How do accommodation providers in Austria use Revenue Management?

Abstract

This work explains the evolution of the digital hotel distribution from 1978-2018 and how software and cloud computing currently affect revenue managment. Answers from 1,056 accomodation providers in Austria show, that pricing for frequent guest, price fairness and price dumping from competitors are perceived as the biggest challenges. The current market power of OTA and the price transparency in the market SME-accomodation provider need to focus on value creation and experience design.

Full text

Salzburg University of Applied Sciences
Innovation & Management in Tourism
master's thesis
How do accommodation providers in Austria
use Revenue Management?
Understanding challenges and opportunities in adapting prices to demand.
CHRI STIAN FURTNER, MA
https://revenue-management.at/
Supervised by:
Mag. Dr. Roman Egger
Mag. (FH) Mag. Dr. Mario Jooss, Bakk.
Graduation: 18.06.2018

2
I. Table of contents
I. Table of contents .................................................................................................................................. 2
II. List of abbreviation.............................................................................................................................. 5
III. List of illustrations ............................................................................................................................... 7
IV. List of tables ......................................................................................................................................... 9
V. Abstract ............................................................................................................................................... 10
1. The aim, relevance and contribution of this thesis ....................................................................... 12
1.1 Evaluating Revenue Management implementation .......................................................... 12
1.2 Why adopting RM is relevant and important for SME ..................................................... 13
1.3 State of research: Which question to ask about RM? ......................................................... 14
2. How RM evolved together with the digital hotel room distribution ......................................... 17
2.1 Rate parity agreements hindered revenue management .................................................. 17
2.2 The end of rate parity and the airline deregulation act of 1978 ....................................... 18
3. Traditional Revenue Management .................................................................................................. 19
3.1 The traditional capacity-based approach ............................................................................ 19
3.2 The classic hotel revenue management problem ............................................................... 22
4. Pricing the hotel experience ............................................................................................................. 23
4.1 Special characteristics of the hotel product ........................................................................ 23
4.2 Attributes of hotel choice ...................................................................................................... 24
4.3 Pricing an experience: Taking online reputation into account......................................... 24
4.4 Price transparency: The end of traditional RM .................................................................. 25
5. Defining Integrated RM: What modern RM should look like ..................................................... 27
5.1 Current state of research ....................................................................................................... 27
5.1.1 An interdisciplinary academic discourse ............................................................................. 28
5.1.2 Literature included for this thesis ......................................................................................... 28
5.2 Critical Successfactors of integrated RM ............................................................................. 29
5.3 Implementing integrated RM 2.0 – a process innovation ................................................. 39
5.3.1 Defining integrated RM .......................................................................................................... 39
5.3.2 Integrated RM from a systems point of view ...................................................................... 40
5.3.3 Managing organisational and technical systems within an organisation ........................ 40
5.3.4 Technical RM solutions ........................................................................................................... 41
5.3.5 Technological pull factor: cloud computing and SaaS........................................................ 41
5.4 Defining success in integrated revenue management ....................................................... 42
5.4.1 Non-financial success – customer satisfaction & delight ................................................... 42
5.4.2 Evaluating financial performance of RM ............................................................................. 43

3
6. Evaluating integrated RM in Austria.............................................................................................. 45
6.1 Integrated RM can’t be evaluated as a process .................................................................. 45
6.2 The technical system: software or SaaS adoption .............................................................. 45
6.2.1 Evidence on technology adoption of SME-accommodation providers ............................ 46
6.2.2 Lack of empirical evidence on integrated RM ..................................................................... 47
7. SME-accommodation providers: organisational dynamics ......................................................... 51
7.1 Definition of SME in Austria ................................................................................................ 51
7.2 Business model and organizational characteristics ........................................................... 52
8. Research question and research design .......................................................................................... 54
8.1 Online survey as a self-administered questionnaire ......................................................... 55
8.2 Sampling and web data extraction ...................................................................................... 56
8.2.1 Web data extraction from wko.at .......................................................................................... 56
8.2.2 Web data extraction from holidaycheck.de.......................................................................... 57
8.3 Overview of survey questions .............................................................................................. 60
8.4 Obligatory questions and prompting messages ................................................................ 62
8.5 A mixed method approach ................................................................................................... 62
8.6 Ethical considerations: anonymity & informed consent ................................................... 64
8.7 Addressing major limitation of online surveys: response bias, low response rates and
anti-spam laws ........................................................................................................................ 64
8.7.1 Low response rates .................................................................................................................. 64
8.7.2 Response bias ........................................................................................................................... 65
8.7.3 Mass distribution without prior authorization.................................................................... 66
8.8 Data analysis and visualisation ............................................................................................ 69
9. Outcome and findings ...................................................................................................................... 71
9.1 Comparing the population to the sample ........................................................................... 71
9.1.1 Business location ..................................................................................................................... 72
9.1.2 Number of employees and guest rooms............................................................................... 73
9.1.3 Number of guest rooms .......................................................................................................... 74
9.1.4 Job role of respondents ........................................................................................................... 78
9.2 The technical system: technology adoption ........................................................................ 80
9.2.1 Rates and availabilities are still managed manually ........................................................... 84
9.2.2 Channel management is key to integrated RM ................................................................... 85
9.2.3 A conceptualisation of technology adoption ....................................................................... 86
9.2.4 Overbooking Practices ............................................................................................................ 88
9.3 Social system: challenges & opportunities in adapting prices to demand ..................... 89
9.3.1.1 Major leverage points and the real issues ....................................................... 91
9.3.2 Cost coverage and profitability ............................................................................................. 93

4
9.3.3 Pricing for frequent guests and the fairness of pricing....................................................... 93
9.3.4 Pricing and value creation ...................................................................................................... 98
9.3.5 Price dumping........................................................................................................................ 100
9.3.6 Distribution environment: challenges and opportunities ................................................ 103
10. Discussion: RM is about value creation ........................................................................................ 105
VI. List of references .............................................................................................................................. 108
VII. Annex ................................................................................................................................................ 126

5
II. List of abbreviation
BAR ...................................................................................................................................... Best available rate
CS .................................................................................................................................. Customer Satisfaction
CSF .............................................................................................................................. Critical Success Factors
CRS ................................................................................................................ Computer Reservation System
CRM .................................................................................................... Customer Relationship Management
DMO ............................................................................................... Destination Management Organisation
EU .......................................................................................................................................... European Union
eWOM ................................................................................................................. Electronic Word of Mouth
GDP .......................................................................................................................... Gross domestic product
GDS ...................................................................................................................... Global Distribution System
GOPPAR ................................................................................................................... Gross Operating Profit
HR ....................................................................................................................................... Human Resources
IBE ............................................................................................................................ Internet booking engine
ICT ........................................................................................................ Internet and Computer Technology
IDS ................................................................................................................... Internet Distribution Systems
IMF ................................................................................................................... International Monetary Fund
MICE ........................................................................................... meetings, incentive, congress and events
OECD .......................................................... Organization for Economic Cooperation and Development
OTA ................................................................................................................................ Online Travel Agent
OTAs .............................................................................................................................. Online Travel Agents
ÖHV ..................................................................................................... Österreichische Hoteliervereinigung
PMS ................................................................................................................ Property Management System
Q .......................................................................................................................................................... Question
RevPAR .......................................................................................................... Revenue per Available Room
RM ................................................................................................................................ Revenue Management
UCG ........................................................................................................................... User generated content
UNWTO .............................................................................. United Nations World Tourism Organization
SaaS ................................................................................................................................. Software as a Service

6
VAT ..................................................................................................................................... Value Added Tax
WKO ............................................................................................................... Austrian Economic Chambers
WTO ..................................................................................................................... World Trade Organization
WTO ............................................................................................................... World Tourism Organization
WTTC ...................................................................................................World Travel and Tourism Council
YM ...................................................................................................................................... Yield Management

7
III. List of illustrations
Figure 1: The integrated RM system ............................................................................................................... 41
Figure 2: Technical Solutions for integrated RM ........................................................................................... 45
Figure 3: Technology Adoption of ÖHV member hotels ............................................................................. 47
Figure 4: Overview of the survey instrument ................................................................................................ 55
Figure 5: Data Extraction from firmen.wko.at ............................................................................................... 57
Figure 6: Web Data Extraction Process ........................................................................................................... 59
Figure 7: Hotel details ....................................................................................................................................... 60
Figure 8:Survey when accessed from a tablet ................................................................................................ 61
Figure 9: Final Test Score after Improvements were made .......................................................................... 65
Figure 10: Delivery and Bounce Rates ............................................................................................................ 68
Figure 11: Survey Responses by day ............................................................................................................... 69
Figure 12: Survey Statistics, exported form SurveMonkey .......................................................................... 71
Figure 13: Organizational Characteristics ...................................................................................................... 71
Figure 14: Number of Survey Responses ....................................................................................................... 73
Figure 15: Sample ............................................................................................................................................... 74
Figure 16: Cumulative Histogram of Guest Rooms ...................................................................................... 75
Figure 17: Number of Accommodation Providers with up to 50 guest rooms ......................................... 76
Figure 18: Number of Accommodation Providers with 100-400 rooms..................................................... 77
Figure 19: Overview of Job Roles .................................................................................................................... 79
Figure 20: Number of Employees .................................................................................................................... 80
Figure 21: The role of Technology Adoption in integrated RM .................................................................. 80
Figure 22: Technology Adoption ..................................................................................................................... 82
Figure 23: Comparison of Technology Adoption Q4 2016 vs. Q1 2018 ...................................................... 83
Figure 24: Histogram of accommodation providers without Channel Manager ...................................... 85
Figure 25: Accommodation Provider without Channel Manager .............................................................. 86
Figure 26: Integrated RM and Channel Management .................................................................................. 87
Figure 27: Basic system for maintaining rates and availabilities ................................................................. 88
Figure 28: Overbooking practise ...................................................................................................................... 89

8
Figure 29: Evaluating the social system .......................................................................................................... 90
Figure 30: Perceived challenges and opportunities in adapting prices to demand .................................. 92
Figure 31: Pricing Fairness................................................................................................................................ 96
Figure 32: Rates offered .................................................................................................................................... 99
Figure 33: Price Dumping ............................................................................................................................... 102
Figure 34: Distribution challenges and opportunities ................................................................................ 104
Figure 35: Perceived Value lies at the heart of integrated RM .................................................................. 106

9
IV. List of tables
Table 1: Research on RM ................................................................................................................................... 16
Table 2: Critical Successfactors of RM............................................................................................................. 38
Table 3: Performance Measurement System, based on (Yilmaz & Bititci, 2006) ....................................... 44
Table 4: Research on RM implementation in Austria ................................................................................... 50
Table 5: SME Definition, European Commission (2012) .............................................................................. 51
Table 6: Number of E-Mail Adresses collected from firmen.wko.at .......................................................... 57
Table 7: Outcome Web Scrapping Holidaycheck.de ..................................................................................... 58
Table 8: Persuasion Techniques Invitation E-Mail ........................................................................................ 67
Table 9: Guest Rooms – Descriptive Statistic ................................................................................................. 74
Table 10: Pricing approaches, slightly modified after (Phillips, 2005) ....................................................... 97

10
V. Abstract
The aim of this thesis is to explore the gap between the state of research on Revenue Management (RM)
and the technology adoption and challenges of small and medium sized (SME)-accommodation pro-
viders in Austria. The implementation of rate parity agreements hindered RM adoption, but the re-
moval of these restrictions in January 2017 facilitated it. Meanwhile the classic hotel revenue manage-
ment problem and traditional approach of RM has changed with the economic crisis of 2008, the rise of
OTA, user generated content, online reviews and electronic word of mouth. Price transparency brought
the end of traditional RM and integrated RM emerged. The research stream on Critical Success Factors
(CSF) of RM already describes very well how modern RM should look like. In order to evaluate RM
adoption of Austrian SME, integrated RM is described in this thesis as an Internet and Computer Tech-
nology (ICT) process innovation. It is an accommodation providers’ ability to manage technical and
social systems within a complex distribution environment.
In order to explore RM-related technology adoption and perceived challenges in adapting prices to
demand, a web survey with quantitative and qualitative questions was used. Closed questions evalu-
ated the use of RM related software and Software as a service solutions (SaaS) – or the technical system
of Revenue Management.
Two open questions asked about challenges and opportunities in adapting prices to demand and gen-
erated qualitative text that was further analysed. Both questions investigated the social or organiza-
tional system of RM. The electronic survey was distributed via e-mail to all accommodation providers,
who displayed their e-mail address on the platform holidaycheck.de. Before sending the survey invita-
tion, these E-Mail addresses were collected through automated web scrapping using Mozenda. In total
the survey was delivered to 9,626 participants and achieved a response rate of 11%.
This work also explains how RM evolved in line with the digital hotel distribution system it is embed-
ded in. It then conceptualises integrated RM, as being at the intersection of technical systems and or-
ganisational systems, within a complex distribution landscape.
The main findings from the survey regarding technology adoption is that Austrian SME-accommoda-
tion providers are still maintaining rates and availabilities manually, because 50% of all surveyed ac-
commodation providers don’t use a channel manager, but without the use of a channel manager rates
and availabilities are not updated in real time across different distribution channels. Adopting a Chan-
nel Manager is key for implementing a real time direct booking option for guests and for more ad-
vanced overbooking policies. It is the necessary first step towards integrated RM. The analysis of the
open questions revealed that one of the major challenges for SME-accommodation providers is pricing
for frequent guest, because providers would like to treat all guests equally. This narrow understanding
of price fairness actually hinders the use of pricing rules.
Cost Coverage, the competition, price dumping and finding the right price level within a complex dis-
tribution system are perceived as major challenges, whereas value creation and personal and individual
guest service are the biggest opportunities, according to the survey participants.

11
The key outcome of this work is the message, that the current digital distribution environment requires
a shift from the traditional, capacity and product-based understanding of RM towards an integrated
approach and culture of value creation. By providing a conceptualisation of integrated RM, quantitative
data on RM related technology adoption and insights about the perception of challenges and opportu-
nities of adapting prices to demand, this work aims to bridge the gap between the state of research and
our understanding of the actual day to day challenges SME-accommodation providers face in Austria.

