This project aims to identify the behavioral measures such as purchase patterns and search patterns from the exiting online channel to predict consumers' m-commerce adoption. Findings from this study are useful to identify and target consumers who are more likely to adopt m-commerce by using exiting e-commerce transaction/search data.

1. Youngsok Bang, Kunsoo Han, Animesh Animesh, Minha Hwang
Desautels Faculty of Management
McGill University
September 24, 2013
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From Online to Mobile: Impact of
Consumers’ Online Purchase Behaviors
on Mobile Commerce Adoption

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Motivation (1/2)
• Growing importance of mobile commerce transactions
Global mobile transactions expected to reach $617 billion with 448 million
users by 2016, up from $106 billion with 160 million users in 2011
(Gartner, 2012).
eBay’s mobile sales volume was expected to exceed $8 billion in 2012, up
from $5 billion in 2011 (Brewer-Hay, 2012)
Driving forces: mobile technologies
Progress of mobile Internet technology and the improvement of user interface
of mobile devices (mainly smartphones and tablets)
New transaction technologies, such as Near Field Communication and Mobile
Wallet technology, are making mobile transactions much easier and more
convenient
• E-commerce firms are increasingly adding mobile commerce
channels to their traditional e-commerce channels
Companies and retailers are increasingly considering mobile commerce as a
new venue for future growth

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Motivation (2/2)
• Understanding e-commerce users’ m-commerce adoption
Key for e-commerce firms
• Yet there is a paucity of research on the factors affecting
the adoption of m-commerce channels
Behavioral research based on the technology adoption theory has greatly
advanced our knowledge in the domain (e.g., TAM, TAM2, UTAUT, TAM3)
Criticisms about its practicality
Call for measures beyond perceptions (Davis and Kotteman 1994)
Call for a new paradigm (Bagozzi, 2007; Benbasat and Barki, 2007)
• This study complements prior adoption literature by
Linking adoption of a new IT (mobile channel) with past usage behaviors
(e-commerce usage)
Using large-scale objective data on actual behavior rather than relying on
perceptual measures

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Research Objectives
• To develop a new theoretical framework to study adoption
of m-commerce channel
• To examine whether consumers’ e-commerce transaction
patterns can predict adoption of m-commerce
• To identify key factors that impacts m-commerce adoption

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Research Framework: An Adapted Task-
Technology Fit Model
Technology
Characteristics
Task
Characteristics
Technology
adoption/
Usage/
Performance
Fit
Task-Technology Fit
(Goodhue & Thompson 1995)
Mobile
Channel’s
Capabilities
Consumers’
Purchase
Patterns
Mobile
Commerce
Adoption
Fit
Our Framework

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Channel capability framework (Avery et al., 2012)
Channel capability: “an enabling characteristic of a channel
that allows consumers to accomplish their shopping goals”
Each capability of a new channel can be evaluated based on:
whether a given capability substitutes for or complements the
capabilities of the pre-existing channels
Easier to apply to the case of mobile and online
channels, because the number of new capabilities of the
mobile channel is limited.
Unlike the case of offline and online channels, mobile and online channels
share most capabilities (e.g., offers the same product assortment, no ability to
touch/feel products, no face-to-face communication with the seller, etc.)

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Capabilities of Mobile vs. Online Channels
• Two main technological characteristics of mobile channels
Ubiquity of the mobile Internet
Mobile channels facilitate anytime, anywhere transactions
Smaller screens and lower usability
Relatively less time spent per visit and less complex navigation is expected on
mobile channels
ESPN mobile web page, for example, records about 12 minutes per visit on
average, which is much less than the dot-com page, and is mostly driven by
simple tasks, such as score-checking and fantasy sports (Walsh, 2011)
• Two distinct capabilities of mobile channel (vis-à-vis online
channel) (Bang et al. forthcoming)
Ubiquitous access capability
C.f., Online: constrained access capability (constrained by location)
Limited search capability
C.f., Online: extensive information search capability

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Consumers’ Online Purchase Patterns
(Preferences)
• Access
• Consumers differ with respect to their preference (or needs) for ubiquitous
access to the transaction channel (in terms of frequency and time
irregularity)
• Search
• Consumers differ with respect to their preference (or needs) for extensive
search (e.g., complexity, breadth and depth of search)

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Fit between Channel Capabilities and Consumers’
Online Purchase Patterns
Mobile Channel’s
Capabilities
- Access
- Search
Consumers’
Purchase Patterns
- Access
- Search
Mobile Commerce
Adoption
Fit
A consumer is more likely to adopt the mobile channel when
there is a greater fit between the channel’s capabilities and
his/her purchase patterns.

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Hypotheses: Access
H1 (Purchase frequency): Consumers who use
e-commerce more frequently are more likely to adopt
mobile commerce.
H2 (Purchase time irregularity): Consumers who use
e-commerce more irregularly (in terms of time of the day) are
more likely to adopt mobile commerce.

