Despite the significance of IT for small-scale agribusinesses (SSAs) in rural areas, little work has been done to identify the determinants of their informationization. Prior studies also overlook the social influences of informationization among SSAs in rural areas. Therefore, this study presents a model to explore and describe the roles played by institutional forces in the informationization of rural SSAs. To empirically test the presented model, partial least squares (PLS) analysis was conducted using the survey responses of 180 ginseng farmers. The results show that mimetic forces related to other farmers’ successful adoption and usage of IT positively affect states of informationization and intentions towards future informationization among rural SSAs. Innovative SSAs are less likely to be affected by these forces, and normative forces from partners and customers are positively related to states of informationization. For the government, the results suggest that policy interventions should focus on mimetic forces rather than coercive forces to aid in creating more effective SSAs and foster rural development.
AgarwalRPrasadJ (1998) A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research9(2): 204–215.
2.
AndersonJCGerbingDW (1988) Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin103(3): 411–423.
3.
BarclayDHigginsCThompsonR (1995) The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technology Studies2(2): 285–309.
4.
BrancheauJCWetherbeJC (1990) The adoption of spreadsheet software: testing innovation diffusion theory in the context of end-user computing. Information Systems Research1(2): 115–143.
5.
BurtRS (1982) Toward a Structural Theory of Action: Network Models of Social Structure, Perception, and Action. New York, NY: Academic Press.
6.
BurtRS (1987) Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology92(6): 1287–1335.
7.
CarlssonCCarlssonJHyvonenKPuhakainenJWaldenP (2006) Adoption of mobile devices/services—searching for answers with the UTAUT. In: HICSS ‘06, Proceedings of the 39th Annual Hawaii International Conference on System Sciences, Kauia, HI, USA, 4–7 January 2006, p. 132a. Kauia, HI: IEEE.
8.
CenLZhangFL (2010) Study on information technology adoption in rural areas based on network externality. Applied Mechanics and Materials33: 509–512.
9.
ChenLYChenAF (2005) Present situations and development orientations of China’s rural information development [J]. Review of China Agricultural Science and Technology2.
10.
ChinWW (1998) The partial least squares approach to structural equation modeling. Modern Methods for Business Research295: 295–336.
11.
ChinWWFryeT (2001) PLS-Graph. Soft Modeling Inc.
12.
ChinWWNewstedPR (1999) Structural Equation Modeling Analysis with Small Samples Using Partial Least Squares. Thousand Oaks, CA: Sage Publications.
13.
CsakiCde HaanC (2003) Reaching the Rural Poor: A Renewed Strategy for Rural Development. Washington, DC: World Bank Publications.
14.
DiamantopoulosAWinklhoferHM (2001) Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research38(2): 269–277.
15.
DiMaggioPJPowellWW (1983) The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review48(2): 147–160.
16.
DixonJTaniguchiKWattenbachH (2003) Approaches to assessing the impact of globalization on African smallholders: Household and village economy modeling. In: Proceedings of a working session on Globalization and the African Smallholder Study. FAO (Agricultural Support Systems Division [AGS] and Agricultural and Development Economics Division [ESA]) and the World Bank. Rome: Food and Agriculture Organization of the United Nations.
17.
DoyleCS (1992a) Development of a Model of Information Literacy Outcome Measures Within National Educational Goals of 1990. PhD Dissertation, Northern Arizona University, USA.
18.
DoyleCS (1992b) Outcome measures for information literacy. Final Report to National Forum on Information Literacy. ERIC Clearinghouse, ED 351033, Syracuse NY: 1–18.
19.
DyerWGHandlerW (1994) Entrepreneurship and family business: Exploring the connections. Entrepreneurship Theory and Practice19: 71–71.
20.
FlanaginJ (2000) Social pressures on organizational website adoption. Human Communication Research26(4): 618–646.
21.
FornellCLarckerDF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research18(1): 39–50.
22.
FuruholtBKristiansenS (2007) A rural-urban digital divide? Regional aspects of Internet use in Tanzania. The Electronic Journal of Information Systems in Developing Countries31.
