the science of organizational design · organization studies encompass two areas: organization...

Rich BurtonDuke University

September 8, 2016Aarhus University

Børge Obel Dorthe Døjbak Håkonsson Jacob Kjær Eskildsen Dan Mønster Panos Mitkidis Many others: ICOA, MindLab, Management

Aarhus University 2016 Copyright by Rich Burton

Science & Organizational Design Organization Theory, Design &

Multicontingency Information processing: the basis of our science The triangle: experimentation, model & question Two studies: one old, one new Design rules Quick Summary Challenges

Science: explanation, understanding of what is or what has been

Definition: knowledge about or study of the natural world based on facts learned through experiments and observationThe state of knowing : knowledge as distinguished from ignornaceor misunderstanding

Design: imagine and create what might be or what should beDefinition:

-to conceive and plan out in the mind <he designed the perfect crime>-to have as a purpose : intend <she designed to excel in her studies>

Experiment : a test, trial, or tentative procedure; an act or operation for the purpose of discovering something unknown or of testing a principle, supposition, etc. An experiment is a procedure carried out to verify, refute, or validate an hypothesis. Experiments provide insight into cause-&-effect by demonstrating what outcome occurs when a particular factor is manipulated. (Wikipedia)

Empirical studies are evaluating what is; while simulation & experimentation can help in finding what might be and what should be.


Organization theory: explanation and understanding of organizational structure writ largefocusing on what is and what has been i.e., what we observe: focus on the past and present

Organizational design: imagination and creation of organizational structure writ large focusing on what might be and what should be i.e., what we have not seen yet, but could: future oriented

N.B. organization = organizational


Georges Romme (2003: 558), building upon Simon (1996), argues that the ‘idea of a design involves inquiry into systems that do not yet exist – either complete new systems or new states of existing systems’.


Organization studies encompass two areas: organization theory as a positive science to explain and understand the structure, behavior, and effectiveness of an organization; and organizational design as a normative science to recommend better designs for increased effectiveness and efficiency. Organization theory attempts to understand and explain; organizational design creates and constructs an organization.

Richard M. Burton and Børge Obel, "Organization" Saul I. Gass and Michael C. Fu (editors), The Encyclopedia of Operations Research and Management Science, Boston, Springer/Kluwer Academic Publishers, 1996, 476-481. Revised 1998. Revised 2012.

(cont.)


Jacob Marschak and Roy Radner, 1972, Economic Theory of Teams

“Who talks to whom about what, who makes which decisions based upon what information”

Kenneth Arrow, 1974, The Limits of OrganizationUncertainty, costs and imperfectness of information, decisions and imperfect information, information channels, organizational agendas, efficiency of codes, coordination, value of authority

Jay Galbraith, 1973, Designing Complex Organizations“greater task uncertainty, greater information processing demands by decision makers” organizational strategies: reduce need for information by creating semi-independent units; or increase capacity with greater communications, hierarchical or lateral communications


Uncertainty has been defined as an incomplete description of the world (Arrow 1974), unpredictability, or perhaps more precisely as Knightian uncertainty where the probability distribution is not well defined; and further uncertainty has included complexity or the number of variables in the environmental space (Burton & Obel 2004).

Interdependency can be defined as the correlation among the variables in the environmental space or task space. Simon (1996) examined interdependencies as the degree of decomposability using a matrix representation of the connections: the more connected or dense the matrix, the more interdependent the tasks; and the sparser the matrix entries, the less connected and the more decomposable the tasks.

"The division of labor is quite as important in organizing decisionmaking as in organizing production, but what is being divided isdifferent in the two cases. From the information-processing point ofview, division of labor means factoring the total system of decisionsthat need to be made into relatively independent subsystems, each oneof which can be designed with only minimal concern for itsinteractions with the others. The division is necessary because theprocessors that are available to organizations, whether humans orcomputers, are very limited in their processing capacity in comparisonwith the magnitude of the decision problems that organizations face.The number of alternatives that can be considered, the intricacy of thechains of consequences that can be traced -- all these are severelyrestricted by the limited capacities of the available processors."

