Interaction Value Organizational Assessment correlation to Financial Performance Data

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Interaction Value Organizational Assessment
correlation toFinancial Performance Data

• Walid Nasrallah
• American University of Beirut
• WEHIA 2005
• Essex UK

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Overview

• Introduction
• Concepts of Interaction Value Analysis
• Survey Results
• Future

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The Big Picture

• What are we trying to do?
• Make Contingent Org. Design practical
• By considering multiple factors at once
• Why?
• Because things are changing
• But nothing changes

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What will I do today?

• Contrast two systems
• Multi-contingency by rules MYCIN
• Network-model-based (IVA)
• Partial validation
• by correlation to financial measures
• Speculation why these results ?

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Interaction Value Analysis

• Rational optimizing agents
• Described by six parameters
• Two ways to optimize ? org. forms
• Global optimum ? structured/autocratic
• Nash equilibrium ? self-org. / ad-hoc
• Model mimics org. theory results

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Interacting Agents
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Allocate Attention
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Generating Value

• Organizations Effectiveness
• Sum over all possible pairs of people (i and j)
  of
• Frequency of communication (pij)
• x Value of successful communication (hij)
• x Probability of successful completion (sij)

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Six Model Parameters

• Number of distinct org. roles Diversity
• Highest-to-lowest skill ratio Differentiation
• Role cooperation need Interdependence
• M/M/1 priority queue w/ reneging gives
• Arrival to departure ratio Load
• Reneging rate to service rate Urgency
• Criterion for choosing whom to serve Climate

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Diversity independent roles
x
KEY3x3 matrix 3 roles ? Low Diversity6x6
matrix 6 roles ? Medium Diversity
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Differentiation cost of settling

• One parameter fully determines H matrix.
• How
• Comparative idealization
• h(my best) h(your best) h(his best)
• Relational idealization
• h(1st)/h(2nd)h(2nd)/h(3rd) etc.
• No special treatment for value of self-interaction

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Failure mode 1 Interdependence

• Sij s1ij x s2ij Probability of failure from
  over-use x Prob. of failure from waiting too
  long
• Over-use (s1)
• Depends only on pij
• Models neglect of other obligations
• too long (s2)
• Queuing (next slide)

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Failure Mode 2 impatient queuing

• M/M/1 queue gives failure probability at
  steady state
• IF response time f(load) gtimpatience time
  f(urgency)THEN FAILURE
• Load depends on aggregate demand
• i.e. column sum of matrix ?pij for a given j.

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Failure Mode 2. priority

• Priority reflects org. Climate
• Who gets served first?
• First come Disciplined climate
• Least Needy Capitalist climate
• Most Needy Fraternal climate

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Optimizing Effectiveness (F)
17.8
H matrix (given)
S matrix f(P)
P matrix (free variable)
18.75
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Global optimum (structured org.) vs. Nash
equilibrium (ad-hoc org.)
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Percent of Time Allocated pij
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15
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0
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Members Ranking of Interaction Partners
Legend
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Fraternal Climate
Climate Fraternal
Urgency Medium
Interdep. High
Diversity Low
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Capitalist Climate
Climate Capitalist
Urgency Medium
Interdep. Medium
Diversity Low
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vs. Multi-Contingency
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Survey Description

• 23 companies surveyed
• All Publicly listed
• on a small Middle-Eastern exchange
• Directed Interview w/ CEO
• Published data on Return on Investment (ROA)

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Results
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Discussion

• We have a correlation
• T-test is OK (90 confidence level )
• Not as good as OrgCon model
• Excluding uncharted environments
• Tracked ROA growth vs. average ROA
• Why?

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Possible conclusions

• Can never control for all effects
• Fishing for right indicator?
• Mature vs. developing economy?
• IVA-subset measures opposing effect?

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Future Research Directions

• Must do more surveys
• Cross-reference old surveys
• Represent change/uncertainty in IVA

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THE END