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P ersonal before business in requirements p rior -IT- ization

P ersonal before business in requirements p rior -IT- ization. Johan F. Hoorn & Mark E. Breuker Vrije Universiteit Computer Science Information Management and Software Engineering jfhoorn@few.vu.nl. Contents. Problem Analysis Predictions Method Results Conclusions Discussion.

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P ersonal before business in requirements p rior -IT- ization

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  1. Personal before business in requirements prior-IT-ization Johan F. Hoorn & Mark E. Breuker Vrije Universiteit Computer Science Information Management and Software Engineering jfhoorn@few.vu.nl

  2. Contents • Problem • Analysis • Predictions • Method • Results • Conclusions • Discussion M M I 9 9 0 0 9 Johan F. Hoorn, 2005

  3. Problem • How to anticipate requirements change, for example, in prioritization? Johan F. Hoorn, 2005

  4. Analysis • Where do change requests come from? Business model 1 Business model 2 • Change in business sub goals - Main goals: Profit - Sub goals: Cost-effectiveness, efficiency • How come business goals change? • Change in personal sub goals (strategic management) - Main goals: Earn my living - Sub goals: Fire employees (not me), improve IT to guarantee same output Johan F. Hoorn, 2005

  5. Predictions • If business or personal goals change, requirements prioritization will change accordingly BM1: Traditional office BM2: Flexible workplace 1 Mainframe 1 Laptops 2 Thin clients 2 Bluetooth 3 Laptops 3 Mainframe 4 Bluetooth 4 Thin clients Priority change

  6. Method (1) • Requirements rank-ordering test • System: Blackboard (BM1) vs. Didactor (BM2) • Internet survey (the e-learning hoax) • Stakeholders: Science students (N= 154) • Four conditions of goal change: 1 From business egotistic to business altruistic (n= 36) 2 From business altruistic to business egotistic (n= 39) • 3 From personal egotistic to personal altruistic (n= 43) • 4 From personal altruistic to personal egotistic (n= 36) Johan F. Hoorn, 2005

  7. Condition 1: From business egotistic… Different randomization between and within students Students put rank numbers 1 up to 16

  8. …to business altruistic For business altruistic to egotistic, the Motivation order was reversed Students, again, put rank numbers 1 up to 16

  9. Likewise for personal egotistic… Interviews with students of the Free University showed that students want to benefit more from the knowledge economy. They belief that students should enter the market while being top qualified. To get a well paid job, they think that governmental subsidies should be invested in advanced technology and learning materials that support the individual student. From several options, the students chose the new digital learning environment Didactor® as the best alternative.

  10. …to personal altruistic and v.v. Interviews with students of the Free University showed that students feel socially responsible for developing the knowledge economy. They belief that investments on top qualified masters should show some returns to society. They think that governmental subsidies should be invested in advanced technology and learning materials with which students can support one another. From several options, the students chose the new digital learning environment Didactor® as the best alternative.

  11. 6 ΣD2 S = 1 – N(N2 – 1) Method (2) • Calculating priority change Spearman’s rho (S) is a rank order correlation coefficient that analyzes whether a bivariate set of paired rankings correlates by rank sum S was calculated for each student in a condition S (altru to ego) S (ego to altru) S (ego to altru) S (altru to ego) (Business) (Personal) 16 14 2 3 7 9 11 10 8 1 5 15 9 5 13 6 1 12 4 8 14 5 1 6 7 11 9 3 10 6 14 15 8 2 3 7 9 12 1 16 4 13 14 6 Johan F. Hoorn, 2005

  12. Method (3) • Four measures of priority change S1  over data of those who filled in both lists (N= 103) S2  over data of the 10 features that best contributed to S= -1 (N= 92) S3  feature to feature rank-order total-scores,* using data of all those who filled in the first list (N= 154) • S4  feature to feature rank-order total-scores* over • data of the 10 features that best contributed • to S= -1 (N= 154) *(see paper or last slides) Rho moves between 1 and -1. The closer S approaches -1, the higher the disagreement between the two sets of ranked features (= priority change) Johan F. Hoorn, 2005

  13. Results (1) Only S3 (feature to feature rank-order total-scores),* using data of all students who filled in the first list (N= 154), rendered significant results *(see paper or last slides) Johan F. Hoorn, 2005

  14. Mean S3 = .60 (priority change is low) The only significant difference Mean S3 = .48 (priority change is high) Main effect (Business vs. Personal): F(1,146)= 4.09, p< .05, ηp2= .03 Results (2) Johan F. Hoorn, 2005

  15. Conclusions • RE should be oriented to personal goals • Changes in personal goals had the most impact on changes in requirements prioritization • This effect occurred irrespective of the type of goal change (from egotistic to altruistic or v.v.) • Business model change had less impact on changes in requirements prioritization Johan F. Hoorn, 2005

  16. Discussion • Effects were not too strong (ηp2= .03). Replication in a business case is urgent Johan F. Hoorn, 2005

  17. Appendix (1) Calculating S3 (feature to feature rank-order total-scores) Only the data of the first requirements list were used (N= 154) For each feature, the sum of rank-order scores was computed across all students in a condition (e.g., Business egotistic (Be) or Personal altruistic (Pa)) On the basis of the rank-order total score per feature (which were between 91 and 576), the 16 features were then rank-ordered from the lowest to the highest rank-order total score Johan F. Hoorn, 2005

  18. Appendix (2) Subsequently, the actual rank-order total score of a feature was replaced by the rank order number of their relative position in this general priority list. The feature with the lowest rank-order total score (= 91) received a 1 and the feature with the highest rank-order total score (= 576) received a 16 The feature to feature rank-order total-scores were established by calculating, for each student in a condition, s between Be (as based on the raw data) and the revised Ba (as based on the rank-order total scores) Ba (as based on the raw data) and the revised Be (as based on the rank-order total scores) Pe (as based on the raw data) and the revised Pa (as based on the rank-order total scores) Pa (as based on the raw data) and the revised Pe (as based on the rank-order total scores) Johan F. Hoorn, 2005

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