23.
GoodhueDLewisWThompsonR (2012) Does PLS have advantages for small sample size or non-normal data?MIS Quarterly36(3): 891–1001.
24.
GopalAMukhopadhyayTKrishnanMS (2005) The impact of institutional forces on software metrics programs. IEEE Transactions on Software Engineering, 31(8): 679–694.
25.
HartPJSaundersCS (1998) Emerging electronic partnerships: antecedents and dimensions of EDI use from the supplier’s perspective. Journal of Management Information Systems14(4): 87–111.
26.
HeidhuesFBrüntrupM (2003) Subsistence agriculture in development: Its role in processes of structural change. In Subsistence agriculture in Central and Eastern Europe: How to break a vicious cycle?AbeleS.FrohbergK., eds. Studies on the Agricultural and Food Sector in Central and Eastern Europe, Vol. 22. Halle, Germany: Institute of Agricultural Development in Central and Eastern Europe (IAMO). 1–30.
27.
HenselerJFassottG (2010) Testing moderating effects in PLS path models: An illustration of available procedures. In: Esposito VinziVChinWWHenselerJWangH (eds.) Handbook of Partial Least Squares. Berlin: Springer, pp. 713–735.
28.
JungGHChoeYCParkHDJangIH (2010) Study on the relationship between factors of farmers’ adoption and continuous use of innovative technology. Journal of Agricultural Education and Human Resource Development42:109–137.
29.
KaplowitzMDHandlockTDLevineR (2009) A comparison of web and mail survey response rates. Public Opinion Quarterly68(1): 94–101.
30.
KeWLiuHWeiKKGuJChenH (2009) How do mediated and non-mediated power affect electronic supply chain management system adoption? The mediating effects of trust and institutional pressures. Decision Support Systems46(4): 839–851.
31.
KibwanaOHaileMvan VeldhuizenLWatersBA (2001) Clapping with two hands: Bringing together local and outside knowledge for innovation in land husbandry in Tanzania and Ethiopia. A comparative case study. The Journal of Agricultural Education and Extension7(3): 133–142.
32.
KimHJLeeSW (2012) The spatial distribution characteristics and determinants of strong small farm: Focusing on apples. Journal of Agricultural Extension & Community Development19(4): 961–987.
33.
Korea Institute for Health & Social Affairs (2014) The Korea Welfare Panel Study - Descriptive Report. Research Report 2014-30. Available: https://www.kihasa.re.kr/
34.
LiYFuZLiH (2007) Evaluating factors affecting the adoption of mobile commerce in agriculture: An empirical study. New Zealand Journal of Agricultural Research50(5): 1213–1218.
35.
LiangHSarafNHuQXueY (2007) Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Quarterly31(1): 59–87.
LiptonM (2005) The Family Farm in a Globalizing World: The Role of Crop Science in Alleviating Poverty. Washington, DC: International Food Policy Research Institute.
38.
LuJYaoJEYuCS (2005) Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems14(3): 245–268.
39.
MachfudAKKartiwiM (2013) E-commerce adoption by Indonesian small agribusiness: Reconsidering the innovation-decision process model. In: 2013 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M), Rabat, Morocco, 26 - 27 March 2013, pp. 1–6. Rabat: IEEE.
40.
MaleckiEJ (2003) Digital development in rural areas: potentials and pitfalls. Journal of Rural Studies19: 201–214.
MoonJHossainMDKangHGShinJ (2012) An analysis of agricultural informatization in Korea: the government’s role in bridging the digital gap. Information Development28: 102–116.
43.
NakasoneEToreroMMintenB (2014) The power of information: The ICT revolution in agricultural development. Annual Review of Resource Economics6: 533–550.
44.
NarayananSGulatiA (2002) Globalization and the Smallholders. Washington, DC: International Food Policy Research Institute.
45.
National Information Society Agency (2012)2011National informatization white paper. Available: http://eng.nia.or.kr
46.
National Information Society Agency (2014)2013National informationization white paper. Available: http://eng.nia.or.kr
47.
