Using cognitive modeling to examine the effects of competition on strategy and effort in races and tournaments

We investigated the effects of two types of competition, races and tournaments (as well as an individual challenge and a do-your-best condition), on two different aspects of performance: effort and strategy. In our experiment, 100 undergraduate participants completed a simple cognitive task under four experimental conditions (in a repeated-measures design) based on different types of competitions and challenges. We used the Linear Ballistic Accumulator to quantify the effects of competition on strategy and effort. The results reveal that competition produced changes in strategy rather than effort, and that trait competitiveness had minimal impact on how people responded to competition. This suggests individuals are more likely to adjust their strategy in competitions, and the uncertainty created by different competition types influences the direction of these strategy adjustments.

Clarifying dynamics for organizational research and interventions: A diversity example

Interventions backfire for many reasons, one being that the dynamics governing a system are not well-understood. To better explain organizational phenomena, and to intervene in ways that yield desired consequences, an appreciation of the core insights from dynamics may be necessary to include into a researcher's toolkit. Although substantial buzz surrounds the term dynamics in organizational science, conceptual missteps are present in the literature, ultimately limiting its application. We provide a coherent description of what dynamics encompasses by (1) advancing a definition and of dynamics and comparing it to other longitudinal concepts, (2) proposing theoretical principles to help researchers apply dynamics concepts to their own research, and (3) demonstrating how dynamics may refine our ability to explain organizational phenomena and devise practical implications. Plain Language Summary Organizational researchers and practitioners are faced with many complex issues and understanding how they unfold over time is difficult. Those difficulties often lead to unexpected results when we try to address those issues. Viewing organizations from a more dynamic lens can shed light on how phenomena evolve over time as governed by the iterative application of mathematical rules. Illumination of dynamics can assist with better targeting interventions and understanding their potential effects. A dynamics lens also emphasizes continuing needs to improve research methods, such as collecting longitudinal data and engaging in computational modeling. To illustrate these points, we discuss how diversity interventions in organizations may be better understood once dynamics are applied. For example, organizations wishing to improve the diversity of their work forces must focus both on why underrepresented groups enter the organization and why they might leave. Focus on only one or the other is unlikely to generate the desired effect. Once identifying the drivers of both entering and leaving and how those factors might reinforce each other, interventions to improve diversity might be better targeted.

Computational Modeling of the Value Co-Creation Process in Customer Service: An Application of the NK Model

There has been an increasing interest to explore and gain knowledge about customer engagement behavior among academia and practitioners. Particularly, the value co-creation process in customer services is essential to explore the interaction structure. In this study, we applied the computational simulation of the NK model to identify the value co-creation process between service employees and customers in the service context. To specifically explore the dynamic interaction among them, we identified what kind of service is provided for what type of customers and when service performance improves according to the degree of interaction between service employees and customers. The simulations show that the greatest service value can be achieved when employees and customers jointly perform local search (90%) and long jump (10%). However, if both employees and customers jointly perform local search only, the service value can be stuck in a local optimum. In cases where employees and customers make their independent improvement, either through local search or long jump, the overall service value varies depending on the complexity of interactions between employees and customers. For example, the improvement in service value is the worst when employees and customers make long jumps at independent timings in high complex interactions. Our computational simulations offer visible experimental-based insights into understanding the value co-creation process with customers and promising results for customer service studies.

An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data

Some of the most influential theories in organizational sciences explicitly describe a dynamic, multilevel process. Yet the inherent complexity of such theories makes them difficult to test. These theories often describe multiple subprocesses that interact reciprocally over time at different levels of analysis and over different time scales. Computational (i.e., mathematical) modeling is increasingly advocated as a method for developing and testing theories of this type. In organizational sciences, however, efforts that have been made to test models empirically are often indirect. We argue that the full potential of computational modeling as a tool for testing dynamic, multilevel theory is yet to be realized. In this article, we demonstrate an approach to testing dynamic, multilevel theory using computational modeling. The approach uses simulations to generate model predictions and Bayesian parameter estimation to fit models to empirical data and facilitate model comparisons. This approach enables a direct integration between theory, model, and data that we believe enables a more rigorous test of theory.