박학다식(薄學多識) 선무당 블로그

원피스 549화 번역 - 알폰소님 입니다.

캡틴 버기가 흰수염의 목을 쥐겠답니다. 세계를 쥐겠답니다. ㅎㅎㅎ

http://blog.naver.com/kcd9369/90051844858

세계 2, 3위 휴대폰 제조사로 부상한 삼성과 LG전자의 애플리케이션 마켓플레이스 전략이 구체화되고 있다. 23일 삼성전자와 LG전자에 따르면, 양사는 최근 앱스토어 베타서비스를 마치고 하반기 상용서비스 준비에 나서고 있다.

삼성전자는 지난 2월 베타서비스를 시작한 `삼성 애플리케이션 스토어' 상용서비스를 3분기 중 유럽에서 시작할 방침이다. 삼성은 현재 윈도와 심비안용 애플리케이션을 기반으로 서비스를 준비중이며 애플처럼 신용카드 정보를 입력해 로그인하는 형태의 인증시스템도 구현했다. 다만 무료 애플리케이션의 경우 내려 받을 때 별도 로그인과정을 없애 편의성을 높였다. 또 게임로프트, EA, 게임빌, 프롬포트, 지오인터랙티브 등 게임 및 플랫폼 업체를 통해 옴니아2 등 자체 단말에 최적화된 콘텐츠도 확보했다.

삼성은 앞서 최근 싱가포르에서 열린 `커뮤닉아시아 2009'에서 앱스토어 코너를 마련해 앱스토어 구축 상황을 소개한 바 있다. 이와관련 삼성전자 고위관계자는 "앞으로 이통사와 제휴를 통해 앱스토어를 운영할 예정이며 이를 위한 수익공유 모델을 준비중"이라고 밝혔다.

이와 함께 삼성은 PC기반으로 다양한 디바이스에서 콘텐츠를 공유하는 `삼성 멀티스테이지'와 PC에서 무선(와이파이)으로 콘텐츠를 내려 받는 `삼성라이브', 기존 `삼성무비'와 뮤직 등 다양한 서비스를 함께 제공해 시너지를 확대할 계획이다.

LG전자도 내달 14일부터 전 세계에서 앱스토어를 공식 가동한다. LG에 따르면 현재 1400여개 애플리케이션을 내려 받을 수 있으며 연말까지 2000개로 확대할 계획이다. 또 현재 50여종 이상의 프로그램은 LG휴대폰 사용자가 무료로 내려 받을 수 있도록 했다.

현재 확보된 애플리케이션들은 면밀한 품질 테스트를 거쳤다고 회사측은 덧붙였다. 다만 윈도기반 프로그램 위주로 구성된 것으로 알려졌다.

LG는 일단 한국을 포함해 아시아를 시작으로 15개국 언어로 앱스토어를 서비스할 예정이며 연말까지 24개국으로 대상국가를 확대할 방침이다. LG전자 관계자는 "향후 이통사의 부족한 부분을 보충하는 형태로 서비스를 운용할 계획"이라면서도 "아직 이통사와의 구체적 협력방향이나 수익분배 모델에 대해서는 결정된 게 없다"고 밝혔다.

조성훈기자 hoon21@

■ 2009 상반기 히트상품 소비자추천부문 - BPM

T9ㆍETCS 등 제조IT 부문 구조조정… 중장기전략 변화 촉각

와이브로 사업 구조조정에 돌입한 포스데이타가 T9(차세대 복합 디지털 단말기)와
ETCS(통행료자동징수시스템) 부문을 구조조정 대상으로 추가했다. T9과 ETCS는
포스데이타가 신성장 동력으로 삼고 추진해온 전략사업들이어서 이번 구조조정의 방향에 관심이 쏠리고 있다.



포스데이타는 학부때 교수님이 아르바이트를 하나 주셔서 한 일주일 간 있었던 기억이 납니다.

전기를 절약하기 위해서 점심시간이 되면 최소한의 조명만을 남겨두고 소등이 되더군요.

덕분에 점심시간은 맘 놓고 편히 쉬어도 된다고 하던....


'이런 회사에 다니게 되면 좋겠다'라고 생각했던 회사지만,

이후에 계속 주시해 보니 포스코의 명성에 미치지 못하는 큰 성과를 내지 못하는 모습을 보면서 안타깝더군요.


어떠한 부분을 전략적으로 개발해야하는지가 중요하다는 것을 새삼 느낍니다.

포스데이타가 와이브로에 손을 댈 때 부터 조금씩 불안감이....

ETCS같은 경우에는 잘 됬다고 생각되는데도 불구하고 이런 결과가 나타나버리네요....

Contingency theory

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Acronym

N/A

Alternate name(s)

N/A

Main dependent construct(s)/factor(s)

Efficiency, organizational performance

Main independent construct(s)/factor(s)

Strategy, technology, task, organizational size, structure, and culture

Concise description of theory

There are many forms of contingency theory. In a general sense, contingency theories are a class of behavioral theory that contend that there is no one best way of organizing / leading and that an organizational / leadership style that is effective in some situations may not be successful in others (Fiedler, 1964). In other words: The optimal organization / leadership style is contingent upon various internal and external constraints.

Four important ideas of Contingency Theory are: 1. There is no universal or one best way to manage 2. The design of an organization and its subsystems must 'fit' with the environment 3. Effective organizations not only have a proper 'fit' with the environment but also between its subsystems 4. The needs of an organization are better satisfied when it is properly designed and the management style is appropriate both to the tasks undertaken and the nature of the work group.

