组织沟通中错误传递的研究
摘要
在每一项管理工作中相互沟通都是必不可少的。沟通是人与人之间特有的联系方式，是维持人与人之间关系的基础，是一种重要的管理方式。
本文在总结已有的沟通涵义、沟通方法、沟通过程、沟通目的、沟通作用、沟通渠道和沟通网络的基础上，提出沟通的本质是信息的传递，沟通的过程就是信息的传递过程。把这个传递过程抽象为一个系统，它包括3个基本要素：信息源、传递渠道和信息接收者。这个系统不断地受到外界的干扰以及信息源与信息接收者之间的知识程度不同，理解的不同，情感支配等的不同，以及传递渠道本身缺陷等使得这个系统经常不能有效运行，即沟通不能有效地进行。为了解决这个问题，文章引入了消错理论、信息论、系统论。从组织中错误信息传递的角度来分析错误传递过程中，错误源发出的错误信息在传递过程中的失真问题，从而揭示组织中沟通失败的原因。其研究过程如下：
首先把错误源抽象为一个错误源空间，这个错误源空间有两个符号组成，一个是错误源，发出n个信号；另一个是出现这n个信号的概率。然后通过这些概率求出一个错误源所能带来的平均错误(信息)量。在此基础上，得出了扩展错误源定理、错误量，联合错误量、错误量的极限的计算公式。并分析了一种典型错误源——马尔可夫错误源。
在对错误源的研究基础上，再分析错误的传递过程。它反映了如何定量地估计错误接收者对收到的信息中获取这个信息含有错误量的多少问题。文章首先把传递过程抽象为一个简单的渠道模型，在这个传递渠道上求出错误传递过程中的错误平均交互量、错误平均条件交互量及传递渠道的疑义度，其次研究了四种传递渠道容量及其计算方法。最后分析了错误源与传递渠道的匹配问题，并得到有关错误平均交互的6个定理。
Communication is absolutely necessarily to every management work. It is a special contact way among the individuals, and is an amplifier. That is to say, it is an important management way.
This paper put forward that the essence of communication is the pass of information, and the process of communication is the pass of communication on based of the conclusion that is already exist about meaning, way, process, purpose, function, channel and net of communication. So the pass process is abstracted a system, which involves three basic elements: namely information resources, pass channel, and the receiver of information. This system cannot efficiency run because it is endlessly impacted by outside factor and fault of pass channel. The paper inducts the theory of error eliminating, information theory, system theory to solve this problem. The distortions of error in the pass process of information are analyzed from the point of view of the error information pass in the organization, then to reveal the cause of communication failing in the organization, and table a proposal.
First, the error recourses is attracted a error recourse room, which consist of two signs, one is the error recourse that give out n sign, the other is the probability that the sign present, and then the average error information quantity that bring out by error recourse can be gotten by seeking the probability. On based of it, the extend error recourse definition, error quantity, united error quantity, limited calculation formula of error quantity can be acquired, and a typical error recourse-Markov error recourse is analyzed.
After researching the error sources, we analyze the error transfer channels, which is the core content of the paper. It includes how much error receivers can receive. Then, the transferring channels are considered as one simply system model, from which average error mutual value, doubtful value, capacity of channels are researched. At last, six theorems are received.
本文在总结已有的沟通涵义、沟通方法、沟通过程、沟通目的、沟通作用、沟通渠道和沟通网络的基础上，提出沟通的本质是信息的传递，沟通的过程就是信息的传递过程。把这个传递过程抽象为一个系统，它包括3个基本要素：信息源、传递渠道和信息接收者。这个系统不断地受到外界的干扰以及信息源与信息接收者之间的知识程度不同，理解的不同，情感支配等的不同，以及传递渠道本身缺陷等使得这个系统经常不能有效运行，即沟通不能有效地进行。为了解决这个问题，文章引入了消错理论、信息论、系统论。从组织中错误信息传递的角度来分析错误传递过程中，错误源发出的错误信息在传递过程中的失真问题，从而揭示组织中沟通失败的原因。其研究过程如下：
首先把错误源抽象为一个错误源空间，这个错误源空间有两个符号组成，一个是错误源，发出n个信号；另一个是出现这n个信号的概率。然后通过这些概率求出一个错误源所能带来的平均错误(信息)量。在此基础上，得出了扩展错误源定理、错误量，联合错误量、错误量的极限的计算公式。并分析了一种典型错误源——马尔可夫错误源。
在对错误源的研究基础上，再分析错误的传递过程。它反映了如何定量地估计错误接收者对收到的信息中获取这个信息含有错误量的多少问题。文章首先把传递过程抽象为一个简单的渠道模型，在这个传递渠道上求出错误传递过程中的错误平均交互量、错误平均条件交互量及传递渠道的疑义度，其次研究了四种传递渠道容量及其计算方法。最后分析了错误源与传递渠道的匹配问题，并得到有关错误平均交互的6个定理。
Communication is absolutely necessarily to every management work. It is a special contact way among the individuals, and is an amplifier. That is to say, it is an important management way.
