Modeling Effectiveness at a B-School student club

292 views

Published on

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
292
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Modeling Effectiveness at a B-School student club

  1. 1. A 15 year old socially inclined consultancy-cum-student organisation in a reputed B-School tries to achieve higher effectiveness in project execution Modeling Effectiveness @ INCA Business Dynamics MI/SM - 271 Harshit Krishna - 0143/48
  2. 2. Modeling Effectiveness @ INCA Purpose Faculty Advisor/Ch airperson To model how effectiveness / performance of the club changes in response to recruitment practices, number of projects and involvement of faculty members. In so doing recommendations to change these factors, expected results therefrom and optimal practices shall be recommended. INCA has been identified for the purpose of this study as a student run organisation at IIM Calcutta providing consultancy to socially active yet resource constrained NGOs in the vicinity of the Kolkata city. The club hierarchy and relationships are shown in the adjoining chart. Voluntary faculty advisors Student Organizing Committee Teams of Members PGP1 PGP2 PGPEX Reference Behaviour Patterns and Hypotheses Choice of variables Popularity: No of applicants / No of students Effectiveness: No of projects completed / No of projects taken Performance: No of projects taken Selectiveness: No of selections / No of applicants Enthusiasm: PGP2’s renewed / PGP1’s taken last year Experience: Proportion of PGP2’s in the membership Archetypes and Relations (Underlying Causes) Limits to Growth Harshit Krishna – 0143/48 Page 1 FP
  3. 3. Modeling Effectiveness @ INCA Limits to growth Reinforcing Loops with Delay Another reinforcing loop There are several other factors, but modelling each is not possible. Furthermore, most effects can be combined under fewer heads or captured using appropriate equations while modelling. The Causal Loop Diagram Harshit Krishna – 0143/48 Page 2
  4. 4. Modeling Effectiveness @ INCA Explanations and Mapping Number of people applying to INCA is determined by several factors, which can be given different amount of importance for different candidates as per whether they value CV points or the experience or the knowledge or just have passion in such work. Important among these are: Performance : Popularity 25 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 20 15 10 5 0 Projects : Recruitment 100 Projects abandoned Projects delivered PGP1's taken 80 60 40 20 Pgpex & FP 0 Projects delivered % of Batch applying 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 1. PGP2’s influence from the presentation stage to interactions during CV mentoring and prior contact before entering the institute 2. Number of projects taken by the club in the previous year assuming a 1 year memory span of the involved people 3. Faculty involvement in the affairs of the club Popularity : Selectiveness 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% % of Batch applying Selectiveness This is the case also because the current faculty advisor prefers to guide from an arm’s length giving more autonomy. Hence OC and faculty members very much determine the effectiveness of the club. Recruitment of PGP1’s is done from the applicants on subjective criteria yet an upper limit is imposed by the requirement as per number of projects taken. Moreover since the number of applicants is partly controllable by PGP2 influence (as said above) that can ultimately determine the recruited number. PGP1’s recruited in a year renew membership based on their experience in that one year, their interest in the social cause and the work. Many leave as CV points don’t accrue without significant Harshit Krishna – 0143/48 Page 3
  5. 5. Modeling Effectiveness @ INCA contribution. This largely determines the PGP2 proportion next year and the churn factor. These two become critical in the club’s popularity in the coming year. Number of projects taken depends on the amount of work the Organizing Committee puts in. Number of projects delivered, a measure of effectiveness, comes from the amount of faculty and OC involvement and the amount of work or enthusiasm put in by the members. The selectiveness followed in recruitment and the toughness of requirements portrayed by PGP2’s have been known to lead to better effectiveness in a given year. Whenever too many projects have been taken, the effectiveness variable has declined. Selectiveness : Performance 25 Projects : Effectiveness 80.00% 30 50.00% 15 35 60.00% 20 40 70.00% 25 40.00% 10 30.00% 20.00% 5 Projects delivered 20 Selectiveness 100.00% 80.00% 10 15 10.00% 5 0.00% 0 120.00% 0 60.00% 40.00% 20.00% 0.00% Conclusions      PGP1 applications reinforce themselves in a positive feedback but can be controlled by PGP2 recommendations, number of projects and faculty involvement There is a reinforcing loop with delay between Projects Taken and Popularity There is another reinforcing loop with delay between PGP2s’ renewals and effectiveness through the former’s effect on PGP1s in the course of project execution (not the networking effect discussed above) Lack of effectiveness makes certain members more active and certain others seeing their CV points in threat tend to give up. The latter (reinforcing ineffectiveness) impacts the former (balancing ineffectiveness) in a limits to growth archetype Selectiveness directly leads to improved effectiveness Recommendations   Use PGP2 networking influence on PGP1s to control the amount of applications. Since application count is affected by this and in turn determines the quality of students coming in, a fair picture of work requirements should be portrayed. Since the memory span can be considered not over 2 years at most (the duration of course) and batch size is on the rise, the hiring can be regulated as per the requirements each year. Harshit Krishna – 0143/48 Page 4 Projects taken Projects delivered Effectivene ss
