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Inferring final organizational outcomes from intermediate outcomes of exploration and exploitation: the complexity link

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Abstract

The difficulty in definitively linking outcomes of managerial action to organizational outcomes has been a festering issue in organizational research. The problem arises because it is not easy to separate the distinctive contributions of managers at intermediate stages, as well as the contribution of external factors beyond the control of managers. Specifically, certain managerial actions focusing on exploratory or exploitative innovation produce an intermediate output, organizational knowledge. From this base of organizational knowledge, further management actions craft the final output that eventually faces the market test. Drawing from complexity concepts, I argue that the probability of correctly fashioning the subset of key elements in the intermediate output may be a good measure of the probability of organizational success. I use March’s iconic computational simulation model to demonstrate the merits of this principle. I model the effect of complexity on managerial intentionality towards exploratory and exploitative innovation. I elicit important insights for research and practice by comparing organizational knowledge outcomes with the outcomes for probability of organizational success, in stable and moderately turbulent environment.

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Notes

  1. Consumers may assign utility on a bundle of features. The argument above can be extended to suggest that an automaker having ability and resources to (cost effectively) provide a higher-utility bundle will do better than an automaker not having the ability (and resources) to offer such a bundle.

  2. In Table 1 we provide a comparison of the flavors of complexity discussed. For additional topics on complexity please refer to Mckelvey (2001, 2004).

  3. http://stoa.org.uk/topics/emergence/index.html, Date accessed 13th March, 2016. This website refers to http://www.iep.utm.edu/emergenc/ as the source.

  4. For an example, please see Axelrod (1997).

  5. Though Schumpeter used the term “equilibrium”, he was probably one of the earliest economists to invoke the concept of irreversibility arising from time. The term “creative destruction”, coined by him, caught the imagination of generations of management scholars. This term unambiguously suggests that certain businesses are eliminated for good, i.e., get replaced by others operating on new formats, products, services and so forth.

  6. Alternately, in the realm of philosophy, this story is narrated by invoking a metaphor of the struggle between a thesis and an anti-thesis. Over time a synthesis emerges, emphasizing the commonalities between the thesis and the anti-thesis. This leads to a state of order, often temporary. Eventually the original differences that were suppressed in the formation of the synthesis raise their head once more, and become the new anti-thesis to it (the synthesis), setting off one more cycle of contestation and change.

  7. Interestingly, Alan Turing, another decoder scientist in the UK, used the omnipresence of exchange of information about the weather and the reference to the dictator, to decode messages of military interest, though he had very little knowledge of German.

  8. Chen and Katila (2008) cites Apple and Amazon as firms that are strongly into exploratory innovation and IBM and Dell as firms that focus on exploitative innovation.

  9. I thank an anonymous reviewer for this suggestion.

  10. Cattani (2005) labels accumulation of knowledge without an explicit, pre-specified purpose as “pre-adaptation”. However, for empirical purposes Cattani has to conceive of a dividing line in time, after which pre-adaptation ceases to apply and knowledge acquisition is more purpose-driven. I argue that acquisition of knowledge by experimentation is an ongoing activity in an organization.

  11. I thank an anonymous reviewer for alerting us to this aspect of the study.

  12. I thank an anonymous reviewer for directing our attention to this aspect.

  13. I thank an anonymous reviewer for suggesting this avenue to us.

  14. Please see Table 3 in the Appendix.

  15. The values of parameters and optima attained are given in Table 2, for Figs. 3, 4 and 5. For Figs. 1, 2 and 6, the parameter values are given alongside the graphical results.