Simon, 1947: 293


Herbert Simon, 1996, The Sciences of the Artificial Design“is concerned with things ought to be, with devising structures to attain goals.”Decomposing, partitioning a big task into a set of smaller tasks (not unique)This decomposition creates interdependencies which creates the need for a “System of consciously coordinated activities of two or more persons” - integrationCoordination mechanisms require: information, communications, cooperation, decisions, rules, routines, trust, cooperation, incentives, leadership, among others.


What is

What might beWhat is

What might be

What is not possible

What should be

A View of the Organizational Design Space


Strategic Organizational Diagnosis & Design Fit


Fits & Misfits


Burton, Obel and Håkonsson, Organizational Design: A step by step Approach, 2015

Information processing view: Integrating what we know from these classic studies (and others) what should be a good design – using both what is and what might be

Approach or model: questions, analysis, misfits, possible actions


Experimentation, Model and Question: Together


Administrative Science Quarterlyvol. 25, No. 3, Sept. 1980, pp. 457-466.

Building the Multicontingency ModelThe question: The M form hypothesis; under what conditions can it be confirmed?The experiment: 2X2 – structure and task interdependency The structure or form choices are the M form and U formThe task interdependency of two levels: high and lowThe coordination process is the Dantzig Wolf decomposition model which is a price based allocation or transfer price coordination schemeThe goal is to max profits.

The Model


Hypothesis Testing


Experimental design

Task interdependency

More nearly independent

Less independent

Form or structure U formInformation processing:Coordination of departmental subunits

Less observed, possible, What might be, quite Inefficient, Misfit,

Observed in real world with centralized decision making; what is, but here decentralized decision making slightly inefficient, misfitThis is a surprising result.

M formInformation processing:Allocation of resources to divisional subunits

Observed in real world: what is, efficient, fit, what should beClassic M form

Less observed, Possible, imagined what might be, marginally efficient,FitThis is a surprising result. Bastardized M form

Interpreting results & real world comparisons


An expert system software with 400+ organizational relationships, e.g., a functional configuration and individual based incentives do not fit (as performance is likely to suffer)

Knowledge base from multicontingency model and validated by executives

Asks the user for same data as the five steps Offers insights and recommendations as a

decision aid – not a solution without thought


Strategic Management JournalStrat. Mgmt. J., 37: 985–1001 (2016)

Published online EarlyView 6 June 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI:

10.1002/smj.2380Received 1 March 2013; Final revision received 2

February 2015DORTHE DØJBAK HÅKONSSON,

JACOB KJÆR ESKILDSEN,LINDA ARGOTE, DAN MØNSTER,

RICHARD M. BURTON,BØRGE OBEL

Beyond bounded rationality: emotions

Beyond self report black box measures: psycho-physiological

Order of things is important: exploit or explore first for performance

Teams as organizations

QuestionUnder what conditions do teams explore or exploit when they have succeeded or failed previously; teams not individuals; emotions and performance; longitudinal study, p.998;Put in hypotheses: H1 H2 H3 H4ModelLab study using 3 person teams; 2 possible task designs or 2 ways to build a boat; Holding constant: Incentives all teams are played using the same metrics, how many boats they build; information processing possibilities, i.e., teams can communicate as they like; see p. 989 and 990.Experimental design and experimental task build a boat and choose explore or exploit.emotions are manipulated – happy or sad by experimenter; relate to leadership style – happy to development style of leadership, sad to internal process manager of leadership;Put in the timeline of the experiment, p. 990, Figure 1Discuss the manipulation of task design possibilities: explore or exploit as a different way to build a boat. See trials 4 and 5 on the timeline:


https://youtu.be/Yp5GYWec7Mc

Relating to our hypotheses, we found, supportive of Hypothesis 1, that performance declines led to a higher likelihood that teams adopted an innovative routine and that performance increases led to a lower likelihood of adoption.


Contrary to Hypothesis 2, we did not find evidence that either self-reported emotions or the psychophysiologicaldata in the period preceding adoption decisions (Q2)/(t3) affected the decision to adopt.

Interestingly however, we found somesupport of Hypothesis 2 for both self-reported and physiological emotions at Q1. This relationship, even if only marginally significant, suggests that people who reported higher valence and smiled more at the beginning of the experiment were more likely to adopt later on than those who did not report high valence and did not smile a lot at the beginning of the experiment.


