407
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

Information Foraging—A Model for Exploration Breadth and Depth

, &

References

  • Abbass, H. A., Sastyy, K., & Goldberg, D. (2004). Oiling the wheels of change: The role of adaptive automatic problem decompositioning in nonstartionary environments (IlliGAL Report No. 2004029).
  • Abraham, A. (2006 September, 26), Tuning evolutionary algorithm performance using nature inspired heuristics. In Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing SYNASC, Timisoara, Romania.
  • Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. doi:10.2307/3250951
  • 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. doi:10.1287/isre.9.2.204
  • Andrews, B. W., Passino, K. M., & Waite, T. A. (2004). Foraging theory for decision-making system design: Task-type choice. CDC 43rd IEEE Conference on Decision and Control, 5, 4740–4745.
  • Avineri, E. (2005). Soft computing applications in traffic and transport systems: A review. In Soft computing: Methodologies and applications (pp. 17–25). Berlin: Springer.
  • Awad, N. F., Jones, J. L., & Zhang, J. (2006). Does search mater? Using online clickstream data to examine the relationship between online search and purchase behavior. In Proceedings of the Twenty-Seventh International Conference on Information Systems, pp. 1159–1174.
  • Bandura, A. (1997). Self-efficacy: toward a unifying theory of behavioral change. Psychology Review, 84(2), 191–215. doi:10.1037/0033-295X.84.2.191
  • Bigne-Alcaniz, E., Ruiz-Mafé, C., Aldás-Manzano, J., & Sanz-Blas, S. (2008). Influence of online shopping information dependency and innovativeness on internet shopping adoption. Online Information Review, 32(5), 648–667.
  • Bolt, M. A., Killough, L. N., & Koh, H. C. (2001). Testing the interaction effects of task complexity in computer training using the social cognitive model. Decision Sciences, 32(1), 1–20. doi:10.1111/deci.2001.32.issue-1
  • Branke, J. (2002). Evolutionary optimization in dynamic environments. Norwell, MA: Kluwer Academic Publishers.
  • Burton-Jones, A., & Straub Jr., D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228–246.
  • Castaneda, J. A., Frías, D. M., & Rodríguez, M. A. (2009). Antecedents of internet acceptance and use as an information source by tourists. Online Information Review, 33(3), 548–567.
  • Chaiyaratana, N., & Zalzala, A. M. S. (1997). Recent developments in evolutionary and genetic algorithms: Theory and applications. Genetic Algorithms In Engineering Systems: Innovations And Applications, GALESIA 97, Second International Conference On (Conf. Publ. No. 446), Glasgow, Scotland, pp. 270–277.
  • Chan, S. C., & Lu, M. (2004). understanding internet banking adoption and use behavior: A Hong Kong perspective. Journal of Global Information Management, 123, 21–44. doi:10.4018/jgim.2004070102
  • Charnov, E. L. (1976). Optimal foraging: The marginal value theorem. Theoretical Population Biology, 9(2), 129–136. doi:10.1016/0040-5809(76)90040-X
  • Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–341). Thousand Oaks, CA: Sage Publications.
  • Chow, I. H.-S., & Shan, S. L. (2007). Business strategy, organizational culture, and performance outcomes in China’s technology industry. Human Resource Planning, 30(2), 47.
  • Cobb, H., & Hoyer, C. (1985). Direct observation of search behavior. Psychology and Marketing, 2(3), 161–179. doi:10.1002/mar.4220020304
  • Coleman, H. L. (2009). The personality traits of instrumentality and expressiveness in relation to microcomputer playfulness. Austin, TX: The University of Texas.
  • Colley, A., & Maltby, J. (2008). Impact of the internet on our lives: Male and female personal perspectives. Computers in Human Behavior, 245, 2005–2013.
  • Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118–142. doi:10.1287/isre.6.2.118
  • Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145–158. doi:10.2307/249749
  • Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco, CA: Jossey-Bass.
  • Cuttler, C., & Graf, P. (2007). Personality predicts prospective memory task performance. An adult lifespan study. Scandinavian Journal of Psychology, 48(3), 215–231. doi:10.1111/j.1467-9450.2007.00570.x
  • Czaja, S. J., & Sharit, J. (1997 September 22). The influence of age and experience on the performance of a data entry task. In Proceedings of the Human Factors and Ergonomics Society 41st Annual Meeting, pp. 144–147, Albuquerque, NM.
  • Djamasbi, S., & Loiacono, E. T. (2008). Do men and women use feedback provided by their Decision Support Systems DSS differently? Decision Support Systems, 44, 854–869.
