1. Online auctions for selling
accommodation packages – A
Readiness-Intensity-Impact Analysis
Matthias Fuchs a, Wolfram Höpken b, Alexander Eybl a, Andreas
Flöck c
a
The European Tourism Research Institute (ETOUR)
Mid-Sweden University, Östersund, Sweden
The Business Informatics Group
University of Applied Sciences Weingarten, Ravensburg, Germany
b
c
Management Center Innsbruck (MCI), Innsbruck, Austria
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2. Agenda
• Introduction
• Literature Review
• Research Framework
– Model
– Method
• Evaluation Results
• Implications
• Limitations & Outlook
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3. Introduction
• T&T leading e-business adopter (Buhalis & Law 2008)  varying rates (93%
micro firms) (E-Business Watch 2007)
• T&T sells services through online auction  market potentials, low
entry & exit barriers
– eBay Germany: 5 million visitors /month 14,000 items listed in the ‘shortterm lodging category’ (Fuchs et al. 2011, p. 1166)
– Supply side dominated by few sellers only (Ho 2008)
• Lack of adoption and impact studies for online auctions in T&T
– Austrian accommodation sector
• Factors facilitating adoption / use of online auctions
• Impact from online auctions on firm performance
– Readiness-Intensity-Impact Framework (Zhu & Kraemer 2005) tested
through PLS and Logistic Regression using survey data
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4. Literature Review
• Auctions  adjust product prices to volatile market conditions (Klein 1997)
– Functions  Coordination, Price setting, Allocation (excess capacity),
Distribution
• Opportunities of Online auctions (Pinker et al. 2003)
– Reduced transaction costs
– Easy access and extended duration  Increased pool of bidders 
Sniping = Last Minute Bidding/Duelling, Automatic Proxy Bidding,
Retailing = BIN)
– Promotion channel
– Auction data  Business Intelligence
• Online auction research in T&T
– Market structure & dynamics (Ho 2008)
– Determinants affecting final price, Intelligent SA optimally listing
accommodation packages (Fuchs et. al. 2008; Fuchs et al. 2011)
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5. Literature Review
• Adoption theories  Individual Behaviour vis-à-vis Technological
Innovations (micro)
– Technology Acceptance Models (Davies 1989): Perceived Usefulness,
Ease of Use
– Innovation Diffusion Theories (Rogers 2003): Relative Advantage,
Compatibility, Simplicity, Trialability, Observability
– Technology-Organization-Environment Framework (Zhu & Kraemer 2005)
– Unified Theory of Acceptance and Use of Technology (Venkatesh et al.
2003): Performance and Effort Expectancy, Social Influence, Facilitating
Conditions
• Technology diffusion  Spread of innovations through social
systems  certain number of people adopts (macro)
– Position on Technology Life Cycle of specific e-business application
(Colechia 1999)
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6. Literature Review
• Research Framework
(Zhu & Kraemer 2005)
– Technology-Organization-Environment components refer to Readiness
– e-Business adoption refers to the Use Intensity
– e-Business value creation refers to Impact
Fig. 1: Diffusion Curve (Colechia 1999)
– Early adopters  Infrastructural conditions and use limitations
– Early majorities  Usage figures on technological systems (benchmarks)
– Late majorities /Laggards  Impact induced by e-business application
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7. Literature Review
E-Business-Readiness
E-Business-Impact
E-Business-Intensity
Technological Context
Front-end
Functions
Technological Competence
Impact on Sales
Organisational Context
Size
Internationalization
Financial Commitment
Environmental Context
Competitive Constraints
Support
E-Business
Adoption
E-Business added
value
Back-end
Integration
Impact on internal
operations
Impact on
sourcing
Fig. 2: Readiness-Intensity-Impact Framework (Zhu & Kraemer 2005, p. 66)
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8. Model Building
• Readiness  technical, economic, social infrastructure necessary for
adoption and use of e-Business applications (Colecchia 1999)
– Organisational Context
• ICT expertise (Premkumar 2003; Hafeez et al 2006)
– ICT competencies; Experiences with online auctions
• Costs related to online auctions (Walczuch et al 2000)
– Set up fees, Fix costs, Final value fee (% of final price), Time
• Commitment to online distribution (Zhu & Kraemer 2005)
– Attitude towards online distribution
– Budget for e-marketing
– Company context
• Larger companies inertia/rigid decision structures although resources
endowment (Premkumar 2003)
– Hotel size: Number of beds (Ching & Ellis 2004)
– Chain vs. Family owned
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9. Model Building
• Readiness (cont.)
