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Design for maintenance: how KMS document linking decisions affect maintenance effort and use

  • Research Article
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Journal of Information Technology

Abstract

Information system maintenance is an important aspect of information system development, especially in systems that provide dynamic content, such as Web-based systems and Knowledge Management Systems (KMS). Design for Maintenance (DFM) is an approach that argues that maintenance effort should be considered during the design of information systems in addition to the usual system design considerations. This research examines how the design of links among knowledge documents in a KMS affects both their maintenance and use. We argue that providing links among knowledge documents increases the cost of maintenance because when a document changes, the documents that link to and from that document are more likely to need changes. At the same, linking knowledge documents makes it easier to locate useful knowledge and thus increases use. We examine this tension between use and maintenance using 10 years of data from a well-established KMS. Our results indicate that as the number of links among documents increases, both maintenance effort and use for these documents increase. Our analyses suggest two DFM principles for dynamic content in practice. First, knowledge coupling (i.e., linking) to documents internal to the KMS rather than sources external to the KMS better balances maintenance effort and use. Second, designing small, knowledge cohesive documents (e.g., 250–350 words) leads to the best balance between maintenance effort and use.

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Notes

  1. We are not the first to apply coupling outside the software engineering literature as it has been successfully applied in other fields such as work flow design (Reijers and Vanderfeesten, 2004).

  2. The measurement error inherent in the data makes our model more conservative than it would be otherwise be. For example, we theorized that the number of In-Links increases use. If a document had few In-Links through most of its life until the last change that doubled this number, and we use this largest number in our statistical model, then this will weaken the likelihood that we will find a relationship between In-Links and use.

References

  • Abedin, B. and Sohrabi, B. (2009). Graph Theory Application and Web Page Ranking for Website Link Structure Improvement, Behaviour & Information Technology 28 (1): 63–72.

    Article  Google Scholar 

  • Adamov, R. and Richter, L. (1990). A Proposal for Measuring the Structural Complexity of Programs, Journal of Systems and Software 12 (1): 55–70.

    Article  Google Scholar 

  • Alavi, M., Kayworth, T. and Leidner, D. (2006). An Empirical Examination of the Influence of Organizational Culture on Knowledge Management Practices, Journal of Management Information Systems 22 (3): 191–224.

    Article  Google Scholar 

  • Alavi, M. and Leidner, D.E. (2001). Knowledge Management and Knoweldge Management Systems: Conceptual fonudations and research issues, MIS Quarterly 25 (1): 107–136.

    Article  Google Scholar 

  • Alavi, M. and Tiwana, A. (2002). Knowledge Integration in Virtual Teams: The potential role of KMS, Journal of the American Society for Information Science and Technology 53 (12): 1029–1037.

    Article  Google Scholar 

  • Albert, R., Jeong, H. and Barabasi, A.L. (1999). Internet – Diameter of the world-wide web, Nature 401 (6749): 130–131.

    Article  Google Scholar 

  • Almind, T.C. and Ingwersen, P. (1997). Informetric Analyses on the World Wide Web: Methodological approaches to ‘webometrics’, Journal of Documentation 53 (4): 404–426.

    Article  Google Scholar 

  • Amadieu, F., Tricot, A. and Mariné, C. (2009). Prior Knowledge in Learning from a Non-Linear Electronic Document: Disorientation and coherence of the reading sequences, Computers in Human Behavior 25 (2): 381–388.

    Article  Google Scholar 

  • Antony, S., Batra, D. and Santhanam, R. (2005). The use of a Knowledge-Based System in Conceptual Data Modeling, Decision Support Systems 41 (1): 176–188.

    Article  Google Scholar 

  • Barjak, F., Li, X. and Thelwall, M. (2007). Which Factors Explain the Web Impact of Scientists’ Personal Homepages? Journal of the American Society for Information Science and Technology 58 (2): 200–211.

    Article  Google Scholar 

  • Berard, E.V. (1993). Essays on Object-Oriented Software Engineering. Vol. 1, Upper Saddle River, NJ: Prentice-Hall, ISBN:0-13-288895-5.

    Google Scholar 

  • Bieman, J. and Ott, L. (1994). Measuring Functional Cohesion, IEEE Transactions on Software Engineering 20 (8): 644–657.