This paper put forward that the essence of communication is the pass of information, and the process of communication is the pass of communication on based of the conclusion that is already exist about meaning, way, process, purpose, function, channel and net of communication. So the pass process is abstracted a system, which involves three basic elements: namely information resources, pass channel, and the receiver of information. This system cannot efficiency run because it is endlessly impacted by outside factor and fault of pass channel. The paper inducts the theory of error eliminating, information theory, system theory to solve this problem. The distortions of error in the pass process of information are analyzed from the point of view of the error information pass in the organization, then to reveal the cause of communication failing in the organization, and table a proposal.
First, the error recourses is attracted a error recourse room, which consist of two signs, one is the error recourse that give out n sign, the other is the probability that the sign present, and then the average error information quantity that bring out by error recourse can be gotten by seeking the probability. On based of it, the extend error recourse definition, error quantity, united error quantity, limited calculation formula of error quantity can be acquired, and a typical error recourse-Markov error recourse is analyzed.
After researching the error sources, we analyze the error transfer channels, which is the core content of the paper. It includes how much error receivers can receive. Then, the transferring channels are considered as one simply system model, from which average error mutual value, doubtful value, capacity of channels are researched. At last, six theorems are received.
引文
1．陈春花．管理沟通．广州：华南理工大学出版社，2001．8
2．曹艳秋．管理制度的缺陷．哈尔滨：黑龙江科学技术出版社，2002．1 pp147-150
3．王积俭．管理学原理．广州：华南理工大学出版社，1995．1 pp7
4. Landauer, T. K.,. How much do people remember? Some estimates on the quantity of learned information in long-term memory,Cognitive Science,10 (4) Oct-Dec, 1999.pp 477-493.
5. Brynjolfsson, E., Hitt, L.M. and Yang, S., Intangible Assets: How the Interaction of Computers and Organizational Structure Affects Stock Market Valuations. In ICIS-98, 9th International Conference on Information Systems: ICIS, 1998 pp8-29.
6. Fulk, J.,Schmitz, J. and Steinfield, C.W.,. A Social Influence Model of Technology Use. In Organizations and Communication Technology: Newbury Park, Sage. 1990, ppl17-140.
7. R Ahlswede. Multi-way communication channels. In proc. 12nd int. sump. 2001, PP23-53
8. Wagner M. Reminder systems that learn from utility feedback. PhD Thesis, Intelligent Systems Program. Pittsburgh: University of Pittsburgh; 1996.pp27-29
9. Contractor, N.S. and Eisenberg, E.M., Communication Networks and New Media in Organizations. Organizations and Communication Technology: Newbury Park, Sage, 1990. pp143-172.
10. Daft, R.L., Lengel, R.H. and Trevino, L.K., Message Equivocality, Media Selection, and Manager Performance: Implications for Information Systems. MIS Quarterly, 11, September, 1997.pp355-366.
11. Daft, R.L. and Lengel, R.H.,. Organizational Information Requirements, Media Richness and Structural Design. Management Science, 31, 5, 1996, pp554-571.