RigdonEE (1995) A necessary and sufficient identification rule for structural models estimated in practice. Multivariate Behavioral Research30(3): 359–383.
48.
RogersEM (1995) Diffusion of Innovations, 4th ed. New York, NY: The Free Press.
49.
RogersEM (2000) Informatization, globalization, and privatization in the new millennium. Asian Journal of Communication10(2): 71–92.
50.
RolfeJGregorSMenziesD (2003) Reasons why farmers in Australia adopt the Internet. Electronic Commerce Research and Applications2(1): 27–41.
SeoJW (2000) Measurement of local resident’s informationization level: Using information literacy. Korean Public Administration Review34: 309–325.
53.
ShererSA (2010) Information Systems and Healthcare XXXIII: An institutional theory perspective on physician adoption of electronic health records. Communications of the Association for Information Systems26: 7.
54.
Statistics Korea (2015) Agriculture, Forestry and Fishery Survey in 2014. Available: http://kostat.go.kr
55.
StenbergPMorehartMVogelSCromartieJBrenemanVBrownD (2009) Broadband Internet’s value for rural America. Report for United States Department of Agriculture Economic Research Service, Economic research report no. 70 (ERR-78), August.
56.
TanKSChongSCLinBEzeUC (2010) Internet-based ICT adoption among SMEs: demographic versus benefits, barriers, and adoption intention. Journal of Enterprise Information Management23(1): 27–55.
57.
TanKSChongSCLinB (2013) Intention to use Internet marketing: A comparative study between Malaysians and South Koreans. Kybernetes42(6): 888–905.
58.
TeoHHWeiKKBenbasatI (2003) Predicting intention to adopt interorganizational linkages: an institutional perspective. MIS Quarterly27(1): 19–49.
59.
TingCYiF (2013). ICT policy for the “socialist new countryside” – A case study of rural informatization in Guangdong, China. Telecommunications Policy37(8): 626–638.
60.
UgrinJC (2009) The effect of system characteristics, stage of adoption, and experience on institutional explanations for ERP systems choice. Accounting Horizons23(4): 365–389.
61.
VenkateshVMorrisMGDavisGBDavisFD (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27(3): 425–478.
62.
VerheesFJMeulenbergMT (2004) Market orientation, innovativeness, product innovation, and performance in small firms. Journal of Small Business Management42(2): 134–154.
63.
WhitacreBGallardoRStroverS (2014) Broadband’s contribution to economic growth in rural areas: Moving towards a causal relationship. Telecommunications Policy38(110): 1011–1023.
64.
WhitacreBEMillsBF (2007) Infrastructure and the rural-urban divide in high-speed residential Internet access. International Regional Science Review30(3): 249–273.
65.
YiMYJacksonJDParkJSProbstJC (2006) Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management43(3): 350–363.
66.
YuCJ (2003) Situation and implication of agricultural information systems in Korea. The Korean Rural Sociological Society13: 115–141.
67.
YuBMKimSWParkSY (2009) Strategies for improving agricultural ICT literacy. Journal of Agricultural Education and Human Resource Development41: 93–119.
68.
ZhangLLiuXFuZLiD (2011) Evaluation of the rural informatization level in central China based on catastrophe progression method. In: LiDLiuYChenY (eds.) Computer and Computing Technologies in Agriculture IV. Berlin: Springer, pp. 672–679.
69.
ZhangLLiuXLiDFuZ (2013) Evaluation of the rural informatization level in four Chinese regions: A methodology based on catastrophe theory. Mathematical and Computer Modeling58(3-4): 868–876.
70.
ZhangZLvY (2009) Information intention: A concept to explain the digital gap – a research on the differences of individual informatization in China. In: ICCIT ‘09 Fourth International Conference on Computer Sciences and Convergence Information Technology (eds SungwonSohnKae DalKwackKyhyunUmGye YoungLeeFranzKo), Seoul, Korea, 24–26 November 2009, pp. 1520–1523.Seoul: IEEE.