There are also contingency theories that relate to decision making (Vroom and Yetton, 1973). According to these models, the effectiveness of a decision procedure depends upon a number of aspects of the situation: the importance of the decision quality and acceptance; the amount of relevant information possessed by the leader and subordinates; the likelihood that subordinates will accept an autocratic decision or cooperate in trying to make a good decision if allowed to participate; the amount of disagreement among subordinates with respect to their preferred alternatives.

It is worth noting that since the mid 1980s contingency theory has been fairly dead within the originating field of organization theory. Apart from Lex Donaldson, professor at Australian Graduate School of Management, and a few other people, nobody within the field attempt to contribute to a further development of contingency theory, foremost because of what can be perceived as the lacking explanatory power of the theory.

Sources: http://www.valuebasedmanagement.net/methods_contingency_theory.html and http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Organizational%20Communication/Contingency_Theories.doc/

Diagram/schematic of theory

Source: Weill, Peter; Olson, Marorethe H. (1989). An Assessment of the Contingency Theory of Management Information Systems. Journal of Management Information Systems, 6(1), 63.

Originating author(s)

Fred Fiedler (contingency theory of leadership)

Seminal articles

Burns, T., Stalker, G.M. (1961). The Management of Innovation. London: Tavistock.

Fiedler, F. E. (1964). A Contingency Model of Leadership Effectiveness. Advances in Experimental Social Psychology (Vol.1). 149-190. New York: Academic Press.

Kast, F., Rosenzweig, J. (1973). Contingency Views of Organization and Management. Chicago: Science Research Associates.

Lawrence, P. R., Lorsch, J. W. (1967) . Organization and Environment. Cambridge, MA: Harvard University Press.

Otley, D. T. 1980. The contingency theory of management accounting: Achievement and prognosis. Accounting, Organizations and Society 5(4): 413-428.

Vroom, V.H. and Yetton, P.W. (1973). Leadership and decision-making. Pittsburgh: University of Pittsburgh Press

Originating area

Organization theory, psychology, strategy

Level of analysis

Firm, individual

IS articles that use the theory

Heeks, Richard (2002) Information Systems and Developing Countries: Failure, Success and Local Improvisations, The Information Society, 18:2, pp. 101-112.

Andres, Hayward P.; Zmud, Robert W. (2001/2002). A Contingency Approach to Software Project Coordination. Journal of Management Information Systems, 18(3), 41-71.

Andrew D. Luzi; Kenneth D. MacKenzie (1982). An Experimental Study of Performance Information Systems. Management Science (pre-1986), 28(3), 243-259.

Arinzn, Bay. (1991). A Contingency Model of DSS Development Methodology. Journal of Management Information Systems, 8(1), 149-166.

Barki, Henri; Rivard, Suzanne; Talbot, Jean (2001). An Integrative Contingency Model of Software Project Risk Management. Journal of Management Information Systems, 17(4), 37-69.

Becerra-Fernandez, Irma; Sabherwal, Rajiv. (2001). Organization Knowledge Management: A Contingency Perspective. Journal of Management Information Systems, 18(1), 23-55.

Belanger, France, Collins, Rosann Webb, Cheney, Paul H. (2001). Technology Requirements and Work Group Communication for Telecommuters. Information Systems Research, 12(2), 155-176.

Blanton, J Ellis, Watson, Hugh J, Moody, Janette (1992). Toward a better understanding of information technology organization: A comparative case study. MIS Quarterly, 16(4), 531-555.

Brown, Carol V.; Bostrom, Robert P. (1994). Organization designs for the management of end-user computing: Reexamining the contingencies. Journal of Management Information Systems, 10(4), 183-211.

Chang, Ruey-Dang, Chang, Yeun-Wen, Paper, David (2003). The effect of task uncertainty, decentralization and AIS characteristics on the performance of AIS: an empirical case in Taiwan. Information & Management, 40(7), 691-713.

Cheon, Myun J.; Grover, Varun; Teng, James T.C. (1995). Theoretical perspectives on the outsourcing of information systems. Journal of Information Technology, 10(4), 209-219.

Chin, Wynne W.; Marcolin, Barbara L.; Newsted, Peter R. (2003). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study. Information Systems Research, 14(2), 189-217.

Croteau, Anne-Marie, Raymond, Louis (2004). Performance outcomes of strategic and IT competencies alignment. Journal of Information Technology, 19(3), 178-190.

Danziger, James N. (1979). Technology and Productivity: A Contingency Analysis of Computers in Local Government. Administration & Society, 11(2), 144-171.

Devaraj, Sarv, Kohli, Rajiv (2000). Information technology payoff in the health-care industry: A longitudinal study. Journal of Management Information Systems, 16(4), 41-67.

Edström, Anders (1977). User Influence and the Success of MIS Projects: A Contingency Approach. Human Relations, 30(7), 589-607.

Fiedler, Kirk Dean, Grover, Varun, Teng, James T C. (1996). An empirically derived taxonomy of information technology structure and its relationship to organizational structure. Journal of Management Information Systems, 13(1), 9-34.

Franz, Charles R. (1985). User Leadership in the Systems Development Life Cycle: A Contingency Model. Journal of Management Information Systems, 2 (2), 5.

Galegher, Jolene; Kraut, Robert E. (1994). Computer-mediated Communication for Intellectual Teamwork: An Experiment in Group Writing. Information Systems Research, 5(2),110-138.

Giaglis, George M.; Klein, Stefan; O'Keefe, Robert M. (2002). The role of intermediaries in electronic marketplaces: developing a contingency model. Information Systems Journal, 12(3), 231-246.

