Online auctions for selling accommodation packages – A Readiness-Intensity-Impact Analysis

ENTER 2014 Research Track Slide Number 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

b The Business Informatics Group

University of Applied Sciences Weingarten, Ravensburg, Germany

c Management Center Innsbruck (MCI), Innsbruck, Austria


Agenda• Introduction

• Literature Review

• Research Framework– Model

– Method

• Evaluation Results

• Implications

• Limitations & Outlook


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 ‘short-term 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


• 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)

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)

Literature Review


– Early adopters Infrastructural conditions and use limitations – Early majorities Usage figures on technological systems (benchmarks)– Late majorities /Laggards Impact induced by e-business application

Literature Review

Fig. 1: Diffusion Curve (Colechia 1999)

• 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


Technological Context Technological Competence

Environmental Context Competitive Constraints Support

Organisational Context Size Internationalization Financial Commitment

E-Business Adoption

E-Business added value

Front-end Functions

Back-end Integration

Impact on Sales

Impact on internal operations

Impact on sourcing

E-Business-Intensity E-Business-Impact E-Business-Readiness

Fig. 2: Readiness-Intensity-Impact Framework (Zhu & Kraemer 2005, p. 66)

Literature Review


• 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

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)

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)

Model Building


INTENSITY

Online-auctions

Adoption & Use Routine

Internal processes

Other benefits

Sales

- +

+

+ +

+ +

- + + -

+ + -

+

+

+

IMPACT READINESS

Organisational context IKT Expertise Wahrgenommene Kosten Commitment

ICT expertise Perceived cost Commitment

Company context Betriebsgröße Typ Company type

Environemntal context Wahrgen. Konkurrenzdruck Wahrgen. Kundendruck Customer demand

Company size

Competitive pressure

Entrepreneurial context Alter Ausbildung Branchenspezifische Erfahrung Wahrgen. Sicherheitsbedenken

Education

Security concerns

System context Wahrgen. relativer Vorteil Kompatibilität Komplexität Compatibility Complexity

Age

Experience

Perceived advantage

Test Model & Hypotheses


• 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

Method


• 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%)

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

Empirical Results


– 24% adopters • 60% will continue to list accommodation products on eBay

• 70% auction-off rooms through intermediaries0 0,5 1 1,5 2 2,5 3 3,5

regularly sell hotel room vouchers as auction

sell hotel room vouchers as auction to increase occupancy rates

sell last-minute offers as auction

regularly sell hotel room vouchers as buy-it-now listing

sell hotel room vouchers as buy-it-now listing to increase occupancy rates

sell last-minute offers as buy-it-now listing

Fig. 4: Usage intensity of online auction types

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

Empirical Results


PLS modelling Preparatory steps (Iacobucci 2011)

Average Average Construct reliability

Convergence

reliability

Discriminant validity

Construct and indicator

Adopters

Non

adopters Cronbach’s

Alpha Std.ized Loadings

t Value (CR) SMC AVE

ICT Expertise 0.512 0.529 Entrepreneur 5.35 5.00 0.797 – a 0.634 Employees 5.78 5.26 0.454 1.876 0.207 Perceived Cost 0.680 0.662 Set up fees 3.08 3.93 0.574 – a 0.330 Time investment

3.55 4.70 0.871 3.698 0.758 Commitment 0.743 0.726 Rel. advantage 3.67 3.08 0.667 – a 0.445 Compatibility 4.59 4.09 0.876 4.056 0.768 eBay-Security 0.921 0.930 Non pay. buyers 2.22 3.61 1.057 – a 1.118 Bid retraction 2.18 3.86 0.810 4.844 0.656 Usability 0.856 0.848 Ease of use 5.45 4.39 0.941 – a 0.886 Ease of process 5.53 4.48 0.788 6.160 0.662

Cmin/df = 0.422; p = 0.987; AGFI = 0.911; RMSEA = 0.001; CFI = 0.99 CR = Critical Ratio; SMC = Squared Multiple Correlations; AVE = Average Variance Extracted; a. value set to 1 for parametric rating

Table 1: Confirmatory Factor Analysis Readiness Model

Empirical Results


Table 2: Confirmatory Factor Analysis Intensity Model Average Construct

reliability

Convergence reliability

Discriminant

validity


Adopters Cronbach’s Alpha

Std.ized Loadings

t Value (CR)

SMC AVE Auction type listing 0.666 0,564 Last minute 2.55 0.869 – a 0.755 Inc. occupancy rate 2.84 0.542 3.355 0.293 Periodic sale 3.18 0.546 3.509 0.298 BuyItNow listing 0.887 0.835 Last minute 2.00 0.778 – a 0.605 Inc. occupancy rate 2.02 0.838 6.612 0.702 Periodic sale 2.22 0.949 7.167 0.901