  6. 6. Modeling Effectiveness @ INCA   OC efforts being an important factor in increasing effectiveness, number of OCs should be accordingly allocated to different verticals as per the results that are desired Faculty involvement is not only a plus for the quality of work but also affects attractiveness for new applications. This end can be pursued by the OC and should be monitored. Simulation Variables and Relations Batch Strength: Input as a time varying number as per given data. PGP1 Applied = Attractiveness of INCA*Batch Strength*0.5/max(Faculty Size/25,1) PGP1 Recruited = SMOOTH(PGP1 Applied, 3)*0.4*Projects Taken/25 PGP2 Proportion = DELAY FIXED(0.4*PGP1 Recruited, 1 , 30)at 0.76 Churn factor: Constant at 0.74 which is the current year figure and can be varied during simulation Inflow of Students = PGP1 Recruited+PGP2 Proportion Outflow of Students = Strength of INCA*Churn Factor Strength of INCA (a stock with initial value 30) = Inflow of Students-Outflow of Students Enthusiasm of OCs = RANDOM UNIFORM(0.5, 1 , 0.73) Number of OCs: Taken as constant at 4 as per current year data and can be varied during simulation Cumulative Effort of OCs = Enthusiasm of OCs*"No. of OCs" Faculty Involvement = 6*Cumulative Effort of OCs Effectiveness of INCA = min(Strength of INCA/40,1)*min(Faculty Involvement/18,1) Projects Taken = Cumulative Effort of OCs*Effectiveness of INCA*20 Attractiveness due to Projects = min(Projects Taken/25,1) Attractiveness due to networking = min(PGP2 Proportion/30,1) Attractiveness of INCA = Attractiveness due to Networking*Attractiveness due to Projects Behaviour and Explanations The behaviour on simulation mirrors that observed in the reference behaviour patterns and predicted in the discussion above. The explanations remain the same as have been presented above in the hypotheses stage. Some graphs follow: Harshit Krishna – 0143/48 Page 5
  7. 7. Modeling Effectiveness @ INCA Plotting Strength of INCA and Effectiveness of INCA Plotting ‘Faculty Involvement’ and ‘Attractiveness of INCA’ with ‘PGP1 Applied’ Plotting ‘Effectiveness of INCA’ and ‘Cumulative Effort of OCs’ with ‘Projects Taken’ Harshit Krishna – 0143/48 Page 6
  8. 8. Modeling Effectiveness @ INCA Annexure: Interview notes Prof Vidyanand Jha (Faculty Advisor and Chairperson) Started in 1997 as a students’ organisation, students approached Prof Surendra Munshi to become faculty advisor. He brought in a good monitoring structure. Vision: We help those who help others. Not a direct volunteer group (like ‘Pehla Kadam’), carry out organised managerial consultancy work. INCA fills a gap of managerial involvement in social sector besides faculty doing some consultancy. First of its kind in Indian B schools, now emulated elsewhere. INCA activities highlighted in campus publications etc. Campus had around 45 SIGs and clubs around 10 years ago, when a census conducted on their activities. Till then student council was weak with no institutional funding (T-shirts and certificates the only expenses any way though), was streamlined thereafter and gained more power then. Funds allocated high and increasing since then, disclosed every year. Chairperson’s mandate:   Projects on fixed timelines (jul-aug to jan-feb) Knowledge management of all projects and awareness for NGOs etc. with institutional upkeep of records Current view: Numbers (of members, of projects) not the motive but the social and knowledge impact. Numbers nevertheless are increasing, recruitments increasing. Have ensured subjective yet transparent criteria for selection etc. yet subjectivity makes these numbers and quality volatile. Over time importance of CV points has increased and hence competition and event organising take precedence over other motives. Hence new trends are being seen (volunteer work and events, fundraising) despite erstwhile OC’s contrary recommendations. Moreover, scope broadened over the years beyond NGOs, have worked with WB and Kerala state govt. Cycle of events: Faculty volunteers invited, PGP1 and other students selected, projects looked out for. No interviews from NGOs and of NGOs, because all NGOs (from CSR initiative of an MNC to a small time NGO) same and all members too, plus client mostly doesn’t have testing expertise. Selection of org’n regulated therefore based on recommendations to and by OC and faculty volunteers (a major factor), project cycle rigid. Short life of news in campus. If people fired (rarely so) for non-performance fewer application received next year and low recruitments (or lesser interest shown). Too many projects and people mean performance reduced, as the excess numbers are mostly motivated by CV points. Keep constantly monitoring for issues in work and quality. As CV verifications done before end of projects so people either incentivised to work or get frustrated and leave. Certificates given out in 2nd year, if not worked in 1st year then membership not renewed mostly (many drop out voluntarily). Some want to work more in 2nd year, not so much in 1st year so laterals are norm. No shortage of students and projects, time constraints are on faculty members. Harshit Krishna – 0143/48 Page 7
