Technology Investment Decision-Making under Uncertainty in Mobile - - PowerPoint PPT Presentation

Technology Investment Decision-Making under Uncertainty in Mobile Payment Systems

Robert J. Kauffman, Jun Liu and Dan Ma

School of Information Systems Singapore Management University

Introduction

• 2012, the year in payments
• Mobile payments:
• Near field communication

(NFC)-enabled

• Cloud-based
• Third party app

Apple Passbook

Motivation

• Uncertainties
• Consumer demand
• Multi-sided business platform
• Revenue model and collaboration
• NFC smartphones and merchant terminals
• Technology standard and regulation
• Banks are key stakeholders in payments.
• Decision making under uncertainty
• Research Questions:
• How can a bank maximize the business value of m-

payments technology adoption under uncertainty?

• How long can a bank postpone its investment and

commitment to a specific technological solution?

Cost, Benefits and Deferral

• Investment time

horizon [0, T]

• Investment cost I

follows geometric Brownian motion:

• Benefit flows B from time t to T follow geometric Brownian

motion:

2 4 6 8 10 12 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Value Time

Geometric Brownian Motion

Investment Cost Benefit Flows

Model Preliminary

• No competitor, no correlation
• The value equals to integration over the interval (t, T) is:
• Expected investment cost I is:

Invest I Wait t T

Receive Benefits Flows

Real Option Value

• Deferral Option:

𝑂𝑄𝑊 𝐵 = 𝑊 − 𝐽 + 𝑆𝑃𝑊 = max⁡ (𝑊 − 𝐽, 0) 𝑆𝑃𝑊 = 𝑛𝑏𝑦 0, 𝐽 − 𝑊

• By Bellman optimality equation, 𝑠

𝑔𝑆𝑃𝑊𝑒𝑢 = 𝐹(𝑒𝑆𝑃𝑊), we

• btain:

1 2 𝜏𝐶2𝐶2𝑆𝑃𝑊

𝐶𝐶 + 1

2 𝜏𝐽2𝐽2𝑆𝑃𝑊

𝐽𝐽 + 𝛽𝐶 − 𝜃𝐶 𝐶𝑆𝑃𝑊 𝐶 + 𝛽𝐽 − 𝜃𝐽 𝐽𝑆𝑃𝑊 𝐽

+ 𝑆𝑃𝑊

𝑢 − 𝑠 𝑔𝑆𝑃𝑊 = 0

Two boundary conditions: 𝑆𝑃𝑊 𝐶, 𝐽, 𝑈 = 0,

𝑆𝑃𝑊 𝐶, 𝐽, 𝑢 ≥ 0⁡⁡⁡⁡∀⁡0⁡ ≤ ⁡𝑢⁡ < ⁡𝑈

Numerical Analysis

Description Value Description Value 𝐽0 Initial investment $10 million 𝐶0(t) Initial benefit flow $0.1-1.0 million 𝛽𝐽 Rate of cost change

• 0.1

𝛽𝐶 Rate of benefit change 0.7 𝜏𝐽 Cost uncertainty 0.2 𝜏𝐶 Benefit uncertainty 1.0-0.1 T Maximal deferral time 5 years 𝑠

𝑔

Risk-free discount rate 6% N

• No. of simulated paths

100,000 Δt Duration of time step 1 month

Investment timing benchmark simulation

Optimal timing t = 14 month; Maximal payoff is $4.10 million

Sensitivity Analysis

Sensitivity analysis of benchmark simulation with respect to T, rf and αB

Benchmarking, t = 14 When T = 6 years, t = 13 When rf = 0.5, t = 13 When αB = 0.8, t = 11 When T = 4 years, t = 15 When rf = 0.7, t = 16 When αB = 0.6, t = 18

Jump Diffusion Process

• Benefit flows when jump events happen:

Benefit flow = Continuous Benefit Flows + Jump Value 𝑒𝐶 = 𝛽𝐶 + 𝜇𝑙 𝐶𝑒𝑢 + 𝜏𝐶𝐶𝑒𝑨 + (𝑍 − 1)𝐶𝑒𝑟

Description Value Description Value λ Mean jumps number 0.05 K % change of benefits 0.5 Upward Jump at t = 20, t = 12 Catastrophic Jump at t = 10, t = 14 Benchmarking, t = 14

Jump Diffusion Simulation

Upward Jump at t = 10, t = 9 Upward Jump at t = 4, t = 14 Catastrophic Jump at t = 20, t = 20 Catastrophic Jump at t = 40, t = 15

• Least-Squares Monte Carlo Method (Longstaff and Schwartz)

With Jump

Conclusion

• Managerial implication
• Investment in Multi-sided business platform
• First mover advantage vs. second mover advantage
• Rational expectations of senior management
• Contribution
• A new modeling perspective on how financial economics

theory support m-payments decision-making

• Help senior manager estimate optimal timing and payoffs

from m-payments

• Use of jump diffusion process to model the dynamically

changing of value of IT investment