12
1. The aim, relevance and contribution of this thesis
The aim of this thesis is to answer the following research question:
How do small and medium sized (SME)-accommodation provider in Austria use Revenue Management? The
answer should help to bridge the gap between the well-researched understanding of: “What ought to
be done to achieve excellent RM adoption?” and the question: “What is actually done by SME accom-
modation providers in Austria?
This thesis explores the current adoption of Revenue Management (RM)-practises of small and medium
sized (SME) accommodation providers in Austria. Before that it also tries to challenge the common
assumptions (Alvesson & Sandberg, 2013) about RM is.
The literature provides decent body of qualitative research, mainly based on expert interviews on the
Critical Successfactors (CSF) of Revenue Management. This research on CSF provides an answer to the
question “What would make RM use and implementation successful” and could be seen as the ‘blue-
print’ for practitioners to reach a high level of RM adoption.
Unfortunately, these CSF of RM remain very generic and this well-developed body of research on the
critical successfactors does not inform practitioners, especially SME-accommodation providers, what
to do day per day. Revenue Management [RM] encompasses what was previously called Yield Man-
agement, the: “process of allocating the right type of capacity to the right kind of customer at the right price so
as to maximize revenue or yield” (Kimes 1989, p.15). This is one of the most used and agreed upon defini-
tion of Revenue Management. Nevertheless, it represents more an objective rather than the activities
involved. So this means researchers and practitioners would agree on what should be done to make
RM work, but at the same time we don’t quite know what this ‘thing’ RM looks like when it is applied.
This becomes even more pronounced with RM currently transitioning from a room capacity-based ap-
proach of inventory manipulation to an integrated management philosophy of total revenue manage-
ment.
Evaluating Revenue Management implementation
In 2013 Talón-Ballestero & González-Serrano (2013) developed the model for evaluating revenue man-
agement implementation, MERMI, (Talón-Ballestero, González-Serrano, & Figueroa-Domecq, 2014).
This model, based on extensive expert interviews describes RM to be 77 activities in the following 9
categories (see Table 2, p. 38):
• Revenue management culture and resources
• Forecasting
• Analysis of the competition
• Demand segmentation
• Budgeting
• Pricing

13
• Distribution Channel Management
• Updating limits, reservations and sales
• Evaluation
In accordance with Talón-Ballestero et. al. (2014) RM activities, can be subsumed under the areas of:
• information management
• price management
• capacity management
• sales management including distribution channel management
Up to this time the MERMI model has been used to evaluate RM implementation in hotels in Madrid
and Barcelona (Talón-Ballestero & González-Serrano, 2013; Talón-Ballestero & Rodríguez-Algeciras,
2017).
The MERMI model describes for the first time an exhaustive set of activities to achieve the objective of
RM to allocate: “the right type of capacity to the right kind of customer at the right price so as to maximize
revenue or yield” (Kimes 1989), but the evaluation of RM implementation is based on a questionnaire
with 50 items and due to its precise description many of the items are rather difficult to understand,
especially for SME-accommodation providers that apply RM more intuitively (European Commission,
1997a) rather than institutionalised.
Why adopting RM is relevant and important for SME
KMUs, “Klein-und Mittelbetriebe” in German (Austria KMU Forschung, 2017; ÖHT, 2017) are interna-
tionally referred to as small and medium sized enterprises, abbreviated SMEs (European Commission,
2015; OECD, 2016). SMEs are of vital importance for the tourism and accommodation sector. They are
considered to be “key drivers” (Tassiopoulos, 2008) and the “backbone” (Jeansson et al., 2017) of the
worldwide tourist industry (OECD, 2016; Thomas, Shaw, & Page, 2011).
In the EU-28 micro-enterprises, having less than 10 employees, account for 92,8% of all enterprises,
small enterprises, with less than 50 employees account for 6%, while medium enterprises, with more
than 250 employees, account for only 1% off all enterprises. Large enterprises, exuding the ceilings of
the SME-definition account for only 0,2% of all enterprises (European Commission, 2016).
The lodging sector is also a key driver on the economic impact of the Austrian tourism industry (BMWF,
2017) and according to the Eurostat Structural Business Statistics (Eurostat, 2017b) in the year 2015, 95%
of all firms in the accommodation & foodservice sector in Austria were SMEs.
Research findings show, that the successful RM implementation offers tremendous potential for hotels.
According to Skugge (2004) it can increase company profits by 30-50% and revenues by 3-7%. Kimes &
Wirtz (2003) note companies reported revenue increases of 2% - 5%. Therefore successful RM imple-
mentation is very relevant on a micro-economic level for SME hotels to increase profitability, especially
because the capital structure and profitability of accommodation providers is far from beeing perfect,
even in the top-sector of leisure hotels (ÖHT, 2016)

14
Since the Austrian lodging sector is dominated by SME enterprises (Eurostat, 2017b) the overall profit-
ability of SME-hotels is crucial on a macro-economic level for the tourism industries contribution to
Austria’s GDP. Austria’s bimodal demand pattern (Eurostat, 2017a) with summer and winter peak
would allow many hotels to optimise their capacity and rates across summer, winter, pre-and off-sea-
sons.
So the question on how to implement and adopt RM is quite relevant for accommodation providers in
Austria and the Austrian economy overall, because if more SME accommodation providers increased
their profitability by adopting RM practises the overall economic health of the accommodation sector
would benefit as well. An economically strong accommodation sector is important for Austria’s tour-
ism sector to stay competitive and maintain its contribution to the GDP in the long term.
According to the tourism satellite account for Austria, TSA (OECD, 2017), the direct value-added effects
of tourism account for 5.6% of GDP. However the TSA framework only considers the direct effects,
neglecting the wider economic effects on the supply chain (Smeral, 2011). Including an estimation of
the indirect effects of tourism carried out by the Austrian Institute of Economic Research, Statistik Aus-
tria reports that the direct and indirect effects together contribute by 8.7% to the GDP. This number is
excluding business trips and the use of touristic facilities by the domestic population
Furthermore almost 60% of people directly employed in the tourism industry work in the lodging or
catering sector (Statistik Austria, 2017). Jobs in tourism account for 7,9% of overall employment in Aus-
tria and within the tourism sector 22,5% of people are employed in the lodging category (Statistik Aus-
tria, 2017a,b,c).
The OECD (2016; p.129) underlines, that the key challenges for the Austrian lodging industry are the
“high dependence on a small number of core markets […] as well as improving the size and quality of tourism
enterprises”.
As described above RM adoption could support SME-accommodation providers to increase their prof-
itability. Regarding the structure of the Austrian lodging industry, an increased profitability of SME-
accommodation providers would be highly beneficial to the country’s GDP and labour market as well.
State of research: Which question to ask about RM?
RM offers tremendous potential for hotels to increase revenue (Kimes & Wirtz, 2003; Skugge, 2004), but
there is a reasonable gap between research and business practise, because although RM is considered
to be a mature and well researched field of study (Shoemaker & Gorin, 2008; Wang et. al. 2015), there
has been no unified model to describe the RM process, its activities or its implementation (Talón-
Ballestero & González-Serrano, 2013).
Many of the technical aspects of RM, like “data analysis, forecasting, pricing and optimisation” (Josephi,
Stierand, & Mourik, 2016) are well researched (Shoemaker & Gorin, 2008; Wang et. al. 2015) and modern
software solutions can handle these tasks quite well. But RM is not only about software adoption. As

15
stated above, according to (Talón-Ballestero et al., 2014) the activities of RM fall under the following
categories:
• information management
• price management
• capacity management
• sales management including distribution channel management
So, software systems can surely help in these areas. The well-developed research body on the technical
aspects of RM allowed the development of sophisticated RM software solutions. Today SME hotels can
choose from a variety of affordable software solutions due to the advancement in SaaS and Cloud com-
puting. Nevertheless RM is not only about technology or software adoption, but research on RM has
largely focused on the technical aspects of RM (Josephi et al., 2016). Therefore Shoemaker & Gorin,
(2008) and Talón-Ballestero et al., (2014) state that “research lags significantly behind business practise”
(Talón-Ballestero et al., 2014, p. 310).
The following paragraph gives an overview of the reasons that might explain the gap between research
on RM and business practise. It is of uttermost importance to better understand the current gap between
research and business practise and how the research question of this thesis fits within the current state
of research.
Table 1 presents questions relevant to both practitioners and academics and shows how this question
has been addressed by researchers.
The research on critical successfactors of RM would answer the practitioners question of “Which factors
of RM are critical to success? or “What ought to be done for RM to work?”
Questions already answered
How have they been answered?
“Which factors of RM are critical
to success?”
“What ought to be done for RM
to work?”
Defining Critical Successfactors of RM (see chapter 5.2, p.
29)
“How does RM look like?”
“How to do it?”
Expression of linear, sequential or systemic models of RM
(Desinano, Minuti, & Schiaffella, 2006; Emeksiz, Gursoy,
& Icoz, 2006; Hunold, 2014; Ingold, McMahon-Beattie, &
Yeoman, 2012; Phillips, 2005; Shoemaker & Gorin, 2008;
Talón-Ballestero et al., 2014; Ben Vinod, 2004)
RM consists of 77 activities in 9 categories
(Talón-Ballestero et al., 2014)
Concept of Total Revenue Management (Kimes, 2011; X. L.
Wang et al., 2015; Zheng & Forgacs, 2017).

16
Then the question for a practitioner would definitely be: “What should I do? Which are the exact steps of
activities? The research on RM provides quite a wide range of different models of the RM process, but
mainly got stuck in the question of whether these models are of linear, sequential or systemic by nature.
Kimes (1989) definition of RM has not changed much until today, but since 1989 the digital hotel dis-
tribution system has seen drastic changes through the internet and OTAs, but since we still deploy
Kimes definition to RM we lack to acknowledge how much RM has evolved over time in line with the
digital hotel distribution system it is embedded in. RMs technical aspects are already well researched,
but we lack to understand that RM has completely transformed – from the traditional RM 1.0 to inte-
grated RM 2.0. This is a reference to the internet where the so called Web 1.0 evolved into Web 2.0
(Benckendorff, Sheldon, & Fesenmaier, 2014).
Kimes definition (1989, p.15) of RM, being: “a process of allocating the right type of capacity to the right kind
of customer at the right price so as to maximize revenue or yield” states more the objective (Sigala, Lockwood,
& Jones, 2001) when RM is achieved, rather than the activities on how to achieve it in the current digital
hotel room distribution environment.
Before this thesis explores the question whether RM accommodation providers in Austria use RM it is
important to understand how traditional RM looked like and how it has transformed into an integrated
management approach of Total Revenue Management (Kimes, 2011; X. L. Wang et al., 2015; Zheng &
Forgacs, 2017). Total Revenue Management proposes to consider all revenues and not only room reve-
nue, as traditional RM did (ibid.). Thus Total Revenue Management recognises customers’ room and
all auxiliary spending (ibid.) and therefore also takes the overall customer value into account (Milla &
Shoemaker, 2008; von Martens & Hilbert, 2009).
In practise Total Revenue Management not only requires the yield culture, as proposed by the CSF
research, but also the collaboration of all departments (Sfodera, 2006) so that individual departmental
goals are replaced by an overall culture of value creation in the long term. This is in stark contrast to
the traditional RM understanding that mainly considered room revenue on a short-term basis.
“How can its application empiri-
cally be evaluated?”
MERMI, Model of Revenue Management Implementation
(Talón-Ballestero & González-Serrano, 2013; Talón-
Ballestero et al., 2014; Talón-Ballestero & Rodríguez-
Algeciras, 2017)
Table 1: Research on RM

17
2. How RM evolved together with the digital hotel room distribution
The digital hotel room distribution presents a very particular area within the digital distribution of
tourism products (Kracht & Wang, 2010). The rise of the internet and the upcoming OTAs promoted
new possibilities for hotels to electronically distribute their inventory (Benckendorff et al., 2014). OTAs
also strongly shaped the terms of digital distribution and became a key player in digital hotel room
distribution (May, 2016; Stangl, Inversini, & Schegg, 2016). Especially SME-hotels voluntarily and
gladly relied on OTAs as distribution channels (Stangl et al., 2016). The OTA-market developed into
and oligopolistic market, dominated by the Priceline Group and Expedia (May, 2016; Stangl et al., 2016).
Hotels more and more began to see the effects, caused by their practise of strongly relying on OTAS
and therefore almost outsourcing their digital distribution over several years (Cetin, Aydogan Cifci,
Istanbullu Dincer, & Fuchs, 2016). By relying on OTAs (Scaglione & Schegg, 2016) hotels also incurred
high distribution costs (Lohmann, Pechlaner, Smeral, & Wöber, 2015) and did not develop proprietary
digital distribution channels.
Rate parity agreements hindered revenue management
The digital hotel distribution has been characterized by the so called rate parity agreements between
OTAs and hotels during the years 2002-2012 (Buhalis & Kaldis, 2008; Croes & Semrad, 2012; Haynes &
David, 2015). This contractual agreements guaranteed that room rates were consistent across different
distribution channels and allowed OTAs to promote the respective rates to customers with a best rate
or best price guarantee, since hoteliers were not allowed to offer cheaper prices or better conditions on
other channels (Katzenschlager & Koch, 2016). This even included the website of the hotel.
In the year 2012 the European commission began to investigate rate parity clauses set by OTAs in con-
tracts with hotels (Buhalis & Kaldis, 2008; Croes & Semrad, 2012; Haynes & David, 2015) in terms of
their anti-competitive effects. Following this investigation several countries began their own legal in-
vestigations and in response to this development the major OTAs waived their rate parity clauses in
Europe in the subsequent years (Expedia Inc., 2016; Priceline Group Inc., 2017). In Austria rate parity
agreements between OTAs and accommodation providers are not valid anymore since the 1st of January
2017, due to a change in the Austrian law for unfair competition, UWG law and labelling law, PrAG, in
November 2016 (Parlamentsdirektion, 2016b).
The legal argumentation noted the market concentration within the OTA sector and that prices should
be freely set by the caterer and mustn’t be restricted by the booking platform through price-fixing or
rate parity clauses (Parlamentsdirektion, 2016a). Rate parity agreements have a negative influence on
hoteliers, which mainly consists of small and medium sized companies in Austria
(Parlamentsdirektion, 2016a).
The investigations were based on the assumption that rate parity agreements are anti-competitive (Kat-
zenschlager & Koch, 2016).s

18
These legal changes marked a major turning point in the digital hotel room distribution. Room rates
are not de-facto regulated by contractual Price Parity Agreements anymore but can be priced freely by
the hotel (McMahon-Beattie, McEntee, McKenna, Yeoman, & Hollywood, 2016; Rose, 2016).
The end of rate parity and the airline deregulation act of 1978
During the years 2002-2012 with rate parity agreements in place (Buhalis & Kaldis, 2008; Croes &
Semrad, 2012; Haynes & David, 2015) hotels de facto self-limited their pricing ability. Since the legal
ban of rate parity agreements in Austria at the beginning of 2017 (Parlamentsdirektion, 2016a) hotels
gained back the pricing power over their inventory. This means hotels could offer lower room rates on
their own website again, compared to the room rate they provided for OTAs. While the main challenge
for hotels in the past has been to master distribution channel selection (Beritelli & Schegg, 2016; Pearce
& Schott, 1177; Stangl et al., 2016) after the rate parity agreements have been banned hotels face the
challenge to manage room capacity profitably across an increasing number of (online) distribution
channels (Beritelli, & Schegg, 2016, Stangl et. al. 2016, Pearce & Schoott, 2010).
Since the electronic distribution landscape has changed so much, selling the right rooms, to the right
customer, at the right price and also through the right channels (Goerlich & Spalteholz, 2014) became
more important than ever for hotels. This is precisely what Revenue Management is about (Kimes 1989).
Since effective RM is about matching price with demand the firm need to be able to control both price
and demand (Ingold et al., 2012) without being restricted by contractual agreements. The ban of rate
parity agreements resembles the deregulation of the US-airline sector in the year 1978. Before the airline
deregulation act of 1978 (B Vinod, 2009) the US-airline sector and fare structures were tightly regulated
(Bitran & Caldentey, 2003). Similar to the described market situation of hotels with rate parity agree-
ments in place the airline companies were also not able to freely price their inventory. After the US-
airline deregulation act airline companies started to adopt practices of price and inventory control
(Bitran & Caldentey, 2003). At this time their goal was to increase the yield per available seat mile. For
this reason these practices were commonly referred to as Yield Management (Kimes, 1989b).
The end of rate parity could lead to an increase in RM-adoption of accommodation providers, similar
to the adoption of YM practises after the US-airline deregulation act, because external pricing re-
strictions have been removed.

