Discussion

3.7.1 Research Question

The goal for this case study is to investigate adopter characteristics and environmental, technological and organizational factors for the post-adoption of an emerging dairy management information system in Korea. We answer the following research question:

To what extent does the relationship of technological, organizational and environmental factors drive/inhibit post-adoption of a dairy management information system in Korea?

3.7.2 Findings

We used this particular dairy management information system in this case study as an example of an agricultural information system. The basic premise of a dairy management information system is that adoption of the system by dairy managers can be regarded as the driving strength for operational efficiencies, improved milk production and better return on investment. We illustrate that a dairy management information system can be adopted for individual adopter characteristics, and environmental, technological and organizational factors. Our case study also illustrates factors that determine adoption of a technology can be adapted to the agriculture and dairy industry. The case study can be used to support the adoption of precision agriculture and precision livestock farming. The farmers interviewed were selected by the dealer or vendor based on the farmers knowledge of the system and influence within their community.

The key findings for this study suggest that education level, dealer support and other social influences such as friends, community, and normative pressures affected the early adoption of the system by small-size farms in Korea. We found that lack of information, limited knowledge of information technology, and

uncertainty and risk associated with the lack of financial resources are some of the more noted barriers to adoption. Our case study found that education background (e.g., a 2 year professional or a college degree) is an important driving force for adoption. University and technical college background may be an important driving force for adoption. The farmers that represented the four farms in Korea were well educated. Current literature for small-size farms suggests that farm managers should be innovative and have information technology knowledge. Our analysis shows that the managers or other family members (i.e. wife/son) were knowledgeable and demonstrated ownership of the system. They felt that the system provided them with a relative advantage. The past stream of literature suggests that small-size farms have more barriers to adoption than large-size farms.

Factors such as information sharing, information technology knowledge, and uncertainty and risk associated with the lack of financial resources are noted barriers for adoption that we encountered in this investigation. The adoption of this particular dairy management information system will transpire slowly when we consider the small size of dairy farms in Korea.

Farmers or other designated system users who were experienced with the system assert that the system provided them with an economic advantage. The farmers saved time and reduced manual labor. Farmers stated that trust in the system and the dealer or vendor was more important than economic factors. The general sentiment is that the technology itself is a better feature as opposed to the benefit for return on investment. However, the managers did articulate economic advantages for adopting the system. The system provided better feeding optimization as feeding costs increased. The feature used for heat detection has helped farmers find cows ready for impregnation, therefore improving product cost.

The farmers saved money on insemination, feed and labor. However, cash flow appeared uncertain.

Dealer and farmer relations had a positive relationship for the four farms in this study. There would not be the awareness and adoption of this particular technology

if it were not for the positive relationship and mutual trust developed between the dealer and farmer. In general, the farms investigated were early adopters of the system. Farm 3 was an early majority adopter and was socially influenced by the manager at farm 2. The farmers said that others in the community were looking closely at the system. These other farmers will evaluate at a later date if they would adopt the system. Therefore, normative beliefs from community contacts and social interaction are having a positive effect. A summary of key findings for all of the case study factors are shown in Table 3-8.

Table 3-8 Key Findings

Variables Findings

Farm size Korean dairy farms are small-size suggesting a low and slow adoption rate

Experience Mostly 20 or more years in dairy industry

Age Two generations; parent and adult children operate system Education University and technical college background;

Driving force for adoption for Korea; Limited education for management in California also suggests slow adoption rate

Social Influences Community members are looking at system; early adopters are role model; Could also be a barrier if unsuccessful with another technology (i.e. California)

Sponsorship Uncertain if existing government-sponsored program for funding is still in service

Information Sharing

Uncertainties exist between the sharing of information (i.e. farmer to farmer; farmer to dealer and vice versa)

Dealer Trust Positive relationship between dealer and manager; Mutual trust and respect carries awareness of technology

Advantage As feeding costs increased, the Feed feature provided better feeding optimization; Heat detection support the managers to discover cows ready for impregnation

Knowledge Demonstrated ownership of the system; Sharing and sourced varied Compatibility manually operated prior to adoption; First time users of technology Planning 4 of 9 modules used: Farm, Act, Tag, Meter, and Feed;

Mostly feeding operations; California: manual operations Complexity On-the-job training helps; Depends on user motivation;

Need easier interface

Profitability Save money on insemination, feed and labor; Diseased cows prevent grade A production; Low wage labor in California prevents adoption Cash Flow/Loans Undetermined

Uncertainty/Risk Trust with the system is more important than return on investment

A non-adopting dairy farm in California was also investigated as a control. There were two issues that hindered adoption of an information system. Technology was not adopted on this farm because there is cheap farm labor in California. A nearby farm also had an impact. The neighboring farm did not succeed with the adoption of a similar dairy management information system. The cow identification tag and heat detecting system was problematic and became an issue with a nearby farmer.