  16. I thank an anonymous reviewer for alerting us to this limitation.

References

  • Argote L, Miron-Spektor E (2011) Organizational learning: from experience to knowledge. Organ Sci 22(5):1123–1137

    Article  Google Scholar 

  • Axelrod R (1997) The dissemination of culture: a model with local convergence and global polarization. J Confl Resolut 41(2):203–226

    Article  Google Scholar 

  • Bak P (1996) How nature works: the science of self-organized criticality. Copernicus, New York

    Book  Google Scholar 

  • Barney JB (1986) Strategic factor markets: expectations, luck, and business strategy. Manag Sci 32(10):1231–1241

    Article  Google Scholar 

  • Barney JB (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99–120

    Google Scholar 

  • Baum JAC, Dahlin KB (2007) Aspiration performance and railroads’ patterns of learning from train wrecks and crashes. Organ Sci 18(3):368–385

    Article  Google Scholar 

  • Benner MJ, Tushman ML (2002) Process management and technological innovation: a longitudinal study of the photography and paint industries. Adm Sci Q 47(4):676–706

    Article  Google Scholar 

  • Broad CD (1925) The mind and its place in nature. Routledge and Kegan Paul, London

    Google Scholar 

  • Brusoni S, Prencipe A, Pavitt K (2001) Knowledge specialization, organizational coupling, and the boundaries of the firm: Why do firms know more than they make? Adm Sci Q 46(4):597–621

    Article  Google Scholar 

  • Burgelman R (1983) Corporate entrepreneurship and strategic management: insights from a process study. Manag Sci 29(12):1349–1364

    Article  Google Scholar 

  • Burton RM (2003) Computational laboratories for organization science: questions, validity and docking. Comput Math Organ Theory 9(2):91–108

    Article  Google Scholar 

  • Cattani G (2005) Preadaptation, firm heterogeneity, and technological performance: a study on the evolution of fiber optics, 1970–1995. Organ Sci 16(6):563–580

    Article  Google Scholar 

  • Chanda SS, Ray S (2015) Optimal exploration and exploitation: the managerial intentionality perspective. Comput Math Organ Theory 21(3):247–273

    Article  Google Scholar 

  • Chen EL, Katila R (2008) Rival interpretations of balancing exploration and exploitation: simultaneous or sequential? In: Shane S (ed) Handbook of technology and innovation management. Wiley, Chichester, pp 197–214

    Google Scholar 

  • Child J (1972) Organizational structure, environment and performance: the role of strategic choice. Sociology 6(1):1–22

    Article  Google Scholar 

  • Cyert RM, March JG (1963) A behavioral theory of the firm. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Davis JP, Eisenhardt KM, Bingham CB (2009) Optimal structure, market dynamism, and the strategy of simple rules. Adm Sci Q 54(3):413–452

    Article  Google Scholar 

  • Dess GG, Beard DW (1984) Dimensions of organizational task environments. Adm Sci Q 29(1):52–73

    Article  Google Scholar 

  • Duncan RB (1972) Characteristics of organizational environments and perceived environmental uncertainty. Adm Sci Q 17(3):313–327

    Article  Google Scholar 

  • Epstein JM (1999) Agent-based computational models and generative social science. Complexity 4(5):41–60

    Article  Google Scholar 

  • Ethiraj SK, Levinthal DA, Roy RR (2008) The dual role of modularity: innovation and imitation. Manag Sci 54(5):939–955

    Article  Google Scholar 

  • Fiol CM (1996) Squeezing harder doesn’t always work: continuing the search for consistency in innovation research. Acad Manag Rev 21(4):1012–1021

    Article  Google Scholar 

  • Frankort HTW (2016) When does knowledge acquisition in R&D alliances increase new product development? The moderating roles of technological relatedness and product-market competition. Res Policy 45(2):291–302

    Article  Google Scholar 

  • Gavetti G, Levinthal DA, Rivkin JW (2005) Strategy making in novel and complex worlds: the power of analogy. Strateg Manag J 26(8):691–712

    Article  Google Scholar 

  • Ghemawat P (1991) Commitment—the dynamic of strategy. Free Press, New York

    Google Scholar 

  • Ghemawat P, del Sol P (1998) Commitment vs. flexibility? Calif Manag Rev 40(4):26–42

    Article  Google Scholar 

  • Gibson CB, Birkinshaw J (2004) The antecedents, consequences, and mediating role of organizational ambidexterity. Acad Manag J 47(2):209–226

    Article  Google Scholar 

  • Grant RM (1996) Towards a knowledge-based theory of the firm. Strateg Manag J 17(S2):109–122

