The Role of Dynamic Incentives in Customer Engagement: Applications in Gaming and Gamification Pubblico

Abstract

Dynamic incentive schemes are rewards structures widely used to motivate and engage customers in marketing practice. Compared to the traditional loyalty programs, dynamic incentives in the digital era are more flexible and accessible with more features at firms’ disposal (outcome uncertainty, social status etc.). This evolvement of dynamic incentives raises academically and managerially important questions.

My first essay studies how customer decisions are influenced by their past investments in a product and by expectations of future rewards. Investments in learning product related knowledge, purchases of add-ons or the accumulation of loyalty points can all create switching costs that result in behavioral loyalty. Similarly, dynamic incentive schemes like loyalty programs can increase loyalty measures because consumers base current decisions on expectations of future benefits. With a data set from the video game industry, I find that the effectiveness of rewards varies based on reward types and the level of customer investments. Rewards that help players explore the game become less attractive after commitment while rewards that help players progress are effective with or without commitment. Furthermore, I find that players’ decisions to invest in the game depend on the breadth of game content experienced. These findings have implications for designing rewards systems in product categories involving customer learning.

My second essay focuses on the impact of outcome uncertainty in gamification: the usage of gaming principles and elements in non-gaming contexts. While sharing some common elements with loyalty programs including dynamic incentives and status, games and gamification are unique in their outcome uncertainty, rendering consumers less confident of whether they can achieve goals. Using data from a mobile app, I find that points pressure effects exist in gamification settings: as players approach the next prize level, their motivation to participate increases. I also find that for some customers multiple losses motivate continued play, consistent with Gambler’s fallacy. Moreover, I find status comparisons can only motivate behavior to a point: positive status comparisons can fuel continued effort until players achieve the highest status, after which status decreases future effort. My results have important implications for firms who apply game concepts to nongaming applications.

Table of Contents

Chapter 1 ......................................................................................................................................... 1

Chapter 2 ......................................................................................................................................... 4

2.1 Introduction ...................................................................................................................... 4

2.2 Related Literature ............................................................................................................. 7

2.1.1 Background ........................................................................................................................... 7

2.2.2 Dynamic Incentive Schemes ............................................................................................... 10

2.2.3 Customer Investment and Switching Costs......................................................................... 13

2.2.4 Summary ............................................................................................................................. 16

2.3 Data and Preliminary Analysis ....................................................................................... 17

2.3.1 The Game ............................................................................................................................ 17

2.3.2 Sample ................................................................................................................................. 20

2.3.3 Preliminary Analysis ........................................................................................................... 22

2.4 Model ............................................................................................................................. 25

2.4.1 Observation Equations ........................................................................................................ 26

2.4.2 State Equation ..................................................................................................................... 30

2.4.3 Estimation ........................................................................................................................... 30

2.5 Results and Analysis ...................................................................................................... 33

2.5.1 Model Results ..................................................................................................................... 33

2.5.2 Simulation Studies .............................................................................................................. 39

2.6 Implications and Limitations .......................................................................................... 41

Chapter 3 ....................................................................................................................................... 45

3.1 Introduction .................................................................................................................... 45

3.2 Related Literature ........................................................................................................... 48

3.2.1 Uncertainty .......................................................................................................................... 48

3.2.2 Uncertainty and Reward Proximity ..................................................................................... 49

3.2.3 Uncertainty and Status ........................................................................................................ 51

3.2.4 Uncertainty in Performance Outcomes ............................................................................... 52

3.3 Data and Model Free Analysis ....................................................................................... 53

3.3.1 The Mobile Application ...................................................................................................... 54

3.3.2 Sample ................................................................................................................................. 55

3.3.3 Model Free Analysis ........................................................................................................... 58

3.4 Model ............................................................................................................................. 59

3.4.1 The Nonhomogeneous Hidden Markov Model ................................................................... 60

3.4.2 Likelihood and Estimation .................................................................................................. 64

3.5 Empirical Analysis ......................................................................................................... 66

3.5.1 Static Model Results ........................................................................................................... 66

3.5.2 Nonhomogeneous HMM Results ........................................................................................ 68

3.6 Discussion ...................................................................................................................... 72

Chapter 4 ....................................................................................................................................... 76

Bibliography ................................................................................................................................. 79

About this Dissertation

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School	Laney Graduate School
Department	Business
Subfield / Discipline	Marketing
Degree	Ph.D.
Submission	Dissertation
Language	English
Research Field	Business Administration, Marketing
Parola chiave	Loyalty Programs Dynamic Icentives Gamification Video Games Bayesian Methods
Committee Chair / Thesis Advisor	Mike Lewis, Emory University
Committee Members	Panagiotis Adamopoulos , Emory University Michelle Andrews, Emory University Yi Zhao, Georgia State University

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