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Hypotheses: Search
H3 (Search complexity): Consumers who tend to
purchase a greater number of items or categories at a
time are less likely to adopt mobile commerce.
H4 (Search mode): Consumers who tend to click display
ads rather than type in keywords or browse categories to
search for products are more likely to adopt mobile
commerce.
H5 (Search propensity): Consumers who tend to search
products more thoroughly are less likely to adopt mobile
commerce.

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Measures
Key Variables Measures
Purchase
Frequency
-Mean of the time gap between the current transaction and the last
transaction (FQ)
Purchase
Time
Irregularity
-Mean of the difference in purchase time of the day between the
current transaction and the last transaction (TF)
-Standard deviation of the difference in purchase time of the day
between the current transaction and the last transaction (TP)
Search
Complexity
-Proportion of the transactions involving multi-items (MI)
-Proportion of the transactions involving multi-categories (MC)
Search Mode
-Proportion of the transactions initiated by clicking on display ads
rather than typing in keywords or browsing categories to search for
products (PD)
Search
Propensity
-Mean of the display rank of transactions (TS). Display rank is
calculated based on the location of the display. If a product is listed
at the top of the first search result page, the display rank is 1; the
rank value is greater for products listed lower.

13. Data
• Collected from a large e-marketplace in Korea
• Initially provided an online channel only, and launched a mobile channel in
June 2010
• Dataset covers 15 months (March 1, 2009 – May 31, 2011) and captures
online & mobile transactions before and after mobile channel introduction
2009. 3. ~ 2011. 6.
N/A 2010. 6. ~ 2011. 6.
Online
Transaction
Mobile
Transaction
2009. 3. ~ 2011. 6.Online
Transaction
t0 t1 t2
Dataset 1
Dataset 2
(Mobile Non-adopters)
(Mobile Adopters)
June 1, 2010
Mobile Channel Launch
March 1, 2009 May 31, 2011

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Main sample
Total Transactions 1,440,525
Before the mobile channel
launch
Online Transactions
(2009/03/01~2010/05/20)
540,883
Online Transactions
(2010/05/21~2010/05/31)
20,567
After the mobile channel
launch
Online Transactions
(2010/06/01~2011/06/30)
877,793
Mobile Transactions
(2010/06/01~2011/06/30)
12,823
Total Subjects 29,227
Mobile Commerce Adopters 3,450
Non-adopters 25,777

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Method: Survival Analysis
Originated in the medical field, but increasingly applied in
economics, engineering, and social sciences
- used to analyze “time to events” (e.g., death, failure)
f(t) = the failure rate of a subject per unit of time
Survival function, S(t) = 1 – F(t), where F(t) is the cumulative
distribution function of the time to failure
Hazard function, λ(t), provides the instantaneous failure rate that a
subject having not failed by time t will fail during the
infinitesimally small interval (t+∆t)

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M-commerce adoption example
Mobile channel
introduction, t0
Data collection
ends, t1
Subject A
Subject B
Subject C
Subject D
Right-censored part:
Do not know the time to adopt
Know the time to adopt
after introduction
t*

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Cox Proportional Hazard (PH) model
We can construct hazard models as many as baseline
hazard functions (i.e., Weibull, exponential, etc.)
We use Cox PH model (Cox, 1972)
One of the most general and robust models (Li et al., 2010)
No restrictions on the shape of the baseline hazard function
Hazard function :
Z : a vector of explanatory variables
β : a vector of the parameter to be estimated

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Control Variables
• Risk preference
- Rate of order confirmation requests (email, SMS)
- Use of a more secure log-in system (e.g., certificate-
based log-in)
• Amount of prior e-commerce transactions
• Rate of product return
• Demographic information
- Age
- Gender
• Time dummies
- Time of the day
- Day of the week

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Results
Dependent Variable: Time to adopt mobile commerce
Model Specifications
Number of observations 29227
No. of adoptions 3450
Model fit log likelihood = -34487.965, Prob. > χ2 = 0.0000
Indep. Variables Coef. z Hypothesis
Purchase Frequency FQ -2.31e-11 -4.710*** H1 supported
Purchase Time Irregularity
TF 0.098 7.720***
H2 supported
TP 0.070 3.630***
Search Complexity
NI -0.042 -3.570***
H3 supported
MC -1.193 -7.570***
Search Mode PD 1.353 18.680*** H4 supported
Search Propensity TS -0.017 -9.470*** H5 supported

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Robustness Checks
• Re-estimate the same model using different samples
• No significant difference in the results
• Out-of-sample prediction
• Using the coefficients from the main results, we generated
the hazard rate for each individual in another sample.
• Significant positive correlation between the hazard rate and
the actual adoption time
• Linear regression with time-to-adopt as the dependent
variable
• No significant difference in the results

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Implications
Research Implications
Among the first to empirically examine mobile commerce adoption
based on a large dataset
Provides a new theoretical perspective based on the fit between
channel capabilities and consumers’ purchase patterns.
Linking adoption of a new IT with the actual usage patterns in the
pre-existing IT is novel in the adoption literature
Managerial Implications
Provides online retailers a understanding of their current customers
regarding who is more (or less) likely to adopt a mobile channel
Allows them to target their efforts to encourage mobile commerce
adoption

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Questions?