19
3. Traditional Revenue Management
Successful RM is about matching price with demand. In order for RM to be effective the firm need to
be able to control both variables price and demand (Ingold et al., 2012).
In a concise literature review on the effect of reviews and price on customer decisions Book et al.( 2015)
notes that “Previous literature has documented a relationship between price and consumers’ perceptions of qual-
ity and value” (ibid. p.5). So traditionally a lower price would signify more value to the customer. This
was certainly true in times when customers could only evaluate hotels based on their star qualification,
the official information by the hotel or reviews in guide books or from friends. The rise of user gener-
ated content (UCG) and reviews lowered the importance of the price. Price is still used by customers to
evaluate value (Dunn et al., 2010) and consumers use it also beside reviews to estimate service quality
(Hung, Shang, & Wang, 2010). With reviews being equal customers still prefer a lower price over an
higher price (Noone & McGuire, 2014).
The traditional capacity-based approach
Revenue Management is also referred to as Perishable-Asset Revenue Management (Bodily &
Weatherford, 1995; Farrell & Whelan-Ryan, 1998), Yield Management (Talluri, 2012) or Pricing and
Revenue Management (Liozu & Hinterhuber, 2015; Ng, 2008), pricing and revenue optimization,
revenue process optimization, demand management or demand-chain management (Talluri & Van
Ryzin, 2004a).
RM refers to “demand management decisions and the methodology and system required to make them” (Talluri
& Van Ryzin, 2004, p.1). Revenue Management encompasses what was previously called Yield Man-
agement, the: “process of allocating the right type of capacity to the right kind of customer at the right price so
as to maximize revenue or yield” (Kimes 1989). In the academic discussion the term Revenue Management
is now more common (Baltazar, 2008; Josephi et al., 2016; Ben Vinod, 2004; X. L. Wang et al., 2015;
Yeoman & McMahon-Beattie, 2011) and the terms YM and RM are used interchangeably (Baltazar,
2008).
RM aim is to successfully manage perishable demand. It addresses structural decisions, price decisions
and quantity decisions (Talluri & Van Ryzin, 2004a).These decisions can be divided into more tactical
short term decisions regarding operational aspects and strategical decisions with a long-term time
horizon (Emeksiz et al., 2006). Structural decisions regard the selling format, market segmentation, the
terms of selling including cancelation policy and product bundling. Price decision include decisions on
how to set prices or how to apply discounts. Quantity decisions regard capacity control on how to
allocate available capacity to defined segments, distribution channels and when to accept or reject offers
and withhold a room from the market (Talluri & Van Ryzin, 2004a).
According to Ingold et al., (2012) the traditional approach to RM involved the following “ingredients”:
• market segmentation

20
• historic sales and booking patterns (including the booking time) by rate level
• decision on the rate structure (one rate for all rooms, rate by room type, “fenced” discounts)
• integration with other IT-Systems
• overbooking policies
Although it is widely argued that Yield Management came into existence after the US airline
deregulation act in 1978 (Ben Vinod, 2016) the business practice of matching supply with demand and
adjusting prices according to the time of purchase has been used by sellers far longer (Cleophas &
Frank, 2011; Talluri & Van Ryzin, 2004). Cleophas & Frank (2011) argues that farmers traditionally
lowered prices for their perishable products at the end of the day, since they preferred to sell them for
a lover price rather to transport them back home or let them go to waste.
In his concise overview about the origins of research on RM Baltazar (2008) refers to Cross (1997).
According to Cross (ibid.) Hayek’s (1945) paper “The Uses of Knowledge in a Society” marks the beginning
of the modern discourse about RM. Cross’ (1997) argument is especially valid in reference to the price
transparency of the market.
Between the years 1958-1967 various papers discussing the practise of overbooking (Talluri & Van
Ryzin, 2004d) were published.
Littlewood provided a rule for optimal seat inventory allocation in 1978 (Baltazar, 2008; Belobaba, 2016;
Littlewood, 2005). In his seminal PhD-thesis Belobaba (1987) further developed Littlewood’s rule into
the “Expected Marginal Seat Revenue (EMSR) approach” (Belobaba, 2016; Yeoman & McMahon-Beattie,
2011). Refined by Belobaba (1992) again it became well known as the EMSRb’ model (Belobaba, 2016).
Even though the first research on overbooking and inventory control was carried out in the 1960ies and
1970ies the application of these practices of price and inventory control were limited due to the fact that
the US-airline sector was tightly regulated until 1978. (Bitran & Caldentey, 2003). The airline
deregulation act of 1978 (B Vinod, 2009), the above described advances of inventory control models by
Belobaba (1987, 1992) and improvements in electronic data processing lead to the wider use of RM
systems. Since at this time these practices were mainly used by airlines to increase the yield per
available seat mile these practices were commonly referred to as Yield Management (Kimes, 1989b).
Kimes (ibid) paper “Yield Management: A Tool for Capacity-Constrained Service Firms” and her paper “The
Basics of Yield Management” (Kimes, 1989a) established YM concepts as a wider known operational
practice by hoteliers.
This was about the same time when hotels managed to solve their challenges with automated
distribution through connecting their PMS systems with GDS, which at these time where the most
important electronic distribution channel, since the internet was only in its early stadium (Benckendorff
et al., 2014). This is because the HTTP-protocol, enabling the world wide web as we know it today, was
only introduced in 1991 and the first browser with a graphic interface in 1993.
Within the 1990ies RM became quite a broadly discussed phenomenon among hoteliers and industry

21
experts (Lieberman, 1993).
Although various authors contributed different definitions of RM (Burgess & Bryant, 2001) the
definition of RM did not change over time (Josephi et al., 2016). One of the first, most widely used and
impactful definition of YM came from Kimes (1989b), from her articles “Yield Management: A Tool for
Capacity-Constrained Service Firms” and “The Basics of Yield Management” (Kimes, 1989a). Accordingly
YM is a tool, process or method: “of allocating the right type of capacity to the right kind of customer at the
right price so as to maximize revenue or yield” (ibid, p.15). This is often done through “the application of
information systems and pricing strategies” (Kimes & Wirtz, 2003, p.125).
The implementation of RM is especially applicable to industries with the following characteristics
(Ingold et al., 2012; Kimes, 1989b; Legohrel, Poutier, & Fyall, 2013; Talluri, 2012; Talluri & Van Ryzin,
2004d):
• Fixed capacity
• Perishable inventory
• Ability to segment markets
• Fluctuating Demand over time, which can be managed or predicated
• Product sold in advance
• Cost structure with high fixed-costs and low variable costs
Traditional RM can be considered as a special form of price discrimination (Talluri, 2012), whereby
customer-segmentation strategies are used to charge “different prices for different customer segments for
the consumption of the same product” (Talluri, 2012, p. 655; Bodea & Ferguson, 2014). With its aim to
increase revenue per available unit of capacity it is equal to archive a perfect price discrimination. This
pricing strategy seeks to charge every customer the maximum amount that he or she is willing to pay
(Hannak, Soeller, Lazer, Mislove, & Wilson, 2014).
An accommodation provider can either charge different prices to different customers and or at the same
time create different versions of their product. Different room types naturally resemble different
products in the eye of the consumer. Yet also a hotel with only one type of rooms can easily create 30
different products by adding different cancelation or payment terms or additional services (Talluri,
2012).
In RM a “product is a combination of resources and a price, along with the associated restrictions and conditions”
(Talluri, 2012). Customers are then able to decide on their own which price-resource combination best
suits their needs and budget (Hanks, Cross, & Noland, 1992). The conditions and restrictions placed on
a room rate are called price fences, hurdles or barriers (Hanks et al., 1992; Hayes & Miller, 2011). They
can be physical, i.e. the type of room or non-physical (Noone, 2016). These conditions are acting as a
hurdle to access the designated room rate. Traditionally non-physical fences are: advance requirement,
refundability, changes allowed or the required time of stay (Hanks et al., 1992).

22
For the accommodation provider the goal is to “accommodate the most profitable mix of customers at each
property” (Ben Vinod, 2004) so it neither misses out on sales nor sells inventory at a price that is too low,
which equals lost profit. The goal is to optimize the mix of occupancy and profit (Hayes & Miller, 2011).
Traditionally YM resembles more a capacity based revenue management, based on different inventory
allocation models tasked: “with opening and closing predefined room rates” (Noone, McGuire, & Niemeier,
2011). The underlying intent in the capacity based approach of RM is to prevent customers with a higher
willingness to pay to access lower fares, not designated to them (Noone, 2016).
The classic hotel revenue management problem
The hotel product shares many similarities with the airline product. Compared to RM in other indus-
tries RM for airlines and hotels must account for the network effect and no-shows. This is done through
overbooking policies and length of stay controls.
Similar to airlines hotels face the challenge that some guests book, but then don’t show up without
cancelling their stay. Accommodation providers, like airlines, account for this uncertainty of arrival
(Ingold et al., 2012) by actually overbooking their inventory and by accounting for no-shows in their
rate strategy (Hayes & Miller, 2011). Moreover, the hotel product, like the airline product typically is
sold as a bundle that uses several units of inventory. A flight sold as one flight could consists of two or
three single flights and a hotel stay is commonly a multiple-night stay. This challenge is dealt by
applying a network RM approach (Belobaba, 2016) with length of stay control (Talluri & Van Ryzin,
2004b).
Overall hotels could easily adapt the YM approach from airlines since their product had similar
characteristics and faced similar challenges. In the traditional offline-distribution environment airlines
and hotels could segment their markets very easily (ibid.), because customers were quite heterogenic
in their time of buying, their evaluation of the product and also their willingness-to pay. By introducing
measures of price- and product segmentation accommodation providers were able to cater both to price
sensitive customers and customers with a higher willingness to pay for certain product features. The
classic and most important segmentation was between leisure and business guests.

23
4. Pricing the hotel experience
The current market environment without price parity agreements, the complex distribution landscape,
price transparency and the high importance of non-price factors on hotel choice (see section 4.3) require
hotels to adopt an integrated approach to RM and thus taking the special characteristics of the hotel
product and the price transparency of the market into account.
Pricing is a source of competitive advantage and the most important economic leverage point for a
company (Dutta, Zbaracki, & Bergen, 2003; Gallego & Stefanescu, 2012). However it is one of the least
understood elements of the marketing mix (Ng, 2008; Rothenberger & Siems, 2008), not widely taught
in business schools and few companies dedicate research or a job role solely to the pricing function
(Hinterhuber & Liozu, 2012). Pricing can be understood as a set of decisions and also an overall ability
of the firm.
In regard to RM pricing deals with the decision a hotel makes on how much it charges for the hotel
experience (Qi & Chen, 2017; Varkaris & Neuhofer, 2017) in order to achieve the objective of the firm
(Ng, 2008) while at the same maximizing the value or utility delivered to the customer (Kimes & Wirtz,
2003). Thus pricing is a firm’s ability to “to meet both its own and its customers’ requirements” (Dutta,
Zbaracki, & Bergen, 2003, p.616). The customers of the accommodation product are generally tourists,
who are also referred to as overnight visitors (UNWTO, 2014) In contrary to excursionist, only staying
for a day, a tourist’ trip includes an overnight stay” (ibid.). In order for overnight visitors to qualify as
tourists their trip has to be less than a year and outside their usual environment (UNWTO, 2010).
Tourists may travel for business, leisure or other purposes, but must not be employed “by a resident
entity in the country or place visited “(ibid.).
Special characteristics of the hotel product
The hotel product is referred to as a product, service or experience that is often purchased in advance
and as part of a bundle of other services (Aldebert, Dang, & Longhi, 2011).
Compared to tangible products services and experiences are intangible, multi-sensory, perishable, com-
plex and heterogenic. In order for the hotel ‘product’ to be consumed it requires the guest to travel to
the location of the accommodation (Aldebert, Dang, & Longhi, 2011; Benckendorff et al., 2014; Ek,
Larsen, Hornskov, & Mansfeldt, 2008; Yilmaz & Bititci, 2006). Due to the fact that production takes place
simultaneously alongside consumption, the service quality differs in scope and quality (Zehrer, 2009).
The inseparability (Benckendorff et al., 2014) between production and consumption is further described
and discussed in the literature as either co-production or co-creation, meaning that the hotel experience
is a result of the interaction between the tourist and the accommodation establishment. (Binkhorst &
Den Dekker, 2009; Chathoth, Altinay, Harrington, Okumus, & Chan, 2013; Prahalad & Ramaswamy,
2004). Co-creation is based on a service dominant logic (Vargo & Lusch, 2004; 2017) and a “joint creation
of value” (Prahalad & Ramaswamy, 2004). Co-creation is routed in the view that companies cannot design
the hotel experience per se, but rather provide the right setting for the tourist to have the kind of

24
meaningful and memorable experience that he or she seeks for (Moscardo, 2017).
Attributes of hotel choice
The decision which hotel to choose is a high-risk decision (Amaro & Duarte, 2015; Book et al., 2015; P.-
J. Lin, Jones, & Westwood, 2009), where emotions play a key role (Varkaris & Neuhofer, 2017). This is
due to the information intensity, complexity and the intangible nature of the hotel product and the
various different experiences that it could enable for guests (Yilmaz & Bititci, 2006). Therefore, the hotel
product is a high-involvement, co-created experience product. It is evaluated based according to all
interactions the guest has with the accommodation provider: Before the trip, during and after the trip
(Tung & Ritchie, 2011; Preveden & Tiefengraber, 2016). Stickdorn & Zehrer (2009, p.8) speak of the: Pre-
service, service and post service period. In the lodging context Zach & Krizaj (2017) define the pre-
arrival, arrival, stay, departure and post departure phase to be important touchpoints in the customers
journey
Pricing an experience: Taking online reputation into account
The literature seem to propose that the location, price of a hotel and its location are the most important
hotel attributes in the decision process (Dolnicar & Otter, 2003; Lockyer, 2005; Xiang & Krawczyk, 2016).
Beside these factors the cleanliness, the facilities and staff service are also among the most important
factors for customers when selecting an accommodation (Book et al., 2015; Lockyer, 2005; McGuire,
2016)
Traditionally a lower price would signify more value to the customer. This was certainly true in times
when customers could only evaluate hotels based on their star qualification, the official information by
the hotel or reviews in guide books or from friends. The rise of User Generated Content (UCG) and
reviews lowered the importance of the price as a significate of value (Book et al., 2015; McGuire, 2016).
By reading UCG and ratings from other travellers customers are better able to evaluate the intangible
hotel experience before their trip (Qi & Chen, 2017; Varkaris & Neuhofer, 2017). Customers can now
anticipate the potential value of the hotel experience in advance and not only after their stay has been
completed.
For that reason in deciding for a hotel photos (Pan, Zhang, & Law, 2012; Salleh, Hashim, & Murphy,
2016), ratings, user generated content and electronic-word of mouth (Litvin, Goldsmith, & Pan, 2008;
Serra Cantallops & Salvi, 2014; Viglia, Minazzi, & Buhalis, 2016) on social media play a key role in the
hotel decision journey (Book et al., 2015; Casaló, Flavián, Guinalíu, & Ekinci, 2015; Noone & McGuire,
2013; Varkaris & Neuhofer, 2017).
Electronic word of mouth is defined by Litvin et al. (2008, p.461) as: “all informal communications directed
at consumers through Internet-based technology related to the usage or characteristics of particular goods and
services, or their sellers”. E-wom or in other words the online reputation of an hotel play a key role in the
decision and text reviews written by previous customers are one of the most trusted information source,

