Personal before businessin requirements prior-IT-ization

Johan F. HoornVrije Universiteit

Computer ScienceInformation Management and Software Engineering

jfhoorn@few.vu.nl

Contents

Status Problem Analysis Model Predictions Method Results Conclusions Discussion

Johan F. Hoorn, 2005

M M I 9 9 0 0 9

Status

Sept. 1, 2001 – Aug. 31, 2005 Supervisors: Gerrit van der Veer Hans van Vliet Four international publications Industries:

Human-Machine Interaction

Johan F. Hoorn, 2005

Problem Requirements change

Business model 1

Traditional office

Mainframe withthin clients

Business model 2

Flexible workplace

Laptops withbluetooth

A change request during development isextremely costly and frustrating

Johan F. Hoorn, 2005

Nobody knows howchanges in

requirementspriorities

can be predicted

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

Model Change of Stakeholder Requirements (CoStaR) (Hoorn & Van der Veer, 2003a; 2003b)

One of the hypotheses:

Johan F. Hoorn, 2005

Goals Relevance Requirements

Stakeholder evaluation:Is a system feature important to my goals?Is a system feature trivial to my goals?(after Frijda, 1986)

Predictions If business or personal goals change, requirements prioritization – as an expression of relevance assessment – will change accordingly

BM1: Traditional office BM2: Flexible workplace1 Mainframe 1 Laptops2 Thin clients 2 Bluetooth3 Laptops 3 Mainframe4 Bluetooth 4 Thin clients

Priority change

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

Condition 1: From business egotistic…

Students putrank numbers1 up to 16

Differentrandomizationbetween andwithin students

…to business altruistic

For business altruistic toegotistic, theMotivation order wasreversed

Students, again,put rank numbers1 up to 16

Uit eerder onderzoek onder studenten van de Vrije Universiteitis gebleken dat zij zelf meer willen kunnen profiteren van dekenniseconomie. Daartoe, vinden zij, moeten studentenhooggekwalificeerd de markt op kunnen. Om straks een goedbetaalde baan te krijgen moeten overheidssubsidies ge-investeerd worden in hoogstaande technologie en leermiddelendie de individuele student ondersteunen. Uit de alternatieven kozen de studenten de nieuwe digitale leeromgeving The Didactor® als de meest geschikte kandidaat.

Likewise for personal egotistic…

Uit eerder onderzoek onder studenten van de Vrije Universiteitis ook gebleken dat zij zich maatschappelijk verantwoordelijkvoelen voor de ontwikkeling van de kenniseconomie. Destudenten stellen dat de investeringen in hooggekwalificeerdeafgestudeerden de maatschappij ook weer ten goede moetkomen. Daartoe zijn hoogstaande technologie en leermiddelennoodzakelijk waarmee studenten elkaar kunnen ondersteunen.Uit de alternatieven kozen de studenten de nieuwe digitaleleeromgeving The Didactor® als de meest geschikte kandidaat.

…to personal altruistic and v.v.

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 (altru to ego) S (ego to altru)

(Business) (Personal)

161423791110815

159513611248145

167119310614158

237912116413146

S (ego to altru)

Johan F. Hoorn, 2005

6 ΣD2

S = 1 – N(N2 – 1)

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 higherthe disagreement between the two sets of ranked features

(= priority change) Johan F. Hoorn, 2005

Original hypothesis:

Johan F. Hoorn, 2005

Goals Relevance Requirements

Only S3 (feature to feature rank-order total-scores),* usingdata of all students who filled in the first list (N= 154),rendered significant results

*(see paper or last slides)

Results (1)

Main effect (Business vs. Personal):F(1,146)= 4.09, p< .05, ηp

2= .03

The onlysignificant difference

Mean S3 = .60(priority changeis low)

Mean S3 = .48(priority changeis high)

Johan F. Hoorn, 2005

Results (2)

RE should be oriented to personal goals Only changes in personal goals had an 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

Conclusions

Effects were not too strong (ηp2= .03).

Replication in a business case is urgent

Johan F. Hoorn, 2005

Discussion

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., Businessegotistic (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

Subsequently, the actual rank-order total score of a featurewas replaced by the rank order number of their relative position in this general priority list. The feature with thelowest rank-order total score (= 91) received a 1 and thefeature 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

Appendix (2)