12. John.M,. The Wansmission of information, http://www. cs.ucl.ac.uk/staff/S.Bhatfi/
13．郭开仲，张式强．消错学引论．广州：华南理工大学出版社，1995
14．郭开仲，张式强．错误集．长沙：中南理工大学出版社，2001
15．刘永清，郭开仲．复杂大系统的冲突和错误理论及应用．广州：华南理工大学出版社，2000
16. Liu Shiyong, Guo Kaizhong. Interaction among error-methods for error-elimination of decision making. Amse,2002 vol139
17. Liu Shiyong, Guo Kaizhong. Decision making under conditions of uncertainty-entropy change of fuzzy error system. Amse,2002 vol39
18. C E Shannon. A mathematical theory of communication. B.S.T.J.vol.27,July, and Oct. 1948
19. E C Van der Meulen. The discrete memoryless channel with two sends and one receiver. In pro. 12nd int. sump. Inform theory. Tsakadsor, 1999
20. H Liao Mutiple-access channels PhD Thesis, Depamnent of E.E univ. of Hawaii, Honolulu, 1992
21. E C Van der meulen recent coding theorems and conversers for multi-way channels part Ⅰ: the broadcasts channel. Department, springer-verlag, New York 1997
22. A El Gamal, T M Cover. Multiple user information theories. IEEE, vol.68, no 12 1990
23. T Berger. Multi-terminal source coding in the information theory approach to communication. Springer-verlag, New York 1997
24. K marton. General broadcast channels with degraded message sets, ieee trans. Infor. Theory, vol IT-23, NO1 1997
25. Slepaian. Coding of correlated information sources. IEEE Trans. Information theory, vol-49 pp471-480, July,1993
26. A Application of information theory to biological evolution. Journal of theoretical biology, 85, 789-806,1990
27. Information parameters of an eactDNA base sequence. Journal of theoretical biology, 86, 401-403, 1990
28. Cover Thomas. Elements of information theory. New York, wiley, 1991
29. Roman. Coding and information theory. New York: springer-verlag, 1992
30. Robert j. theory of information and coding: a mathematical framework of communication. Reading, mass.: Addison-wesley pub. Co. advanced book program,
1997
31. Wells Richard. Applied coding and information theory for engineers. Upper saddle river, n.j.: prentice hall, 1999
32. Jones duglas. Elementary information theory. Oxford: clarendon, 1997
33. Lubbe. Information theory. New york: Cambridge university press, 1997
34. Anderson. Source and channel coding: an algorithmic approach. Boston: kluwer acaemic publishers, 1991
35. Ash Robert. B. information theory. New york: dover publications, 1998
36. Kapur j n. entropy optimization principles with application. Boston: academic press, 1992
37. Verdu Sergio. Information theory: 50years of discovery, Piscataway. Nj: ieee pres, 2000
38. Usher m j. information theory for information technologists. London: Macmillan, 1994
39. Guiasu Silviu. Information theory with applications. New York: mcgraw-hill, 1977
40. Gallager R.G information theory and reliable communication. New York, willey, 1998
41. Li Ming, Vitanyi Paul. An introduction to kolmogorv complexity and its applications. New york: springer-verlag, 1993
42. Blahut r e. principles and practice of information theory. Reading, ma: Addison-wesly, 1987
43. Berger t. rate distortion theory. Englewood cliffs, n j: prentice-hall, 1987
44. Kullback. Information theory and statistics. New york, willey, 1995
45. Pawlak z. rough sets-theoretical aspects of reasoning about. Dordrecht: kluwer academic publishers, 1991
46. Ivy duntch. Uncertainty measures of rough set prediction. Artificial intelligence,2002.10
47. Bell. Rogh computational methods for information systems. Artificial intelligence,2002.9
48. Ahn,B S. the integrated methodology of rough set theory and artificial neural
network for business failure prediction. Expert systems with application, 2001.1
49. banerjee M. rough fuzzy set. Information science, 1999
50. beaibpief T, pal SK. Rough fuzzy, MLP: knowledge encoding and classification. IEEE Transactions on neural networks,2001.4
51．孟庆生．信息论．西安：西安交通大学出版社，1986
52．仇佩亮．信息论及其应用．杭州：浙江大学出版社，1991
53．贾世楼．信息论基础．哈尔滨：哈尔滨工业大学出版社，1986
54．傅祖芸．信息论—基础理论与应用．北京：电子工业出版社，2001．8
55. Hripsack G, Clayton PD, Jenders RA, Cimino JJ,Johnson SB. Design of a clinical event monitor. Comp Biomed Res. 1996;29:194-221.
56. .Kuperman GJ, Teich JM, Bates DW, et al. Detecting alerts, notifying the physician, and offering action items: a comprehensive alerting system. JAMIA Symposium Supplement. 1996:704-8.
57. Rind DM, Safran C, Phillips RS, et al. Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Archives of Internal Medicine. 1994;154:1511-7.