25
because reviews are easy to process cognitively and thereby facilitate the customers decision making
process (Book et al., 2015)
Accordingly based on their well received and cited consumer choice experiment Noone & McGuire
(2014) report that price “had no significant relationship with quality” (McGuire, 2016, p.90), because “con-
sumers do not use price as an indication of quality if ratings and reviews are available” (McGuire, 2016,
p.93). Customers remove hotels with low ratings and negative reviews from their choice-set. So, low-
ering the price does not provide additional value to the customer (Noone & McGuire, 2014). Although
with reviews being equal customers still prefer a lower price over an higher price (Noone & McGuire,
2014).
The role of UCG and reviews in the hotel selection process requires accommodation provider to include
these quantitative review scores and qualitative review content in their revenue management
strategies. Modern Integrated RM systems can support hotels doing this (Mews, 2017).
Accommodation provider must understand, the hotel experience, as being part of a wider touristic
experience. The hotel experience not only consists of the active stay at the accommodation provider,
but begins before the trip and ends after the trip, when the guest reflects upon his or her experience
and its overall value. Value is only attributed by the customer to the accommodation product ex-ante.
Nevertheless also the booking, information or inspiration phase before the trip is important in value
creation. This might strongly affect decisions about pricing strategies, display of pricing or discounting.
Price transparency: The end of traditional RM
The following factors contributed to the end of traditional RM: the price transparency enabled through
OTAs, reduced information asymmetry and economic downturn after the financial crisis in the year
2008. The transparency in the market made it difficult for hotels to segment demand and apply the
traditional rate fences of traditional RM. While price transparency marked the end of traditional RM
Integrated RM emerged as an Internet and Computer Technology and managerial process innovation.
Traditional RM could be mainly considered as a special form of price discrimination (Talluri, 2012)
according to the time of purchase (M. Collins & Parsa, 2006) and it relied on the ability to clearly seg-
ment customers, but in times of economic downturn in the tourism industry beginning with the terror-
istic attacks of September 11 in 2001, soon followed by the global financial crisis 2008 (Cross, Higbie, &
Cross, 2009) the approach of opening and closing rates became less and less appropriate and effective
(Noone et al., 2011; Schwartz, 2008). During these times of recession hotels had to drive demand.
The rise of the internet with OTA and meta-search engines and rate parity across distribution channels
increased the price transparency for customers (Noone, 2016) because they were suddenly able to
quickly find the best price or last-minute deals. (In the same time the information asymmetry between
supplier and customer has been reduced tremendously (Chen & Schwartz, 2006). McGuire, (2016)
claims, that price transparency “is probably the largest and most widely discussed change to revenue
management analytics” (ibid, p. 18). Therefore RM expanded from a pure capacity management to an

26
integrated management approach. Successful integrated RM supports accommodation provider to
meet both their financial and non-financial goals (Grissemann, Plank, & Brunner-Sperdin, 2013). Price
transparency made it also more difficult for hotels to segment demand. During the years 2002-2012
with rate parity agreements in place (Buhalis & Kaldis, 2008; Croes & Semrad, 2012; Haynes & David,
2015), the usage and utility of rate fences diminished. Furthermore during the same time hotel ratings
became an essential factor for customers in their hotel choice (Noone & McGuire, 2014).

27
5. Defining Integrated RM: What modern RM should look like
Compared to traditional RM integrated RM has to be more customer focused (McMahon-Beattie et al.,
2016). As described above the current online distribution system with a vast number of channels made
inventory control more difficult for hotels (Cross et al., 2009) and therefore RM moved from a capacity
based approach towards price-based RM, relying more on price optimization (Gregory, 2010;
Lieberman, 2010; Noone, 2016) and dynamic pricing (Ingold et al., 2012; Talluri, 2012). The goal of price
optimization is to set an optimal price “for each combination of room type, arrival date, and length of stay”
over the booking horizon (McGuire, 2016), since ”Price optimization solutions derive an optimal price
recommendation by quantifying price elasticity of the hotel’s demand as well as that of their competitive set to
forecast how consumers will respond to price change” (Dietz, Osborn, & Sanli, 2012; Noone & McGuire, 2013)
An integrated approach to RM acknowledges the price transparency of the market. Compared to this,
within a traditional RM approach hotels often rely on a short term approach of discounting (Hanks et
al., 1992), that is based on the view that lowering the room rate in times of low demand would lead to
an increase in demand. In the current market environment competing solely on price is not a useful
strategy for hotels anymore. In fact it is contra-productive since it lowers the reference price for the
customers in the long term (Noone & McGuire, 2014). Integrated RM considers that in the context of
co-creation prices cannot just be ‘set’ by companies according to their cost of production but are
continuously negotiated with customers on the market place (Prahalad & Ramaswamy, 2004).
So while traditionally hotels influenced revenue by protecting their inventory through rate fences, now-
adays hotels more and more rely on manipulating the price in order to foster change in demand levels
(Cross et al., 2009; McGuire, 2016).
Or how Noone & McGuire (2013, p.387), some of the leading experts in the field, precisely conclude:
“The scope of RM is in transition away from a stand- alone tactical approach to inventory management, to a
strategic, customer-centric approach to demand creation that encompasses marketing, sales and channel strategy”
(Noone & McGuire, 2013). This view is also shared by others (Cross et al., 2009; Kimes, 2011; X. L. Wang
et al., 2015). The move to price optimization came also due to the availability of information systems
that can solve sophisticated mathematical price optimization problems (Cross et al., 2009; Dietz et al.,
2012).
Current state of research
RM is currently conceptually well understood as a multi-disciplinary business process (Buckhiester,
2011) and an innovative management approach (Legohrel et al., 2013) or even management
philosophy (Talón-Ballestero et al., 2014).
Seminal works by Talluri & Van Ryzin (2004d), Ingold et al. (2012) and Phillips (2005) have advanced
the general understanding of RM as a managerial concept. Today RM is not only about price and
demand management anymore.

28
In line with Talón-Ballestero, González-Serrano, & Figueroa-Domecq, (2014) RM includes:
• price management
• capacity management
• sales management including distribution channel management
With the recent transition of RM from capacity management to price optimization (X. L. Wang et al.,
2015) modelling customer behaviour beside from standard economic models (Su & Zuo-Jun, 2007)
became more relevant in operation research. Advanced RM and price optimization might confuse
customers. This confusion could result in consumer inertia (Özer & Zheng, 2012; Su, 2009), difficulties
in the mental accounting of rates or anger that prices were unfair.
5.1.1 An interdisciplinary academic discourse
RM belongs under the Operation Research and management science field of study (Baltazar, 2008) and
also stretches into marketing management and pricing research (Ivanov, 2014), IT, mathematics
(Springer Link, 2017) and it also includes questions of organizational development (Jones, 1999b; Jones
& Hamilton, 1992; Sfodera, 2006).
Since its first volume, issued in May 2002 (Springer Link, 2017), the Journal of Revenue and Pricing
Management, have established itself as the main platform for the interdisciplinary academic discourse
(McMillan, 2017), about RM. The journal publishes 6 issues a year and is edited by Ian Yeoman an Una
McMahon-Beattie (ibid.). Papers are peer-reviewed and cover theoretical findings as well as applied
research and views of practitioners.
5.1.2 Literature included for this thesis
The Journal of Revenue and Pricing Management is not licensed by Salzburg University of Applied
Sciences. The author gained access to articles of this journal through the “ProQuest ABI/INFORM
Global” database at WU Vienna University of Economics and Business (WU Vienna University of
Economics and Business, 2017). Due to a paywall restriction the most current 6 issues could not be
accessed (WU Vienna University of Economics and Business, 2017). The author did an extensive
literature research within this Journal. This process resulted in 89 articles that were considered for this
thesis. The 89 included articles represent about 11% of all papers published in this journal since its first
issue in 2002. Due to the fact, that the 6 most current issues were not available, the author accessed
some of the most recent papers by contacting the researchers through the Online platform ResearchGate
(Researchgate, 2017).
The special edition on the occasion of the 10 and 15th anniversary of the Journal provide a good
overview of the current state of discourse (Yeoman, 2011, 2016). In her surveys of revenue management
professionals Kimes (2011, 2017) provides a great overview of emerging trends.
Due to the inherent interdisciplinarity of RM the author widened the literature, whenever reasonable,
to include contributions from a wide scope of Journals, because a lively academic discourse about
Revenue Management is also taking place in journals like Cornell Hospitality Quarterly (Anderson &

29
Xie, 2010; Kimes, 1989a, 2017) or the International Journal of Contemporary Hospitality Management.
The latter is double-blind reviewed and interdisciplinary (Emerald Publishing, 2017).
Critical Successfactors of integrated RM
The factors that make RM and its implementation successful have been extensively researched through
qualitative interviews with revenue managers, academic experts and case studies. Table 2 (p. 38) pre-
sents this stream of qualitative research and the research method applied.
The Critical Successfactors (CSF) approach has been applied extensively to the field of Information
Systems (Griffin, 1994), but is now understood more as a generic lens to questions of operation and
general management (Brotherton, Heinhuis, Miller, & Medema, 2002)
Brotherton & Shaw (1996) state that: „the essence of the CSF approach to management is, what we would call,
Focused Specialisation, i.e. the concentration of resources and effort upon those factors capable of providing the
greatest competitive leverage” (ibid, p.48). CSF don’t represent the business objective itself, but rather:
”combinations of activities and processes designed to support the achievement of such desired outcomes”
(Brotherton et al., 2002).
Table 2 (p. 38) displays an overview of the research on CSF for Revenue Management systems, relevant
to the integrated RM approach.
After Kimes (1989) published her famous definition of YM, it was broadly discussed among hoteliers
and industry experts (Lieberman, 1993). At this time scholars also started to investigate implementation
models and critical success factors of RM (Shoemaker & Gorin, 2008).
After (Orkin, 1988), Jones & Hamilton (1992) proposed one of the first success model of RM, consisting
of 7 stages. A report by the European Commission (1997), based on a study carried out in 1995, identi-
fied four success factors of Yield Management (see Table 2, p. 38). Following his PhD thesis Griffin
(Griffin, 1994, 1995, 1997) published about the Critical Success Factors (CSF) of Lodging Yield Manage-
ment Systems. Hansen & Eringa (1998) grouped CSF taken from a literature review and merged them
with their findings from qualitative interviews of revenue managers in hotels. Selmi & Dornier, (2011)
and Queenan, Ferguson, & Stratman (2011) also gained their findings from a literature review and qual-
itative interviews. Liebermann (2003) summarized the “managerial, organizational, and operational issues“
(ibid, p.3) and formulated CSF based on his consulting experience.
Luciani (1999), Brotherton & Turner (2006) also contributed to the discussion of CSF in Revenue Man-
agement. Their research design included a literature review and case studies of Dutch hotels and hotels
in Florence.
Talón-Ballestero & González-Serrano, (2013) developed a model (MERMI) to evaluate Revenue Man-
agement implementation. The model is based on a literature review and two rounds of expert inter-
views following the Delphi Method.

30
The model is based on a survey and was used to evaluate RM-implementation of 142 hotels in Madrid
(Talón-Ballestero & González-Serrano, 2013) and 17 hotels in Barcelona (Talón-Ballestero & Rodríguez-
Algeciras, 2017). The MERMI-evaluation model consists of 77 items in 9 categories (ibid). Each item
represents an RM activity. If the underlying question about the implementation of the activity is an-
swered with yes, the hotel yields the according score of the item. The score for each item ranges from
3,5-4 points. The highest score that can be achieved is 1617,10 points (ibid.) and represents an ‘excellent’
level of RM-implementation.

31
Critical Success factors to successful Revenue Management Research Method applied
Jones & Hamilton (1992)
Expert Interviews
Griffin (1995)
Literature Review, Survey
Griffin (1997)
Survey
Seven-Stage Success Modell
1. Develop a yield culture
2. Analyze overall demand
3. Establish the price-value relationships
4. Create appropriate market segments
5. Analyze the pattern of demand
6. Track declines and denials
7. Evaluate and revise the system
System factor variables
- Functionality
- System controls
- Information quality
- Match with business objectives
- Steady maintenance
- Data management
- System design
- System quality
- Existing information systems
- User-to-computer interface
- Computer-to computer interfaces
- Supplier system
User-education
- User’s competence
- User’s understanding
- User’s decision making
- System training
User-traits
- Commitment to system
- Positive attitude towards system
Lodging Yield Management Systems
Success Factors
- property is better off with system
- focuses property on goals and strategies
- improved sales decisions
- improves image of computer technology
- System
- accuracy
- adaptability
- completeness
- flexibility
- friendliness
- manuals
- reliability
- reports
- relevancy
- security
- timelines
- usefulness
- meets overall expectations
- positively impacts job

32
External environment
- Middle agent behaviour
- Customer behavior
- Environment benevolence
Organizational support
- Top management support
- Marketing support
- Sales Support
- Operations support
- Reservations support
- Work atmosphere
Improved communication
- between reservations and sales
- between operations and marketing
Employees
- become committed to system
- reduces employee's workload

33
European Commission, (1997)
Desk Research, Survey
Hansen & Eringa (1998)
Literature Review, Case Study
Luciani (1999)
Case Study,
Qualitative Interviews
Liebermann (2003)
Experience
- Efficient communication between the
supplier and the consumer
- organizational issues
- Free flow of information in the
market
- Generally dynamic markets
Organisation of YM Function
- Yield Culture
- Top Management Commitment
- Development of a YM culture, YM
themes and YM communication (incl.
feedback to employees
Empowerment
- employee commitment
- unwritten rules
- training of employees
- incentive & reward systems
Employee Behaviour
- Loyalty
- Productivity
Decision support systems
- Technology
- Human resources
Information system
Actions outside SMEs
- Measuring performance
- Developing supporting business
policies and processes
- Ensuring decision-making authority
and accountability
- Integrating revenue management
with other departments
- Knowing the limits of your revenue
management system
- Providing for career path support
and progression: Life during and
after revenue management

