Introducing OU MIS Faculty Research

1. Introducing OU MIS Faculty Research

2. MIS Research Approaches Computer Science Organization Sciences Engineering Mathematical Modeling Economic Modeling

3. OU’s Research Approach Computer Science Organization Sciences Engineering Mathematical Modeling Economic Modeling

4. Topics for Today • Managing complex projects: Lessons from the field • Blending learning with e-learning: How to have your cake and eat it too • Data warehousing: Developing an effective user culture • Governing outsourcing relationships for alternate outcomes • IT management in the public and private sectors • Determinants of budgeted IT expenditures

6. Managing Complex Projects:Lessons from the Field Pamela E. Carter [pcarter@ou.edu 405-325-3213]

7. Project Complexity high low

8. The Sustainable Waters Project • Goal: Technologies to Develop and Allocate Sustainable Groundwater Resources in South Africa • Countries: US & RSA • Organizational Membership: Universities, Government, Quasi-Government, Consultant, Non-Profit • Disciplines: Hydrogeology, Petroleum Engineering, Mechanical Engineering, Environmental Sociology, Education, Media Communications, Information Management

9. Major Problems Encountered • Communication • Speaking Different Languages • Being “In the Dark” • Leadership • Coordination • Trust • Complexity Management • Decomplexification • Being Stuck

10. Problem Resolutions • Communication • Management of Interpretive Communications • Emphasis on “Speaking Out” • Leadership • Meta-Leadership: Seeing and Conveying a Meta-View of the Project • Complexity Management • Knowledge Transfer • Broad Learning

11. Current Research Issues • Communication • How to increase interpretive communication skills at individual and project levels • Leadership • Who should be charged with meta-leadership role • How to create buy-in from other project members • Complexity Management • Under what conditions advantageous to decomplexify…and when better to maintain complexity • How best to manage knowledge transfer and broad learning

13. Blending Learning with e-Learning: How to Have Your Cake and Eat it Too Timothy R. Hill, San Jose State University Laku Chidambaram, University of Oklahoma [laku@ou.edu, 405-325-8013]

14. Context of the Study • A Technology-based Learning Continuum: • Very interactive, face-to-face vs. no interaction, distributed • Expensive vs. efficient Traditional Classroom Environment e-Learning Computer- Aided Instruction

15. Relevance of the Study • Big picture: Organizations spend billions of dollars on educating and training their employees. Are there ways to leverage these investments? • Our objective: To examine whether e-learning can be integrated effectively with “traditional” learning so as to provide added value

16. The System: ExStreamIS • Digitized classroom lectures synchronized with slides • Fast download and startup due to streaming • Is easy to navigate, i.e., stop, repeat, skip ahead • Integrated e-mail facility within ExStreamIS • slide/session numbers embedded in subject line • Audio search for keywords • Completely web-enabled • Anytime/anywhere access

17. Study in a Nutshell Section 1 (Had to use ExStreamIS) Section 2 (Optional Use) Section 3 (No Use – Control Group) A total of 138 students participated (about 45 in each section) Used system over a four-week period We monitored various measures (usage, performance, attendance etc.)

18. Performance Differences system introduced

19. Optional Use: Performance Differences system introduced

20. Results in a Nutshell • System use helped reduce differences in performance across all three groups • Among optional users, system really helped those users who used the system compared to those who did not • Among users, “good” students did even better than before • “Poor” students showed no improvement • No differences in attendance levels across the various groups

21. What Does It All Mean? • Perhaps the most important part of classroom instruction (or training) is what the instructor imparts • Ironically that is the part that “disappears” instantly • Digitally archiving the contents of classroom training can provide a valuable resource to: • people who have been through the training (as a way to refresh themselves on the material) • people who have not been through the training (as a way to learn the material themselves) • Over time, blending e-learning with traditional learning • can help organizations get more value out of their investments in training • will, as a by product, lead to a valuable digital library of readily accessible content (that can be served over an organization’s intranet, for instance)

23. Data Warehousing:Developing an Effective User Culture Al Schwarzkopf [aschwarz@ou.edu, 405-325-5703] Terrie Shaft [tshaft@ou.ed, 405-325-2880] Traci Carte Bob Zmud

24. Research Question What enables an organization to obtain business value from a data warehouse?

25. Allegiance Healthcare • Effective data warehouse program • Wide-spread use • Appreciation of the business value of the data warehouse • Offered to support an in-depth interview research program

26. Research Progress • Senior management interviews (March 2002) • Mid-management user interviews (April 2002) • Phone interviews (May – September 2002) • Group interviews (October - November 2002)