Ginberg, Michael J. (1980). An Organizational Contingencies View of Accounting and Information Systems Implementation. Accounting, Organizations & Society, 5(4), 369-382.

Goodhue, Dale L., Quillard, Judith A.,Rockart, John F. (1988). Managing The Data Resource: A Contingency Perspective. MIS Quarterly, 12(3), 372-382.

Gordon, Lawrence A., Miller, Danny.A (1976). Contingency Framework for the Design of Accounting Information Systems. Accounting, Organizations & Society, 1(1), 59-70.

Hardgrave, Bill C.; Wilson, Rick L. (1999). Toward a Contingency Model for Selecting an Information System Prototyping Strategy. Journal of Management Information Systems, 16(2), 113-136.

Huber, George (1982). Organizational Information Systems: Determinants of Their Performance and Behavior. Management Science, 28(2), 138-155.

Jae-Nam Lee; Miranda, Shaila M.; Yong-Mi Kim (2004). IT Outsourcing Strategies: Universalistic, Contingency, and Configurational Explanations of Success. Information Systems Research, 15(2), 110-131.

Khazanchi, Deepak. (2005). Information Technology (IT) Appropriateness: The Contingency Theory of "Fit" and IT Implementation in Small and Medium Enterprises. Journal of Computer Information Systems, 45(3), 88-95.

Kyu Kim, K.; Umanath, Narayan S. (1992/1993). Structure and Perceived Effectiveness of Software Development Subunits: A Task Contingency Analysis. Journal of Management Information Systems, 9(3), 157-181.

Lai, V.S. (1999). A Contingency Examination of CASE-task Fit on Software Developer's Performance. European Journal of Information Systems, 8(1), 27-49.

Lee, Choong C., Grover, Varun (1999/2000). Exploring mediation between environmental and structural attributes: The penetration of communication technologies in manufacturing organizations. Journal of Management Information Systems, 16(3),187-217.

Lin, Winston T.; Shao, Benjamin B.M.(2000). The relationship between user participation and system success: a simultaneous contingency approach. Information & Management, 37(6), 283-295.

Markus, M. Lynne; Bjørn-Andersen, Niels. (1987). Power Over Users: Its Exercise by System Professionals. Communications of the ACM, 30(6), 498-504.

McKeen, James D. Guimaraes, Tor, Wetherbe, James C. (1994). The relationship between user participation and user satisfaction: an investigation of four contingency factors. MIS Quarterly, 18(4), 427-451.

McKeen, James D., Guimaraes, Tor (1997). Successful strategies for user participation in systems development. Journal of Management Information Systems, 14(2), 133-150.

Nidumolu, Sarma R. (1996). A Comparison of the Structural Contingency and Risk-based Perspectives on Coordination in Software-development Projects. Journal of Management Information Systems, 13(2), 77-113.

Panagiotis Kanellis, Ray J Paul (2005). User Behaving Badly: Phenomena and Paradoxes from an Investigation into Information Systems Misfit. Journal of Organizational and End User Computing17(2), 64-91.

Pinsonneault, Alain; Heppel, Nelson. (1997/1998). Anonymity in Group Support Systems Research: A New Conceptualization, Measure, and Contingency Framework. Journal of Management Information Systems, 14(3), 89-108.

Premkumar, G, King, William R. (1992). An empirical assessment of information systems planning and the role of information systems in organizations. Journal of Management Information Systems, 9(2), 99-125.

Ratbe, Dina, King,William R., Kim, Young-Gul (1999/2000). The fit between project characteristics and application development methodologies: A contingency approach. The Journal of Computer Information Systems, 40(2), 26-33.

Raymond, Louis (1990). Organizational Context and Information Systems Success: A Contingency Approach. Journal of Management Information Systems, 6(4), 5-20.

Sabherwal, Rajiv; King, William R.(1992). Decision Processes for Developing Strategic Applications of Information Systems: A Contingency Approach. Decision Sciences, 23(4), 917-943.

Schonberger, Richard J. (1980). MIS Design: A Contingency Approach. MIS Quarterly, 4(1), 13-20.

Seliem, Ahmed A.M.; Ashour, Ahmed S.; Khalil, Omar E.M.; Millar, Stephen J. (2003). The Relationship of Some Organizational Factors to Information Systems Effectiveness: A Contingency Analysis of Egyptian Data. Journal of Global Information Management, 11(1), 40-71.

Sugumaran, Vijayan, Arogyaswamy, Bernard (2003-2004). Measuring IT Performance: "Contingency" Variables and Value Modes. Journal of Computer Information Systems, 44(2), 79-86.

Teo, Thompson S.H. (2003). A contingency perspective on Internet adoption and competitive advantage. European Journal of Information Systems, 12(2), 78-92.

Umanath, Narayan S. (2003). The concept of contingency beyond “It depends”: illustrations from IS research stream. Information & Management, 40(6), 551-562.

Venkatraman, N. (1985/1986).Research on MIS Planning: Some Guidelines from Strategic Planning Research. Journal of Management Information Systems, 2(3), 65-77.

Weill, Peter; Olson, Marorethe H. (1989). An Assessment of the Contingency Theory of Management Information Systems. Journal of Management Information Systems, 6(1), 59-85.

Wetherbe, Jim C.; Whitehead, Canton J. (1977). A Contingency View of Managing the Data Processing Organization. MIS Quarterly, Mar77, Vol. 1 Issue 1, p19, 7p

Zhu, Zhichang (2002). Evaluating contingency approaches to information systems design. International Journal of Information Management, 22(5), 343-356.