    Article  Google Scholar 

  • Binkley, A.B. and Schach, S.R. (2007). A classical view of object-oriented cohesion and coupling, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.5909.

  • Björneborn, L. and Ingwersen, P. (2004). Toward a Basic Framework for Webometrics, Journal of the American Society for Information Science and Technology 55 (14): 1216–1227.

    Article  Google Scholar 

  • Boehm, B.W., Brown, J.R. and Lipow, M. (1976). Quantitative evaluation of software quality, 2nd International Conference on Software Engineering, Los Alamitos, CA: IEEE Computer Society Press, pp. 592–605.

  • Boehm, B., Brown, J., Kaspar, H., Lipow, M., MacLeod, G. and Merrit, M. (1978). Characteristics of Software Quality, Amsterdam: North-Holland Pub. Co.

    Google Scholar 

  • Boling, E., Cai, W., Brown, J.P. and Bolte, J. (2000). Knowledge Base Development: The life cycle of an item in the Indiana university knowledge base, Technical Communication: Fourth Quarter 47 (4): 530–543.

    Google Scholar 

  • Burton-Jones, A. and Straub, D.W. (2006). Reconceptualizing System Usage: An approach and empirical test, Information Systems Research 17 (3): 228–246.

    Article  Google Scholar 

  • Chakrabarti, S., Dom, B.E., Kumar, S.R., Raghavan, P., Rajagopalan, S., Tomkins, A., Gibson, D. and Kleinberg, J. (1999). Mining the Web’s Link Structure, Computer 32 (8): 60–67.

    Article  Google Scholar 

  • Chidamber, S. and Kemerer, C. (1994). A Metrics Suite for Object Oriented Design, IEEE Transactions on Software Engineering 20 (6): 476–493.

    Article  Google Scholar 

  • Chowdhury, I. and Zulkernine, M. (2010). Can complexity, Coupling, And Cohesion Metrics Be Used As Early Indicators Of Vulnerabilities?, in Proceedings of the 2010 ACM Symposium on Applied Computing, Sierre, Switzerland: ACM, pp. 1963–1969.

  • Chung, L. and Prado Leite, J. (2009). On Non-Functional Requirements in Software Engineering, in A. Borgida, V. Chaudhri, P. Giorgini and E. Yu (eds.) Conceptual Modeling: Foundations and Applications, Berlin Heidelberg: Springer, pp. 363–379.

    Chapter  Google Scholar 

  • Croasdell, D.T. (2001). It’s Role in Organizational Memory and Learning, Information Systems Management 18 (1): 1–4.

    Article  Google Scholar 

  • Darcy, D.P., Kemerer, C.F., Slaughter, S.A. and Tomayko, J.E. (2005). The Structural Complexity of Software an Experimental Test, IEEE Transactions on Software Engineering 31 (11): 982–995.

    Article  Google Scholar 

  • Dekleva, S.M. (1992). The Influence of the Information Systems Development Approach on Maintenance, MIS Quarterly 16 (3): 355–372.

    Article  Google Scholar 

  • Dennis, A., Wixom, B.H. and Tegarden, D. (2009). System Analysis Design UML Version 2.0: An Object-Oriented Approach, Hoboken, NJ: John Wiley & Sons.

    Google Scholar 

  • Dennis, A.R. and Vessey, I. (2005). Three Knowledge Management Strategies: Knowledge hierarchies, knowledge markets, and knowledge communities, MIS Quarterly Executive 4 (4): 399–412.

    Google Scholar 

  • Desouza, K.C. and Awazu, Y. (2005). Maintaining Knowledge Management Systems: A strategic imperative, Journal of the American Society for Information Science and Technology 56 (7): 765–768.

    Article  Google Scholar 

  • Dobrica, L. and Niemelä, E. (2002). A Survey on Software Architecture Analysis Methods, IEEE Transactions on Software Engineering 28 (7): 638–653.

    Article  Google Scholar 

  • Dzidek, W.J., Arisholm, E. and Briand, L.C. (2008). A Realistic Empirical Evaluation of the Costs and Benefits of UML in Software Maintenance, IEEE Transactions on Software Engineering 34 (3): 407–432.

    Article  Google Scholar 

  • Eder, J., Kappel, G. and Schreft, M. (1994). Coupling and Cohesion in Object-Oriented Systems, Technical Report, Austria: University of Klagenfurt.

