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Youngsok Bang, Kunsoo Han, Animesh Animesh, Minha Hwang
Desautels Faculty of Management
McGill University
September 24, 20...
2
Motivation (1/2)
• Growing importance of mobile commerce transactions
Global mobile transactions expected to reach $617 ...
3
Motivation (2/2)
• Understanding e-commerce users’ m-commerce adoption
Key for e-commerce firms
• Yet there is a paucity...
4
Research Objectives
• To develop a new theoretical framework to study adoption
of m-commerce channel
• To examine whethe...
5
Research Framework: An Adapted Task-
Technology Fit Model
Technology
Characteristics
Task
Characteristics
Technology
ado...
6
Channel capability framework (Avery et al., 2012)
Channel capability: “an enabling characteristic of a channel
that allo...
7
Capabilities of Mobile vs. Online Channels
• Two main technological characteristics of mobile channels
Ubiquity of the m...
8
Consumers’ Online Purchase Patterns
(Preferences)
• Access
• Consumers differ with respect to their preference (or needs...
9
Fit between Channel Capabilities and Consumers’
Online Purchase Patterns
Mobile Channel’s
Capabilities
- Access
- Search...
10
Hypotheses: Access
H1 (Purchase frequency): Consumers who use
e-commerce more frequently are more likely to adopt
mobil...
11
Hypotheses: Search
H3 (Search complexity): Consumers who tend to
purchase a greater number of items or categories at a
...
12
Measures
Key Variables Measures
Purchase
Frequency
-Mean of the time gap between the current transaction and the last
t...
Data
• Collected from a large e-marketplace in Korea
• Initially provided an online channel only, and launched a mobile ch...
14
Main sample
Total Transactions 1,440,525
Before the mobile channel
launch
Online Transactions
(2009/03/01~2010/05/20)
5...
15
Method: Survival Analysis
Originated in the medical field, but increasingly applied in
economics, engineering, and soci...
16
M-commerce adoption example
Mobile channel
introduction, t0
Data collection
ends, t1
Subject A
Subject B
Subject C
Subj...
17
Cox Proportional Hazard (PH) model
We can construct hazard models as many as baseline
hazard functions (i.e., Weibull, ...
18
Control Variables
• Risk preference
- Rate of order confirmation requests (email, SMS)
- Use of a more secure log-in sy...
19
Results
Dependent Variable: Time to adopt mobile commerce
Model Specifications
Number of observations 29227
No. of adop...
20
Robustness Checks
• Re-estimate the same model using different samples
• No significant difference in the results
• Out...
21
Implications
Research Implications
Among the first to empirically examine mobile commerce adoption
based on a large dat...
22
Questions?
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From Online to Mobile - Impact of Consumers' Online Purchase Behaviors on Mobile Commerce Adoption

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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.

Publicada em: Negócios, Tecnologia
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From Online to Mobile - Impact of Consumers' Online Purchase Behaviors on Mobile Commerce Adoption

  1. 1. Youngsok Bang, Kunsoo Han, Animesh Animesh, Minha Hwang Desautels Faculty of Management McGill University September 24, 2013 1 From Online to Mobile: Impact of Consumers’ Online Purchase Behaviors on Mobile Commerce Adoption
  2. 2. 2 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
  3. 3. 3 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
  4. 4. 4 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
  5. 5. 5 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
  6. 6. 6 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.)
  7. 7. 7 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
  8. 8. 8 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)
  9. 9. 9 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.
  10. 10. 10 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.
  11. 11. 11 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.
  12. 12. 12 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. 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
  14. 14. 14 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
  15. 15. 15 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)
  16. 16. 16 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*
  17. 17. 17 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
  18. 18. 18 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
  19. 19. 19 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
  20. 20. 20 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
  21. 21. 21 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
  22. 22. 22 Questions?

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