바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

The Study on User's Continuance Intention of Traceability System between Agricultural and Marine Products

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2016, v.14 no.4, pp.67-79
https://doi.org/https://doi.org/10.15722/jds.14.4.201604.67
Lee, Seung-Yook
Park, Hyeon-Suk

Abstract

Purpose - Over recent years, we have concerned about safety and quality on food products because of delivery complexity. The dependence of foreign food products escalate supply of products. And there are often negligent accident of marine and agricultural products. Therefore, the complexity increases the importance of safety on food and information quality for consumers. In spite of the interest augmentation of various interested parties, there is decrease in reliability and effectiveness, if it would be established without the right directivity. For the study, we tried to examine the first considerations the point of - view in service environment and information quality with accepting and diffusing the Traceability System. Then, we tried to verify the relationships between the factors of TS and the determinants of behavior decision. Next, we made efforts to find the mutual relationship among distributors, producers, consumers and the other prerequisite factors from the point of view in service environment and information quality in order to operate effectively the information perspective and system. Research design, data, and methodology - For the purpose of this study, the samples of consumers were targeted to Traceability System, and 661 people have been investigated. Through theoretical discussion of previous research, nine hypotheses were established, the influence of Continuous User Intention in TS. In order to test the hypotheses, a survey had conducted for 661 consumers as opinion leaders in their 20s-60s as data, and structural equation modeling was used. The difference analysis between Agricultural and Marine Products in TS; SPSS 22.0 and AMOS 22.0 were used for statistical analysis. Results - The major findings from this study were as follow; all factors of information quality excluding completeness and a social-impact had effects on the ease of use; all factors excluding understand ability in information quality and a social-impact had effects on the usefulness; completeness and social-impact had effects on perceived value; the ease of use had effects on usefulness and perceived value; usefulness had effects on perceived value and the intention of continuous use. From the results of different analysis, the CPLT(Completeness) factor has positive effects on Easy of USE and PV(Perceived Value) strongly in agricultural products. On the other hand, Social Duty has positive effects on Easy of Use strongly in marine products. Conclusion - In the age of information overflowing, TS will be a burden for users if it places too much emphasis upon accessibility. To accept and diffuse TS safely, therefore, Information System should be settled first into initial market formation. In addition, if TS elements are considered in conjunction with information factors and user environment, the acceptance and diffusion of TS would make synergy effect, even better. That is, this study contributes to the acceptance and diffusion of Traceability System. Accordingly, information quality will be settled into initial market formation. Also, social-impact element will be considered in conjunction with information quality's factors, and it will make synergy effect.

keywords
Information Quality, Service Environment, Traceability System, Agricultural and Marine Products, Technology Acceptance Model

Reference

1.

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 16(2), 227-248.

2.

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2). 179-211.

3.

Anandrajan, M., Igbaria, M., & Anakwe, U. (2002). IT Acceptance in a Less-Developed Country: A Motivational Factor Perspective. International Journal of Information Management, 22(1), 47-65.

4.

Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and Test of a Theory of Technological Learning and Usage. Human Relations, 45(4), 660–686.

5.

Bailey, J. E., & Pearson, W. S. (1983). Development of a Tool of Measuring and Analyzing Computer User Satisfaction, Management Science, 29(5), 530-545.

6.

Bhattacherjee, A. (2001). An Empirical Analysis of the Antecedents of Electronic Commerce Service Continuance. Decision Support Systems, 32(2), 201-214.

7.

Chae, Y. I. (2011). Factors Affecting Continuous Customer Acceptance of Internet Banking. The Korea Contents Society, 11(6), 372-384.

8.

Chau, P. Y. K., & Hu, P. J. H. (2002). Examining a Model of Information Technology Acceptance by Individual Professionals: An Exploratory Study. Journal of Management Information Systems, 18(4), 191-229.

9.

Cheng, M. J., & Siummons, J. E. L. (1994). Traceability in Manufacturing Systems. International Journal of Operations and Production Management, 4(10), 4-16.

10.

Chin, W. W., & Todd, P. A. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS research: A Note of Caution. MIS Quarterly, 19(2), 237-246.

11.