58. Hripcsak G, Cimino JJ, Johnson SB, Clayton PD.The Columbia-Presbyterian Medical Center decision-support system as a model for implementing the Arden Syntax. Proc 15thSCAMC. 1991:248-52.
59. Tate KE, Gardner RM, Scherting K. Nurses, pagers, and patient-specific criteria:three keys to improved critical value reporting. Proc 19th SCAMC. 1995:164-8.
60. Shabot M, LoBue M, Leyerle B, Dubin S. Inferencing strategies for automated alerts on critically abnormal laboratory and blood gas data. Proc 13th SCAMC. 1989:54-57.
61. Buntine W. A guide to the literature on learning probabilistic networks from data.IEEE Trans Knowl ata Eng. 1996;8:1-17.
62. Haddawy P, Jacobson J, Kahn CE, Jr. BANTER: a Bayesian network tutoring shell. Artificial Intelligence in Medicine. 1997;10:177-200.
63. Suermondt HJ, Cooper GF. An evaluation of explanations of probabilistic inference. Comp Biomedical Res 1993;26:242-54.
64. Bacos, J.Y. and Kemerer, C.E, 1992. Recent applications of economic theory in Information Technology Research, Decision Support Systems, 8, 365-386.
65. Bazerman,C., 1994. Systems of Genres and the Enactment of Social Intentions, in Freedman, A. and Medway, P.(ed.) Genre and the New Rhetoric London, Taylor & Francis, 79-101.
66. Berghout, E. and Renkema, T., 2001. Methodologies for IT investment evaluation: a review and assessment, in van Grembergen, W. Information Technology Evaluation Methods and Management, Hershey, PA: Idea Group Publishin, 78-97.
67. J.R. and Zmud, R.W., 1999. Channel Expansion Theory and the Experiential Nature of Media Richness Perceptions. Academy of Management Journal, 42, 2, 153-170.
68. Dans, E., 2001. IT Investment in Small and Medium Enterprises: Paradoxically Productive? Electronic Journal of Information Systems Evaluation. 4, 1.
69. Dennis, A.R. and Valachich, J.S., 1999. Rethinking Media Richness: Towards a Theory of Media Synchronicity. In HICSS-32, 32nd Annual Hawaii International Conference on System Sciences.
70. Los Alamitos CA, IEEE Computer Society. Francalanci, C. and Maggiolini, P., 2002. Measuring the Financial Benefits of IT Investments on Coordination, in Information Systems Evaluation Management, IRM Press, 54-74.
71. Katjalainen, A., P(?)iv(?)rinta, T., Tyrv(?)inen, P., and Rajala, J., 2000. Genre-Based Metadata for Enterprise Document Management, in 33rd Annual Hawaii International Conference on System Sciences: Los Alamitos CA, IEEE Computer Society.
72. Lesk, M., 1997. How much information is there in the world Technical report, lesk.com, available at http://www, lesk.com/mlesk/ksg97/ksg.html.
73. Lyman, P. Varian, H.R. Dunn, J. Strygin, A. and Swearingen K., 2000. How Much Information School of Information Management and Systems at University of California at Berkeley, http://www, sims.berkeley, edu
74. Markus, M.L., 1990. Toward a "Critical Mass" Theory of Interactive Media. In Organizations and Management.
2．曹艳秋．管理制度的缺陷．哈尔滨：黑龙江科学技术出版社，2002．1 pp147-150
3．王积俭．管理学原理．广州：华南理工大学出版社，1995．1 pp7
4. Landauer, T. K.,. How much do people remember? Some estimates on the quantity of learned information in long-term memory,Cognitive Science,10 (4) Oct-Dec, 1999.pp 477-493.
5. Brynjolfsson, E., Hitt, L.M. and Yang, S., Intangible Assets: How the Interaction of Computers and Organizational Structure Affects Stock Market Valuations. In ICIS-98, 9th International Conference on Information Systems: ICIS, 1998 pp8-29.
6. Fulk, J.,Schmitz, J. and Steinfield, C.W.,. A Social Influence Model of Technology Use. In Organizations and Communication Technology: Newbury Park, Sage. 1990, ppl17-140.
7. R Ahlswede. Multi-way communication channels. In proc. 12nd int. sump. 2001, PP23-53
8. Wagner M. Reminder systems that learn from utility feedback. PhD Thesis, Intelligent Systems Program. Pittsburgh: University of Pittsburgh; 1996.pp27-29
9. Contractor, N.S. and Eisenberg, E.M., Communication Networks and New Media in Organizations. Organizations and Communication Technology: Newbury Park, Sage, 1990. pp143-172.
