The Emergence of Norms in Competitive Decision-Making Groups

Bettenhausen, Kenneth L.; Murnighan, J. Keith

doi:10.2307/2392667

Cited by 471 publications

(267 citation statements)

References 27 publications

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“…For example, at SCC a large amount of adjustment and In almost every case, the existence of a later spun of adaptive activity at BBA was associated with a specific, disruptive event in the project life cycle (see Table 7 (Weick, 1990:21). Weick points to Barley's (1986) (Hedberg, Nystrom and Starbuck, 1976), groups (Gersick, 1988;Bettenhausen and Murninghan, 1985) and individuals (Luchins, 1942;Langer and Imber, 1979 (Hackman, 1990). Bettenhausen and Muminghan (1985) find that when group members threaten or challenge group norms, they create an opponunity to consider new altemanves and to build a new, stronger consensus.…”

Section: Methodsmentioning

confidence: 99%

Windows of Opportunity: Temporal Patterns of Technological Adaptation in Organizations

Tyre

Orlikowski

1994

Organization Science

689

411

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(Rosenberg, 1982;Dutton and Thomas, 1985). These problems, in turn, require adaptation of the technologies already in use.A close understanding of the process of adaptation is critical for several reasons. First, users' adaptations to technologies-in-use often help to shape funher development and research activities (von Hippel. 1988;Dutton and Thomas, 1985). Second, the operating efficiency ultimately achieved with a new technology depends heavily on users' modifications (Enos, 1958;Hollander, 1965;Dutton and Thomas. 1984). Third, modifications affect not just the technologyin-use, but also its physical and organizational context (Leonard-Banon, 1988 (1983) states that when organizations try to rush the introduction process, they fail to identify and correct problems that later hamper productive use of the technology. Thus, "too-rapid implementation of the innovation ... can lead to disastrous results" (Rogers, 1983:364). Similarly, Hughes (1971:152) maintains that "trying to force the pace" of adaptation is counterproductive, while Hage and Aiken (1970:106) suggest that "the longer the elite allow [the] period of tnal and error to continue, the greater the chances of the new program achieving its intended objectives." Finally, Imai (1986) (Kiesler and Sproull, 1982;Starbuck, 1989). Groups in organizations also develop tendencies toward routine behaviors. Over time, they become increasingly unlikely to recognize and respond to new kinds of problems (Kelley and Thibaut, 1954;Katz, 1982;Hackman, 1990). Even research teams have been shown to be reluctant to alter a given technical approach once it has been selected, and the longer the approach has been used, the greater their rigidity (Allen, 1966).At the individual level, research suggests that people's arousal, attention, and motivation to engage in effortful problem solving is not constant over time. Specifically, active problem solving and information processing appear to drop sharply as soon as tasks become familiar or manageable (Langer and Imber, 1979;Kruglanski and Freund, 1983). With increasing exposure, observers tend to "chunk" activities into larger units that convey less information than fine-grained observauons, although a sudden surprise can sometimes reverse the process (Newtson, 1973; Louis and Sutton, 1991). Familiarity also breeds rounnized response patterns; once activities are well entrenched, even superficial resemblance to a known stimulus is sufficient to tngger a familiar response (Luchins, 1942).One of the few scholars to have considered the implications of these behavioral tendencies for technological adaptation is Weick (1990). Following Winner (1986), Weick (1990:21) suggests that "the point at which technology is introduced [may be) the point at which it is most susceptible to influence." Weick argues that "beginnings are of special imponance ... because they constrain what is learned about the technology and how fast it is learned " (1990:21-22 We deliberately sought vanety in the settings studied, the technologies...

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Section: Methodsmentioning

confidence: 99%

Windows of Opportunity: Temporal Patterns of Technological Adaptation in Organizations

Tyre

Orlikowski

1994

Organization Science

689

411

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“…Note that the Shapley value is not necessarily in the core, even if the core is nonempty. § See also, e.g., the role of equity considerations in coalition bargaining (38) and a survey of recent evidence in various non-cooperative games (39). We caution that, because unlike Nash we implement a finite multistage game, Nash's simulation results are only suggestive for our data analysis, which is why we complement our analysis with hypotheses from cooperative and non-cooperative game theory.…”