  • Foster, A. (2004). A nonlinear model of information seeking behavior. Journal of the American Society for Information Science and Technology, 55(3), 228–264. doi:10.1002/asi.10359
  • Freedman, D., Pisani, R., and Purves, R. 2007. Statistics (4th ed.). W.W. Norton & Co.
  • Gefen, D., & Straub, D. (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. doi:10.17705/1jais.00008
  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization and optimization and machine learning. Boston, MA: Addison-Wesley Longman.
  • Gonzalez-Benito, J. (2007). A theory of purchasing’s contribution to business performance. Journal of Operations Management, 25(4), 901–917. doi:10.1016/j.jom.2007.02.001
  • Hackbarth, G., Grover, V., & Mun, Y. Y. (2003). Computer playfulness and anxiety: Positive and negative mediators of the system experience effect on perceived ease of use. Information & Management, 40(3), 221–232.
  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. D. (1998). Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall.
  • Harman, H. H. (1967). Modern factor analysis. Chicago, IL: University of Chicago Press.
  • Harrison, A. W., & Rainer, R. K. (1992). The influence of individual differences on skill in end-user computing. Journal of Management Information Systems archive, 9, 93–111.
  • Hasan, B., & Ali, J. M. H. (2004). An empirical examination of a model of computer learning performance. The Journal of Computer Information Systems, 44(4), 27–34.
  • Hinz, O., & Eckert, J. (2010). The impact of search and recommendation systems on sales in electronic commerce. Business & Information Systems Engineering, 2(2), 67–77. doi:10.1007/s12599-010-0092-x
  • Ho, R. (2006). Handbook of univariate and multivariate data analysis and interpretation with SPSS. Boca Raton, FL: Chapman & Hall/CRC.
  • Huang, H. M. (2003). Design website attributes to induce experimental encounters. Computers in Human Behavior, 19(4), 425–442. doi:10.1016/S0747-5632(02)00080-8
  • Jansen, B. J., Booth, D., & Smith, B. (2009). Using the taxonomy of cognitive learning to model online searching. Information Processing & Management, 45(6), 643–663.
  • Jasperson, J. S., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS quarterly, 29(3), 525–557.
  • Kang, E. K., & Yoon, W. C. (2008). Age- and experience-related user behavior differences in the use of complicated electronic devices. International Journal of Human-Computer Studies, 66(6), 425–437. doi:10.1016/j.ijhcs.2007.12.003
  • Kao, G. Y. M., Lei, P. L., & Sun, C. T. (2008). Thinking style impacts on Web search strategies. Computers in Human Behavior, 24(4), 1330–1341. doi:10.1016/j.chb.2007.07.009
  • Karson, E. J., & Fisher, R. J. (2005). Predicting intentions to return to the Web site: Extending the dual mediation hypothesis. Journal of Interactive Marketing, 19(3), 2–14. doi:10.1002/dir.20040
  • Katila, R., & Ahuja, G. (2002). Something old, something new: A longitudinal study of search behavior and new product introduction. Academy of Management Journal, 45(6), 1183–1194. doi:10.2307/3069433
  • Kennedy, T., Wellman, B., & Klement, K. (2003). Gendering the digital divide. IT & Society, 1(5), 72–96.
  • Kim K. W., Chan, H. C., & Chan, Y. P. 2007. A balanced thinking–feelings model of information systems continuance. International Journal of Human-Computer Studies, 65, 511–525.
  • King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. doi:10.1016/j.im.2006.05.003
  • Kuo, F. Y., Chu, T. H., Hsu, M. H., & Hsieh, H. S. (2004). An investigation of effort-accuracy trade-off and the impact of self-efficacy on Web searching behaviors. Decision Support Systems, 37(3), 331. doi:10.1016/S0167-9236(03)00032-0
  • Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. In Critical issues in information systems research book contents (pp. 227–251). New York, NY: Source, Wiley Series in Information Systems archive.
  • Larsen, E., & Rainie, L. (2002). The rise of the e-citizen: How people use government agencies’ Websites. Washington, DC: Pew Internet and American Life Project.
  • Lee, J., Podlaseck, M., Schonberg, E., & Hoch, R. (2001). Visualization and analysis of click stream data of online stores for understanding web merchandising. Journal of Data Mining and Knowledge Discovery, 5(1–2), 59–84. doi:10.1023/A:1009843912662
  • Lee, M., Garrow, L. A., & Post, D. (2009, September 27). Airline passengers’ online search and purchase behaviors: New insights from an interactive pricing response model. In AGIFORS Annual Symposium, Georgia Institute of Technology, Atlanta, GA.