– Environmental context (Premkumar 2003)
• Competitive pressure  ICT use to remain competitive
• Competitors are using online auctions
• Demanding customers (Wu et al 2003)
– Entrepreneurial Context (Grandon & Parson 2004)
•
•
•
•
Age
Formal education
Professional experience
Security concerns (Pinker et al. 2003)
– System Context (Thong 1999)
• Perceived relative advantage
• Compliance with existing distribution channels (Comatibilty)
• Ease of understanding and use (Complexity)
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10. Model Building
• Use intensity
– Current use of online auctions (Zhu et al 2006)
• Adoption  yes/no
• Intention to use (Non adopters)
• Routine (Adopters)
– Type
» eBay auction vs. Buy-it Now listing (BIN)
» (i) last-minute offers (ii) room vouchers of free capacities (iii)
room vouchers on a regular base
– Intensity
• Impact: adopters  perception; non-adopters  beliefs
– Perceived impact on increased bookings/occupancy (Dedrick et al 2003)
– Internal processes (advertising leverage) (Amit & Zott 2001)
– Other benefits (satisfied customers, data for BI) (Zhu & Kraemer 2005)
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11. Test Model & Hypotheses
I MPACT
I NTENSITY
R EADINESS
Organisational context
+
+
Company context
+
+
IKT Expertise
ICT expertise
Wahrgenommene Kosten
Perceived cost
Commitment
Commitment
Betriebsgröße size
Company
Typ
Company type
Environemntal context
Wahrgen. Konkurrenzdruck
Competitive pressure
Wahrgen. Kundendruck
Customer demand
Sales
+
+
Entrepreneurial context
Age
Alter
+
+
-
System context
Online-auctions
Adoption &
Use Routine
+
+
Internal processes
+
+
+
-
Education
Ausbildung
Experience
Branchenspezifische Erfahrung
Security concerns
Wahrgen. Sicherheitsbedenken
Wahrgen. relativer Vorteil
Perceived advantage
Kompatibilität
Compatibility
Komplexität
Complexity
Other benefits
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12. Method
• Generation of experience-based survey data (Wu et al 2003)
– ‘(...) individuals’ perceptions of the attributes of an innovation, not
the attributes as classified objectively by experts or change agents,
affect the rate of adoption (...)’ (Rogers 2003, p. 223)
– Items measurement  7-point scale
• ‘I fully agree’ to ‘I fully disagree’  ‘I don’t know’
• Online survey June 2009 targeting owners/managers of 5,000 AUT
accommodation companies (adopters and non-adopters)
– 206 fully completed questionnaires from all over Austria
• Model Testing
– Measurement: E/CFA (Hair et al 2006)
– Causal Model: PLS (Iacobucci 2010) , Logistic Regression
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13. Empirical Results
• Descriptive
– 32% are hotels, 30% apartments, 21% bed & breakfast, 9% farm
and guest houses
– 93% family businesses  92% in urban areas  67% < 40 beds
– 24% (51) adopters  76% (155) non-adopters
• 74% of non-adopters < 40 beds  70% of adopters > 40 beds
– 30% 31-40 years, 29% 41-50 years 28% > 50 years 12% < 30 years
• T-Test: adoption of online auctions higher for younger
entrepreneurs (sig. 99%)
– 20% academics, 26% high school degree 48% vocational training
• Mann-Whitney-U-Test: entrepreneurs using online auctions
show higher education levels (sig. 99%)
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14. Empirical Results
• Readiness
–
–
–
–
–
–
65% experience with eBay
63% eBay easy to use
61% online auctions advantageous additional distribution channel
40% security concerns (non paying buyers, bid retractions)
37% know competitors using online auctions
34% rate cost for online auctions as high (20% unknown)
• Intensity
– 76% Non-adopters
• 41% intend to use online auctions
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15. Empirical Results
– 24% adopters
• 60% will continue to list accommodation products on eBay
sell last-minute offers as buy-it-now listing
sell hotel room vouchers as buy-it-now
listing to increase occupancy rates