Section: ‡mentioning

confidence: 99%

The agencies method for coalition formation in experimental games

Nash

Nagel

Ockenfels

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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In society, power is often transferred to another person or group. A previous work studied the evolution of cooperation among robot players through a coalition formation game with a noncooperative procedure of acceptance of an agency of another player. Motivated by this previous work, we conduct a laboratory experiment on finitely repeated three-person coalition formation games. Human players with different strength according to the coalition payoffs can accept a transfer of power to another player, the agent, who then distributes the coalition payoffs. We find that the agencies method for coalition formation is quite successful in promoting efficiency. However, the agent faces a tension between short-term incentives of not equally distributing the coalition payoff and the long-term concern to keep cooperation going. In a given round, the strong player in our experiment often resolves this tension approximately in line with the Shapley value and the nucleolus. Yet aggregated over all rounds, the payoff differences between players are rather small, and the equal division of payoffs predicts about 80% of all groups best. One reason is that the voting procedure appears to induce a balance of power, independent of the individual player's strength: Selfish subjects tend to be voted out of their agency and are further disciplined by reciprocal behaviors.leader | rules | reciprocity | fairness T he evolution of human altruism and cooperation is a puzzle. Unlike other animals, people frequently cooperate even absent of any material or reputational incentive to do so. In this paper we show how a voting procedure to transfer power to another person successfully promotes cooperation by balancing the tension between short-term incentives to defect and long-term incentives to keep cooperation going. Our work is inspired by John Nash (1), who theoretically studied the evolution of cooperation among robot players through acceptance of an agency of another player.Beyond Nash's (1) work, there is virtually no work on the agencies method in (experimental) economics as we apply it in our paper. The underlying idea is simple and important: Human subjects can transfer the power to an agency, who determines the final payoff distribution within the group.* Our game reflects that, often, efficiency requires people's willingness to accept the agency of others, such as political, social, or economic leaders (for voting of an expert, see ref. 7).In Nash's (1) work, the robots employed optimal strategies, being the computational result of complex systems of equations. Motivated by Nash's paper, we study laboratory three-person coalition formation games with a non-cooperative procedure of acceptance of an agency of another player. The base games are finitely repeated for 40 rounds with the same three subjects, allowing cooperation and coordination to evolve. In our games non-cooperative game theory cannot organize behavior because it is basically consistent with any outcome. Thus, even the strategies of fully rational agents cannot be predi...

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“…Organizational routines consist of visible and invisible factors that define how members interact among themselves and with technologies, such as culture, rules, standards, codes, procedures, and forms (Levitt and March 1988;Becker 2004;Pentland and Feldman 2005). Those routines evolve as members retain workable ones and discard unworkable ones each time they encounter uncertain situations, and thus routines are repositories of results of organizational learning (Nelson and Winter 1982;Feldman 1984;Battenhausen and Murninghan 1985;Gersick and Hackman 1990;Cohen 1996;Cohen and Bacdayan 1996;Feldman and Pentland 2003;Becker 2004;Pentland and Feldman 2005). Organizational learning occurs when members cannot attain their aspiration levels-in other words, organizational performance is below their expectations (March and Simon 1958;Levitt and March 1988).…”

Section: Literature Review: How Justification Shift May Occurmentioning

confidence: 99%

“…Each time members encounter uncertain situations, they first rely on similar cases to decide what to do, and then evaluate how their choices do or do not work (Weick 1979(Weick , 1995Battenhausen and Murninghan 1985). Workable choices are selected and retained, becoming routines, whereas unworkable ones are discarded.…”

Section: Literature Review: How Justification Shift May Occurmentioning

confidence: 99%

Justification shift and uncertainty: why are low-probability near misses underrated against organizational routines?

Shimazoe

Burton

2012

Comput Math Organ Theory

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Knowledge of near misses is helpful in preventing accidents, but it does not always lead to changes in organizational routines or contribute to accident prevention. In this article, the authors argue that low-probability near misses reinforce beliefs of experts and professionals in existing routines, proposing a concept of justification shift. Justification shift is underestimation of risks of known near misses vis-à-vis overestimation of reliabilities of existing routines. Consequently, signals of "close calls" tend to be disregarded, and accidents become more likely. When justification shift occurs, experts and professionals who want changes in routines face burden of proof about risks of those routines. Uncertainty in requirements and data may increase the burden and make justification shift more likely. To explore how justification shift occurs and how uncertainty influences the shift, the authors have developed an agent-based model based on theories in organizational studies and the case of the space shuttle Challenger accident in 1986. From the simulation using the model, it becomes clear that uncertainty does not make a difference in frequencies and degrees of justification shift. The authors also discuss implications of this finding to efforts to utilize knowledge of near misses and to further research on the efforts and organizational decisions.

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The Emergence of Norms in Competitive Decision-Making Groups

Cited by 471 publications

References 27 publications

Windows of Opportunity: Temporal Patterns of Technological Adaptation in Organizations

Windows of Opportunity: Temporal Patterns of Technological Adaptation in Organizations

The agencies method for coalition formation in experimental games

Justification shift and uncertainty: why are low-probability near misses underrated against organizational routines?

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