and Rasaul, 2006; Conley and Udry, 2001; Frambach and Schillewaert, 2002; Lu, 2005; Morris and Venkatesh, 2000; Zack and McKenney, 1995)

Farm size

IT adoption for small firms is often a decision made by the owner or executive.

There have been many studies that have focused on the adoption of IT in small firms. The size of small firms in rural communities is a critical factor for the adoption of new technologies and IT use (Palvia et al., 1994). Other factors also exist in combination with the adoption of IT by small firms. Relative advantage, top management support, firm size, and external and competitive pressures are

important factors for adoption (Prekumar and Roberts, 1999). Three factors for adoption on small firms – organizational readiness, external pressures and perceived benefits that influence Electronic Data Interchange (EDI) have been investigated (Iacouvou et al. 1995). The study findings indicate that efforts should be made to improve perceptions of EDI benefits. Small firms with low knowledge access should also be provided financial and technological assistance. Selecting and applying influence strategies to reduce barriers for adoption of IT should also be implemented. Firm size and executive characteristics are the most significant factors for adopters and non-adopters of IT (Thong and Yap, 1995). Large firms are more likely to adopt IT and small firms are slow to adopt IT. Executives that are more innovative and have a positive attitude for adoption, have greater IT knowledge. Small firms are more likely to adopt IS with executives that are innovative and have a high level of IS knowledge, and understand IS advantage, compatibility and complexity (Thong, 1999). Yet, innovation characteristics of executives from small firms do not affect the extent of IT adoption. Instead, firm size and employee IS knowledge have a better effect on the extent of IT adoption.

It was also identified that adoption relies heavily on individual executive qualities (Al-Qirim, 2007). In contrast, a study using the Theory of Planned Behavior (TPB) to explain and predict small firm executive decisions to adopt IT was conducted (Harrison et al., 1997). Firm size and executive characteristics had no effect on adoption. However, as firm-size increased, potential adoption barriers decreased.

Therefore, we propose the following:

Proposition 1 (P1): Small-size farms are less likely to adopt the system.

Experience

Social norms have more effect in determining consumer behavior when it is prior to adoption. As users gain more experience, social norms influence on behavior will decrease (Triandis, 1971). Inexperienced IT users are more influenced by

social norms than experienced IT users, and the ease of use can influence inexperienced users more than experienced users (Thompson et al., 1994). In a study on adoption beliefs, it is assumed that pre-adoption beliefs are formed on indirect experience (i.e. cognition) and post-adoption beliefs are based on past experiences (Karahanna et al., 1999). Social norms alone induce initial adoption and post-adoption usage and therefore are based on the attitude of the user.

Therefore, without prior knowledge of the IT, social norms influenced adoption.

However, perceived usefulness and image influenced attitude when experienced users have knowledge of the IT. Firms that already have more IT experience or IT in use (post-adoption) are more likely to adopt IT (Fink, 1998; Yap et al., 1992).

Smaller firms that have strong managerial influence, supportive external environment, and available experiences within the firm largely benefit from adoption (Yap et al., 1992). Finally, small-size firms are significantly challenged by changes in technology. Top management support and IT experience are necessary to meet these challenges (Fink, 1998). Therefore, we propose the following:

Proposition 2 (P2): Experienced information technology users are more likely to adopt the system.

Age

Younger workers that use technology are more influenced by attitude towards that technology. Older workers are more subjected to the influence of other people in their social environment and the perception for their performance and difficulty to use the technology (Morris and Venkatesh, 2000). Age can negatively affect PA technology adoption (Daberkow and McBride, 2003). Gender can also have an effect on adoption and can vary based on age. Gender differences are less clear for younger workers. Social influences were more important for older women.

Performance and difficulty to use the technology were more important for older men (Morris et al., 2005). A relationship between age and computer anxiety

indicates that older users have less computer knowledge and training (Raub, 1981).

Therefore, we propose the following:

Proposition 3 (P3): Younger users are more likely to adopt the system.

Education

There have been many studies focused on the adoption of technology and the education level of senior management. Senior management support and user education level assisted in Material Requirements Planning (MRP) adoption (Cooper and Zmud, 1990). Human capital and information about the technology are significant factors for the adoption of technology (Wozinak, 1987). Education and information about the technology improve the probability for adoption over costs and uncertainty. Farmer education, improvement and information accumulation can increase the probability that a farmer will adopt new irrigation technology (Koundouri et al., 2006). Education can facilitate the implementation of IT (Chun, 2003). Farmers with a high level of education tend to adopt technology earlier than farmers with less education (Chun, 2003; Nelson and Phelps, 1966).

Therefore, we propose the following:

Proposition 4 (P4): Educated users are more likely to adopt the system.

Social influences

The adoption of IT can be influenced by a social network of family and friends within a community. Individual perceptions and ease of use towards technology (i.e.

Internet services via mobile technology) are significantly credited to social influences, and more specifically, informal social networks (Lu, 2005). Users within an organization will show more positive attitudes if other users in their social environment also use the technology (Frambach and Schillewaert, 2002).

Social relationships can have an influence at the sub-community level (Zack and

McKenney, 1995). Adoption may vary across an organization or may not be needed in other parts of the organization. This results in varying degrees of adoption by a sub-community. As mentioned, age has an impact for older managers. Older managers can develop a positive attitude about the new technology through opinion leaders within the organization (Morris and Venkatesh, 2000). In agriculture, farmers decisions to adopt a new crop relates to choices made by family and friends (Bandiera and Rasaul, 2006). Paradoxically, they found social influences to be positive in a smaller network of adopters and negative in a larger network.

Conversely, farmers that have access to better information about a new crop are less influenced by adoption choices of others in their social network. Learning about technology occurs through communication networks within a community.

Social learning is required for optimal learning behavior such as tracking and finding history of production performance for members in that farmer’s community or social network (Conley and Udry, 2001). Therefore, we propose the following:

Proposition 5 (P5): Farmers with social influences are more likely to adopt the system.