  • Edwards, C. (1984). Information Systems Maintenance: An integrated perspective, MIS Quarterly 8 (4): 237–256.

    Article  Google Scholar 

  • Evans, B. (2009). Global CIO: The top 10 CIO issues for 2010, in InformationWeek, Manhasset, NY: InformationWeek Business Technology Network, http://www.informationweek.com/security/risk-management/global-cio-the-top-10-cio-issues-for-2010/d/d-id/1085701.

    Google Scholar 

  • Foltz, P.W. (1996). Comprehension, Coherence and Strategies in Hpyertext and Linear Text, in J.-F. Rouet, J.J. Levonen, A.P. Dillon and R.J. Spiro (eds.) Hypertext and Cognition, Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Goffin, K. (2000). Design for Supportability: Essential component of new product development, Research-Technology Management 43 (2): 40–47.

    Google Scholar 

  • Grover, V. and Davenport, T. (2001). General Perspectives on Knowledge Management: Fostering a research agenda, Journal of Management Information Systems 18 (1): 5–21.

    Google Scholar 

  • Gui, G. and Scott, P.D. (2008). New Coupling and Cohesion Metrics for Evaluation of Software Component Reusability, The 9th International Conference for Young Computer Scientists, 2008. ICYCS 2008, 18–21 November, 1181–1186.

  • Gui, G. and Scott, P.D. (2009). Measuring Software Component Reusability by Coupling and Cohesion Metrics, Journal of Computers 4 (9): 797–805.

    Article  Google Scholar 

  • Hansen, M.T., Nohria, N. and Tierney, T. (1999). What’s your Strategy for Managing Knoweldge, Harvard Business Review (March-April): 106–116.

  • Hitz, M. and Montazeri, B. (1995). Measuring coupling and cohesion in object-oriented systems, In Proceedings of the International Symposium on Applied Corporate Computing, 75–76.

  • Ingwersen, P. (1998). The Calculation of Web Impact Factors, Journal of Documentation 54 (2): 236–243.

    Article  Google Scholar 

  • Jarzabek, S. (1993). Domain Model-Driven Software Reengineering and Maintenance, Journal of Systems and Software 20 (1): 37–51.

    Article  Google Scholar 

  • Jennex, M.E., Smolnik, S. and Croasdell, D. (2008). Towards Measuring Knowledge Management Success, Proceedings of the 41st Annual Hawaii International Conference on System Sciences, 7–10 January, Waikoloa, Big Island: Hawaii, 360.

  • Jennex, M.E., Smolnik, S. and Croasdell, D. (2012). Where to Look for Knowledge Management Success, 45th Hawaii International Conference on System Sciences (HICSS), 4–7 January, Maui, Hawaii, pp. 3969–3978.

  • Juristo, N., Moreno, A. and Sanchez-Segura, M.-I. (2007). Guidelines for Eliciting Usability Functionalities, IEEE Transactions on Software Engineering 33 (11): 744–758.

    Article  Google Scholar 

  • Kao, H.Y., Lin, S.H., Ho, J.M. and Chen, M.S. (2004). Mining Web Informative Structures and Contents Based on Entropy Analysis, IEEE Transactions on Knowledge and Data Engineering 16 (1): 41–55.

    Article  Google Scholar 

  • Kaushik, A. (2010). Web Analytics 2.0, Indianapolis, Indiana: Wiley Publishing.

    Google Scholar 

  • Kearney, J., Sedlmeyer, R., Thompson, W., Gray, M. and Adler, M. (1986). Software Complexity Measurement, Communications of the ACM 29 (11): 1050.

    Article  Google Scholar 

  • Kemerer, C. and Slaughter, S. (1999). An Empirical Approach to Studying Software Evolution, IEEE Transactions on Software Engineering 25 (4): 493–509.

    Article  Google Scholar 

  • Kleinberg, J.M. Authoritative Sources in a Hyperlinked Environment (1999). Journal of the ACM 46 (5): 604–632.

    Article  Google Scholar 

  • Ko, D.G. and Dennis, A.R. (2011). Profiting from Knowledge Management: The impact of time and experience, Information Systems Research 22 (1): 134–152.

    Article  Google Scholar 

  • Krebs, V. (2000). Working in the Connected World Book Network, International Association for Human Resource Information Management Journal 4 (1): 87–90.

