Marylène Gagné

Bonuses are notoriously used to motivate workers. How effective are they at doing so, and might there be unintended consequences? We investigated the effectiveness of bonuses based on game profitability on the motivation of video game developers by examining specific bonus characteristics that align with advice derived from expectancy theory. We also investigated if bonuses encouraged or discouraged moral engagement and corner‐cutting behavior through their effects on work motivation as conceptualized through self‐determination theory. Company data on bonus characteristics coupled with surveys from 1024 game developers in a video game company indicated that the size of the last received bonus did not influence current work motivation. Uncertain probability of getting the next bonus installment was related to a lack of motivation, while more certain probability was related to higher intrinsic motivation. The probable size of the next bonus was related to lower external regulation and to higher intrinsic motivation, contrary to predictions from most motivation theories. Both probability certainty and bonus size probability were indirectly negatively associated with moral disengagement and corner‐cutting behaviors via decreasing amotivation and external regulation. Overall, results show that the bonus system in this company had limited effects on motivation and on discouraging deviant behaviors and point to how such systems can be improved using advice from theory.

Summary: Implementing successful organizational change is challenging, and to better understand it, researchers and practitioners have proposed a variety of models of change management. Paradoxically, while the behavior change of organizational members lies at the core of organizational change, theories and models of behavior change are rarely applied to organizational change management. Such knowledge could improve our understanding of underlying psychological mechanisms behind organizational change adoption and maintenance. Using self‐determination theory as the framework, we developed a model of organizational change that integrates knowledge across the organizational and behavior change fields by (i) unifying change practices derived from organizational change models and from behavior change techniques, (ii) identifying and unifying underlying psychological mechanisms derived from theories that explain how organizational and behavior change occurs, and (iii) linking change practices to psychological mechanisms. The resulting model aims to provide theoretical and practical guidance for those involved in the planning and implementation of organizational change.

Management practice is informed by fundamental assumptions about human motivation. We review two contrasting perspectives: agency theory – which assumes that humans are self‐interested rational beings whose actions should be constrained to achieve organizational goals (which are opposing) – and self‐determination theory – which assumes that individuals will thrive when they have autonomy to pursue activities and can internalize external goals when their needs are satisfied. We highlight how the assumptions of agency theory continue to dominate the design and implementation of management practices and management education, despite decades of evidence that individuals are not solely driven by economic rationality. We suggest that attempts to refine these assumptions have so far fallen short of adequately representing human motivation and highlight an important aspect of self‐determination theory which is often neglected from these debates: how people come to internalize goals. Placing motivation internalization as more central to management thinking yields practices that more effectively align the interests of employees and organizations.

Algorithmic management (AM) refers to a system of control where algorithms are used to manage workers. With the notion of control at the centre of its definition, it is no surprise that the rapidly growing literature on AM has revealed its deleterious impact on workers’ autonomy and on other employee outcomes. In this chapter, we draw on the well-established self-determination theory of motivation as a key framework to 1) discuss the evidence on the motivational effect of current forms of algorithmic management and 2) move beyond the current research inertia characterised by a somewhat fatalistic investigation of negative consequences. We develop a framework of motivation-enhancing algorithmic management characteristics and practices to guide future research and AM implementation in a way that is more conducive to satisfying workers’ psychological needs and enhancing their self-determined motivation.

Despite decades of research on teams, there are still gaps in our understanding of motivational dynamics within teams and the emergence of team‐level motivation. We advance a new team motivation model that invokes self‐determination theory (SDT), multilevel theory, emergence processes, and identity construction. Using the conceptualization of motivation offered by SDT, we define team motivation as a collective source of energy driving the direction, intensity, and persistence of team activities. By using SDT to develop the process‐based team motivation emergence model, we describe the role of human psychological needs that are involved in the emergence of this collective construct. An interpersonal feedback loop intertwined with a team process feedback loop predict how team members' individual motivations converge and then transform into team‐level motivation through a process of identity construction. Propositions for testing the model are advanced, as well as suggestions for methodological and analytical considerations.

This 2-day seminar is designed to enhance participants' understanding and application of Self-Determination Theory (SDT) across various fields including psychology, education, organization science, and public health. Participants will explore SDT's theoretical foundations as a theory of motivation, recent methodological approaches, and practical applications, gaining tools to implement SDT principles effectively in their research and design more effective interventions.