10. Daft, R.L., Lengel, R.H. and Trevino, L.K., Message Equivocality, Media Selection, and Manager Performance: Implications for Information Systems. MIS Quarterly, 11, September, 1997.pp355-366.
11. Daft, R.L. and Lengel, R.H.,. Organizational Information Requirements, Media Richness and Structural Design. Management Science, 31, 5, 1996, pp554-571.
12. John.M,. The Wansmission of information, http://www. cs.ucl.ac.uk/staff/S.Bhatfi/
13．郭开仲，张式强．消错学引论．广州：华南理工大学出版社，1995
14．郭开仲，张式强．错误集．长沙：中南理工大学出版社，2001
15．刘永清，郭开仲．复杂大系统的冲突和错误理论及应用．广州：华南理工大学出版社，2000
16. Liu Shiyong, Guo Kaizhong. Interaction among error-methods for error-elimination of decision making. Amse,2002 vol139
17. Liu Shiyong, Guo Kaizhong. Decision making under conditions of uncertainty-entropy change of fuzzy error system. Amse,2002 vol39
18. C E Shannon. A mathematical theory of communication. B.S.T.J.vol.27,July, and Oct. 1948
19. E C Van der Meulen. The discrete memoryless channel with two sends and one receiver. In pro. 12nd int. sump. Inform theory. Tsakadsor, 1999
20. H Liao Mutiple-access channels PhD Thesis, Depamnent of E.E univ. of Hawaii, Honolulu, 1992
21. E C Van der meulen recent coding theorems and conversers for multi-way channels part Ⅰ: the broadcasts channel. Department, springer-verlag, New York 1997
22. A El Gamal, T M Cover. Multiple user information theories. IEEE, vol.68, no 12 1990
23. T Berger. Multi-terminal source coding in the information theory approach to communication. Springer-verlag, New York 1997
24. K marton. General broadcast channels with degraded message sets, ieee trans. Infor. Theory, vol IT-23, NO1 1997
25. Slepaian. Coding of correlated information sources. IEEE Trans. Information theory, vol-49 pp471-480, July,1993
26. A Application of information theory to biological evolution. Journal of theoretical biology, 85, 789-806,1990
27. Information parameters of an eactDNA base sequence. Journal of theoretical biology, 86, 401-403, 1990
28. Cover Thomas. Elements of information theory. New York, wiley, 1991
29. Roman. Coding and information theory. New York: springer-verlag, 1992
30. Robert j. theory of information and coding: a mathematical framework of communication. Reading, mass.: Addison-wesley pub. Co. advanced book program,
1997
31. Wells Richard. Applied coding and information theory for engineers. Upper saddle river, n.j.: prentice hall, 1999
32. Jones duglas. Elementary information theory. Oxford: clarendon, 1997
33. Lubbe. Information theory. New york: Cambridge university press, 1997
34. Anderson. Source and channel coding: an algorithmic approach. Boston: kluwer acaemic publishers, 1991
35. Ash Robert. B. information theory. New york: dover publications, 1998
36. Kapur j n. entropy optimization principles with application. Boston: academic press, 1992
37. Verdu Sergio. Information theory: 50years of discovery, Piscataway. Nj: ieee pres, 2000
38. Usher m j. information theory for information technologists. London: Macmillan, 1994
39. Guiasu Silviu. Information theory with applications. New York: mcgraw-hill, 1977
40. Gallager R.G information theory and reliable communication. New York, willey, 1998
41. Li Ming, Vitanyi Paul. An introduction to kolmogorv complexity and its applications. New york: springer-verlag, 1993
42. Blahut r e. principles and practice of information theory. Reading, ma: Addison-wesly, 1987
43. Berger t. rate distortion theory. Englewood cliffs, n j: prentice-hall, 1987
44. Kullback. Information theory and statistics. New york, willey, 1995
45. Pawlak z. rough sets-theoretical aspects of reasoning about. Dordrecht: kluwer academic publishers, 1991
46. Ivy duntch. Uncertainty measures of rough set prediction. Artificial intelligence,2002.10
47. Bell. Rogh computational methods for information systems. Artificial intelligence,2002.9
48. Ahn,B S. the integrated methodology of rough set theory and artificial neural
network for business failure prediction. Expert systems with application, 2001.1
49. banerjee M. rough fuzzy set. Information science, 1999
50. beaibpief T, pal SK. Rough fuzzy, MLP: knowledge encoding and classification. IEEE Transactions on neural networks,2001.4
51．孟庆生．信息论．西安：西安交通大学出版社，1986
52．仇佩亮．信息论及其应用．杭州：浙江大学出版社，1991
53．贾世楼．信息论基础．哈尔滨：哈尔滨工业大学出版社，1986
54．傅祖芸．信息论—基础理论与应用．北京：电子工业出版社，2001．8
55. Hripsack G, Clayton PD, Jenders RA, Cimino JJ,Johnson SB. Design of a clinical event monitor. Comp Biomed Res. 1996;29:194-221.