27. Practices AH rolled out their data warehouse in 7 cubes using Business Objects (tm) for access. SAP was introduced at same time, without any independent reporting capability. • Data/information reporting structures ‘rationalized’ • The data warehouse was only path for accessing SAP data. • ‘Finance’ power users ‘seeded’ within every functional area and region • DW is now accepted as the everyday pathway for information access

28. Data Warehouse Use Patterns • Through existing Business Objects templates • Ad hoc • Template modification/creation • Downloaded into Excel, Access or SAS • Large queries • Supplemental data

29. Keys to Success • Data-driven culture • SAP & data warehouse rolled out simultaneously • “Power users” have extensive business knowledge • Data warehouse “prospecting” is not seen as different from reporting • Data warehouse knowledge distributed throughout the organization • Multiple levels of assistance

30. Limitations • Non-financial data is not in the data warehouse • Solutions that require other data (i.e., pricing, marketing, etc.) are difficult to roll-out to the organization • Only 12 months of data is generally available

31. Remaining Questions • Where do problems come from? • How are groups formed? • What is the perceived reward structure? • How are solutions rolled out?

32. OU Long term issue Publishable results Business Partner Publicity Internal insights Campus recognition Win-Win To Make it Work • Active senior management support • User access • Time for meaningful interaction

34. Governing Outsourcing Relationships forAlternate Outcomes Shaila Miranda, University of Oklahoma [shailamiranda@ou.edu, 405-325-5732] Bruce Kavan, University of North Florida

35. The Xerox-EDS Story “‘We believe that we are developing a contract that will guarantee us a competitive price throughout the period. We are going to build into the contract productivity guarantees [and] price performance guarantees…’” “‘We need people on the EDS side looking at opportunities for consolidating existing applications and beginning to come up with some ideas for the retirement strategy that will complement the new development strategy” “The very existence of ‘price’ based control clauses within the contract ensured that price controls would be operative, [which created a] disconnect between the contract and the need for cooperative controls”

36. Contracts govern Client/provider behaviors Outcomes Too tight a contract prevents psychological synergy (However, too loose a contract provides inadequate communication for supporting the development of a shared understanding) Failure to anticipate needs can Prevent desirable behaviors/outcomes and Promote undesirable behaviors/outcomes The Moral of the Story

37. Desired Outcomes • Asset Protection • Focus on preserving existing competencies and internal resources • e.g., cost reduction, predictability, preventing knowledge leakage • Asset Development • Focus on leveraging co-competencies to develop new resources • e.g., learning, access to new tangible resources

38. Moments of Governance • Moment 0: Adoption decision • Make versus buy decision • Moment 1: Promissory Contract • Legal specification of quid pro quo • Moment 2: Psychological Contract • Shared understandings about what constitutes acceptable behaviors

39. Governance Remedies(What do you do when things fall apart?) • Remedies may be located in the • Legal-system: Sue • Authority-system: Negotiate • Social-system: Cooperate

40. Governance Alternatives X = Viable remedy at specific governance moment

41. Knowledge Gaps • What kind of relationship is appropriate? • What are clients’ governance options at each moment? • How do governance choices at each moment constrain and enable? • future governance choices • outcomes

42. Strategies in Structuring IS Outsourcing Relationships COMMODITY PARTNERSHIP Legal Authority PROMISSORY CONTRACT Authority Social PSYCHOLOGICAL CONTRACT Asset Protection Asset Development OUTCOME

43. Where Do We Go From Here? • What are the contingency rules for each strategy? • To what extent are each of these strategies effective? • When is it viable to switch from one strategy to another? • How can both strategies be pursued within a single relationship at the same time?

45. IT Management in the Public and Private Sectors We have a political economy based in large part on the assumption that it makes a difference whether activities are controlled by government authority or economic markets. Yet much of the management literature has treated this distinction as irrelevant or harmful Traci Carte [tcarte@ou.edu, 405-325-0741]

46. How Does ‘Publicness’ Impact IT Implementation? • Publicness can be defined as an organizational characteristic that reflects the extent to which the organization is influenced by political authority • Generally believed that public organizations exhibit differences in the form of more rules and regulations and more centralized decision making

47. Publicness Examples

48. The Model Publicness Organizational Context IT Implementation Context Champion Performance Formalization Resource Adequacy IT Implementation Success Centralization of Decision Making Data Quality Functional Area Participation

49. Results Organizational Context IT Implementation Context Formalization Champion Performance Resource Adequacy IT Implementation Success Data Quality Centralization of Decision Making Functional Area Part. No impact of publicness Publicness matters

50. What Does This Mean? • The good news… • Champion performance, resource adequacy, data quality, and participation are related to success regardless of “publicness”