Choi, W. S. (2013). The Effect of Eco-friendly Agricultural Products Traceability System has on Perceived Value, and Behavioral Intentions: by Applying Expanded Technology Acceptance Model(TAM). Seoul, Korea: thesis for Doctorate in Kyunghee University.

12.

Chung, S. Y., & Park, C. (2007). Factors Influencing Acceptance of Mobile Services: Moderating Effects of Service Type. Information Systems Review, 9(1), 23-44.

13.

Chung, M. R., Choe, Y. C., Moon, J. H., & Lee, C. H. (2007). Understanding Producers` Continuing Use of Food Traceability System. Korean Agricultural Economic Association, 48(4), 133-160.

14.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.

15.

Davis F. D., Bagozzi, R. P., & Warshaw, P. P. (1989). User Acceptance of Computer Technology : A Comparison of Two Theoretical Models. Management Science, 30(2), 361-391.

16.

DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for Dependent Variable. Information Systems. 8(2). 60-95.

17.

DeLone, W. H., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-year Update. Journal of Management Systems, 19(4), 9-30.

18.

Devinder, P. S. (2014). Online Shopping Motivations, Information Search, and Shopping Intention in an Emerging Economy. The East Asian Journal of Business Management, 4(3), 5-12.

19.

Doll, W. J., & Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction. MIS Quarterly, 12(2), 259-274.

20.

Etezadi-Amoli, J., & Farhoomand, A. F. (1996). A Structural Model of End User Computing Satisfaction and User Performance. Information & Management, 30(2), 65-73

21.

Fornell, C., & Larker, D. F. (1981). Evaluation Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory. Journal of Marketing Research, 18(1), February, 39-50.

22.

Ha, S., & Stoel, L. (2009). Consumer e-shopping Acceptance:Antecedents in a Technology Acceptance Model. Journal of Business Research, 62(5), 565-571.

23.

Hardius, Usman. (2015). Customer Communication Strategy for Islamic Banks. The International Journal of Industrial Distribution & Business, 6(2), 17-24.

24.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th ed.). New Jersey:Prentice Hall Inc.

25.

Ham, D. C. (2010). Application of TAM Model into Consumers'System Usage Intention and Factors Affecting. Seoul, Korea: thesis for Doctorate in Sejong University.

26.

Hillier, D., & Shears, P. (2005). Radio Frequency Identification and Food Retailing in the UK. British Food Journal, 107(6), 356-360.

27.

Jeon, H. K. (2008). A study on the Structural Relations of Characteristics in Tourism Site, Tourists' Perceived Value, Satisfaction and Behavioral Intention. Daegu, Korea: thesis for Doctorate in Keimyung University.

28.

Jeon, S. M., Cho, S. D., & Kim, S. H. (2011). The Effects of Characteristics of Mobile Coupon Service on Consumers’Intention of Using Mobile Coupons. ASIA Marketing journal, 13(3), 103-134.

29.

Jung, H. Y. (2003). Comprehensive Evaluation Model of Information Systems in Public Sector. Seoul, Korea: thesis for Doctorate in Kwanwoon University.

30.

Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer Adoption of Mobile TV: Examining Psychological Flow and Media Content. Computers in Human Behavior, 25(1), 123-129.

31.

Kang, B. J. (2008). The study on factors affecting the intention to use Traceability System. Jeju, Korea: thesis for Doctorate in Jeju University.

32.

Kang, J. J., & Moon, T. S. (2007). An Extension of the Technology Acceptance Model in Web-Based Learning System. Korea Internet e-Commerce Association, 7(1), 201-227.

33.

Karahanna, E., & Strabu, D. W. (1999). The Psychological origins of Perceived Usefulness and Ease-of-Use. Information and Management, 35(4), 237-250.

34.

Karlygash, S. M., & Aknur, Z. (2015). Development of Green Economy via Commercialization of Green Technologies:Experience of Kazakhstan. The Journal of Asian Finance, Economics and Business, 2(4), 21-29.

35.

Kim, K. K., & Park, S. W. (1997). An Empirical Study of User Information Satisfaction. Korean Management Review, 26(1), 93-113.

36.

Kim, G. N., Song, Y. M., & Kim, S. H. (2010). Smart Service:Determinants Influencing Individual users` Intention to Adopt Appstore and the Moderating Effect of Value. The Journal of Information Systems, 19(3), 181-208.