34
Brotherton & Turner (2006)
Literature Review, Case Study
Selmi & Dornier (2011)
Qualitative Interviews
Queenan, Ferguson, & Stratman (2011)
Qualitative Interviews, Literature
- 'Critical' issues
- Awareness Building and the
Development of a Yield Culture
- Encouraging Involvement and Creating
Commitment
- Initial and Ongoing YM Training
- Effective Communication, Co-ordination
and Clarification of Responsibilities
- Organisational and Job Re-structuring
- Motivation and Incentivisation
People-Perspective
- Developing a yield culture and
employee commitment
- Establishing a forecasting committee/YM
team, including rooms, reservations,
sales, marketing, food and beverage,
banqueting and front office managers to
meet weekly
- Identifying human resource implications
- Providing appropriate training and
education
- Clarifying interdepartmental
relationships
- Amending job descriptions
- Management commitment
- Importance of a YM system
- Contribution made by the tool
(software program)
- The human factors
RM Technical Performance Drivers
-Market Segmentation
-Pricing
-Forecasting
-Capacity Allocation
-IT
RM Social Performance Drivers
-Organizational Focus
-Aligned Incentives
-Organizational Structure
-Education & Training

35
Rodríguez-Algeciras & Talón-Ballestero, (2017)
Talón-Ballestero, González-Serrano, & Figueroa-Domecq, (2014)
Talon, Pilar, Gonzales, Talón-Ballestero, & González-Serrano, (2013)
Delphi Expert Interviews, Case Study Madrid, Case Study Barcelona
Revenue management culture and resources
- The hotel management, owner or chain supports the implementation of RM strategies
- The RM team is up to date in RM questions
- The RM team took RM training
Forecasting
- Historical data are taken into consideration on:
o type of customers
o type of rooms sold
o occupancy rate (OR)
o No. of denials (rooms involved)
o No. of no-shows (rooms involved)
o No. of walk-ins (rooms involved)
o average room rate (ARR)
o revenue per available room (RevPAR)
o gross operating profit (GOPAR)
o length of stay
o key accounts in each segment
o group conversion ratio
o group denials, cancellations and decline
- Forecasts compare current to past reservation trends
- Forecasts take existing room reservations into consideration

36
- The advance notice given for reservations by each market segment is known
Pick-up is analysed:
o daily
o weekly
o by total room
- Environmental factors are analysed.
- Future events are analysed
- Future trends in the environment are taken into consideration
Analysis of the competition
- Competitors are identified
- The hotel’s position is determined:
o in the long term (over 12 months)
o in the short term
o in search engines, with respect to its competitors
o in distribution channels, with respect to its competitors
- The following are analysed:
o competitive advantage (location, price, marketing strategies) held by competitors
o competitors' pricing strategies
o competitors' distribution channel strategies
- Competitors' pricing strategies are analysed with rate shopping and benchmarking tools
- The following are measured:
o market penetration index (MPI)
o average rate index (ARI)
o revenue generation index (RGI)
- The competition is analysed periodically

37
Demand segmentation
- More than four market segments are defined.
- The following are analysed
o segment typology
o segment origin
o each segment's buying patterns
o each market segment's contribution to profit
o the segments sourced from distribution channels
Budgeting
- Revenue Management Department forecasts are taken into consideration
- The budget is broken down by market segment
Pricing
- Both the Sales and the Revenue Management Departments are responsible for pricing
- Differential pricing is in place
- Different rates are applied to different market segments
- Restrictive criteria or barriers are applied to the lowest rates
- Package deals (room plus other services) are offered
- Rooms are differentiated by installing facilities that entail no extra cost of any significance
- Costs, demand, competition and distribution channels are taken into consideration in pricing
- Agreements with tour operators and corporate accounts contain provisions for varying rates
- The BAR (best available rate) model is used
- Pricing parity is in place in all distribution channels
- Information on the highest/lowest rate applied is available
- Discounts are subject to compliance with pre-established requirements
- The effect of local events is taken into consideration when revising rates

38
Table 2: Critical Successfactors of RM
Distribution channels
- The position of the various distribution channels is analysed
- The most cost-effective channels are selected
- Customers can reserve on-line through the hotel's website
Updating limits, reservations and sales
- Updated information is at hand on the number of rooms available
- Upselling and cross-selling are practised
- The hotel overbooks
- Reservations are accepted or denied depending on:
o length of stay
o season
o reservation volume
- Reservations are always accepted when the customer revenue/profit/value involved is greater than the customer
revenue/profit/value that would be generated by having one extra room
- Rates are opened and closed depending on demand-side forecasts
- Rates can be changed simultaneously in all channels (channel manager)
- Lower rates cannot be found on other organizations' websites
Evaluation
- The profits resulting from applying RM are evaluated on the grounds of variables such as occupancy, mean rate or RevPAR
- Results are reviewed daily
- Real and budget figures are compared
- Deviations are analysed
- Incentives are in place to encourage reservation and front office staff to upsell and cross-sell

39
Implementing integrated RM 2.0 – a process innovation
Integrated RM can be described as a multi-disciplinary business process (Buckhiester, 2011), manage-
ment approach (Legohrel et al., 2013) or even management philosophy (Talón-Ballestero et al., 2014).
The implementation is a process innovation or more specifically an Information and Computer
Technology (ICT) process innovation since the customer is not part or does not co-create this back-stage
process. According to Hjalager (2002, p.466): “Process innovations tend to raise the performance of existing
operations by means of new or improved technology, or by redesigns of the entire production line, e.g. as a result
of process re-engineering” (Hjalager, 2002). It is also a form of a managerial innovation, since it involves
new “new ways of organising internal collaboration” (Hjalager, 2010, p.3).
Integrated RM is also a process of organisational learning and knowledge-management (Aubke, Wober,
Scott, & Noel, 2014). This is in line with Sfodera (2006, p. 10), who argues that Yield Management is “a
process in which knowledge and information flow from the various divisions and whose actuation improves the
satisfaction of the hotel and its employees and increases knowledge and the ability to satisfy the clients.”
Knowledge is to considered to be a key driver of innovation performance (Nieves, Quintana, & Osorio,
2014).
Parts of this complex process can also be outsourced. Based on qualitative interviews Altin (2017) de-
scribes the following 4 RM-implementation strategies:
• An in-house strategy
• Centralization
• Corporate outsourcing
• Third party outsourcing
5.3.1 Defining integrated RM
The author defines integrated RM as follows:
“Integrated RM is the sum off all the strategic, tactical and operational activities and decisions, undertaken with
the intent to maximize revenue of perishable inventory units with fixed capacity. Thus, it refers to activities,
decisions and abilities in the areas of:
• information management
• price management
• capacity management
• sales management including distribution channel management
to maximise profitability. It includes an accommodation providers ability to manage technical and social systems
to meet its own and its customers’ requirements within a complex distribution landscape.”

40
5.3.2 Integrated RM from a systems point of view
Jones, (1999b) introduced a conceptual model of the hotel yield management system. The model was
based on extensive qualitative interviews with hotel executives from mostly four and five-star hotels in
London with more than 150 rooms. Jones (ibid.) distinguishes between a decision system including the
strategic and operational decision-making and a decision support system with its sub-systems: Tech-
nology, Human Resources and the Information system that deals with demand analysis and reserva-
tions. El Haddad, (2015) further elaborated Jones model by adding new interconnections. Ivanov made
another attempt to describe the RM system from a systems point of view.
In their systems model of the RM-process Yeoman & Watson (1997) speak of YM as “A human activity
system” with 3 interlinked sub-systems: The Forecasting system, the people system and the strategic
system. Thus interfunctional coordination, organisational learning (Tajeddini, Altinay, & Ratten, 2017)
and internal collaboration are key for integrated RM (Aubke et al., 2014) and the overall performance of
the company.
Tajeddini, Altinay, & Ratten, (2017) state that “Inter-functional coordination is defined as the synchronization
of communication, information dissemination and other resources along with integration and collaboration of dif-
ferent functional units throughout the organization to generate value for customers and buyers” (ibid, p.103).
Integrated RM requires the collaboration of all departments (Sfodera, 2006) where individual
departmental goals are replaced by an overall culture of value creation. In this regard scholars within
the research stream of CSF of RM (see Table 2, p. 38) often describe “developing a yield culture”
(Brotherton & Turner, 2006; Hansen & Eringa, 1998; Jones & Hamilton, 1992) within the organisation in
order to make the RM-process successful. In fact, it is one of the most agreed upon CSF of RM by experts.
This ‘yield culture’ is a culture of value co-creation and it can be better achieved by understanding
integrated RM.
5.3.3 Managing organisational and technical systems within an organisation
Integrated RM is not a linear activity with a clear starting or ending point (Talón-Ballestero et al., 2014).
It is rather a complex and dynamic process of organisational learning (Crossan, Lane, & White, 1999)
and knowledge management (Aubke et al., 2014; Sfodera, 2006; Tajeddini et al., 2017).
Integrated RM (see Figure 1, p.41) is about the intersection of managing the technical and social perfor-
mance drivers of an organisation (Queenan, Ferguson, & Stratman, 2011) within a complex distribution
landscape. So, it’s complexity is like X3.
An integrated RM system includes technical and organizational systems, (Melis & Piga, 2016) and there-
fore represent a socio-technical system (Kirk, 1995), that combines so called ‘hard’ technological systems
(Jones, Ball, Kirk, & Lockwood, 2011), and so called ‘soft’ or social or systems (Hansen & Eringa, 1998;
Jones, 1999a; Kirk, 1995).

41
Figure 1: The integrated RM system
5.3.4 Technical RM solutions
Technical solutions within an integrated RM system are not something that can be installed by the push
of a button. Technical RM solutions, previously called Lodging Yield Management Systems (Griffin,
1994, 1995, 1997) are not just software. They can be described as Computerised management and mon-
itoring systems (Hjalager, 2002), or “Expert systems (ES)” (Yeoman & McMahon Beattie, 2010, p.3) which:
“are ‘knowledge-based’ software packages” (ibid.). Guadix, Cortés, Onieva, & Muñuzuri (2010) and (Talluri
& Van Ryzin, 2004d) propose an overview of the system flow of a Technology revenue management
system. This systems can improve all areas of integrated RM - price management, capacity management,
distribution and sales management (Liozu, 2016; Talón-Ballestero et al., 2014; Weatherford, 2016).
5.3.5 Technological pull factor: cloud computing and SaaS
Currently a major technological pull factor (Benckendorff et al., 2014) to adopt RM is the wide availa-
bility of cloud computing (Hsu, Ray, & Li-Hsieh, 2014; Oliveira, Thomas, & Espadanal, 2014) and Soft-
ware as a Service (Lian, Yen, & Wang, 2014).
SME can currently choose from a variety of revenue management solutions and software systems
(HotelPartner, 2017; HQplus, 2017; SiteMinder, 2017), embedded in a well-developed technological eco-
system (Mews, 2017). In the past sophisticated RM software was only available and affordable for bigger
chain hotels (European Commission, 1997a), and even in the year 2013 the choice of semi or fully-auto-
mated software solutions available was limited and required a significant financial investment (Goerlich
& Spalteholz, 2014). In the year 2018 many of the RM-software systems (Rateboard, 2018) are offered
through cloud-computing on a Software as a Service base (Lian et al., 2014). Cloud based, Software as a
service solutions (SaaS) only require the user to pay a lower monthly fee, instead of buying the software
Distribution
Environment
Technical
System
Social/
Organisational
System
RM
2.0

42
upfront at its full price. (ibid.) Furthermore the software is accessed through the internet and there is no
need to install it on premise. In general providers constantly improve the software and automatically
provide the latest version to the user (Corey, 2017). So from a standpoint of technology adoption this
would be pull factors for SME-accommodation providers to implement cloud based RM-software solu-
tions (Benckendorff et al., 2014).
With the implementation of new information systems integrated RM often leads to organizational
change (H. F. Lin, 2014) and might require business process re-engineering (Turban, King, Lee, Liang,
& Turban, 2015). Thus, RM implementation is not only about which software to buy. Successful RM
implementation deals with the sum of all technical and non-technical or human aspects of an integrated
RM system (Kimes 2012). Matters of Change Management, Organizational Learning and Development
are therefore very relevant. This view is widely shared by many authors (Brotherton & Turner, 2006;
Donaghy, McMahon-Beattie, & McDowell, 1997; Jones & Hamilton, 1992; Lieberman, 2003; Selmi &
Dornier, 2011; Sfodera, 2006; Yeoman & Watson, 1997).
RM is not solely a question of technology adoption but technology adoption plays a key role in the
readiness to engage in more sophisticated RM activities like forecasting, since modern PMS- systems,
channel managers, CRM systems, web booking engines or even Revenue & Yield Management Systems
provide the underlying data and assist accommodation providers to move from a more intuitive ap-
proach of RM to a more strategic one.
Defining success in integrated revenue management
In general success is defined by indicators of financial performance. Beside these ‘objective’ measures
success can also be defined by so called ‘perceptive’ measures regarding the non-financial performance.
The overall business performance of an hotel is defined by the level of which an accommodation
provider achieves both financial and non-financial goals (Grissemann et al., 2013).
According to Skugge (Skugge, 2004) The successful RM implementation increases company profits by
30-50% and revenues by 3-7%. Kimes & Wirtz (2003) note in a literature review, companies reported
revue increases of 2% - 5%.
5.4.1 Non-financial success – customer satisfaction & delight
A major goal of every accommodation provider is to satisfy customers and enable delightful experiences
(D. Wang, Park, & Fesenmaier, 2012). The academic discussion around customer satisfaction evolved in
distinguishing the new concept of customer delight from the traditional views of customer satisfaction
(Cronin, Brady, & Hult, 2000; Roth & Bösener, 2015). Customer delight refers to a positive emotion,
arousal and satisfaction level, that arises when accommodation provider positively exceed customers’
expectations: „to an unexpected and surprising degree” (Roth & Bösener, 2015, p. 25). Apart from the dis-
cussion to establish customer delight as an extra construct according to Roth & Bösener (2015, p.2): “it
is widely assumed that CS [customer satisfaction] stimulates customer loyalty and commitment“ (ibid.). This in
turn positively influences the long-term success. Customer Satisfaction is described as “the result of a

43
customer’s perception of the value received in a transaction or relationship […]relative to the value expected from
transactions or relationships with competing vendors” (Hallowell, 1996).
The ‘value’ received’ “equals: “perceived service quality relative to price” (ibid., p.28). According to (Dunn,
Baloglu, Annaraud, & Brewer, 2010) perceived value rather than satisfaction is the key driver to pur-
chase intentions and customer loyalty. It is to note that beside this general linkages the constructs of
satisfaction, quality and value are connected in a very complex linkage structure (Cronin et al., 2000).
5.4.2 Evaluating financial performance of RM
When airlines introduced yield management to increase the profitability of their seat inventory, yield
either referred to “yield per available seat mile or yield per revenue passenger mile” (Kimes, 1989, p.348). In
traditional RM capacity normally refers to a bundle of room nights only (see paragraph 3.1, p. 19). The
recent approach of Total Revenue Management considers all revenue streams in a hotel (Zheng & For-
gacs, 2017).
One of the most used performance indicators of accommodation providers is occupancy rate, as much
as that occupancy rate is included in the “European Tourism Indicator System for Destination Perfor-
mance” (European Commision, 2016).
The de-facto industry standard for RM performance measurement is Revenue per Available Room,
RevPAR. (Schwartz, Altin, & Singal, 2016). It is calculated as follows: Average Daily Rate (ADR) * Oc-
cupancy percentage (Hayes & Miller, 2011; Mattimoe & Tivnan, 2017) or by “dividing the hotel’s total
guestroom revenue by the number of available rooms and the number of days during the measured period.” (ibid,
p. 4.). The shortcoming of the RevPAR metric is that even though hotels aim to achieve greater profita-
bility (Guadix et al., 2010) RevPAR is based on revenues rather than profits. The according metric based
on profitability is Gross Operating Profit (GOPPAR), hence the “average gross operating profit (GOP) gen-
erated by each available guest room during a specific accounting period” (Hayes & Miller, 2011, p. 313). The
widespread use of the RevPAR is because it can be computed more easily and the needed data is more
readily available and provided by benchmarking firms such as STR (Schwartz et al., 2016).
Due to the shortcomings of RevPAR (Enz, Canina, & Walsh, 2001; Hayes & Miller, 2011; Schwartz et al.,
2016), based solely on room revenue, research and RM practitioners agree (Kimes, 2011) that financial
performance should rather be evaluated based on Total Revenue per available room, GOPPAR or total
revenue per available square foot (Kimes & Renaghan, 2011; Mattimoe & Tivnan, 2017). In his concep-
tual paper on performance measurement in the touristic value chain Yilmaz & Bititci (2006) propose the
following performance measurements, displayed in Table 3:

44
Measurement
Profitability/
Productivity
Capacity Management
Cost
Customer related
Approach
Metric
− Revenue
per Customer
− % of repeat
customer
− % of no show
− % of capacity used to
total
− % of overbooking
problem
− Forecasting
accuracy
− Value Chain Cost
(fixed)
− Marketing Cost
(variable)
− Marketing
Effectiveness
− Cost of sales
− Conversion rate
− Customer
Satisfaction & Feed-
back
− Number of
complaints
− Critical Successfactors
− Balanced Scorecard
− Service Quality
Table 3: Performance Measurement System, based on (Yilmaz & Bititci, 2006)
Sainaghi, Phillips, & Corti (2013) propose a balanced scorecard (BSC) approach to measure hotel performance, since this framework includes
financial and non-financial elements and acknowledges the diverse stakeholder interests. Moreover, further research on performance manage-
ment is needed.