Zmud, R. W. 1982. Diffusion of modern software practices: Influence of centralization and formalization. Management Science (28): 1421-1431.

Links from this theory to other theories

N/A

External links

http://www.valuebasedmanagement.net/methods_contingency_theory.html, management summary of contingency theory

http://changingminds.org/disciplines/leadership/theories/contingency_theory.htm, brief summary of contingency theory

http://www.stfrancis.edu/ba/ghkickul/stuwebs/btopics/works/fied.htm, website focused on Fiedler's contingency theory of leadership

http://en.wikipedia.org/wiki/Fiedler_contingency_model, another description of Fiedler's contingency theory of leadership

http://www.12manage.com/methods_contingency_theory.html, provides definitions of didefinitions of types of contingency theory (organization, leadership, decision making)

http://www.geocities.com/kstability/learning/management/contingency.html, description of contingency theory.

http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Organizational%20Communication/Contingency_Theories.doc/, contingency theory summary from Twente

Original Contributor(s)

Mike Wade and Sally Tomasevic

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Technology acceptance model

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Acronym

TAM

Alternate name(s)

N/A

Main dependent construct(s)/factor(s)

Behavioral intention to use, System usage

Main independent construct(s)/factor(s)

Perceived usefulness, Perceived ease of use

Concise description of theory

TAM is an adaptation of the Theory of Reasoned Action (TRA) to the field of IS. TAM posits that perceived usefulness and perceived ease of use determine an individual's intention to use a system with intention to use serving as a mediator of actual system use. Perceived usefulness is also seen as being directly impacted by perceived ease of use. Researchers have simplified TAM by removing the attitude construct found in TRA from the current specification (Venkatesh et. al., 2003). Attempts to extend TAM have generally taken one of three approaches: by introducing factors from related models, by introducing additional or alternative belief factors, and by examining antecedents and moderators of perceived usefulness and perceived ease of use (Wixom and Todd, 2005).

TRA and TAM, both of which have strong behavioural elements, assume that when someone forms an intention to act, that they will be free to act without limitation. In practice constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act.

Diagram/schematic of theory

Originating author(s)

Davis (1986); Davis (1989)

Seminal articles

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology).

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

Originating area

Information Systems, Technology Adoption

Level of analysis

Individual

IS articles that use the theory

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information: A replication. MIS Quarterly, 16(2), 227-247.

Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.

Al-Gahtani, S. (2001). The applicability of TAM outside north america: An empirical test in the united kingdom. Information Resources Management Journal, 14(3), 37-46.

Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745.

Brosnan, M. J. (1999). Modeling technophobia: A case for word processing. Computers in Human Behavior, 15(2), 105-121.

Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., & Burkman, J. R. (2002). Do I really have to? user acceptance of mandated technology. European Journal of Information Systems, 11(4), 283-295.

Chan, S., & Lu, M. (2004). Understanding internet banking adoption and use behavior: A hong kong perspective. Journal of Global Information Management, 12(3), 21-43.

Chau, P. K. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems, 13(2), 185-204.

Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311.

Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology).

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of b2C channel satisfaction and preference: Validation e-commerce metrics. Information Systems Research, 13(3), 316-333.

Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21.

Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1-13.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of E-commerce adoption. Journal of the Association for Information Systems, 1(8), 1-28.

Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of E-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400.

Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955.

Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374.

Hsu, C. L. and Lin, J. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation, Information & Management, 45, 65-74.

Hsu, C. L. and Lu, H. P. (2007). Consumer behavior in on-line game communities: a motivational factor perspective Computers in Human Behavior, 23, 1642-1659.

Hsu, C. L. and Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience, Information & Management, 41(7), 853-868.

Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87-114.

Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279-305.

Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357-389.

Kamel, S., & Hassan, A. (2003). Assessing the introduction of electronic banking in egypt using the technology acceptance model. Annals of Cases on Information Technology, 5, 1-25.

Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755.

Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer E-commerce. Information Technology, Learning, and Performance Journal, 22(1), 35-48.

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223.

Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29(3), 269-282.

Lim, J. (2003). A conceptual framework on the adoption of negotiation support systems. Information and Software Technology, 45(8), 469-477.

Lu, H., Hsu, C., & Hsu, H. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2/3), 106-120.

Lucas, H. C.,Jr, & Spitler, V. K. (1999). Technology use and performance: A field study of broker workstations. Decision Sciences, 30(2), 291-311.

Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72.

Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.

McCloskey, D. (2003). Evaluating electronic commerce acceptance with the technology acceptance model. The Journal of Computer Information Systems, 44(2), 49-57.

McCoy, S., Everard, A., & Jones, B. M. (2005). An examination of the technology acceptance model in uruguay and the US: A focus on culture. Journal of Global Information Technology Management, 8(2), 27-45.

Ndubisi, N. O., Gupta, O. K., & Ndubisi, G. C. (2005). The moguls' model of computing: Integrating the moderating impact of users' persona into the technology acceptance model. Journal of Global Information Technology Management, 8(1), 27-47.

Ndubisi, N. O., & Jantan, M. (2003). Evaluating IS usage in malaysian small and medium-sized firms using the technology acceptance model. Logistics Information Management, 16(6), 440-450.

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

Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Research report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system. Information Systems Research, 12(2), 208-222.

Riemenschneider, C. K., & Hardgrave, B. C. (2001). Explaining software development tool use with the technology acceptance model. The Journal of Computer Information Systems, 41(4), 1-8.