  9. 9. Modeling Effectiveness @ INCA End terms (event organising) vs. continuous evaluation (projects) kind of trade-off faced by students in deciding where they want to work, thus the increasing preference for events. Change in direction has also come from faculty advisor change. Current advisor acts as a guide from arm’s length, thus OC and faculty presence a determinant of performance. A meeting with faculty once in a while is the norm, they were more important till OC hadn’t take over. More structured and effective then, more intensive now because of personal accountability despite reduced amount of monitoring and. Need to keep it restrictive so that accounts (related to NGOs) and event funding in control. Monitoring may reduce intensity of work as people can be thrown out and practices regulated. Optimal size of teams based on EOIs and interest. From min 3 to max 6-7, beyond a level issues crop up in managing their own conflicts. No team leader appointed directly. OC’s energy and commitment depends on if he is involved with other clubs etc. Optimum OC around 7, above that (even in case of normal recruitment) people mostly turn up for the sake of CV so not as good. Purpose is social and learning oriented, but if threatened to remove CV point conjecture loss of interest. Positive guiding effect of more PGP2s on PGP1s’ effectiveness Sachin (1st year student) Motives for joining: Interest based pursuits (several groups on campus), learning based outside curriculum, networking with similar people (both reasons for joining INCA). Other factors are interest / passion, time available from other activities in 1st year, clubs’ past performance (important), and brand name of NGOs worked with, association with seniors (important), involvement of faculty. Besides, amount of knowledge sharing and discussions that occur, amount of involvement shown during the year from others (passion factor), and amount of unexpected workload in first year (if can’t do much then might give up from frustration). Vivek Jha (2nd year student, OC member) Common motives for joining: Initial push from PGP2, networking (the kind of external people involved liek Ms Shaheen Mistry of Teach for India), social purpose, consulting experience, CV points (majority of cases), PGP2s initial push and level of commitment both play a role, Student’s background Presentations and advertisements on past year performance and some idea (vague) of coming year work is a huge determinant of applicants People continue because: passion, club performance in 1st year, interest in social work and similar other motivations, CV point ultimately biggest motivation, satisfaction on positive impactful work, and not just work done for CV points, voluntary work (like Pehla Kadam). PGP2s: Previous experience, enthusiasm for club, interest in such work, competence. PGP2s in laterals come in with more knowledge and competence but may not have same interest. Harshit Krishna – 0143/48 Page 8
  10. 10. Modeling Effectiveness @ INCA Showed the work quotient part and commitment required in presentations etc. in response to loafing by students: hence fewer applications and expecting better quality work this year Recognition: certificate given at convocation, CV point only when quality Project completed (majority students working because of this and the interest in consulting work: knowledge gain) Faculty feedback says student so not contact much and hence voluntary contribution has reduced. 4 OCs for projects, 1 for events as of recently, members divided into these verticals too Project allocation to students as per EOI and profile matching in case of ties by july end, each OC handles fair share of total, OC responsible for liasoning and information sharing. 60 hours per member required over 6 month period. Projects landed based on OCs and faculty volunteers’ work and suggestions, besides campus’ brand name, all in or around Calcutta. Responses from NGOs and no projects increasing but increasingly higher recruits accordingly would dilute quality. Project selection based on breadth of project and value-add possible by us to the project. Need continuous feedback from students on effectiveness of project and quality of inputs coming from both students and NGOs. Mid-term review with chairperson (progress, feasibility, support form stakeholders, quality of work). NGOs not involved in selecting students and making presentations as they are low on resources and personnel for such a task, best way is for INCA to do the scouting for appropriate people, information and resources. Maintaining relations with NGOs important to get projects over the years although mostly new NGOs come year on year. Harshit Krishna – 0143/48 Page 9

×