The conceptual model can be used for further research into the topic of IO. Future
research can attempt to test the propositions put forth in this chapter. We argue that
information overload can partly be reduced by a change in a person’s personal
characteristics. Also, information overload can partly be reduced by presenting the
information provided by the information system in a clear and unambiguous way.
The insights from this study can be useful for information intensive organizations in
developing countermeasures for information overload and thereby increasing overall
health conditions with the organization (and thus increasing sustainability). These
organizations could also use the outcomes of this research to advise other firms about
dealing with information overload.
In this chapter, we focused on two determinants of an individual’s perceived
degree of information overload. We examined several cognitive and personality
traits of decision-makers and studied three concepts of information quality provided
by information systems. However, for both determinants other characteristics could
have an effect on the perceived IO. In future research these other characteristics
could be studied.
Recommendations go out to the IT departments of organizations. These departments can reduce the information overload significantly by improving the relevance,
accurateness and understandability of information. Information technology can be
applied to reduce the amount of irrelevant information and improve the accurateness
of information. Applying intelligent agents from the field of artificial intelligence
could make information systems ‘smarter’ which would reduce information
overload.
References
Bawden, D., & Robinson, L. (2008). The dark side of information: Overload, anxiety and other
paradoxes and pathologies. Journal of Information Science, 35, 180–191.
Conway, A. R. A., & Engle. (1996). Individual differences in working memory capacity: More
evidence for a general capacity theory. Memory, 4(6), 577–590.
DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use:
Adaptive structuration theory. Organization Science, 5(2), 121–147.
Eppler, M., & Mengis, J. (2004). The concept of information overload: A review of literature from
organization science, accounting, marketing, MIS, and related disciplines. Information Society,
20(5), 325–344.
Good, H. H. (1958). Greenhouses of science for management. Management Science, 4(4), 365–381.
Hall, A., & Walton, G. (2004). Information overload within the health care system: A literature
review. Health Information Libraries Journal, 21, 102–108.
Jang, K. L. (2001). Behavioural-genetic perspectives on personality function. Canadian Journal of
Psychiatry, 46(3), 234–244.
Just, M. A., & Carpenter, P. A. (1992). A capacity theory of comprehension: Individual differences
in working memory. Psychological Review, 99(1), 122–149.
Kim, K., Lustria, M. L. A., & Burke, D. (2007). Predictors of cancer information overload: Findings
from a national survey. Information Research, 12, 1–29.
Klapp, O. E. (1986). Overload and boredom: Essays on the quality of life in the information society.
Westport: Greenwood Publishing Group Inc.
9 Strategic and Managerial Decision-Making for Sustainable Management: Factors. . .
173
research can attempt to test the propositions put forth in this chapter. We argue that
information overload can partly be reduced by a change in a person’s personal
characteristics. Also, information overload can partly be reduced by presenting the
information provided by the information system in a clear and unambiguous way.
The insights from this study can be useful for information intensive organizations in
developing countermeasures for information overload and thereby increasing overall
health conditions with the organization (and thus increasing sustainability). These
organizations could also use the outcomes of this research to advise other firms about
dealing with information overload.
In this chapter, we focused on two determinants of an individual’s perceived
degree of information overload. We examined several cognitive and personality
traits of decision-makers and studied three concepts of information quality provided
by information systems. However, for both determinants other characteristics could
have an effect on the perceived IO. In future research these other characteristics
could be studied.
Recommendations go out to the IT departments of organizations. These departments can reduce the information overload significantly by improving the relevance,
accurateness and understandability of information. Information technology can be
applied to reduce the amount of irrelevant information and improve the accurateness
of information. Applying intelligent agents from the field of artificial intelligence
could make information systems ‘smarter’ which would reduce information
overload.
References
Bawden, D., & Robinson, L. (2008). The dark side of information: Overload, anxiety and other
paradoxes and pathologies. Journal of Information Science, 35, 180–191.
Conway, A. R. A., & Engle. (1996). Individual differences in working memory capacity: More
evidence for a general capacity theory. Memory, 4(6), 577–590.
DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use:
Adaptive structuration theory. Organization Science, 5(2), 121–147.
Eppler, M., & Mengis, J. (2004). The concept of information overload: A review of literature from
organization science, accounting, marketing, MIS, and related disciplines. Information Society,
20(5), 325–344.
Good, H. H. (1958). Greenhouses of science for management. Management Science, 4(4), 365–381.
Hall, A., & Walton, G. (2004). Information overload within the health care system: A literature
review. Health Information Libraries Journal, 21, 102–108.
Jang, K. L. (2001). Behavioural-genetic perspectives on personality function. Canadian Journal of
Psychiatry, 46(3), 234–244.
Just, M. A., & Carpenter, P. A. (1992). A capacity theory of comprehension: Individual differences
in working memory. Psychological Review, 99(1), 122–149.
Kim, K., Lustria, M. L. A., & Burke, D. (2007). Predictors of cancer information overload: Findings
from a national survey. Information Research, 12, 1–29.
Klapp, O. E. (1986). Overload and boredom: Essays on the quality of life in the information society.
Westport: Greenwood Publishing Group Inc.
9 Strategic and Managerial Decision-Making for Sustainable Management: Factors. . .
173