    Google Scholar 

  • Lee, A.A. and Hubona, G.S. (2009). A Scientifc Basis for Rigor in Information Systems Research, MIS Quarterly 33 (2): 237–262.

    Google Scholar 

  • Mannaert, H., Verelst, J. and Ven, K. (2012). Towards Evolvable Software Architectures Based on Systems Theoretic Stability, Software: Practice and Experience 42 (1): 89–116.

    Google Scholar 

  • Marchionini, G. (2006). Exploratory Search: From finding to understanding, Communications of the ACM 49 (4): 41–46.

    Article  Google Scholar 

  • Marchionini, G. and Schneiderman, B. (1988). Finding Facts vs. Browsing knowledge in Hypertext Systems, IEEE Computer 21 (1): 70–80.

    Article  Google Scholar 

  • Markus, M.L. (2001). Toward a Theory of Knoweldge Reuse: Types of knoweldge reuse situations and factors in reuse success, Journal of Management Information Systems 18 (1): 57–93.

    Google Scholar 

  • McCullagh, P. and Nelder, J.A. (1989). Generalized Linear Models, 2nd edn London: Chapman and Hall.

    Book  Google Scholar 

  • McInerney, C. (2002). Knowledge Management and the Dynamic Nature of Knowledge, Journal of the American Society for Information Science and Technology 53 (12): 1009–1018.

    Article  Google Scholar 

  • Moreton, R. (1990). A process Model for Software Maintenance, Journal of Information Technology. (Routledge, Ltd.) 5 (2): 100.

    Article  Google Scholar 

  • Nosek, J. and Palvia, P. (2006). Software Maintenance Management: Changes in the last decade, Journal of Software Maintenance: Research and Practice 2 (3): 157–174.

    Article  Google Scholar 

  • Osborne, W.M. (1985). Executive Guide to Software Maintenance, Special Publication 500130, National Bureau of Standards.

  • Palmer, J.W., Bailey, J.P. and Faraj, S. (2000). The Role of Intermediaries in the Development of Trust on the WWW: The use and prominence of trusted third parties and privacy statements, Journal of Computer‐Mediated Communication 5 (3).

  • Papazoglou, M.P. and van den Heuvel, W.J (2007). Business Process Development Life Cycle Methodology, Communications of the ACM 50 (10): 79–85.

    Article  Google Scholar 

  • Park, H.W. and Thelwall, M. (2003). Hyperlink Analyses of the World Wide Web: A review, Journal of Computer‐Mediated Communication 8 (4).

  • Parnas, D. (1972). A Technique for Software Module Specification with Examples, Communications of the ACM 15 (5): 330–336.

    Article  Google Scholar 

  • Pirolli, P., Pitkow, J. and Rao, R. (1996). Silk from a Sow’s Ear: Extracting usable structures from the Web, Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems, Vancouver, British Columbia, Canada: Common ground, ACM.

  • Pfeffer, J. and Sutton, R.I. (2000). The Knowing-Doing Gap, Boston: Harvard Business School Press.

    Google Scholar 

  • Pressman, R. (2005). Software Engineering: A Practitioner’s Approach, 6th edn, Boston, MA: McGraw-Hill.

    Google Scholar 

  • Purao, S. and Vaishnavi, V. (2003). Product Metrics for Object-Oriented Systems, ACM Computing Surveys (CSUR) 35 (2): 221.

    Article  Google Scholar 

  • Reijers, H. and Vanderfeesten, I.P. (2004). Cohesion and Coupling Metrics for Workflow Process Design, in J. Desel, B. Pernici and M. Weske (eds.) Business Process Management, Berlin Heidelberg: Springer, Vol. 3080, pp. 290–305.

    Chapter  Google Scholar 

  • Sarkar, S., Rama, G.M. and Kak, A.C. (2007). API-Based and Information-Theoretic Metrics for Measuring the Quality of Software Modularization, IEEE Transactions on Software Engineering 33 (1): 14–32.

    Article  Google Scholar 

  • Sartipi, K. and Kontogiannis, K. (2003). A User-Assisted Approach to Component Clustering, Journal of Software Maintenance and Evolution: Research and Practice 15 (4): 265–295.