Supportive of Hypothesis 3, we found that theadoption of the new routine led to an increasein the experience of self-reported positive emotions.In support of Hypothesis 4, this increasewas due to improved performance associated with the new routine. Performance development mediated the effect of adopting the new routine on increases in positive, self-reported emotions. Teams that adopted the innovative routine experienced more positive emotions because their performance increased.

Thus, the successful adoption of thenew routine caused teams to experience more positive emotions.


Thus, our experimental approachallows us to reveal insights into a long-standingdebate as to whether or not happy workers areproductive workers (e.g., Lawler and Porter, 1967).

Interestingly however, for the psycho-physiologicaldata, successful implementation of the new routinedid not lead to valence increases.

Hence, managers might influence explicit, self-reported, and moreconscious emotional states by fostering the opportunity to explore and succeed, whereas implicit emotional states might not change as a result of such efforts.


A more powerful way for managers to foster exploration is to provide teams opportunities to explore a new routine and resources to use the routine successfully. Once a new routine has been successfully implemented, positive emotions at the team level ensue. Because emotional management is a key factor in strategic change and implementation (Huy, 2002), our findings add important insights for strategic management in terms of how to manage emotions. Our findings also provide important insights into how to foster exploration and improve performance.


If the goal is to explore, then performance failure is positive motivation.

If the goal is to exploit, then early performance success is positive motivation not to change.

If the goal is happy workers, then let them be successful or perform well.

Can you get it wrong?

Had we only looked at total productivity and the mean self-reported valence of the teams after the final stage of the experiment, we might have erroneously concluded that teams with higher self-reported valence would be more productive. Our longitudinal analysis revealed, however, that the causal sequence was that teams with higher productivityexperienced more positive emotions rather than the reverse sequence.


Science of Organizational DesignWhat is, what might be, what should beInformation processing viewMulticontingency theory of organizationExperimentation – triangle Two studies: M form and emotions in explore-exploit

Herbert Simon, The Sciences of the Artificial(nearly) decomposable systems – a basis for organizational design: Decompose or partition a big task into smaller tasks, which are interdependent, requires coordination,Through: information, decision rules, communications, routines, incentives, trust, cooperation among others.


More exploration in our research of what might be and what should be; go beyond explaining what is and what has beenEmbracing the counterfactual and the created possibility

Beyond bounded rationality: emotions and imagination or creationMicro measures such as brain activity and continuous time measuresAre emotions based upon information; how are they enacted?

Organizational design for entrepreneurial ventures and startups: basic functions are still required; what is newLoose confederations: neither market nor hierarchyTemporary organization: startup and disbandNew organizations; new boundaries beyond ownershipAgile organizations or dynamic fit: not static stability

Incentives and contracts in organizational designOther??

References:

Dorthe Døjbak Håkonsson, Jacob Kjær Eskildsen, Linda Argote, Dan Mønster, Richard M. Burton and Børge Obel, Exploration versus exploitation: Emotions and performance as antecedents and consequences of team decision, Strategic Management Journal, 2016, vol. 37, p. 985–1001.

Richard M. Burton and Børge Obel, "Organization" Saul I. Gass and Michael C. Fu (editors), The Encyclopedia of Operations Research and Management Science, Boston, Springer/Kluwer Academic Publishers, 1996, 476-481. Revised 1998. Revised 2012.

Richard M. Burton, The Future of Organization Design: An Interpretative Synthesis in Three Themes, Journal of Organization Design, 2013, v. 2, n. 1, p. 42 - 44.

Dorthe DØjbak Haakonsson, Richard M. Burton, BØrge Obel, JØrgen Lauridsen, Strategy Implementation Requires the Right Executive Style: Evidence from Danish SMEs, Long Range Planning, 45 (2012) 182 -208.

Richard M. Burton and Borge Obel, Computational modeling for what-is, what-might-be, what-should-be studies – and triangulation, Organization Science, vol. 22, no. 5, September – October 2011, p. 1195 - 1202.

Mark Nissen and Richard Burton, Designing Organizations for Dynamic Fit: System Stability, Maneuverability, and Opportunity Loss, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, vol. 41, no. 3, May 2011, p. 418 - 433.

