Multidisciplinary learning through collective performance favors decentralization

Many models of learning in teams assume that team members can share solutions or learn concurrently. However, these assumptions break down in multidisciplinary teams where team members often complete distinct, interrelated pieces of larger tasks. Such contexts make it difficult for individuals to separate the performance effects of their own actions from the actions of interacting neighbors. In this work, we show that individuals can overcome this challenge by learning from network neighbors through mediating artifacts (like collective performance assessments). When neighbors' actions influence collective outcomes, teams with different networks perform relatively similarly to one another. However, varying a team's network can affect performance on tasks that weight individuals' contributions by network properties. Consequently, when individuals innovate (through ``exploring'' searches), dense networks hurt performance slightly by increasing uncertainty. In contrast, dense networks moderately help performance when individuals refine their work (through ``exploiting'' searches) by efficiently finding local optima. We also find that decentralization improves team performance across a battery of 34 tasks. Our results offer design principles for multidisciplinary teams within which other forms of learning prove more difficult.Comment: 11 pages, 8 figures. For SI Appendix, see Ancillary files. For accompanying code, see https://github.com/meluso/multi-disciplinary-learning. For accompanying data, see https://osf.io/kyvtd

Networked Miscommunication: The Relationship Between Communication Networks, Misunderstandings, and Organizational Performance

In this dissertation, I introduce and define the concept of networked miscommunication – unintentional, aggregated effects of communication practices throughout an organization – and demonstrate its deleterious impacts on organizational performance through case studies and models. While “miscommunication” features prominently in accounts of high-profile complex system accidents, researchers have yet to demonstrate how communicative misunderstandings degrade organizational performance more generally. I show that while miscommunication costs can result from misunderstandings distributed throughout an organization’s communication networks, they also arise whenever a networked communicative interaction falls short of a desired organizational outcome. In my framework, miscommunication is not merely mistakes; practitioners can also be strategically ambiguous. Competitive environments make strategic ambiguity more likely than do cooperative organizational cultures. I therefore hypothesize that fostering cooperation over competition can improve organizational performance while also increasing equity. I begin by exploring the responsibilities organizations bear as they develop, operate, and manage the complex systems that pervade modern society – whether those systems involve manufacturing, healthcare, or finance. Complex systems contain large collections of highly interacting, tightly coupled elements, making them susceptible to “normal accidents” such as Three Mile Island (Perrow, 1981, 2011). Organizations that suppress dissent, as was the case with the Challenger Space Shuttle disaster, will be more prone to these accidents (Vaughan, 1997). More recently, the 2018 Hawaii Ballistic Missile False Alarm highlights how misunderstandings in organizational communication networks affect complex system performance and hence organizational performance. This last type of failure is the primary focus of this dissertation. After a review of the literature on communication and miscommunication, I dually define miscommunication: pragmatically as communication problems that negatively affect goal attainment, and integratively as misunderstandings that prevent participants from balancing their values. I then define networked miscommunication and present three studies that I use to identify a surprising and impactful type of unintentional communicative misunderstandings concerning the meaning of the term “estimates.” I demonstrate how heterogeneous meanings of the word estimate both do and don’t affect organizational performance. My first study reveals that expert practicing engineers use cognitive heuristics and strategic ambiguity to shape estimates of their designs. I then demonstrate how these behaviors increase system uncertainty via an Agent-Based Model and Monte Carlo simulation (Meluso & Austin-Breneman, 2018). To understand the strategic uses of estimates, I study a Fortune 500 company and find widespread variation among practicing engineers about what an “estimate” means independent of their division, title, and phase of product development. While some practitioners define estimates as approximations of current designs, others define them as approximations of future designs, points in a project which could be years apart. Importantly, engineers inadvertently aggregate estimates of different types into single values that inform programmatic decision-making, thereby constituting networked miscommunication (Meluso et al., 2020). The third study, however, reveals a nuanced picture in which varied estimate definitions conditionally degrade organizational performance. In particular, future estimates degrade complex system performance relative to current estimates, constituting networked miscommunication despite a lack of misunderstandings. I also find that some misunderstandings can protect an organization from performance degradation. In organizations with equal use of current and future estimates, current estimates buffer systems against degradation caused by future estimates, indicating that performance degradation depends on communication network structure (Meluso et al., 2019). Collectively, these studies demonstrate the potential of networked miscommunication to affect organizational performance.PHDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155145/1/jmeluso_1.pd

How do managers evaluate individual contributions to team production? A theory and empirical test

Research SummaryOrganizations rely on subjective evaluations to reward employees for team-based performance. However, it is unclear how supervisors determine individuals’ contributions to collective output. We theorize that supervisors rely on the covariance between employees’ presence and their teams’ productivity. If teams are more productive when an employee is present, the supervisor may infer a greater contribution from the employee. Using data from a manufacturing firm, we find that covariation between an employee’s presence and her team’s output has a positive effect on her evaluation. This relationship is stronger when supervisors have more opportunities to observe an employee across various teams and when the employee has more authority to direct team production, supporting counterfactual information as an important component of evaluations for individuals engaged in team production.Managerial SummaryIt is notoriously difficult to evaluate the individual performance of employees when the only available metric is team-based output. We suggest that supervisors help solve this problem by observing how team output correlates with changes in team membership. We construct a measure of the covariance between an employee’s presence in a team and the team’s productivity, and find a positive relationship between this measure and the employee’s annual subjective performance evaluation. Our results indicate that subjective evaluations reflect individual contributions to team production fairly well for employees who (a) have sufficient authority to direct team production and (b) are frequently rotated beyond a single team. We discuss what kinds of organizations might benefit from this measure as an input to their performance evaluation processes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175231/1/smj3433.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175231/2/smj3433_am.pd