Riemenschneider, C. K., Harrison, D. A., & Mykytn, P. P.,Jr. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information & Management, 40(4), 269-285.

Roberts, P., & Henderson, R. (2000). Information technology acceptance in a sample of government employees: A test of the technology acceptance model. Interacting with Computers, 12(5), 427-443.

Shih, H. (2004). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719-729.

Spacey, R., Goulding, A., & Murray, I. (2004). Exploring the attitudes of public library staff to the internet using the TAM. Journal of Documentation, 60(5), 550-564.

Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92.

Szajna, B. (1994). Software evaluation and choice: Predictive validation of the technology acceptance instrument. MIS Quarterly, 18(3), 319-324. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.

Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for dirrections? gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139.

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.

Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297-316.

Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747-762.

Wang, W., & Benbasat, I. (2005). Trust in and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72-101.

Wang, C., Hsu, Y., & Fang, W. (2004). Acceptance of technology with network externalities: An empirical study of internet instant messaging services. JITTA : Journal of Information Technology Theory and Application, 6(4), 15-28.

Wang, Y., Wang, Y., Lin, H., & Tang, T. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501-519.

Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.

Yu, J. L. C., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless internet. Internet Research, 13(3), 206-222.

Links from this theory to other theories

Theory of planned behavior, Theory of reasoned action, Unified theory of acceptance and use of technology, Delone and McLean IS success model

External links

http://en.wikipedia.org/wiki/Technology_acceptance_model, Wikipedia entry for TAM

http://www.guuspijpers.com/Research.htm#Technology%20Acceptance%20Model%20(TAM), Guus Pijpers presents an extensive list of TAM references up to December 2003

Original Contributor(s)

Brent Furneaux

Please feel free to make modifications to this site. In order to do so, you must register.

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Diffusion of innovations

---

Acronym

DOI

Alternate name(s)

Innovation Diffusion Theory (IDT)

Main dependent construct(s)/factor(s)

Implementation Success or Technology Adoption

Main independent construct(s)/factor(s)

Compatibility of Technology, Complexity of Technology, Relative Advantage (Perceived Need for Technology)

Concise description of theory

DOI theory sees innovations as being communicated through certain channels over time and within a particular social system (Rogers, 1995). Individuals are seen as possessing different degrees of willingness to adopt innovations and thus it is generally observed that the portion of the population adopting an innovation is approximately normally distributed over time (Rogers, 1995). Breaking this normal distribution into segments leads to the segregation of individuals into the following five categories of individual innovativeness (from earliest to latest adopters): innovators, early adopters, early majority, late majority, laggards (Rogers, 1995). Members of each category typically possess certain distinguishing characteristics as shown below:

• innovators - venturesome, educated, multiple info sources
• early adopters - social leaders, popular, educated
• early majority - deliberate, many informal social contacts
• late majority - skeptical, traditional, lower socio-economic status
• laggards - neighbours and friends are main info sources, fear of debt

When the adoption curve is converted to a cumulative percent curve a characteristic S curve (as shown in the first figure below) is generated that represents the rate of adoption of the innovation within the population (Rogers, 1995). The rate of adoption of innovations is impacted by five factors: relative advantage, compatibility, trialability, observability, and complexity (Rogers, 1995). The first four factors are generally positively correlated with rate of adoption while the last factor, complexity, is generally negatively correlated with rate of adoption (Rogers, 1995). The actual rate of adoption is governed by both the rate at which an innovation takes off and the rate of later growth. Low cost innovations may have a rapid take-off while innovations whose value increases with widespread adoption (network effects) may have faster late stage growth. Innovation adoption rates can, however, be impacted by other phenomena. For instance, the adaptation of technology to individual needs can change the nature of the innovation over time. In addition, a new innovation can impact the adoption rate of an existing innovation and path dependence may lock potentially inferior technologies in place.

Sources: http://en.wikipedia.org/wiki/Diffusion_of_innovation

Rogers, Everett M. Diffusion of Innovations. 4thed. New York: Free Press,1995

Diffusion of Innovation Theory in IS

Moore and Benbasat (1991), working in an IS context, expanded upon the five factors impacting the adoption of innovations presented by Rogers, generating eight factors (voluntariness, relative advantage, compatibility, image, ease of use, result demonstrability, visibility, and trialability) that impact the adoption of IT. Scales used to operationalize these factors were also validated in the study.

Since the early applications of DOI to IS research the theory has been applied and adapted in numerous ways. Research has, however, consistently found that technical compatibility, technical complexity, and relative advantage (perceived need) are important antecedents to the adoption of innovations (Bradford and Florin, 2003; Crum et. al., 1996) leading to the generalized model presented below (see second figure below).

Diagram/schematic of theory

IS diffusion variance model:

Originating author(s)

Lazarsfeld et. al. (1949); Rogers (1962); Rogers and Shoemaker (1971); Rogers (1995)

Seminal articles

Lazarsfeld, P.F., Berelson, B. & Gaudet, H. (1949). The people’s choice: How the voter makes up his mind in a presidential campaign. New York: Columbia University Press.

Rogers, Everett M. (1962). Diffusion of Innovations. The Free Press. New York.

Rogers, Everett M & Shoemaker, Floyd F (1971). Communication of Innovations: A Cross-Cultural Approach (2nd ed.). New York: The Free Press.

Rogers, Everett M. Diffusion of Innovations. 4thed. New York: Free Press,1995

Originating area

Anthropology/Sociology/Education/Communication/Marketing and Management/Geography/Economics

Level of analysis

Group, Firm, Industry, Society

IS articles that use the theory

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.

Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557-582.