    Article  Google Scholar 

  • Greve HR (1998) Performance, aspirations, and risky organizational change. Adm Sci Q 41:29–60

    Article  Google Scholar 

  • Gupta A, Smith K, Shalley C (2006) The interplay between exploration and exploitation. Acad Manag J 49(4):693–706

    Article  Google Scholar 

  • Hayek FA (1945) The use of knowledge in society. Am Econ Rev 35(4):519–530

    Google Scholar 

  • Hedlund G (1994) A model of knowledge management and the N-form corporation. Strateg Manag J 15(S2):73–90

    Article  Google Scholar 

  • Hilferding R (1910) Finance capital. Routledge and Kegan Paul, London [1981]

  • Holland J (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Huber GP (1991) Organizational learning: the contributing processes and the literature. Organ Sci 2(1):88–115

    Article  Google Scholar 

  • Hutzschenreuter T, Pedersen T, Volberda HW (2007) Internationalization—the role of managerial intentionality and path dependency: a perspective on international business research. J Int Bus Stud 38(7):1055–1068

    Article  Google Scholar 

  • Jansen JJP, Van den Bosch FAJ, Volberda HW (2006) Exploratory innovation, exploitative innovation, and performance: effects of organizational antecedents and environmental moderators. Manag Sci 52(11):1661–1674

    Article  Google Scholar 

  • Jansen JJP, George G, Bosch VD, Volberda HW (2008) Senior team attributes and organizational ambidexterity: the moderating role of transformational leadership. J Manag Stud 45(5):982–1007

    Article  Google Scholar 

  • Kirzner IM (1997) Entrepreneurial discovery and the competitive market process: an Austrian approach. J Econ Lit 35(1):60–85

    Google Scholar 

  • Kogut B, Zander U (1992) Knowledge of the firm, combination capabilities, and the replication of technology. Organ Sci 3(3):383–397

    Article  Google Scholar 

  • Kogut B, Zander U (1996) What firms do? Coordination, identity and learning. Organ Sci 7(5):502–518

    Article  Google Scholar 

  • Kozlowski SWJ, Chao GT, Grand JA, Braun MT, Kuljanin G (2013) Advancing multilevel research design: capturing the dynamics of emergence. Organ Res Methods 16(4):581–615

    Article  Google Scholar 

  • Lavie D, Rosenkopf L (2006) Balancing exploration and exploitation in alliance formation. Acad Manag J 49(4):797–818

    Article  Google Scholar 

  • Lavie D, Stettner U, Tushman ML (2010) Exploration and exploitation within and across organizations. Acad Manag Ann 4(1):109–155

    Article  Google Scholar 

  • Levin DZ (2000) Organizational learning and the transfer of knowledge: an investigation of quality improvement. Organ Sci 11(6):630–647

    Article  Google Scholar 

  • Levinthal DA (1997) Adaptation on rugged landscapes. Manag Sci 43(7):934–950

    Article  Google Scholar 

  • Levinthal DA, March JG (1993) The myopia of learning. Strateg Manag J 14:95–112

    Article  Google Scholar 

  • Lewes GH (1875) Problems of life and mind, vol 2. Kegan Paul, Trench, Turbner, and Co., London

    Google Scholar 

  • March JG (1991) Exploration and exploitation in organizational learning. Organ Sci 2(1):71–87

    Article  Google Scholar 

  • McGrath RG (2001) Exploratory learning, innovative capacity, and managerial oversight. Acad Manag J 44(1):118–131

    Article  Google Scholar 

  • Mckelvey B (1998) What is theory really? Toward a model-centered organization science. http://www.billmckelvey.org/documents/What%20is%20Theory.pdf. Accessed 13 March 2016

  • Mckelvey B (1999) Chapter 15: self-organization, complexity catastrophe, and microstate models at the edge of chaos. In: Baum JC, Mckelvey B (eds) Variations in organization science. Sage Publications, Thousand Oaks, pp 279–307