regularly sell hotel room vouchers as buy-itnow listing
sell last-minute offers as auction
sell hotel room vouchers as auction to
increase occupancy rates
regularly sell hotel room vouchers as auction
0
0,5
1
1,5
2
2,5
3
3,5
Fig. 4: Usage intensity of online auction types
• 70% auction-off rooms through intermediaries
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16. Empirical Results
• Impact
– 76% online auctions distribution channel to attract new
customers
– 73% online auctions to increase firm’s reputation
– 70% online auctions to increase booking rates in low seasons
– 51% online auctions to generate additional sales
– 43% asset from automatically stored auction data for BI
– 32% higher selling prices through online auctions
– 27% increased guest satisfaction
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17. Empirical Results
PLS modelling  Preparatory steps (Iacobucci 2011)
Table 1: Confirmatory Factor Analysis Readiness Model
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18. Empirical Results
PLS modelling  Preparatory steps (Iacobucci 2011)
Table 2: Confirmatory Factor Analysis Intensity Model
Major usage scenarios of eBay
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19. Empirical Results
PLS modelling  Preparatory steps (Iacobucci 2011)
Table 3: Confirmatory Factor Analysis Impact Model
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20. Empirical Results
PLS modelling
• Readiness, intensity & impact constructs integrated into PLS model to
predict variance of dependent variables (Iacobucci 2010)
– Model improvement
• Variable exclusion  accommodation type/size, education level
• Variable integration  age, professional experience = ‘experience’
Table 4: Fit Measures Measurement Model (PLS-based)
Latent Construct
AVE > 0,6
Composite
Reliability > 0,7
Q² > 0
Experience
Commitment
eBay-Security
Usability
Auction type listing
Buy It Now (BIN)
Sales
Other benefits
0.876
0.795
0.923
0.874
0.604
0.818
0.827
0.691
0.934
0.886
0.960
0.933
0.820
0.931
0.966
0.899
0.509
0.337
0.594
0.496
0.206
0.600
0.738
0.391
Table 5: Fit Measures Causal Model (PLS-based)
Latent Construct
Auction type listing
Buy It Now (BIN) listing
Sales
Other benefits
R²
0.465
0.184
0.437
0.273
Remarks: AVE = Average Variance Extracted
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Q² >
00.227
0.121
0.313
0.158

21. Empirical Results
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22. Empirical Results
• Use intensity of online auctions (auction type listing) determined by
– Executives’ commitment (compatibility with distribution channels,
advantage), executives experience, perceived usability
• Use intensity of Buy it Now listings (BIN) determined by
– Executives’ commitment and security concerns (bid retractions or final
auction prices below going market price level)
• Impact from online auctions
– Sales (new customers, increased bookings, occupancy rate, cost coverage)
– Other benefits (reduced transaction costs, higher product prices, customer
satisfaction)
– No effects from BIN listings on firm performance  Possible reasons…
• Fixed prices often set too high, thus, making offers unattractive
• Low usage rates of BIN listings in survey-based sample data (26 firms =
12%)
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23. Empirical Results
Results from logistic regression
• Readiness factors explain dichotomous decision to adopt online
auctions ( ‘to adopt’ vs. ‘not to adopt’)
–eBay security
–Commitment
Tab. 6: Logistic regression results
Log-Regression coefficient
eBay security
Commitment
Constant
1.662
0.778
-0.201
Sig. level
0.000
0.008
0.465
Exp(B)
% Change
5.271
2.178
0.818
427.10%
117.80%
• Model fit
–-2Log-Likelihood = 88.180
–Nagelkerke R² = 0.549
–Total prediction accuracy 0.83
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24. Summary
Adoption
R EADINESS
Routine
Organisational context
+
+
Company context
I MPACT
I NTENSITY
+
+