Armstrong, J. S., & Yokum, J. T. (2001). Potential diffusion of expert systems in forecasting. Technological Forecasting and Social Change, 67(1), 93-103.

Baskerville, R L & Pries-Heje, J (2001). A multiple-theory analysis of a diffusion of information technology case. Information Systems Journal, 11(3), 181-212.

Baskerville, R., & Pries-Heje, J. (2003). Diversity in modeling diffusion of information technology. Journal of Technology Transfer, 28(3-4), 251-264.

Beatty, R. C., Shim, J. P., & Jones, M. C. (2001). Factors influencing corporate web site adoption: A time-based assessment. Information & Management, 38(6), 337-354.

Blake, B. F., Neuendorf, K. A., & Valdiserri, C. M. (2005). Tailoring new websites to appeal to those most likely to shop online. Technovation, 25(10), 1205-1214.

Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205-225.

Brancheau, J. C., & Wetherbe, J. C. (1990). The adoption of spreadsheet software: Testing innovation diffusion theory in the context of end-user computing. Information Systems Research, 1(2), 115-143.

Carter Jr., F. J., Jambulingam, T., Gupta, V. K., & Melone, N. (2001). Technological innovations: A framework for communicating diffusion effects. Information & Management, 38(5), 277-287.

Chen, L., Gillenson, M. L., & 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), 8-31.

Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705-719.

Cheng, J. M. S., Kao, L. L. Y., & Lin, J. Y. (2004). An investigation of the diffusion of online games in taiwan: An application of roger's diffusion of innovation theory. Journal of American Academy of Business, Cambridge, 5(1/2), 439-445.

Cheung, C. M. K., Chan, G. W. W., & Limayem, M. (2005). A critical review of online consumer behavior: Empirical research. Journal of Electronic Commerce in Organizations, 3(4), 1-19.

Chircu, A. M., & Kauffman, R. J. (2000). Limits to value in electronic commerce-related IT investments. Journal of Management Information Systems, 17(2), 59-80.

Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123-139.

Crum, M. R., Premkumar, G., & Ramamurthy, K. (1996). An assessment of motor carrier adoption, use, and satisfaction with EDI. Transportation Journal, 35(4), 44-57.

Dos Santos, B. L., & Peffers, K. (1998). Competitor and vendor influence on the adoption of innovative applications in electronic commerce. Information & Management, 34(3), 175-184.

Eastin, M. S. (2002). Diffusion of e-commerce: An analysis of the adoption of four e-commerce activities. Telematics and Informatics, 19(3), 251-267.

Eder, L. B., & Igbaria, M. (2001). Determinants of intranet diffusion and infusion. Omega, 29(3), 233-242.

Fichman, R. G. (2004). Going beyond the dominant paradigm for information technology innovation research: Emerging concepts and methods. Journal of the Association for Information Systems, 5(8), 314-355.

Fichman, R. G. (2001). The role of aggregation in the measurement of it-related organizational innovation. MIS Quarterly, 25(4), 427-455.

Fichman, R. G., & Kemerer, C. F. (1999). The illusory diffusion of innovation: An examination of assimilation gaps. Information Systems Research, 10(3), 255.

Fichman, R. G., & Kemerer, C. F. (1997). The assimilation of software process innovations: An organizational learning perspective. Management Science, 43(10), 1345-1363.

Forman, C. (2005). The corporate digital divide: Determinants of internet adoption. Management Science, 51(4), 641.

Geroski, P. A. (2000). Models of technology diffusion. Research Policy, 29(4-5), 603-625.

Goslar, M. D. (1987). Marketing and the adoption of microcomputers: An application of diffusion theory. Journal of the Academy of Marketing Science, 15(2), 42-48.

Grantham, A., & Tsekouras, G. (2005). Diffusing wireless applications in a mobile world. Technology in Society, 27(1), 85-104.

Grover, V. (1993). An empirically derived model for the adoption of customer-based interorganizational systems. Decision Sciences, 24(3), 603-640.

Grover, V., Fiedler, K., & Teng, J. (1997). Empirical evidence on swanson's tri-core model of information systems innovation. Information Systems Research, 8(3), 273-287.

Grover, V., & Goslar, M. D. (1993). The initiation, adoption, and implementation of telecommunications technologies in U.S. organizations. Journal of Management Information Systems, 10(1), 141-163.

Hardgrave, B. C., Davis, F. D., & Riemenschneider, C. K. (2003). Investigating determinants of software developers' intentions to follow methodologies. Journal of Management Information Systems, 20(1), 123-152.

Hsu, C. L., Lu, H. P. and Hsu, H. H. (2007). Adoption of the mobile internet: an empirical study of multimedia message service (MMS), OMEGA: International Journal of Management Science, 35, 715-726.

Hu, Q., Saunders, C., & Gebelt, M. (1997). Research report: Diffusion of information systems outsourcing: A reevaluation of influence sources. Information Systems Research, 8(3), 288-301.

Hung, S., Ku, C., & Chang, C. (2003). Critical factors of WAP services adoption: An empirical study. Electronic Commerce Research and Applications, 2(1), 42-60.

Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465-485.

Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183-213.

Kautz, K., & Pries-Heje, J. (Eds.). (1996). Diffusion and adoption of information technology. London: Chapman and Hall.

Kocas, C. (2002). Evolution of prices in electronic markets under diffusion of price-comparison shopping. Journal of Management Information Systems, 19(3), 99-119.

Lai, V. S. (1997). Critical factors of ISDN implementation: An exploratory study. Information & Management, 33(2), 87-97.