37.

Kim, J. K. (2013). A Study on the Usage Intention of Category Types in the Mobile Application Based on the Technology Readiness and Acceptance Model. Kongju, Korea: thesis for Doctorate in Kongju University.

38.

Kim, T. M. (2007). Antecedents and Customer Characteristics affecting User's Purchasing Intentions of Internet Travel Products : Focused on extended TAM. Kyonggi, Korea:thesis for Doctorate in Kyonggi University.

39.

Kim, T. S. (2012). A Study on The Effect of a Food Traceability System with Expanded TAM on Usage Purpose. Kyonggi, Korea: thesis for Doctorate in Kyonggi University.

40.

Kim, T. S., & Jin, Y. H. (2011). The Effect of the Food Traceability System Appication Applied with the TAM on Consumer Confidence. Korean Journal of Culinary Research, 17(4), 74-87.

41.

King, W. R., & Epstein, B. J. (1983). Assessing Information System Value. Decision Sciences, 14(1), 34-35.

42.

Koufaris, M., & Hampton-Sosa, W. (2002). Customer Trust Online: Examining the Role of the Experience with the Web-site. CIS Working Paper Series, New York, NY.:Zicklin School of Business, Baruch College.

43.

Larker, D. F., & Lessing, V. P. (1980). Perceived Usefulness of Information: A Psychometric Examination. Decision Sciences, 11(1), 121-134.

44.

Luarn, P., & Lin, H. H. (2005). Toward an Understanding of the Behavioral Intention to Use Mobile Banking. Computers in Human Behaviour, 21(6), 873-891.

45.

Lee, I. S., & Yoon, H. H. (2011). Effects of Culinary Staff"s Technology Kitchen System upon Perceived Ease of Use, Usefulness, Attitude, and Job Performance in the Foodservice Industry. The Korea Society of Food &Cookery Science, 27(3), 71-79.

46.

Lee, J. W. (2014). The Impact of Product Distribution and Information Technology on Carbon Emissions and Economic Growth: Empirical Evidence in Korea. The Journal of Asian Finance, Economics and Business, 1(3), 17-28.

47.

Lee, J. H., Ock, J. W., & Yun, D. H. (2011). Structural Relationship of Content Trait, Identification, Loyalty on Online Brand Community. The Korea Contents Society, 11(2), 385-396.

48.

Lee, Y. H. (2009). A Study of Mobile Banking Service Quality Affecting to the Market Mavens. Seoul, Korea: thesis for Doctorate in Soongsil University.

49.

Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Source of Influence on Beliefs about Information Technology Use:an Empirical Study of Knowledge Workers. MIS Quarterly, 27(4), 657-678.

50.

Liao, C. H., Tsou, C. W., & Huang, M. F. (2007). Factors Influencing the Usage of 3G Mobile Services in Taiwan. Online Information Review, 31(6), 759-774

51.

Lin, J., & Lu, H. (2000). Towards an Understanding of the Behavioral Intention to Use a WebSite. International Journal of Information Management, 20(3), 197-206.

52.

Lin, P. C., & Huang, Y. H. (2012). The Influence Factors on Choice Behavior Regarding Green Products based on the Theory of Consumption Values. Journal of Cleaner Production, 22. 11-18.

53.

Liu, C., & Arnett, K. P. (2001). Exploring the Factors Associated with Web Site Success in the Context of Electronic Commerce. Information and Management, 381(1), 23-33.

54.

McFarland, D., & Hamiltion, D. (2006). Factors Affecting Student Performance and Satisfaction: Online versus Traditional Course Delivery. Journal of Computer Information Systems, 46(2), 25-32.

55.

Mahmood, M. A. (1987). Systems Development Methods-A Comparative Investigation. MIS Quarterly, 11(1), 293-311.

56.

McKinney, V., Yoon, K., & Zahedi, F. M. (2002). The Measurement of Web-customer Satisfaction: An Expectation and Disconfirmation Approach. Information Systems Research, 13(3), 296-315.

57.

Miller, J., & Doyle, B. A. (1987). Measuring Effectiveness of Computer Based Information Systems in the Financial Services Sector. MIS Quarterly, 11(1), 107-124.

58.

Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information systems Research, 92(3), 192-222.

59.

Mulaik, S. A., James, L. R., Alstine, V., Bennett, N., Lind, S., & Stillwell, C. D. (1989). Evaluation of Goodness-of-Fit Indices for Structural Equation Models. Psychological Bulletin, 105, 430-445.

60.

Na, Y. K. (2010). Transactions : The Effect of the e-CRM on the Purchase Relation Quality, Performance in Fashion e-Commerce: A Comparative Study of Korea and America. Fashion & Textile Research Journal, 12(3), 327-337.

61.

Oh, J. E. (2008). The Influence of Website Quality and Social Influence on Customers’ Repurchase Intention of Internet Travel Products: Focusing on Mediating Role of Online Hedonic and Utilitarian Values in Internet Shopping. Korea Journal of Tourism Research, 32(5), 357-380.

62.

Oh, K. T. (2003). Diagnosis and Course of Propulsion of Korean E-govt in accordance with Synthetic Development Method. The Korea Association for Policy Studies, 12(1), 325-350.

63.

Park, H. S. (2001). Relations of Hotel Information System Quality on User' ValuesㆍSatisfactions and System Use Intentions. Daegu, Korea: thesis for Doctorate in Daeju University.

64.

Park, J. P., & Kim, J. Y. (2010). Study on Response of Consumers for Mobile Advertisement : Based on TAM. Journal of Outdoor Advertising Research, 7(4), 71-103.

65.

Rai A., Lang S. S., & Welker R. B. (2002). Assessing the Validity of IS Sucess Models : An Empirical Test and Theoretical Analysis. Information Systems Research, 13(1):50-69.

66.

Regattieri, A., Gamberi, M., & Manzini, R. (2007), Traceability of Food Products: General Framework and Experimental Evidence. Journal of Food Engineering, 81, 347–356.

67.

Richard, Chinomona. (2015). The Role of Dealers’s Non-Mediated Power in Fostering SEM Manufacturers’ Cooperation:SEM Manufacturers’ Perspective. The International Journal of Industrial Distribution & Business, 3(2), 5-16.

68.

Seddon, P. B., & Kiew, M. Y. (1994). A Partial Test and Development of the DeLone and McLean Model of IS Success (pp.99-110). Proceedings of the International Conference on Information Systems. Vancouver, Canada:ICIS.

69.

Shin, D. H. (2009). An Empirical Investigation of a Modified Technology Acceptance Model of IPTV. Behaviour &Information Technology, 28(4), 361-372.

70.

Sterling, B., & Sparling, D. (2004). Food Traceability in Canada. RCM Technologies. GTIN-RFID Conference. Toronto, October. 14.

71.

Suh, C. K., & Jung, H. J. (2008). The Factors Affecting on Intentions to Use Online Bookstores. The Journal of Information Systems, 17(3), 111-134.

72.

Sultan, F., & Chan, L. (2000). The Adoption of New Technology:The Case of Object-Oriented Computing in Software Companies. IEEE Transaction on Engineering Management, 47(1). 106-126.

73.

Venkatesh, V., & Brown, S. A. (2001). A Longitudinal Investigation of Personal Computers in Homes: Adoption Determinants and Emerging Challenges. MIS Quarterly, 25(1), 71-102.

74.

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.

75.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology:Toward a Unified View. MIS Quarterly, 27(3), 425-478.

76.

Verbeke W., & Ward, R. W. (2006). Consumer Interest in Information Cues Denoting Equality, Traceability and Origin: An Application of Ordered Probit Models to Beef Labels. Food Quality and Preference, 17(6), 453–467.

77.

Yang, Y. S., & Shin, C. H. (2011). Effects of Mobile Phone User Interface Technology and Social Factors on New UI Acceptance in Consumer Use Pattern: From the TAM Perspectives. Korea Corporation Management Association, 18(2), 1-20.

78.

Yun, S. O. (2002). The Development and Application of e-Government Maturity Evaluation Model. The Korea Association for Policy Studies, 11(4), 243-271.

79.

Ziamou, P., & Ratneshwar, S. (2002). Promoting Consumer Adoption of High-technology Products : Is More Information Always Better? Journal of Consumer Psychology, 12(4), 341–351.

The Journal of Distribution Science