    Article  Google Scholar 

  • Schach, S., Jin, B., Yu, L., Heller, G. and Offutt, J. (2003). Determining the Distribution of Maintenance Categories: Survey versus measurement, Empirical Software Engineering 8 (4): 351–365.

    Article  Google Scholar 

  • Shaft, T. (1995). Helping Programmers Understand Computer Programs: The use of metacognition, ACM SIGMIS Database 26 (4): 25–46.

    Article  Google Scholar 

  • Shapiro, A., Niederhauser, D. and Jonassen, D.H. (eds.) (2004). Learning From Hypertext: Research issues and findings, Handbook of Research on Educational Communications and Technology. 2nd edn., Mahwah, NJ: Lawrence Erlbaum Associates Publishers, pp. 605–620.

  • Shmueli, G. and Koppius, O.R. (2011). Predictive Analytics in Information Systems Research, MIS Quarterly 35 (3): 553–572.

    Google Scholar 

  • Simon, H.A. (1962). The Architecture of Complexity, Proceedings of the American Philosophical Society American Philosophical Society, Vol. 106, 467–482.

  • Stevens, W.P., Myers, G.J. and Constantine, L.L. (1974). Structured Design, IBM Systems Journal 13 (2): 115–139.

    Article  Google Scholar 

  • Swanson, E.B. (1999). IS ‘Maintainability’: Should it reduce the maintenance effort? SIGMIS Database 30 (1): 65–76.

    Article  Google Scholar 

  • Tang, R. and Thelwall, M. (2008). A Hyperlink Analysis of US Public and Academic Libraries’ Web Sites, The Library Quarterly 78 (4): 419–435.

    Article  Google Scholar 

  • Taylor, M., Moynihan, E. and Wood-Harper, T. (1997). Knowledge for Software Maintenance, Journal of Information Technology. (Routledge, Ltd.) 12 (2): 155–166.

    Article  Google Scholar 

  • Teece, D.J. (1998). Capturing Value from Knowledge Assets: The new economy, markets for know-how, and intangible assets, California Management Review 40 (3): 55–79.

    Article  Google Scholar 

  • Thelwall, M. (2003). Web Use and Peer Interconnectivity Metrics for Academic Web Sites, Journal of Information Science 29 (1): 1–10.

    Article  Google Scholar 

  • Thelwall, M., Vaughan, L. and Bjorneborn, L. (2005). Webometrics, Annual Review of Information Science and Technology 39 (1): 81–135.

    Article  Google Scholar 

  • Teresko, J. (1994). Service Now a Design Element, Industry Week 243 (3): 51.

    Google Scholar 

  • Újházi, B., Ferenc, R., Poshyvanyk, D. and Gyimóthy, T. (2010). New conceptual coupling and cohesion metrics for object-oriented systems, Source Code Analysis and Manipulation (SCAM), 10th IEEE Working Conference on IEEE, Timisoara, Romania, 33–42.

  • van Vliet, H. (2008). Software Engineering: Principles and Practice, 3rd edn, Hoboken, NJ: John Wiley & Sons.

    Google Scholar 

  • Xue, G.-R., Yang, Q., Zeng, H.-J., Yu, Y. and Chen, Z. (2005). Exploiting the Hierarchical Structure for Link Analysis, Paper presented at the Proceedings of the 28th Annual International ACM SIGIR Conference on Research and development in Information Retrieval, Salvador, Brazil.

  • Yu, B.-M. and Roh, S.-Z. (2002). The Effects of Menu Design on Information-Seeking Performance and User’s Attitude on the World Wide Web, Journal of the American Society for Information Science and Technology 53 (11): 923–933.

    Article  Google Scholar 

  • Zack, M., McKeen, J. and Singh, S. (2009). Knowledge Management and Organizational Performance: An exploratory survey, Journal of Knowledge Management 13 (6): 392–409.

    Article  Google Scholar 

  • Zyngier, S. and Burstein, F. (2012). Knowledge Management Governance: The road to continuous benefits realization, Journal of Information Technology 27: 140–155.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Iris Vessey for her assistance with this research. We would also like to thank the reviewers and Senior Editor for helpful comments on previous drafts.

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Dennis, A., Samuel, B. & McNamara, K. Design for maintenance: how KMS document linking decisions affect maintenance effort and use. J Inf Technol 29, 312–326 (2014). https://doi.org/10.1057/jit.2014.5

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