Attention Competition and Learning

in Multiple-Task Settings

J MinKyushu University

jwmin@econ.kyushu-u.ac.jp

Hitoshi MitsuhashiKeio University

2014 International Conference on Advances in Business and Management

Background(1): Organizational learning theory

1 year 3 year 5 year 7 year 9 year

Increased performance

• Learning = Making and updating routines in response to EXPERIENCES (Schulz, 2001)

• Cumulative experience of a certain task results in learning of the task

Cumulative experience = Learning??NOT ALL THE TIME!!!

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What we have known about ineffective learning…

Un-learning

Erroneous learning

Failing to knowledge/skill acquisition(ex: Haunschild & Sullivan, 2002; Madsen & Desai,

2010)

Learning depreciation (Memory decay)(ex: Algote, 1999; de Holan & Phillips, 2004)

Superstitious learning,

Competency traps(ex: Kim et al., 2009; Zollo, 2009)

Single Task Multiple Tasks

?

Transfer problems(ex: Ellis et al., 2011;

Haleblian & Finkelstein, 1999)

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Background(2): Organizational learning theory

Multiple-tasks settings

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5

Research Question & Purpose

RQ:

How do we learn the execution of a task in a multiple-task setting?

Purpose:

To develop a mechanism of learning in a multiple-task setting by

examining how the learning of simultaneously-performed other tasks

influence the learning of focal task

Research Context

Multiple tasks

Difficulty

Learning

The International Skating Union, 2006-2013

• Senior figure skating 71 games including

Winter Olympic Games, World Championships,

European Championships, Four Continents

Championships, and Grand Prix Series

• Actors: 457 male and female senior skaters

• Project: short + free programs

• Tasks: 326 Jumping techniques

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Learning in Multiple-task settings: Saliency and Attention

Organizational learning theory

• “Organizations learn to pay attention to some parts or their comparative

environment, and to ignore other parts.” (Cyert & March, 1963: p. 123)

• “Multiple types of experience detract from rather than supplement each

other.” (Desai, 2010)

T1

Focal task

T2

T3T5

Ignored

Salient Task

Attention based view (ABV) (Ocasio, 1997)

• Actors’ behaviors depend on what they

attend.

• Actors cannot pay attention to all tasks at

the same time, thus they selectively

allocate their attention based on each

task’s saliency.

Ignored

In a multiple-task setting, learning of task is a function of limited attention to the task, which is determined by other tasks’ saliency!

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LEARN!!

Many other tasks with high saliency

Low attention chance to a focal task

Failing to learn a focal task

FT

FT

FT

t-2

t-1

t

Salient other tasks FT Focal task

...

Low attention stability to a

focal task

Failing to knowledge/skill

acquisition

Learning depreciation

Learn?t+1

Un-learning(Rerup, 2009)

(Cyert & March, 1963; Desai, 2010)

(ex: Ocasio, 1997)

Learning in a Multiple-task Setting

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Hypotheses 1 & 2

Other tasks with high saliency?

Failure of prior tasks

• Problemistic search, strong driver of learning (ex: Greve, 2003; Madsen & Desai, 2010)

Incorporation of new tasks

• Non-routinized tasks Predictable future failure (Levitt & March, 1988 )

Failed other tasks

Incorporation of new other tasks

Focal task learning

Depreciation of learning of focal task H2a(＋)

H1b (－)

H1a (－)

H2b(＋)

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H3: Moderating effects of task difficulty

Failures of difficult other tasks

Difficult new tasks

• High possibility of future failure

• Superstitious learning for easy

new tasks

10Failures of easyother tasks

Difficult new other tasks

Easy new other tasks

<

>More salient!

More salient!

H3a. The negative effect of the failure of easy other tasks on an actor’s focal task

learning is greater than that of the failure of difficult other tasks.

H3b. The negative effect of the difficult new other tasks on an actor’s focal task

learning is greater than that of the easy new other tasks.

Failure of easy tasks

• Strong recognition of failure

• Expectation about prompt

learning effects

Measures

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Dependent var. (t)

• Successful completion of focal task: focal task’s GOE ≥ 0 (H1, H3)

• Weight (Lamda) with maximum log likelihood in models for learning from failure

of focal task (H2)

Independent var. (t-1)

• Cumulative number of failed other tasks that are performed with the focal task

from t-3 to t-1

• Cumulative number of newly incorporated other tasks that are performed with

the focal task from t-2 to t

• Difficult tasks: Tasks with a higher basevalue than an averaged basevalue of

tasks that a focal actor successfully completed at t-2

Control var. (t-1)

• Season, short program, home game, number of other skaters, skater’s age, ISU

points, duplicated tasks, basevalue of tasks, task sequence, days elapsed since

the prior experience of focal task , new task, cumulative experience of focal

task’s failure

Analytical method

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Unit of analysis

• Actor-Competition-Jumping technique

Logistic regression

Fixed effect model

Heckman selection correction to predict challenging to the focal task

Analysis Results (1): Focal task learning

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Standard errors are in parentheses.;† p < .10, * p < .05, ** p < .01, *** p < .001

H1a

H1b

H3a

H3b

Variables

Analysis Results (2): Depreciation of focal task learning

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Standard errors are in parentheses.;† p < .10, * p < .05, ** p < .01, *** p < .001

H2a H2b

Rejected!

Findings

How do we learn the execution of a task in a multiple-task setting?

In a multiple-task settings, actors fail to learn a focal task..

1. when they simultaneously perform many other tasks that they failed to perform well, and particularly when such tasks requires lower skills.

2. when they simultaneously perform many other tasks that they newly incorporate, and particularly when such tasks are difficult and challenging.

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Implications

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Theoretical implications

• Organizational learning theory

Theory of how actors learn from multiple tasks

A new moderator in learning Task difficulty

Change = Good learning?

• Attention based new

New antecedents of limited attention (failures, newness, and difficulty

of tasks)

Attention effects in learning contexts

Practical implications

• Harmfulness of urgent many changes in multiple-task settings

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THANK YOU!!!!!

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Learning depreciation

-100

5-1

00

0-9

95

-990

-985

ll

0 20 40 60 80 100_j

Learning from focal task failure(F) = 𝑘=𝑡−3𝑘=𝑡 F × λ

Lamda = Rate of focal task learning remained (%)

Log-likelihood

Portfolio of tasks (1)

• Many difficult tasks? Increased possibility of multiple failures

• Many easy tasks? Few improvement of entire performance

• Many new tasks? Increased possibility of multiple failures

• Many repeated tasks? Few improvement of entire performance

We know that we have to challenge difficult and new tasks to improve entire performance…but…

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Multiple failures of tasks (in particular, failures of easy tasks)

From previous hypotheses…

Multiple new tasks (in particular, new difficult tasks)

Unlearning of each task

Decreased entire performance

Portfolio of tasks (2)

Difficult tasks Easy tasks

New tasks

Repeated tasks

The highest possibility of multiple failures

Despite low difficulty, new tasks are likely to be failed Major

antecedent of unlearning of each task (H3b).

Despite high difficulty, repeated tasks are likely to

be succeed Improvement of entire performance.

The lowest possibility of multiple failures. But, few

improvement of entire performance.

H4. In a project consisting of multiple tasks, a high proportion of repeated difficult

tasks improves the project’s performance.

H5. In a project consisting of multiple tasks, a small proportion of new tasks improves

the project’s performance.

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Analysis Results (3): Multiple-tasks Portfolio

Standard errors are in parentheses.;† p < .10, * p < .05, ** p < .01, *** p < .001