Richard M. Burton, “Computational Laboratories for Organization Science: Questions, Validity and Docking,” Computational and Mathematical Organization Theory, 2003, vol. 9, Issue 1, pp. 91 - 108.

Richard M. Burton, Jørgen Lauridsen and Børge Obel, “Return on Assets Loss from Situational and Contingency Misfits, Management Science, November 2002, vol. 48, No. 11, 1461 - 1485.

Helmy H. Baligh, Richard M. Burton and Børge Obel, "Organizational Consultant: Creating A Useable Theory for Organizational Design," Management Science, vol. 42, No. 12, December 1996, p. 1648-1662.


Richard M. Burton, “Computational Laboratories for Organization Science: Questions, Validity and Docking,” Computational and Mathematical Organization Theory, 2003, vol. 9, Issue 1, pp. 91 -108.

Richard M. Burton, Jørgen Lauridsen and Børge Obel, “Return on Assets Loss from Situational and Contingency Misfits, Management Science, November 2002, vol. 48, No. 11, 1461 - 1485.

Helmy H. Baligh, Richard M. Burton and Børge Obel, "Organizational Consultant: Creating A Useable Theory for Organizational Design," Management Science, vol. 42, No. 12, December 1996, p. 1648-1662.

Richard M. Burton and Børge Obel, "The Validity of Computational Models in Organization Science: From Model Realism to Purpose of the Model," Computational and Mathematical Organization Theory, vol. 1, No. 1, 1995, pp. 57-71.

Richard M. Burton and Børge Obel, "Opportunism, Incentives, and the M-form Hypothesis - A Laboratory Experiment," Journal of Economic Behavior and Organization, vol. 10, 1988, pp. 99-119.

Richard M. Burton and Børge Obel, "A Computer Simulation Test of the M-form Hypothesis," Administrative Science Quarterly, vol. 25, No. 3, Sept. 1980, pp. 457-466.

Richard M. Burton, Børge Obel and Dorthe D. Håkonsson, “Contingency Theory, Dynamic Fit and Contracts,” Jane Qui, editor, Routledge Publishers, 2016, in press

Richard M. Burton and Borge Obel, “Design Rules for dynamic organization design: the contribution of computational modeling” Anna Grandori, editor, Handbook on Economic Organization: Integrating Economic and Organization Theory, 2013, Edward Elgar Publishing, Cheltenham, UK and Northampton, MA, USA, Chapter 13, p. 223 – 244.


Helmy H. Baligh, R. Burton and Børge Obel, "Validating an Expert System That Designs Organizations", Kathleen M. Carley and Michael J. Prietula, editors, Computational Organization Theory, Hillsdale, N.J. , Erlbaum, 1994, pp. 179-193.

Helmy H. Baligh, Richard M. Burton and Børge Obel, "Organizational Consultant: Learning By Doing," Michael F. Masuch (editor), Artificial Intelligence in Organization and Management Theory, Amsterdam, Elsevier, 1991.

Helmy H. Baligh, Richard M. Burton and Børge Obel, "Designing Expert Systems In Organization Theory: Putting `Design' Into Organizational Design," Michael F. Masuch(editor), Organization, Management and Expert Systems, Berlin, Walter De Gruyer & Co, 1990.

Helmy H. Baligh, Richard M. Burton and Børge Obel, "Creating An Expert System to Design Organizations: The Design First Approach," Michael F. Masuch (editor), Organization,Management and Expert Systems, 1990, Berlin, Walter De Gruyer & Co.

Richard M. Burton, Børge Obel, and Dorthe Døjbak Håkonsson, Organizational Design: A Step By Step Approach, 2006 with Gerardine DeSanctis, Cambridge, Cambridge University Press, third edition, 2015.

Richard M. Burton and Børge Obel, Strategic Organizational Diagnosis and Design: The Dynamics of Fit, with OrgCon Software, Kluwer Academic Publishers, Boston, 2004, Third edition; earlier editions, Strategic Organizational Diagnosis and Design: Developing Theory for Application, Boston, Kluwer Publishers, 1995, Second edition, 1998.

Richard M. Burton and Børge Obel, Designing Efficient Organizations: Modelling and Experimentation, Amsterdam, North-Holland, 1984. Review, Administrative Science Quarterly, 1986.


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