Lee, M. K. O. (1998). Internet-based financial EDI: Towards a theory of its organizational adoption. Computer Networks and ISDN Systems, 30(16-18), 1579-1588.

Leonard-Barton, D., & Deschamps, I. (1988). Managerial influence in the implementation of new technology. Management Science, 34(10), 1252-1265.

Li, S. S. (2003). Electronic newspaper and its adopters: Examining the factors influencing the adoption of electronic newspapers in taiwan. Telematics and Informatics, 20(1), 35-49.

Liao, S., Shao, Y. P., Wang, H., & Chen, A. (1999). The adoption of virtual banking: An empirical study. International Journal of Information Management, 19(1), 63-74.

Martins, C. B. M. J., Steil, A. V., & Todesco, J. L. (2004). Factors influencing the adoption of the internet as a teaching tool at foreign language schools. Computers and Education, 42(4), 353-374.

Moore, G. C. (1987). "End user computing and ofice automation: A diffusion of innovations perspective. INFOR, 25(3), 214-235.

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.

Mustonen-Ollila, E., & Lyytinen, K. (2003). Why Organizations Adopt Information System Process Innovations: A Longitudinal Study using Diffusion of Innovation Theory, Information Systems Journal, 13(3), 275-297.

Nilakanta, S., & Scamell, R. W. (1990). The effect of information sources and communication channels on the diffusion of innovation in a data base development environment. Management Science, 36(1), 24-40.

O'Callaghan, R., Kaufmann, P. J., & Konsynski, B. R. (1992). Adoption correlates and share effects of electronic data interchange systems in marketing channels. Journal of Marketing, 56(2), 45-56.

Park, S., & Yoon, S. (2005). Separating early-adopters from the majority: The case of broadband internet access in korea. Technological Forecasting and Social Change, 72(3), 301-325.

Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information Systems Research, 9(4), 362-379.

Peansupap, V., & Walker, D. (2005). Exploratory factors influencing information and communication technology diffusion and adoption within australian construction organizations: A micro analysis. Construction Innovation, 5(3), 135-157.

Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Research report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system. Information Systems Research, 12(2), 208-222.

Premkumar, G., Ramamurthy, K., & Nilakanta, S. (1994). Implementation of electronic data interchange: An innovation diffusion perspective. Journal of Management Information Systems, 11(2), 157-186.

Purvis, R. L., Sambamurthy, V., & Zmud, R. W. (2001). The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science, 12(2), 117-135.

Raho, L. E., Belohlav, J. A., & Fiedler, K. D. (1987). Assimilating new technology into the organization: An assessment of McFarlan and McKenney's model. MIS Quarterly, 11(1), 46-57.

Rajagopal, P. (2002). An innovation—diffusion view of implementation of enterprise resource planning (ERP) systems and development of a research model. Information and Management, 40(2), 87-114.

Ramamurthy, K., & Premkumar, G. (1995). Determinants and outcomes of electronic data interchange diffusion. IEEE Transactions on Engineering Management, 42(4), 332-351.

Ramamurthy, K., Premkumar, G., & Crum, M. R. (1999). Organizational and interorganizational determinants of EDI diffusion and organizational performance: A causal model. Journal of Organizational Computing & Electronic Commerce, 9(4), 253-285.

Ravichandran, T. (2000). Swiftness and intensity of administrative innovation adoption: An empirical study of TQM in information systems. Decision Sciences, 31(3), 691-724.

Reich, B. H., & Benbasat, I. (1990). An empirical investigation of factors influencing the success of customer-oriented strategic systems. Information Systems Research, 1(3), 325-347.

Rodger, J. A., Pendharkar, P. C., & Bhatt, G. D. (1996). Diffusion theory and the adoption of software innovation: Common errors and future issues. Journal of High Technology Management Research, 7(1), 1-13.

Roman, R. (2003). Diffusion of innovations as a theoretical framework for telecenters. Information Technologies & International Development, 1(2), 53-66.

Seyal, A. H., & Rahman, M. N. A. (2003). A preliminary investigation of e-commerce adoption in small & medium enterprises in brunei. Journal of Global Information Technology Management, 6(2), 6-26.

Shao, Y. P. (1999). Expert systems diffusion in british banking: Diffusion models and media factor. Information & Management, 35(1), 1-8.

Sharma, S., & Rai, A. (2003). An assessment of the relationship between ISD leadership characteristics and IS innovation adoption in organizations. Information and Management, 40(5), 391-401.

Straub, D. W. (1994). The effect of culture on IT diffusion: E-mail and FAX in japan and the U.S. Information Systems Research, 5(1), 23-47.

Swanson, E. B. (1994). Information systems innovation among organizations. Management Science, 40(9), 1069-1092.

Tam, K. Y. (1996). Dynamic price elasticity and the diffusion of mainframe computing. Journal of Management Information Systems, 13(2), 163-183.

Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(5), 1-42.

Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington, Mass.: Lexington Books.

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-45.

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.

Wu, J., & Wang, S. (2005). What drives mobile commerce? an empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.

Zhu, K., & Kraemer, K. L. (2005). Post-adoption variations in usage and value of E-business by organizations: Cross-country evidence from the retail industry. Information Systems Research, 16(1), 61-84.

Zmud, R. W. (1984). An examination of 'push-pull' theory applied to process innovation in knowledge work. Management Science, 30(6), 727-738.

Zmud, R. W. (1983). The effectiveness of external information channels in facilitating innovation within software development groups. MIS Quarterly, 7(2), 43-58.