  • Lefkowitz, J. (1994). Sex-related differences in job attitudes and dispositional variables: Now you see them. Academy of Management Journal, 37, 323–349.
  • Li, L., & Buhalis, D. (2006). E-commerce in China: The case of travel. International Journal of Information Management, 26, 153–166.
  • Li, S., & Chatterjee, P. (2005, June 16-18). Reducing shopping cart abandonment at retail websites. In Proceedings of the Marketing Science Conference, Atlanta, GA.
  • Lian, J.-W., & Lin, T.-M. (2007). Effects of consumer characteristics on their acceptance of online shopping: Comparisons among different product types. Computers in Human Behavior, 24(1), 48–65. doi:10.1016/j.chb.2007.01.002
  • Liaw, S. S., & Huang, H. M. (2006). Information retrieval from the World Wide Web: A user-focused approach based on individual experience with search engines. Computers in Human Behavior, 22(3), 501–517. doi:10.1016/j.chb.2004.10.007
  • Losh, S. C. (2003). Gender and educational digital chasms in computer and Internet access and use over time. IT and Society, 1(4), 73–86.
  • Lu, J., Yaob, J. E., & Yua, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245–268. doi:10.1016/j.jsis.2005.07.003
  • MacArthu, R. H., & Pianka, E. R. (1966). On the optimal use of a patchy environment. American Naturalist 100, 603–609.
  • March, J. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. doi:10.1287/orsc.2.1.71
  • Marcoulides, G. A., & Saunders, C. (2006). Editor’s comments, PLS: A silver bullet? MIS Quarterly, 30, 2. doi:10.2307/25148727
  • Miller, S. (1973). Ends, means, and galumphing: Some leitmotifs of play. American Anthropologist, 75(1), 87–98. doi:10.1525/aa.1973.75.issue-1
  • Money, R. B., & Crotts, J. C. (2003). The effect of uncertainty avoidance on information search, planning, and purchases of international travel vacations. Tourism Management, 24(2), 191–202.
  • Munro, M. C., Huff, S. L., Marcolin, B. L., & Compeau, D. R. (1997). Understanding and measuring user competence. Information and Management, 33(1), 45–57. doi:10.1016/S0378-7206(97)00035-9
  • Murphy, C., Coover, D., & Owen, S. (1989). Development and validation of the computer self-efficacy scale. Educational and Psychological Measurement, 49(4), 893–899. doi:10.1177/001316448904900412
  • Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall.
  • Nielsen//NetRatings. (2002a). Number of female Web surfers grows faster than overall Internet population. Retrieved October 14, 2017, from www.nielsen-netratings.com
  • Nielsen//NetRatings. (2002b). Digital divide for women exists. Retrieved October 3, 2017, from www.nielsen-netratings.com
  • Passino, K. M. (2002). Biomimicry of bacterial foraging. IEEE Control System Magazine, 22(2), 52–67. doi:10.1109/MCS.2002.1004010
  • Pearson, J. M., Bahmanziari, T., Crosby, L., & Conrad, E. (2002). An empirical investigation into the relationship between organizational culture and computer efficacy as moderated by age and gender. The Journal of Computer Information Systems, 43(2), 58–70.
  • Pfeil, U., Arjan, R., & Zhaphiris, P. (2008). Age differences in online social networking – A study of user profiles. Computers in Human Behavior, 25(3), 643–654. doi:10.1016/j.chb.2008.08.015
  • Pirolli, P. (2003). Exploring and finding information. In J. M. Carroll (Ed.), HCI models, theories, and frameworks: Toward a multidisciplinary science. San Francisco, CA: Morgan Kaufmann.
  • Pirolli, P. (2009). Information FORAGING theory: Adaptive interaction with information. Boston, MA: Oxford University Press.
  • Pirolli, P., & Card, S. (1999). Information foraging. Psychological Review, 106(4), 643–675. doi:10.1037/0033-295X.106.4.643
  • Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59, 999–1007.