Empirical Results

Major usage scenarios of eBay

PLS modelling Preparatory steps (Iacobucci 2011)


Average

Average

Construct reliability

Convergence

reliability

Discriminant validity


Adopters

Non

Adopters Cronbach’s

Alpha Std.ized Loadings

t Value (CR) SMC AVE

Sales 0.957 0.876 New customers 4.90 5.27 0.913 – a 0.833 Booking rate 4.49 4.66 0.939 10.587 0.881 Inc. sales 3.90 4.25 0.840 9.047 0.705 Inc. occupancy rate 4.69 4.94 0.990 14.448 0.979 Cost coverage 3.90 4.51 0.756 7.220 0.572 Advertising 4.67 5.02 0.890 12.202 0.792 Other benefits 0.849 0.715 Final prices 2.57 3.25 0.756 – a 0.572 Transaction costs 3.61 3.86 0.750 4.503 0.562 Data Mining 3.61 4.59 0.848 6.012 0.719 Customer satisf. 2.98 3.35 0.709 4.955 0.503


Table 3: Confirmatory Factor Analysis Impact Model

Empirical ResultsPLS modelling Preparatory steps (Iacobucci 2011)


• 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 0.876 0.934 0.509 Commitment 0.795 0.886 0.337

eBay-Security 0.923 0.960 0.594

Usability 0.874 0.933 0.496

Auction type listing 0.604 0.820 0.206

Buy It Now (BIN) 0.818 0.931 0.600

Sales 0.827 0.966 0.738

Other benefits 0.691 0.899 0.391

Remarks: AVE = Average Variance Extracted

Table 5: Fit Measures Causal Model (PLS-based)

Latent Construct R² Q² > 0 Auction type listing 0.465 0.227

Buy It Now (BIN) listing 0.184 0.121

Sales 0.437 0.313

Other benefits 0.273 0.158

Empirical ResultsPLS modelling


Empirical Results

0.834

Experience

Last minute auctions

Auctions to increase

occupancy rate

Periodic auctions

Traditional auction

New customers

Sales

0.776 0.717

0.950Increased

sales

Increased booking rate

Increased occup. rate

Cost coverage

Advertise-ment

Age

0.924

0.896

0.973

0.791

0.912

0.817 Final prices

Transaction costs

Data Mining

Customer Satisfaction

0.796

0.870

0.841

Other benefits

0.900

Last minute auctions

Auctions to increase

occupancy rate

BuyItNow

0.887 0.927

Periodic auctions

Industrial experience

Commitment

Compatibility

Perceived advantage

eBay security

Security concerns

Personal experience

Usability

Ease of use

Ease of process

0.922

0.950

0.867

0.916

0.943

0.979

0.937

0.933

0.313

0.490

0.260

0.326

-0.400

-0.162

0.730

0.523

Fig.4: Global PLS model


• 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%)

Empirical Results


Results from logistic regression • Readiness factors explain dichotomous decision to adopt online auctions ( ‘to

adopt’ vs. ‘not to adopt’)–eBay security –Commitment

• Model fit –-2Log-Likelihood = 88.180–Nagelkerke R² = 0.549 –Total prediction accuracy 0.83

Tab. 6: Logistic regression results

Log-Regression coefficient Sig. level Exp(B) % Change

eBay security 1.662 0.000 5.271 427.10% Commitment 0.778 0.008 2.178 117.80%

Constant -0.201 0.465 0.818

Empirical Results


INTENSITY

Online-auctions

Adoption & Use Routine

Internal processes

Other benefits

Sales

- +

+

+ +

+ +

- + + -

+ + -

+

+

+

IMPACT READINESS

Organisational context IKT Expertise Wahrgenommene Kosten Commitment

ICT expertise Perceived cost Commitment

Company context Betriebsgröße Typ Company type

Environemntal context Wahrgen. Konkurrenzdruck Wahrgen. Kundendruck Customer demand

Company size

Competitive pressure

Entrepreneurial context Alter Ausbildung Branchenspezifische Erfahrung Wahrgen. Sicherheitsbedenken

Education

Security concerns

System context Wahrgen. relativer Vorteil Kompatibilität Komplexität Compatibility Complexity

Age

Experience

Perceived advantage

Summary

1. Coordination Regular sale of accommodation packages

2. Allocation Sale of vacant accommodation capacities in low season

3. Distribution Last minute auction

Adoption

Routine


• 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

Implications


• 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

Conclusions, Limitations & Outlook


• Thank you!

Questions?


• 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.

References

Online auctions for selling accommodation packages – A Readiness-Intensity-Impact Analysis

Technology

Transcript of Online auctions for selling accommodation packages – A Readiness-Intensity-Impact Analysis