IKT Expertise
ICT expertise
Wahrgenommene Kosten
Perceived cost
Commitment
Commitment
Betriebsgröße size
Company
Typ
Company type
Environemntal context
Wahrgen. Konkurrenzdruck
Competitive pressure
Wahrgen. Kundendruck
Customer demand
Sales
+
+
Entrepreneurial context
Age
Alter
+
+
-
System context
+
+
-
Education
Ausbildung
Experience
Branchenspezifische Erfahrung
Security concerns
Wahrgen. Sicherheitsbedenken
Wahrgen. relativer Vorteil
Perceived advantage
Kompatibilität
Compatibility
Komplexität
Complexity
Online-auctions
Adoption &
Use Routine
1. Coordination Regular sale
of accommodation packages
2. Allocation Sale of vacant
accommodation capacities
in low season
3. Distribution Last minute
auction
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+
+
Internal processes
+
Other benefits
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25. Implications
• Although online auctions require minimal tech/org prerequisites,
adoption rate in (Austrian) hospitality industry is low
– 40% plan to give up use online auctions in future
• eBay  increase attractiveness for bidders and sellers in T&T
– Communicate security at eBay
• 128-bit encrypt technology for registration, log-in,
transactions
• Double confirmation before submitting bid
• Due to low entry and exit barriers combined with high success rate
and low operation costs (Ho 2008), suppliers are recommended to test
online auctions, and, according to success rates, consider permanent
use
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26. Conclusions,
Limitations & Outlook
• Readiness-Intensity-Impact model (Zhu & Kraemer 2005), PLS &
Logistic Regression (Iacobucci 2011)  Insights about
– Drivers behind decision to adopt / use online auctions in hospitality
sector
– Effects on firm performance from use of online auctions
• Limitations & Future Research
– Response rate  Bias in sample induced by managers’ willingness to
reply (206 responding subjects likely show different attitudes)
– Empirical results cannot be generalized  restricted by time (i.e.
2009), geographical perspective (i.e. Austria’s accommodation sector)
– Investigation of usage barriers for bidders (i.e. customers) at various
stages of involvement using online auctions in T&T
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27. • Thank you!
Questions?
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28. References
•
•
•
•
•
•
•
•
•
•
Colecchia, A. (1999). Defining and Measuring Electronic Commerce. Paris: OECD Press.
Fuchs, M., Eybl, A., & Höpken, W. (2011). Successfully Selling Accommodation Packages at
Online Auctions – The Case of eBay Austria. Tourism Management, 32(5): 1166-1175.
Fuchs, M., Höpken, W., Föger, A., & Kunz, M. (2010). E-Business Readiness, Intensity, and
Impact – An Austrian DMO Study. Journal of Travel Research, 49(2): 165-178.
Fuchs, M., Höpken, W. Eybl, A., & Ulrich, J. (2008). Selling Accommodation Packages in Online
Auctions - The Case of eBay. In: O’Connor, P., Höpken, W. & Gretzel, U. (eds.), Information
and Communication Technologies in Tourism 2008, Springer, New York: 291-302.
Ho, J. (2008). Online Auction Markets in Tourism. Information Technology & Tourism, 10(1):
19–29.
Iacobucci, D. (2010). Structural Equation Modelling – Fit Indices, Sample Size, and Advanced
Topics. Journal of Consumer Psychology, 20: 90-98.
Pinker, E., Seidmann, A., & Vakrat, Y. (2003). Managing Online Auctions: Current Business and
Research Issues. Management Science, 49(11): 1457–1484.
Rogers, E. M. (2003). Diffusion of Innovations. 5th ed., New York, NY: Free Press.
Sahadev, S. & Islam, N. (2005). Why Hotels adopt Information and Communication
Technologies? Int. Journal of Contemporary Hospitality Management, 17(5): 391–401.
Zhu, K. & Kraemer, K. (2005). Post-Adoption Variations in Usage and Value of e-Business by
Organizations. Information Systems Research. 16(1): 61–84.
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