    Google Scholar 

  • Mckelvey B (2001) What is complexity science? It is really order-creation science. E:CO 3(1):137–157

    Google Scholar 

  • Mckelvey B (2004) Complexity science as order-creation science: new theory, new method. E:CO 6(4):2–27

    Google Scholar 

  • Mead GH (1932) Emergence and identity. In The philosophy of the present (Chapter 2, pp. 32–46), London, England: Open Court Publishing Company. Reproduced in Goldstein JA (2007) Classic paper: emergence and identity. E:CO 9(1):75–96

    Google Scholar 

  • Michaelides P, Milios J (2005) Did Hilferding influence Schumpeter? Hist Econ Rev 41(Winter):98–125

    Article  Google Scholar 

  • Morgan CL (1923) Emergent evolution. Williams and Norgate, London

    Google Scholar 

  • Mosakowski E, McKelvey B (1997) Predicting rent generation in competence-based competition. In: Heene A, Sanchez R (eds) Competence-based strategic management. Wiley, New York

    Google Scholar 

  • Pinder CC, Moore LF (1980) Middle range theory and the study of organizations. Springer, New York

    Book  Google Scholar 

  • Porter ME (1991) Towards a dynamic theory of strategy. Strateg Manag J 12(1):95–117

    Article  Google Scholar 

  • Porter ME (2008) The five competitive forces that shape strategy. Harv Bus Rev 86(1):78–93

    Google Scholar 

  • Priem RL, Butler JE (2001) Tautology in the resource-based view and implications of externally determined resource value: further comments. Acad Manag Rev 26(1):57–66

    Article  Google Scholar 

  • Raisch S, Birkinshaw J, Probst G, Tushman ML (2009) Organizational ambidexterity: balancing exploitation and exploration for sustained performance. Organ Sci 20(4):685–695

    Article  Google Scholar 

  • Rivkin JW, Siggelkow N (2007) Patterned interactions in complex systems: implications for exploration. Manag Sci 53(7):1068–1085

    Article  Google Scholar 

  • Robert-Mitchell J, Shepherd DA, Sharfman M (2011) Erratic strategic decisions: when and why managers are inconsistent in strategic decision making. Strateg Manag J 32(7):683–704

    Article  Google Scholar 

  • Schumpeter JA (1912) The theory of economic development. Harvard University Press, Cambridge

    Google Scholar 

  • Schumpeter JA (1942) [1950] Capitalism, socialism and democracy. Harper and Row, New York

  • Shimizu K (2012) Risks of corporate entrepreneurship: autonomy and agency issues. Organ Sci 23(1):194–206

    Article  Google Scholar 

  • Simon HA (1962) The architecture of complexity. Proc Am Philos Soc 106(6):467–482

    Google Scholar 

  • Sosa ME, Gargiulo M, Rowles C (2015) Can informal communication networks disrupt coordination in new product development projects? Organ Sci 26(4):1059–1078

    Article  Google Scholar 

  • Spender JC (2008) Organizational learning and knowledge management: whence and whither? Manag Learn 39(2):159–176

    Article  Google Scholar 

  • Starbuck WH (1992) Learning by knowledge-intensive firms. J Manag Stud 29(6):713–740

    Article  Google Scholar 

  • Stettner U, Lavie D (2014) Ambidexterity under scrutiny: exploration and exploitation via internal organization, alliances, and acquisitions. Strateg Manag J 35(13):1903–1929

    Article  Google Scholar 

  • Thomas JB, Sussman SW, Henderson JC (2001) Understanding strategic learning: linking organizational learning, knowledge management, and sensemaking. Organ Sci 12(3):331–345

    Article  Google Scholar 

  • Tippmann E, Mangematin V, Scott PS (2013) The two faces of knowledge search: new solutions and capability development. Organ Stud 34(12):1869–1901

    Article  Google Scholar 

  • Vancouver JB, Putka DJ, Scherbaum CA (2005) Testing a computational model of the goal-level effect: an example of a neglected methodology. Organ Res Methods 8(1):100–127

    Article  Google Scholar 

  • von Bertalanffy L (1968) General system theory: foundations, development, applications. George Braziller, New York