56. .Kuperman GJ, Teich JM, Bates DW, et al. Detecting alerts, notifying the physician, and offering action items: a comprehensive alerting system. JAMIA Symposium Supplement. 1996:704-8.
57. Rind DM, Safran C, Phillips RS, et al. Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Archives of Internal Medicine. 1994;154:1511-7.
58. Hripcsak G, Cimino JJ, Johnson SB, Clayton PD.The Columbia-Presbyterian Medical Center decision-support system as a model for implementing the Arden Syntax. Proc 15thSCAMC. 1991:248-52.
59. Tate KE, Gardner RM, Scherting K. Nurses, pagers, and patient-specific criteria:three keys to improved critical value reporting. Proc 19th SCAMC. 1995:164-8.
60. Shabot M, LoBue M, Leyerle B, Dubin S. Inferencing strategies for automated alerts on critically abnormal laboratory and blood gas data. Proc 13th SCAMC. 1989:54-57.
61. Buntine W. A guide to the literature on learning probabilistic networks from data.IEEE Trans Knowl ata Eng. 1996;8:1-17.
62. Haddawy P, Jacobson J, Kahn CE, Jr. BANTER: a Bayesian network tutoring shell. Artificial Intelligence in Medicine. 1997;10:177-200.
63. Suermondt HJ, Cooper GF. An evaluation of explanations of probabilistic inference. Comp Biomedical Res 1993;26:242-54.
64. Bacos, J.Y. and Kemerer, C.E, 1992. Recent applications of economic theory in Information Technology Research, Decision Support Systems, 8, 365-386.
65. Bazerman,C., 1994. Systems of Genres and the Enactment of Social Intentions, in Freedman, A. and Medway, P.(ed.) Genre and the New Rhetoric London, Taylor & Francis, 79-101.
66. Berghout, E. and Renkema, T., 2001. Methodologies for IT investment evaluation: a review and assessment, in van Grembergen, W. Information Technology Evaluation Methods and Management, Hershey, PA: Idea Group Publishin, 78-97.
67. J.R. and Zmud, R.W., 1999. Channel Expansion Theory and the Experiential Nature of Media Richness Perceptions. Academy of Management Journal, 42, 2, 153-170.
68. Dans, E., 2001. IT Investment in Small and Medium Enterprises: Paradoxically Productive? Electronic Journal of Information Systems Evaluation. 4, 1.
69. Dennis, A.R. and Valachich, J.S., 1999. Rethinking Media Richness: Towards a Theory of Media Synchronicity. In HICSS-32, 32nd Annual Hawaii International Conference on System Sciences.
70. Los Alamitos CA, IEEE Computer Society. Francalanci, C. and Maggiolini, P., 2002. Measuring the Financial Benefits of IT Investments on Coordination, in Information Systems Evaluation Management, IRM Press, 54-74.
71. Katjalainen, A., P(?)iv(?)rinta, T., Tyrv(?)inen, P., and Rajala, J., 2000. Genre-Based Metadata for Enterprise Document Management, in 33rd Annual Hawaii International Conference on System Sciences: Los Alamitos CA, IEEE Computer Society.
72. Lesk, M., 1997. How much information is there in the world Technical report, lesk.com, available at http://www, lesk.com/mlesk/ksg97/ksg.html.
73. Lyman, P. Varian, H.R. Dunn, J. Strygin, A. and Swearingen K., 2000. How Much Information School of Information Management and Systems at University of California at Berkeley, http://www, sims.berkeley, edu
74. Markus, M.L., 1990. Toward a "Critical Mass" Theory of Interactive Media. In Organizations and Management.