Zmud, R. W. (1982). Diffusion of modern software practices: Influence of centralization and formalization. Management Science, 28(12), 1421-1431.

Links from this theory to other theories

Technology acceptance model, Theory of planned behavior, Theory of reasoned action, Unified theory of acceptance and use of technology, Evolutionary theory

External links

http://en.wikipedia.org/wiki/Diffusion_of_innovations, Wikipedia provides a brief synopsis of DOI theory

http://www.anu.edu.au/people/Roger.Clarke/SOS/InnDiff.html, Roger Clarke presents a primer on DOI theory as a preparation to reading the relevant IS literature and a resource list including a number of references at http://www.anu.edu.au/people/Roger.Clarke/SOS/InnDiffISW.html

http://www.sigadit.org/, The AIS Special Interest Group on Adoption and Diffusion of Information Technology

http://www.isi.salford.ac.uk/tm/Diffusion.enl, Tom McMaster provides an EndNote library of DOI

http://www.context.org/ICLIB/IC28/AtKisson.htm, A role playing game called The Innovation Diffusion Game that is intended to demonstrate some basic principles of cultural change and DOI theory

http://disc-nt.cba.uh.edu/chin/digit98/panel2.pdf#search='innovation%20diffusion%20theory', A 1998 paper by Agarwal et. al. outlining extensions to DOI theory

http://www.personal.psu.edu/staff/c/a/cam240/litreview.htm, A number of additional web links on DOI

Original Contributor(s)

Brent Furneaux

Please feel free to make modifications to this site. In order to do so, you must register.


<출처: http://www.fsc.yorku.ca/york/istheory/wiki/index.php/Main_Page>

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2. 합격자 오리엔테이션 안내
	ㅇ	일시 : 2009. 7. 1(수) - 10:00 ~ 12:00 지식창업 분야 합격자 - 14:00 ~ 16:00 일반창업·기술창업 분야 합격자 ※ 준비사항 : 신분증 사본 1장,  증명사진 1매
	ㅇ	장소 - 강북권 지원자 : 마포구청 12층 강당(지하철6호선 마포구청역 1번출구 200m) - 강남권 지원자 : 동남권유통단지(가든파이브)공구상가 10층 회의실(지하철8호선 장지역 1번출구 10분거리)
	ㅇ	내 용 : 청년창업 활동 지원내용소개, 창업공간 호실배정, 시설물 이용안내, 입주계약서 작성 등
	▷	효율적인 창업활동을 위하여 같은 창업지원센터 공간이용 희망자 및 여성존 입주 희망자, 입주할 창업공간 변경희망자는 별첨 조사서를 작성 팩스 또는 이메일으로 ‘09. 6. 26일까지 접수하여 주시기 바랍니다.
	※	주의사항
		-	오리엔테이션은 당초 접수시 신청한 지역의 교육장소로 교육에 임하시기 바랍니다.
		-	오리엔테이션 미참석자는 기본교육 시간을 미이수한 것으로 간주 불이익 처분이 부가될 예정이니 반드시 참석하시기 바랍니다.(※대리참석 불허
	▷	최종합격자 오리엔테이션과 입주 등에 관한 사항은 서울산업통상진흥원청년창업T/F팀(☎2657-5846~7),서울시 일자리플러스센터(☎731-9529~33),서울시청 일자리정책담당관(☎731-5319~20)으로 연락주시기 바랍니다.

2030 청년창업 프로젝트 창업아이템 경진대회

GI1665 김연진입니다.

2030 청년창업 프로젝트 창업아이템 경진대회 일반창업분야에
서류통과 하였습니다.

18일 면접준비에 최선을 다할 생각입니다.


IT분야에서 공부를 하고 있지만, 이를 꼭 지식산업 분야에만 활용할 필요는 없다고 생각하고
일반 창업분야에 지원했습니다.

기업에서 가장 필요한 인원은 경영관련 지식과 정보기술을 모두 활용가능한 MIS 인력이라고 저는 확신 합니다.

기획단계에서부터 MIS는 절대적으로 필요한 인적 자원임에도 불구하고
대기업에서는 일단 학벌좋은 어린이들만 뽑아가고 있습니다.

일반 창업분야에서 MIS인력의 우수함과, 정보기술 활용분야의 다양함을 보여주고, 마포구 청사로 명함 뽑아 오겠습니다.

『2030 청년창업 프로젝트』창업아이템 경진대회
서류심사 합격자 및 면접심사 시행안내

창업아이템 경진대회 서류심사 합격자 및 면접시험 계획을 다음과 같이 안내 합니다.

2009. 6. 15

2030청년창업프로젝트 1차심사결과(일반창업분야)

GI1665

김연진

7880

13:00

'지식나눔공동체 꼬뚜' 김연진입니다.


서울 강원 권역별 대회 본선 진출 하였습니다.
8개팀이 본선진출하여 7월에 본선대회를 치르게 됩니다.


open innovation paper를 찾던 도중 발견하게 된 카페에서
스터디와 공모전, 그리고 좋은 사람들까지 많은 것을 얻고 있습니다.

노동부 공고 제2009 - 119호

2009년 소셜벤처(Social Venture) 권역별 경연대회
 
 

창의적인 아이디어를 가진 학생, 일반인을 대상으로 소셜벤처 경진대회를 개최하여 혁신적인 사회적기업모델을 발굴․육성하고자 ꡔ2009년 소셜벤처(Social Venture) 권역별 경연대회ꡕ 시행계획을 공고하니 관심있는 분들의 많은 참가를 바랍니다.

2009. 4. 30.  노 동 부 장 관