    Google Scholar 

  • Walter J, Levin DZ, Murnighan JK (2015) Reconnection choices: selecting the most valuable (vs. most preferred) dormant ties. Organ Sci 26(5):1447–1465

    Article  Google Scholar 

  • Ward-Smith J (2012) Mechanics of fluids, 9th edn. CRC Press, London

    Google Scholar 

Download references

Acknowledgments

I express deep gratitude to Prof. James March and Prof. Bill Mckelvey for their role in making this study possible. I thank Dr. Bhuvanesh Pareek for assistance in developing the mathematical model for the complexity construct. I thank Prof. Terrill L. Frantz, Editor-in-Chief and three anonymous reviewers for their constructive feedback that enhanced the quality of this work. All errors remain the author’s sole responsibility.

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  1. Strategic Management Group, Indian Institute of Management Indore, C-101 Academic Block, Prabandh Shikhar, Rau-Pithampur Road, Indore, MP, 453556, India

    Sasanka Sekhar Chanda

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Appendix: Mathematical structure underlying the complexity framing for computation of probability of organizational success

Appendix: Mathematical structure underlying the complexity framing for computation of probability of organizational success

The essential nature of the problem addressed in the paper constitutes matching a randomly generated reality string R, of size m bits, each of which can have one of two state values (let us call them A and B, standing for “1” and “−1”, respectively), with the corresponding values of another string, MyString (that stands for the organizational code), whose m dimensions can take values from the set {A, B, C}, where “C” stands for a value “0” or no opinion. MyString is generated by March’s adoption of the genetic algorithm, drawing from the work of Holland (1975). Thus, in the paper, different combinations of exploitation (p 1) and exploration (p 3) enable us to generate distinct MyString. Comparing MyString with R, we assess organizational knowledge and success probability.

Let us consider a simplified situation where the R string is generated with a probability P01 for a bit turning up with value “A” and probability (1 − P01) for a bit turning up with a value of “B”. Further, assume that MyString is generated with a probability P02 for a bit turning up with value “A” and probability (1 − P02) for a bit turning up with a value of “B”. Also, let k stand for the proportion of bits that must match in order that MyString may be given payoff. Accordingly, the total number of bits that need to match is given by m * k.

Now, consider a particular bit position, in both R and MyString. In the former, the probability of getting an “A” is P01, in the latter the same probability is P02. Thus, Q, the probability of getting the same value in that particular bit position, is given by:

$$Q = P01\,*\,P02 + (1 - P01)\,*\,(1 - P02).$$
(1)

Accordingly, the expected number of matches is given by m * Q.

Thus, by random draw without replacement from a pool of size m, the probability P overall of getting (m * k) exact matches, is given by:

$$P_{\_ \text{overall}} \,=\, {^{m\,*\,Q} {\rm C}_{m\,*\,k}} /^{m} {\rm C}_{m\,*\,k} .$$
(2)

P overall can be calculated by computational simulation as well. Suppose that, instead of generating MyString by the genetic algorithm (as in the reported results), we generate MyString with a process involving random draws with probability P02. Thereafter we pick random sets of m * k bits, say L = 500 of them in all, and assign payoff of 1 unit only if MyString and R match in all the bit-positions in a given random set. We compute the value of P overall as the proportion of L evaluations that is non-zero.

We observe in Table 3 that that this process generates values fairly close to that given by (2). However, the deviation from the formulaic prediction increases as complexity increases, since smaller numbers introduce larger rounding errors. Using a higher value of L (say L = 1000) does not get a significantly closer match. Moreover, using a high L where each experiment is performed I = 10,000 times becomes quite computation intensive. Therefore we recommend L = 500 for future studies of this kind.

Table 3 Comparison of P overall values attained for P01 = 0.50 by formula and by simulation
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Chanda, S.S. Inferring final organizational outcomes from intermediate outcomes of exploration and exploitation: the complexity link. Comput Math Organ Theory 23, 61–93 (2017). https://doi.org/10.1007/s10588-016-9217-1

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