45
6. Evaluating integrated RM in Austria
The research on RM is far more ahead in describing what RM could or should be, than practitioners are
able to apply this knowledge and make use of RM on a day to day basis. This can be explained consid-
ering again that RM is not just protecting inventory through rate fences anymore (Hanks et al., 1992;
Hayes & Miller, 2011; Noone et al., 2011)
Integrated RM can’t be evaluated as a process
Researchers have tried to address the questions “How does RM look like?” and “How to do it?” by devel-
oping different descriptions of the RM process (Desinano et al., 2006; Emeksiz et al., 2006; Hunold, 2014;
Ingold et al., 2012; Phillips, 2005; Shoemaker & Gorin, 2008; Ben Vinod, 2004). Many of these models
describe RM as a linear or sequential process. While the technical aspects of RM, like mathematical
inventory optimization or overbooking models are certainly linear (Baltazar, 2008; Belobaba, 2016; Lit-
tlewood, 2005), integrated RM in general is absolutely not a ‘linear thing’ (Josephi et al., 2016). It is
neither linear nor a thing. Unfortunately, the various conceptualisations of the RM process, even the
linear or sequential ones, do not provide any measurement scales to assess whether accommodation
providers apply RM. The underlying questionnaire of the MERMI model for evaluating revenue man-
agement implementation (see Table 2, p.38) is too complex to be applied for SME-accommodation pro-
viders, since it is far to technical and detailed.
The technical system: software or SaaS adoption
Since the process of integrated RM is too complex to be evaluated as a whole, this thesis takes the
approach of evaluating the technology adoption of technical RM solutions by SME-accommodation
providers, in order to evaluate the technical system of integrated RM. Then to assess the social system
the accommodation providers will be asked which challenges and opportunities they perceive in adapt-
ing prices to demand.
Technical System / Technology Adoption
• Property Management System (PMS)
• Channel-Manager
• Web Booking Engine
• Booking Request & Management System
• Online Reputationsmanagement
• CRM-System
• Revenue- &
Yield Management System
• Automatic Rate Monitoring
(Rate Shopping)matic Rate Monitoring
(Rate Shopping)
Distribution
Environment
Technical
System
Social/
Organisation
al System
RM
2.0
Figure 2: Technical Solutions for integrated RM

46
Figure 2 lists the components of an optimal technical RM system (Fensel & Werthner, 2017; Goerlich &
Spalteholz, 2014; Gruber, 2012; McGuire, 2016; Noone et al., 2011; ÖHV, 2016). Each individual technical
component of an integrated RM system (see Figure 1, p.41) can be either implemented as software in-
stalled on premise or accessed through Cloud based, Software as a service solutions (SaaS). Regarding
the integrated approach of RM, these systems seem almost necessary, because it would be very difficult
to track, segment or forecast demand, optimise inventory allocation, set and update rates, availabilities
or booking controls, manage and control distribution channels and distribution costs or communicate
and respond to customer reviews or Electronic word of mouth (Fensel & Werthner, 2017; Noone et al.,
2011; Talón-Ballestero & Rodríguez-Algeciras, 2017).
6.2.1 Evidence on technology adoption of SME-accommodation providers
About 20 years ago a report by the European Commission (1997) examined the implementation of Yield
Management in SME in the tourism industry in Austria. It noted that SME hotels practise YM intuitively
with pricing decisions being made “spontaneously and in desperation” (ibid., p.84).
According to this report only international chain hotels and Austrian Airlines had implemented ad-
vanced Yield Management. The report stated that Vienna Marriott hotel had been implementing and
relying on advanced yield management since 1988. The report pointed out the obstacles SME hotels
face in line with the use of YM. Amongst others these were especially the insufficient management
skills and missing awareness of YM combined with the misperception held by SME that YM can only
be applied by bigger hotels.
This tends to hold true also these days, since in general independent SME-hotels are slow to adapt new
technologies (Karamaşa & Acılar; Ali, 2012; Scott et al., 2010).
In the year 1995, when the study by the European Commission (1997) was carried out, the implemen-
tation of effective YM practises has been limited by technological constraints, since off-the-shelf com-
puter YM systems were not readily available and very costly (ibid.).
In 2016 the ÖHV conducted a Web survey on the technology adaption and use within the hotel sector
(ÖHV, 2016). The parent population consisted of all 1350 members of the ÖHV. With a response rate of
11,6% the final sample was 156 hotels. The results of the survey, displayed in figure Figure 3 below
provides some insights on technology adoption of different member hotels of the ÖHV.

47
Figure 3: Technology Adoption of ÖHV member hotels
Almost all the 156 ÖHV member hotels in the sample use a property management system. 3 out of 4
hotels use a channel manager. A real time web booking engine is only used by 40% of all hotels in the
sample. 31% of the accommodation providers in the sample reported to use a distinct Revenue- & Yield
Management system.
Maurer (2012) investigated the e-tourism fitness of Austrian accommodation providers with a sample
of 375 Austrian hotels. For their master’s thesis Thomas (2012) surveyed an unknown sample of
Austrian hotels on their RM adoption and achieved 81 usable responses.
Gruber (2012) surveyed member hotels about their RM adoption and in 2016 the ÖHV conducted a
Web survey on the technology adaption and use within their member hotels (ÖHV, 2016). The parent
population consisted of all 1350 members of the ÖHV. With a response rate of 11,6% the final sample
was 156 hotels.
The “European Hotel Distribution Study”, published by Hotrec only addressed members of the ÖHV
and had rather low sample sizes with 58 valid responses for the year 2013 and 130 for the reference year
2015 (Schegg, 2016).
6.2.2 Lack of empirical evidence on integrated RM
Our understanding of technology adoption as a base for Revenue Management practises is extremely
limited and the valid research only focuses on ÖHV hotels (Gruber, 2012; C. Maurer, 2012; ÖHV, 2016)
97% 93%
75%
60% 54% 48% 43% 42%
31%
0%
25%
50%
75%
100%
Technology Adoption, ÖHV Survey 2016
(n=156)

48
The author also included published and accessible theses on RM. Therefore the author searched the
Austrian Library Network Search Engine (OBV, 2018) for the terms “Revenue Management” and “Yield
Management”. In the category „Form“ the type „Hochschulschrift“ was selected. For this selective review
bachelor’s theses or project theses were not considered, because they have a lower academic relevance
and not all institutions make bachelor theses publicly available.
Furthermore, works with a clear reference to other application in the title other than hotels were ex-
cluded. These other application areas included i.e. airlines, railways, cable cars, restaurants, spa resorts,
business to business contexts or destinations.
Most of the authors relied on qualitative interviews or case studies. By doing so they more or less rep-
licated the findings already well established within the qualitative research stream on critical success-
factors of RM (Bernard, 2016; Egger & Hirvonen, 2001; Hold, 2015; Kylander, 2017). Gruber (2012) and
Thomas (2012) used self-developed online questionnaires to ask accommodation providers about RM-
related activities.
While Thomas (2012) research design showed strong flaws, Gruber (2012) provides a well-rounded
overview of the online distribution landscape. The thesis from Grubers (2012) is not listed in the Aus-
trian Library Network Search Engine (OBV, 2018) but was obtained by contacting the author directly,
since she received the online questionnaire and mentioned her work in the comments field.
Author
Title
Type
Research
Instrument
Sample
Kylander
(2017)
Revenue Management in
der österreichischen
Hotellerie - Eine Analyse
der österreichischen Klein-
und Mittelbetriebe sowie
der Kettenhotels
Master’s
Thesis
Semi-Struc-
tured Inter-
views
9 revenue experts from
chain hotels and SME hotels
RM experts (academic, con-
sultants)
Bernard
(2016)
Revenue Management in
der Hotellerie. Der Preis
als Steuerungsinstrument
zur Gewinnmaximierung.
Master’s
Thesis
Semi-Struc-
tured Inter-
views
Austrian chain hotels:
2 General Manager,
1 Resident Manager
1 Room Operations Man-
ager
South Tyrolean Private Ho-
tels
3 General Manager
1 Marketing Manager

49
Gruber
(2012)
Der Stellenwert von
Revenue Management
und Online Distribution
im Web 2.0 für die
österreichische
Ferienhotellerie
Master’s
Thesis
Not published
due to confi-
dentiality
clause
Work was
provided by
the author
Online Ques-
tionnaire,
+
Unstructured
Interviews
ÖHV member hotels
N=142, response rate: 12%
(1200)
11 hotels (person inter-
viewed not specified)
Thomas
(2012)
Yieldmanagement und
Yieldmanagement-
Methoden in der
österreichischen
familiengeführten KMU
Hotellerie unter
Berücksichtigung der
allgemeinen
Preisproblematik am
Markt.
Master’s
Thesis
Online Ques-
tionnaire,
Invitation to Questionnaire
sent anonymously through
cooperation with compa-
nies
n=81
Response rate unclear
Enzi (2015)
Überbuchungssteuerung
im Revenue-Management
Master’s
Thesis
None / litera-
ture driven
/
Krimbacher
(2007)
Yield Management –
Entwicklung von
Ansätzen und
Anwendungen für das
Management von alpinen
Destinationen”
Master’s
Thesis
Case Study
1 DMO in Tirol, Austria
1 DMO in Laax, Switzerland
Aslan
(2007)
Die Implementierung von
Revenue Management
Verfahren in Excel
Master’s
Thesis
Literature
Based
RM-Formula for Microsoft
Excel
Steiner
(2014)
Einfluss der
Opportunitätskosten auf
die Kapazitätssteuerung
im Revenue Management
Master’s
Thesis
Literature
Based
/
Aubke
(2012)
A model of effective com-
munication structures in
expert-led teams: an appli-
cation to hotel revenue
management teams
Doctoral
Dissertation
Network
Analyses

50
Kössner
(2012)
Analyse und Adaptierung
von Yield Management
Modellen im Online
Booking Sektor am
Beispiel von Saalbach
Hinterglemm
Master’s
Thesis
Work not
seen
Work not seen
Hold (2015)
Der Einsatz von Yield-
Management als
erfolgreiche
Geschäftsstrategie und
dessen Bedeutung sowie
unterschiedliche
Anwendung in der Wiener
Incoming-
Veranstalterbranche in
Zusammenarbeit mit der
4* Hotellerie in Wien
Master's
Thesis
Semi-Struc-
tured Inter-
views
4 managers incoming-
agents
3 hotel managers
Table 4: Research on RM implementation in Austria
Pöchacker (2015) wrote about Change Management processes in 4 and 5* hotels in Vienna. One hotel
reported the introduction of centrally organized RM cluster function and another reported merging the
reservation systems.

51
7. SME-accommodation providers: organisational dynamics
The definitions of what actually constitutes a SME vary significantly internationally and are not defined
universally (Jeansson et al., 2017; Tassiopoulos, 2008; R. Thomas et al., 2011). Therefore scholars often
refer to SME definitions of local or international policy makers (Jeansson et al., 2017). A key variable in
various definitions is the number of people employed (R. Thomas et al., 2011).
Definition of SME in Austria
The Austrian Institute for SME Research follows the definition of the European Union (KMU Forschung
Austria, 2017). According to the harmonized framework by the European Commission (2015): ”The cat-
egory of micro, small and medium-sized enterprises (SMEs) is made up of enterprises which employ fewer than
250 persons and which have an annual turnover not exceeding EUR 50 million, and/or an annual balance sheet
total not exceeding EUR 43 million.” (See Table 5). Furthermore SMEs “must be autonomous or part of a
group of affiliated enterprises that together fall below the ceiling” (European Commission, 2012).
Enterprise category
Staff Headcount
Financial ceilings
Turnover
or
Balance sheet total
Small
<10
≤ € 2 million
≤ € 2 million
Micro
<50
≤ € 10 million
≤ € 10 million
Medium sized
<250
≤ € 50 million
≤ € 43 million
Table 5: SME Definition, European Commission (2012)
In his review on small firms in tourism Thomas et. al, (2011) points out that for SMEs in the tourism
sector authors also use acronyms like (like SMTEs, small and medium tourism enterprises (Buhalis,
2003; Benckendorff, Sheldon, & Fesenmaier, 2014; Beritelli & Schegg, 2016; Cetin, Cifici, Dincer & Fuchs,
2016); SMHOs – small and medium sized hospitality organizations (Buhalis & Main, 1998), SHE small
hospitality businesses (Alonso & O’Neill, 2009), STBs -small tourism businesses (Akbaba, 2012) or STFs
– small tourism firms (Ateljevic, 2007).
In order to define SME for this thesis the SME definition of the European Commission (2012) is
applied. Besides the elements of the SME-definition by the European Commission for accommodation
sector the number of rooms is used to distinguish between small and large enterprises. Providers with
less than 50 rooms are usually considered as “small” (Buhalis & Main, 1998; Buick, 2003; Cetin,
Aydogan Cifci, et al., 2016; Main, Chung, & Ingold, 1997). Buhalis & Main (Buhalis & Main, 1998, p.
198), note that SMHOs “operate in the lower reaches of the market and are often situated in tertiary
locations”.
Following research on small hotels (Buhalis & Main, 1998; Buick, 2003; Cetin, Aydogan Cifci, et al.,
2016; Main et al., 1997). SME-hotels are considered to have up to 50 rooms, but according to Henschel