  • Pyke, G. H. (1984). Optimal foraging theory: A critical review. Annual Review of Ecology and Systematics, (15), 523–575. doi:10.1146/annurev.es.15.110184.002515
  • Pyke, G. H., Pulliam, H. R., & Charnov, E. L. (1977). Optimal foraging: A selective review of theory and tests. The Quarterly Review of Biology, (52), 137–154. doi:10.1086/409852
  • Scarborough Research. (2008). Understanding the digital savvy consumer. Retrieved March 8, 2010, from http://www.scarborough.com/freeStudies.php
  • Serenkoa, A., & Turelb, O. (2007). Are MIS research instruments stable? An exploratory reconsideration of the computer playfulness scale. Information & Management, 44(1), 657–665. doi:10.1016/j.im.2007.08.002
  • Shang, R. A., Chen, Y. C., & Shen, L. (2005). Extrinsic versus intrinsic motivations for consumers to shop on-line. Information and Management, 42(3), 401–413. doi:10.1016/j.im.2004.01.009
  • Shaw, L. H., & Gant, L. M. (2002). Users divided? Exploring the gender gap in internet use. Cyberpsychology & Behavior, 5(6), 517–527. doi:10.1089/109493102321018150
  • Sia, C. L., Lim, K. H., Leung, K., Lee, M. K. O., Huang, W. W., & Benbasat, I. (2009). Web strategies to promote Internet Shopping: Is cultural – Customization needed. MIS Quarterly, 33(3), 491–512. doi:10.2307/20650306
  • Starbuck, W. H., & Webster, J. (1991). When is play productive? Accounting, Management, and Information Technology, 1(1), 71–90. doi:10.1016/0959-8022(91)90013-5
  • Stevens, J. (1996). Applied multivariate statistics for the social sciences (3 ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Suh, E., Lim, S., Hwang, H., & Kim, S. (2004). A prediction model for the purchase probability of anonymous customers to support real time web marketing: A case study. Expert Systems with Applications, 27, 245–255.
  • Tang, W. J., Wu, Q. H., & Saunders, J. R. (2006 July 16-21). Bacterial foraging algorithm for dynamic environments. In Proceedings from the IEEE Congress Evolutionary Computation, Vancouver, BC, Canada, 1324–1330.
  • Thatcher, A. (2008). Web search strategies: The influence of Web experience and task type. Information Processing & Management, 44(3), 1308–1329. doi:10.1016/j.ipm.2007.09.004
  • Thatcher, J. B., Loughry, M. L., Lim, J., & McKnight, D. H. (2007). Internet anxiety: An empirical study of the effects of personality, beliefs, and social support. Information & Management, 44(4), 353–363.
  • Torkzadeh, G., Chang, J. C. J., & Demirhan, D. (2006). A contingency model of computer and Internet self-efficacy. Information and Management, 43, 541–550.
  • Torkzadeh, G., Plfughoeft, K., & Hall, L. (1999). Computer self-efficacy, training effectiveness and user attitudes: An empirical study. Behaviour & Information Technology, 18(4), 299–309. doi:10.1080/014492999119039
  • Torkzadeh, G., & Van Dyke, T. P. (2002). Effects of training on Internet self-efficacy and computer user attitudes. Computers in Human Behavior, 18(3), 479–494. doi:10.1016/S0747-5632(02)00010-9
  • Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behavior. European Journal of Operational Research, 166(2), 557–575. doi:10.1016/j.ejor.2004.04.022
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. doi:10.1287/mnsc.46.2.186.11926
  • Venkatesh, V., Morris, I. S., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83, 33–60.
  • 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. doi:10.2307/30036540
  • Webster, J., & Martochhio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly, 16(2), 201–226. doi:10.2307/249576
  • Wilson, T. D. (1997). Information Behaviour: An interdisciplinary perspective. Information Processing & Management, 33(4), 551–572. doi:10.1016/S0306-4573(97)00028-9
  • Yager, S. E., Kappelman, L. A., Maples, G. A., & Prybutok, V. R. (1997). Microcomputer playfulness: Stable or dynamic trait? ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 28(2), 43–52.
  • Yang, S., & Yao, X. (2005). Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9(11), 815–834. doi:10.1007/s00500-004-0422-3
  • Zaphiris, P., Kurniawan, S., & Ghiawadwala, M. (2007). A systematic approach to the development of research-based web design guidelines for older people. Universal Access in the Information Society Journal, 6(1), 59–76. doi:10.1007/s10209-006-0054-8
  • Zaphiris, P., & Sawar, R. (2006). Trends, similarities, and differences in the usage of teen and senior public online newsgroups. ACM Transactions on Computer-Human Interaction, 13(3), 403–422. doi:10.1145/1183456.1183461
  • Zhang, Y. (2005). Age, gender, and Internet attitudes among employees in the business world. Computers in Human Behavior, 21(1), 1–10. doi:10.1016/j.chb.2004.02.006

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.