Adoption of innovations: modelling the interplay of behavioural biases, incentives and network structure
Creators: name: Feylessoufi, Antoine
Publisher: name: Apollo - University of Cambridge Repository
Year: 2020
Subjects: Innovation Adoption
Population Dynamics
Behavioural Biases
Evolutionary Game Theory
Network

description: A crucial challenge faced by most large organisations concerns their ability to effectively adopt new operational practices. Many streams of literature have looked into this challenge and generally, two reasons have been given to explain the difficulty of adopting: the uncertain utility an individual gets by adopting the new practice and the social influences between adopters. Chapter 2 gives a detailed multi-disciplinary review (sociology, economics, marketing, organisational behaviour, operations management and operational research) exploring the main determinants and mechanisms of practice adoption identified over the years and opportunities for the field of operations management. In particular, one key opportunity unexplored by the existing literature (highlighted by numerous examples and industry cases throughout the dissertation) is the importance of the relative benefits felt by adopters compared to others' choices due to behavioural considerations resulting from social interactions such as social comparisons. To study these effects fully, existing analytical methodologies, such as classic game theory or dynamic programming used in the field, prove to be limiting. Thus, I introduce a novel analytical methodology called evolutionary game theory. I propose a series of evolutionary game theoretic models in the following chapters to give novel insights in the adoption challenge by exploring the impact of these relative benefits in the decision-making process to adopt or not. Chapter 3 is a seminal paper exploring the effects of these relative benefits to the adoption choice. I explore how (i) the types of reward systems (individual and/or collective) employed by an organisation to induce adoption, and (ii) the way social comparisons (namely behind-averse and ahead-seeking) affect individual employee utilities, shape the eventual adoption. Behind-averse social comparisons are felt by individuals when they experience a disutility of having a worse outcome than others. Ahead-seeking comparisons, on the other hand, are felt when individuals experience a utility boost by having a superior outcome than others. I find that under certain circumstances the better practice may not be fully adopted. Behind-averse social comparisons drive a bandwagon effect phenomenon whereby full adoption or no-adoption occurs depending on the critical mass of initial adopters. In contrast, the initial mass of adopters does not have any effect in the presence of ahead-seeking comparisons. These lead to the coexistence of "competing'' practices, because of the attempts of adopters to differentiate from each other. The organisational rewards moderate these two outcomes in non-intuitive ways. Specifically, collective rewards help moderate the coexistence of the "competing'' practices under ahead-seeking comparisons and individual rewards play a key role to the adoption of high risk new practices under behind-averse social comparisons. Chapter 4 further develops the concept of relative benefits of adoption introduced in Chapter 3 and proposes an evolutionary game theoretic model allowing heterogeneity in the teams of employees in their capabilities to successfully adopt the new practice, in the type of social comparisons and in the level of interactions between the two teams. Past literature in operations management advocates that information exchange and collaboration between teams help adoption. I mathematically model this collaboration concept in the form of wide and narrow bridges that represent the level of interactions between the two teams. I find that that these two types of bridges do not impact teams the same way due to the heterogeneous relative benefits. Contrary to past literature in operations management, I find that wide bridges may not always be the best solution to put in place in organisations. The choice that an organisation has to make between allowing wide or narrow bridges also depends on the upfront training investment it is willing to do. This analysis provides evidence for management on when adoption can benefit from increased collaboration between teams. This chapter also brings new theoretical insights to the current academic discussion on wide versus narrow bridges for diffusion of new practices in sociology and the ongoing debate on the creation of star teams in organisations for better performance. Chapter 5 further investigates the impact of heterogeneous social interactions in the adoption of new practices introduced in Chapter 4 by introducing a network structure guiding the social interactions. I study two classic production network topologies, chain (decentralised) and hub-and-spoke (centralised), in the adoption of new practices in two distinct social settings, behind-averse and ahead-seeking. I find that the outcome in terms of average adoption differs between the two networks in each of the two social comparison regimes. Moreover, I find that the impact of economic incentives is heterogeneous among the network plants and depends on their connectivity. Interestingly, periphery nodes in the networks are the most sensitive to economic incentives. These findings give insights on the type of structure to promote for organisations (centralised or decentralised) and the placement of certain units in the network to ensure they stay innovative, such as the R&amp;D departments. These have been significant concerns for manufacturing firms to which this chapter provides guidance. In conclusion, I address in this dissertation a conceptual gap in the literature on the relative benefits experienced by individuals to adopt new practices and innovations. I model how key behavioural biases (ahead-seeking and behind-averse comparisons) trigger these relative benefits. Moreover, I use a novel methodology to the topic (evolutionary game theory) which grasps better the essence of the adoption challenge than existing methodologies. In addition, I study how the type of rewards (individual or collective) and the organisational structure (wide vs narrow bridges, decentralised vs centralised) can enhance or mitigate these behavioural effects.