52
(2001) there is no unified definition about the number of rooms, that define a small accommodation
provider.
Regarding the ownership and management style this works follows the SME-definition and R &
Crowling, (1996). He defined an SME as follows:
“[A] small tourism business is financed by one individual or small group and is directly managed by its
owner(s), in a personalised manner and not through the medium of a formalised management structure [] it is
perceived as small, in terms of physical facilities, production/service capacity, market share and number of
employees” (Morrison, 1996, p. 4491).
“[T]he business has no power to control prices of the products it buys and sells and the credit it gives and receives.
The business is managed by its owners who also control the business. A small business will most likely be a sole
trader or a partnership but may also be a limited company” (R & Crowling, 1996, p. 4491)
Therefore, an independent SME-hotel is defined as follows:
Staff Headcount: <250 employees
Number of Rooms: <50 rooms
Turnover / Balance sheet total: ≤ € 50 million € / ≤ € 43 million
Business model and organizational characteristics
Apart from these different abbreviations for SMEs in the tourism context Thomas et al. (2001) claims
that researchers failed to identify clear distinctions “between small firms in tourism and similar businesses
elsewhere” (R. Thomas et al., 2011). and that the academic discussion does not adequately mirror the vital
importance of SME. Furthermore exhaustive theories and a holistic understanding about organisational
dynamics within these organisations are still to be developed (Thomas et al., 2011; Page, Forer, &
Lawton, 1999).
Contrary to large firms SME often follow a business model that is more lifestyle than growth orientated
(Hjalager, 2010; Peters, Frehse, & Buhalis, 2009; Morris et. al., 2005).
There is no agreed-upon definition of the term ‘business model’ and the components it includes
(Jeansson et al., 2017; Morris, Schindehutte, & Allen, 2005; Runfola, Rosati, & Guercini, 2013). Never-
theless, a business model can be described as: “The logic of the firm, the way it operates and how it creates
value for its stakeholder” Casadesus & Ricart (2010, p.). Thus it “provide[s] a set of generic level descriptors of
how a firm organises itself to create and distribute value in a profitable manner” (Baden-Fuller & Morgan,
2010, p.166). Summaries of the research on the components that make up business models (Jeansson et
al., 2017; Runfola et al., 2013) seem to conclude that a business models in tourism consists of defining a
service that is valued by a certain target group, describing of how this service is delivered with the
according service architecture. This leads to determining which resources are required, the organiza-
tional roles within the company and the relationship the company has with external stakeholders
(Bouwman, Haaker, & Vos, 2008).

53
Thus a business model describes “the rationale of how an organization creates, delivers, and captures value”
(Jeansson et al., 2017;p.53). Or how Morris et. al. (2005, p. 729-730) formulate it: A business model has
to answer the following questions: “How will the firm create value? For whom will the firm create value?
What is the firm’s internal source of advantage? How will the firm position itself in the marketplace? How will
the firm make money? What are the entrepreneur’s time, scope and size ambitions?” SMEs often follow very
different business model than large companies (Morris et al., 2005).
While large firms’ business models often favour growth, speculation, short term profit or value added
reselling (Morris et al., 2005), SME tend to prefer to operate with a long term horizon to provide a secure
steady income for the owners, since SMEs are also often family run. (Peters et al., 2009). They are there-
fore often called lifestyle entrepreneurs in the literature (Hjalager, 2010; Peters et al., 2009) since the
business is deeply integrated in their life and business decisions are also taken with relevance to their
effects on the owners lifestyle.

54
8. Research question and research design
The aim of this thesis is to investigate the current implementation of RM-practises of SME-accommo-
dation providers in Austria. The research question to be explored are:
• How are current RM-practises and RM-related technology adoption of
SME-accommodation providers in Austria?
• How can Austrian SME-accommodation provider move toward integrated Revenue
Management?
The research questions aims to solve a „descriptive and illuminative research puzzle“ (Hart, 2005, p. 59) by
describing and illuminating the research gap between business practise of RM and state of research.
The research question therefore stems from a perceived gap in the literature (Alvesson & Sandberg,
2013). According to Alvesson & Sandberg (2013) this „gap-spotting“ (ibid, p. 29) approach of construct-
ing research questions is the most common approach in the social sciences.
The research question strives to be open (Alvesson & Sandberg, 2013) and acknowledges how much
RM has transformed over time in line with the digital hotel distribution system it is embedded in. At
the same time the research question needs to be answerable (Hart, 2005). Conceptually the main goal
of this work is to describe how the different research streams contribute to our understanding of RM.
The authors understanding of RM and the digital distribution landscape has been improved through a
research project (Maurer & Egger, 2017) whereby the author conducted semi-structured qualitative in-
terviews with various sales representatives of hotel and channel management software, OTAs and hotel
consultants. Additionally, the author interviewed the Head of Business development of a Yield Man-
agement consultancy.
In the authors opinion the research on RM-implementation of Austrian SME-accommodation provider
refers to an overlooked or under-researched area with a „lack of empirical support“ (Alvesson &
Sandberg, 2013, p. 29).
For every research matters of feasibility, accessibility, time and resources play a key role in order to
answer the research question (Bui, 2014; Hart, 2005). While case studies or qualitative interviews with
revenue managers or academics from the field might be very feasible to conduct these methods don’t
provide any insight into the use of RM on a broader scale.
Access to organizations is one of the major obstacles in research of organizational dynamics (Hart,
2005). The author is currently not affiliated with SME-accommodation providers and has already de-
scribed another case study research would not be appropriate to answer the research question.
To evaluate the current RM-practises and implementation of SME-hotels in Austria on a broader scale
an online survey was used. The survey was conducted online, using a questionnaire with descriptive
questions (Gray, 2009) about technology adoption and open questions about the challenges and oppor-

55
tunities in adapting prices according to demand. Figure 4, p. 55 provides an overview about the ques-
tions included and how the questions evaluated the different systems of an integrated RM approach
(see Figure 1, p. 41).
Online survey as a self-administered questionnaire
An online survey is a self-administered questionnaire (Sapsford, 2006).
Respondents receive an e-mail inviting them to participate in the research. The e-mail contains a link
that leads to the web survey containing the survey questions (Bryman, 2012). The respondents self-
complete the questions in their given order at their own pace and time convenient to them.
Thus, web surveys are self-completed interviews but present questions in a certain order. Web surveys
are in such a way similar to a structured interview (ibid.)
The web survey was created and hosted on the platform surveymonkey.com, a robust online platform
to build surveys. Its use is mentioned in books on research methodology (Bryman, 2012; Gray, 2009).
The key advantages of self-completion online surveys are the quick, cheap and easy distribution, be-
cause the invitation can be sent via e-mail and respondents self-complete the questionnaire online at
their own convenience (Bryman, 2012; Gray, 2009). One advantage of a self-administered questionnaire
is that no interviewer effect is introduced and the answers are automatically recorded, the same way
the participants had wrote it without the need of transcription (Bryman, 2012; Sapsford & Jupp, 2006).
This is of special relevance for the open-ended questions: Q2, Q8, Q9, Q10.
Q5: Technical System /
Technology Adoption
•Property Management System (PMS)
•Channel-Manager
•Web Booking Engine
•Booking Request & Management System
•Online Reputationsmanagement
•CRM-System
•Revenue- &
Yield Management System
Social/ Organizational System
Q8: Perceived challenges in adapting
prices according to demand
Q9: Perceived opportunities in
adapting prices according to
demand
Organizational Characteristics
Q1: Number of Employees
Q2: Job Role interviewee
Q3: Postal Code / Location
Q4: Number of Guest Rooms
RM-related activities
Q6 Rates offered
Q7: Overbooking
Figure 4: Overview of the survey instrument

56
The SurveyMonkey platform uses cookies to ensure that participants can only participate in the survey
once (Surveymonkey, 2018a). Another advantage is that all answer choices and the text of open-ended
questions is stored immediately eliminating the need for transcription. This reduces data-collection er-
rors (Bryman, 2012).
Sampling and web data extraction
In order to invite as many accommodation providers in Austria to take part in the research the survey
invitation had been sent via e-mail. The author decided to collect e-mail addresses, that accommodation
providers publish on the internet. This made the research intendent from the cooperation of accommo-
dation provider organisations such as ÖHV or other associations, who would only send the survey
invitation to their members. This would drastically limit the amount of accommodation providers re-
ceiving the survey.
To collect as many e-mail addresses as possible access to a comprehensive database with the e-mail
address of Austrian SME-accommodation providers was necessary.
The Austrian Economic chamber runs a database of all registered Austrian companies on their websites
(WKO, 2017a), including the various types of accommodation providers. Unfortunately, many compa-
nies do not display their e-mail address there, since this is not obligatory and the database does not
offer any possibility to export the e-mail addresses. Copying every e-mail address manually from the
database would be far too time consuming. Therefore, an automatic approach, of Web Data Extraction
was needed. Web Data Extraction refers to software, which: “interacts with a Web source and extracts data
stored in it” (Ferrata, De Meo, Fiumara, & Baumgartner, 2014). This automated process is often also
called Web Scrapping and its use is well established in tourism research (Marine-Roig & Clave, 2015;
Schütze, 2008; Supak, Brothers, Ghahramani, & Van Berkel, 2017).
The author used an Advanced Web Data Extraction system with a graphical user interface (Ferrata et
al., 2014) by Mozenda Inc. Their product Mozenda Cloud (Corey, 2017) is a cloud-based, commercial
SaaS service (Mozenda, 2017) that enables to configure a browser based scrapping process without any
programming knowledge. The web scrapping works by downloading the Mozenda Agent Builder, a
software that is installed on the computer and works as a browser to design the so called “agent” to
carry out the data extraction process. The Mozenda Agent Builder is based on the Microsoft Internet
explorer, since Mozenda Web Scrapping works “by rendering each web page in a browser and performing
actions on the page like a human would“ (Corey, 2017, p.4). According to Mozenda (Corey, 2017) this guar-
antees that the scrapping does not result in a traffic spike to the targeted site.
8.2.1 Web data extraction from wko.at
In order to extract the e-mail address from the company database of the Austrian economic chamber
(WKO, 2017a) the Mozenda Cloud software was used to design a so called Mozenda agent, a set of
rules and directions so the software is then able to extract the E-mail Address. This was repeated for all

57
9 federal states, always by selecting the following categories (ibid.): industry: “Tourismus und Freizeit-
wirtschaft” → “Hotellerie (gesamt)”.
Figure 5: Data Extraction from firmen.wko.at
The web scrapping process from the company database of the Aus-
trian Economic Chamber (WKO, 2017a) resulted in 3.891 e-mail ad-
dresses.
Since this was not a sufficient amount of E-Mail addresses in a second step the author decided to collect
e-mail addresses from the website holidaycheck.de.
8.2.2 Web data extraction from holidaycheck.de
Holidaycheck.de is the biggest hotel rating site in German language (Holidaycheck, 2018) and one of
the most important social media channels for accommodation providers after tripadvisor.com (Akbar,
Toma, & Fensel, 2016; Buhalis & Mamalakis, 2015). In contrast to other OTA-platforms or social media
channels hotels have the possibility to display their contact details on holidaycheck.at, including their
telephone number and e-mail address in plain text in the hotel description. Most hotels provide their
e-mail address. Therefore holidaycheck.at could be used to extract the e-mail of the hotels. Furthermore,
holidaycheck features a wide range of accommodation providers, especially from within the SME sec-
tor.
Figure 6 and Figure 7on the following pages (p.59-60) give a visual overview about the settings, that
were selected by the Mozenda Agent in order to collect the e-mail addresses from all the accommoda-
tion providers in one federal state. The example is for accommodation providers in Vienna. First the
federal state was chosen within the menu “Region in Österreich” in the search bar. Holidaycheck.de
shows an arrival date by default, but if the filter „Nur verfügbare Hotels“ is not selected all accommo-
dation providers, listed in the according federal state, will be displayed. The Mozenda agent would
then, like a human, click on the green button “Angebote ansehen” (View offers) (see Figure 6, p. 59) to
open a page with more details about the provider (see Figure 7, p.60). On this site, by scrolling down,
Economic chamber
Vienna
178
Burgenland
134
Lower Austria
199
Upper Austria
178
Styria
399
Carinthia
897
Salzburg
612
Tirol
1,091
Vorarlberg
203
3,891
Table 6: Number of E-Mail Adresses
collected from firmen.wko.at

58
the Mozenda agent would detect the name of the hotel and e-mail address, save both and then go back
and repeat the process for the next accommodation provider in the list.
Table 7 (p.58) shows the outcome of the Web Scrapping process for holidaycheck.de. In total 10,267 e-
mail addresses could be collected automatically. The collected e-mail addresses and names of the ac-
commodation provider were then exported from the Mozenda Cloud into one Excel file. After manually
checking and deleting duplicate e-mail addresses in Microsoft Excel, 9,860 unique E-Mail addresses
remained. Duplicates were mainly because chain or affiliated hotels often provided the e-mail address
of their central reservation centre. So, two different hotels in fact used the same e-mail address leading
to their central reservation system. Sometimes one provider operated different types of accommodation
at the same location (hotel + apartment houses) but reservation was all dealt by the hotels reservation
team.
Federal State
Number of Ac-
commodation
Providers listed
Number of Accom-
modation Provider
who display E-
Mail
Number of
E-Mail Ad-
dresses col-
lected
Vienna
417
86%
357
Burgenland
259
96%
248
Lower Austria
503
83%
417
Upper Austria
1,625
48%
778
Styria
2,276
58%
1,328
Carinthia
2,877
56%
1,611
Salzburg
3,026
63%
1,911
Tirol
3,007
86%
2,590
Vorarlberg
2,037
50%
1,027
16,027
70%
10,267
Table 7: Outcome Web Scrapping Holidaycheck.de

59
Figure 6: Web Data Extraction Process

60
Figure 7: Hotel details
Overview of survey questions
Different formulations transport different meanings and assumptions about a phenomenon under in-
vestigation (Flick, 2014; Lyn, 2015). The major challenge of self-administered online questionnaires is
that they should not be too long or complicated to increase response rates. The wording has to be both
very precise but at the same time easy and fast to process for participants, since there is no interviewer
that can provide further explanations on how to understand a question or answer choice.
The following paragraph gives an overview about the design of the survey instrument (see Annex D).
All questions, explanations and answer options were stated in German language and are translated
here in English. The survey was divided into 4 sub-pages, so participants could only see the relevant
questions of the active page. (see Figure 8).
In order to increase the response rate (Bryman, 2012), the introduction sentence informed the partici-
pants that the completion of all survey questions would take about 4 minutes. Additionally, a green
bar showed the progress in the survey. This progress bar should motivate respondents to participate in
the survey by emphasizing that the 4 very short questions on the first page already represent 25% of
the total questionnaire. Thus, the entry level for participation is purposely set very low by placing the
statistical questions first. The survey started with the introduction sentence: „Thank you for your contri-
bution to the Austrian tourism research “, reframing the participation to serve a higher purpose.

61
Figure 8:Survey when accessed from a tablet
Question 1 (category question) asked about the number of employees, working at the business location
of the interviewee?
Question 2 (open question) asked the interviewees about their job role in the company.
Question 3 (open question) asked about the postal code, of the company location
Question 4 (open question) let the participants enter the number of guest rooms.
Question 5 (scale question, yes/no) was worded as follows: „For which of the following options did your
company use a software or an Internet service (in the past 12 month)?” The participants had to choose the
answer option “Yes” or “No” for every given option (see Annex A). In order to avoid response error
(Bryman, 2012; Sapsford & Jupp, 2006) the eight items in Q5 were rotated randomly for every survey
participant. This randomization is important, since in this way participants must read every option and
don’t find the most commonly used options first, because then they could get tired an only select the
answer option “No” after the first two software options. This question is essential for the survey, but
since it is quite complex it was placed after some easier to comprehend statistical questions Q1-Q4.
Q5 did not provide the answer choice „not applicable“ or „I don’t know“, in order to prevent response
bias. If participants would not understand an answer choice, it can be assumed that this kind of soft-
ware is not used in the respective company of the interviewee.
Question 6 (category question) was formulated as follows: „Which of the following rates regarding over-
night stays has been offered by your company at least once (during the last 12 month)?”
Question 7 (scale question) asked about the frequency one or several rooms have been overbooked
purposely during the last 12 months. The five answer options provided ranged from “always” to
“never”.

62
Question 8, 9 and 10 (open question) were presented on the 4th page and introduced with the following
paragraph: „The following questions are optional. Your opinion and each input are valuable. The entries will be
analysed anonymously“ (see Annex D).
Q8 and Q9 tried to explore the perceived challenges and opportunities the company of the interviewee
faces in in adapting prices according to demand. They were formulated as follows:
Q8: Which are the biggest challenges, in your opinion for your company, in adapting prices according
to demand?
Q9: Which are the biggest opportunities, in your opinion for your company, in adapting prices accord-
ing to demand?
The last question Q10 provided an opportunity for participants to send „further comments related to the
topic“ (seeAnnex D).
Obligatory questions and prompting messages
Question number 1, 3, 4, 5 were obligatory for participants, so they had to provide an answer to the
question, according to the question format. If no answer was provided the following prompting mes-
sages, were displayed in red and participants could not proceed with the survey:
Q1: “Für diese Frage ist eine Antwort erforderlich“ (This question requires an answer)
Q3: „Bitte Zahl eintragen“ (Please enter a number)
Q4: „Bitte Zahl eintragen. Eine ungefähre Angabe ist dabei ausreichend.“ (Please enter a number. An
approximate indication is sufficient“.
Q5: „Bittte geben Sie für jede Möglichkeit an, ob Ihr Betrieb in den letzten 12 Monaten dazu eine Software oder
einen Internetdientst genutzt hat “ (Please indicate for each option if your company used a software or internet
service during the past 12 month).
Question 2,6,7 and the qualitative open questions Q8, Q9 and the option to provide additional com-
ments in Q10 were not obligatory. So, participants could skip these questions, if they did not like to
answer them.
A mixed method approach
The electronic questionnaire followed a mixed method approach because it generated qualitative and
quantitative data. Thereby it applies quantitative and qualitative research strategies (Bryman, 2012) and
in doing so it crosses the qualitative and quantitative research paradigms (Bryman, 2012; Johnson &
Onwuegbuzie, 2004). Johnson & Onwuegbuzie (2004, p.17) define the approach of mixed methods re-
search as follow: „Its logic of inquiry includes the use of induction (or discovery of patterns), deduction (testing
of theories and hypotheses), and abduction (uncovering and relying on the best of a set of explanations for under-
standing one’s results).“

63
The mixed method approach thereby combines the strength of qualitative and quantitative paradigm
or research strategies by generating and analysing both quantitative and qualitative data (Bryman,
2012; Johnson & Onwuegbuzie, 2004). According to (Bryman, 2012) is a widely used and accepted ap-
proach in social research.
Instead of adopting certain worldviews embedded within the quantitative or qualitative research strat-
egies (Bryman, 2012; Flick, 2014; Gray, 2009) and adopting the research questions and research design
accordingly to fit this research paradigm the aim of this was to adopt a multi-research (Bryman, 2012)
or mixed-method approach. This approach is considered to be creative (Johnson & Onwuegbuzie, 2004)
with an iterative mindset (Bryman, 2012; Edmondson & McManus, 2007) so the research questions,
questions in the survey and research methods followed what emerged from the literature.
Beside the chosen research strategy an integral feature of management field research is the methodo-
logical fit (Edmondson & McManus, 2007). Management field research hereby means the: „systematic
studies that rely on the collection of original data – qualitative or quantitative – in real organizations“ (Edmond-
son & McManus, 2007, p. 1155).
Methodological Fit refers to the internal consistency of the research elements, such as: research ques-
tion, prior work, research design and contribution to literature (ibid.) and it can be achieved: „through
an iterative learning process that requires a mindset in which feed- back, rethinking, and revising are embraced
as valued activities“ (Edmondson & McManus, 2007, p. 1156).
According to Edmondson & McManus‘ (2007) framework on the state of theory the research on CSF
belongs to the intermediate archetype. This is because the MERMI model already provides „a provisional
theory (…) that integrates previously separate bodies of work“ (ibid., p.1160).
Q3 and Q4 were formulated as open questions (Bryman, 2012), but the participants were only allowed
to enter a four digit number for Q3 and a number between 0-600 for Q3.
The introduction section for the statistical questions Q1 -Q4 had the following note: „If your organisation
consists of several business locations, please think of the one with the highest number of employees and guest
rooms“(see Annex D).
Q2, Q8, Q9, Q10 were real open-ended, uncoded questions (Bryman, 2012; Sapsford & Jupp, 2006). All
three questions were optional and allowed participants to enter their perception in their own terms.
Thus the questions generated text, that served as the basis for further qualitive analysis (Flick, 2014;
Lyn, 2015). The aim of these open questions was to gain a deeper understanding on how participants
perceive opportunities and challenges in adapting prices according to demand and how they describe
them in their own words (Flick, 2014).
Since the phenomenon of RM includes information, price, capacity and even channel management Q8
and Q9 had to be narrowed down to focus on a specific area where challenges and opportunities can
be perceived. Therefore, the formulation „Which are the biggest challenges, in your opinion for your com-
pany, in adapting prices according to demand?“ was chosen, because adapting prices to demand presents

64
the core idea of RM. Also the word adapting is neutral in the sense that it might include the aspects of
lowering, increasing prices or not adapting prices at all.
Ethical considerations: anonymity & informed consent
The author took all prerequisites to adhere to the EU code for socio-economic research (Dench, Iphofen,
& Huws, 2004). Therefore, the invitation to the E-Mail provided exhaustive information on the re-
searcher conducting the research, including: full name, home address, link to private website, e-mail
address. The Austrian anti-spam laws (RIS, 2018a, 2018b) also require that the sender, can be clearly
identified. The invitation clearly stated the aim of the research and that it was carried for a master’s
thesis at the Salzburg University of Applied Sciences. The invitation e-mail was sent from the authors
university e-mail address; therefore, participants could see that the authors name matched the name of
the e-mail address. The last sentence of the invitation e-mail stated that if recipients had any questions
they could reply to the e-mail. The author answered all e-mails. Requests were mainly to be informed
about the outcomes of the study or confusion why they had been selected to participate in the research,
although their business was not active anymore. The author then always replied by informing the com-
panies that since their e-mail address was listed on holidaycheck.at they had received the invitation to
participate in the research.
The e-mail clearly stated that the participants are „invited“ (see Annex B), and did not purposely un-
derestimate the time it takes to complete the survey (Bryman, 2012). In fact, the invitation e-mail and
survey introduction stated that the survey would take 4 minutes to complete and the median amount
of time respondents spent answering the survey was 03m:23s (see image XY), recorded by the Survey-
Monkey platform. So respondents could decide whether to participate or not from an informed position
(Dench et al., 2004).
Addressing major limitation of online surveys: response bias,
low response rates and anti-spam laws
The major limitations of self-completed surveys are generally low response rates. Since no interviewer
is present to guide interviewees different response bias, such as careless or social desirability or respond
tendencies are more likely to occur (Bryman, 2012; Gray, 2009; Sapsford & Jupp, 2006). The following
chapter gives an exhaustive overview how the research design of this thesis dealt with these challenges.
8.7.1 Low response rates
Self-completed online surveys are considered to face the problem of low response rates (Bryman, 2012; Gray,
2009; Sapsford & Jupp, 2006).
To increase response data, it was ensured that the questionnaire and invitation e-mail use clear and
easy to understand language. Using the SurveyMonkey platform resulted in a professional layout, ap-
pearance and readability across different devices (Sapsford & Jupp, 2006).

65
The questionnaire was pre-tested using the SurveyMonkey Genius tool (Surveymonkey, 2018b). This
automatic tool provides recommendations on how to improve question order and number of answer
choices. Moreover, it gives an outlook on the estimated completion rate and time for respondents to
complete the survey. The survey instrument has then been sent to one person, who had no previous
knowledge about the topic and is not working in tourism industry. The goal of this short pre-test was
proof reading and to ensure readability and the overall functionality from receiving the invitation e-
mail until completing the survey. Beside this no further pilot testing than this has been done.
Figure 9: Final Test Score after Improvements were made
8.7.2 Response bias
Q8 and Q9 tried to explore the perceived challenges and opportunities the company of the interviewee
faces in in adapting prices according to demand. The term „challenges“ (Herausforderungen, see An-
nex D) was chosen purposefully, because it included problems but also neutral or positive stimuli and
situations, because a challenge can also be something one gladly takes on or part in. Understood as
„Which are the problems your company has in adapting prices according to demand?“ the question might evoke
social desirability responding, „the tendency to provide answers which cause the respondent to look good“
(Hancock & Flowers, 2001, p.173). To prevent these above-mentioned precautions regarding anonymity
and informed consent were taken. The participants could self-complete the questionnaire in their own
environment, that feels „anonym“ (Hancock & Flowers, 2001). Likewise, just above Q8 a short paragraph
informed participants about their anonymity as follows: „The following questions are optional. Your opin-
ion and each input are valuable. The entries will be analysed anonymously“ (see Annex D).
In Q5 participants needed to choose yes or no for eight different software options, which required some
mental effort from participants. To avoid response bias like careless responding, item omission or ac-
quiescence (Hancock & Flowers, 2001) the question was made obligatory and items were rotated ran-
domly for each participant.

66
8.7.3 Mass distribution without prior authorization
The questionnaire was designed to inquiry information about the accommodation provider on an or-
ganizational level, nevertheless many of the e-mail addresses delivered the invitation e-mail to a gen-
eral inbox like office@testhotel.at or reservation@...
This e-mails might have end up in the general inbox together with many other e-mails that might be
more relevant to the business such as booking requests by guests. The e-mail invitation thus needed to
convince the recipients to either answer the survey themselves or forward the e-mail to a colleague in
charge of the topic RM.
The invitation e-mail was considered to be the key to achieve a high response rate (Surveymonkey,
2017) and the text, inviting the respondents to take the survey, made use of the following well known
persuasion techniques and tactics of direct marketing (Gelbrich, Wünschmann, & Müller, 2008)
(Cialdini, 2006; Sapsford & Jupp, 2006):
Technique
Application, Phrases used
Label (See Annex D)
Personalisation
and Engagement (Cialdini, 2006;
Sapsford & Jupp, 2006)
Phrases „Your opinion is
needed”, “I want to invite you
to today (…) to share your per-
sonal view”.
Authority (Cialdini, 2006)
Mail sent from University E-
Mail Address, University Logo
Distinctive Url: revenue-
mangement.at
Clear Call to action
Start survey & dedicated
Link to survey
Link to survey repeated at the
end of e-mail
Seamless experience (Bilgihan,
Okumus, Nusair, & Bujisic, 2013)
Focus on value to the participant
(Sapsford & Jupp, 2006).
Clear communication Who
needs what for what reason
Informed consent
Sincere and friendly approach
(Cialdini, 2006) and trust building
(Srinivasan, 2004).
Providing personal Details
such as full address and per-
sonal website
Personal Introduction
Social Proof (Cialdini, 2006)
Reminder E-Mail stated „many
of your colleagues already took
part“.

67
Table 8: Persuasion Techniques Invitation E-Mail
In order to achieve both personalisation while also sending the e-mail in bulk to all recipients, the e-
mail was sent using „a self-hosted email newsletter application“ called Sendy (Sendy.co, 2018 ). The soft-
ware was bought and installed on the authors own webhosting account. The e-mail addresses from the
web scrapping process were exported from the Mozenda Cloud Software and subsequently saved as a
.cvs file. This file included the name and e-mail address of the accommodation provider in two col-
umns. The .cvs-File could then be imported into Sendy. This allowed to personalise the invitation e-
mail (see Annex A). Sendy uses the Amazon Simple Email Service, SES (AWS, 2012) to distribute e-
mails. Amazon SES is a cloud-based service to send e-mails (AWS, 2018) and it allows to use personal-
ization tags to send personalized e-mails from any e-mail address the sender has access to. The author
decided to use his personal e-mail address from Salzburg University of Applied Sciences to build trust
and authority with participants. In order to use an e-mail address with Amazon SES, the cloud platform
sends a verification e-mail, to verify the ownership of the respective e-mail address (AWS, 2012). Over-
all using Amazon SES ensures personalization, bounce handling and deliverability, so the messages
distributed are not marked as spam by the recipients’ ISP or spam filter (ibid.).
The Austrian anti-spam laws (RIS, 2018a, 2018b) prohibit to distribute electronic messages to more than
50 recipients, without their prior consent.
The invitation text for the survey was sent on 20/12/2017, 01:53 pm to 9.860 recipients. This equals all e-
mail addresses collected through the above described web scrapping process from the website holiday-
check.at. On 27/12/2017 the invitation e-mail was sent to 49 recipients from Burgenland, as collected
through the web scrapping process from the Austrian Economic chamber database (WKO, 2017a).

68
Figure 10: Delivery and Bounce Rates
In line with the anti-spam laws (RIS, 2018a, 2018b) this is legally not allowed, even though the message
did not directly advertise a service or product, but invite participants to take part in the survey. Nev-
ertheless, the fact that the e-mail addresses were not collected through a double-opt in process presents
a limitation.
In accordance with legal regulations the invitation e-mail provided an unsubscribe link, so participants
had the possibility to opt-out.
The delivery statistics (see Figure 11, p.69) shows that about 22% of the 49 messages, sent to the WKO
sample bounced. A bounce indicates a delivery failure (AWS, 2012). That means the e-mail addresses
were wrong or outdated.
In contrast 97% of the messages sent to the e-mail addresses collected from holidaycheck.de could be
delivered successfully. 75 users clicked on the link „Austragen“ (unsubscribe) and only 1 recipient
marked the message as spam.
In order to increase the response rate a second reminder e-mail was sent to 7,188 recipients on
09/01/2018, 09:31AM. The self-hosted e-mail