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Graduate Theses and Dissertations Iowa State University Capstones, Theses andDissertations
2015
The effects of lighting temperature and complexityon hotel guests' perceived servicescape, perceivedvalue, and behavioral intentionsJing YangIowa State University
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Recommended CitationYang, Jing, "The effects of lighting temperature and complexity on hotel guests' perceived servicescape, perceived value, and behavioralintentions" (2015). Graduate Theses and Dissertations. 14694.https://lib.dr.iastate.edu/etd/14694
The effects of lighting temperature and complexity on hotel guests' perceived servicescape, perceived value, and behavioral intentions
by
Jing Yang
A dissertation submitted to the graduate faculty
in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Major: Hospitality Management
Program of Study Committee: Thomas Schrier, Major Professor
Ann-Marie Fiore Frederick Lorenz
Anthony Townsend Tianshu Zheng
Iowa State University
Ames, Iowa
2015
Copyright © Jing Yang, 2015. All rights reserved.
ii
TABLE OF CONTENTS
Page
LIST OF FIGURES ............................................................................................................ v
LIST OF TABLES ............................................................................................................. vi
ACKNOWLEDGMENTS ............................................................................................... viii
ABSTRACT ........................................................................................................................ x
CHAPTER 1 INTRODUCTION ................................................................................... 1
The S-O-R Paradigm (The Mehrabian-Russell Model) .................................................. 1 Overview .................................................................................................................... 1
The development of environmental stimuli ............................................................ 2 The development of organism: Pleasure, arousal, and dominance ......................... 2 The development of response: Approach-Avoidance ............................................. 3 The linkage between environmental stimuli and organism .................................... 4 The linkage between organism and responses ........................................................ 5
Extending the S-O-R paradigm ................................................................................... 6 Intended Servicescape versus Perceived Servicescape ............................................... 8 The Importance of Servicescape ................................................................................. 8 The Importance of Positive Word-of-mouth and Intention to Revisit ...................... 11
Research Contribution .................................................................................................. 12 Definition of Terms ....................................................................................................... 14 Chapter Summary ......................................................................................................... 15
CHAPTER 2 LITERATURE REVIEW ...................................................................... 16
Introduction ................................................................................................................... 16 The Effects of Complexity and Lighting Temperature ................................................. 16
The Effects of Complexity ........................................................................................ 16 The Effects of Lighting Temperature ....................................................................... 21 The Interaction Effect of Lighting and Complexity ................................................. 25
Servicescape .................................................................................................................. 25 From Atmospherics to Servicescape: An Overview ................................................. 25 The Dimensions of Servicescape .............................................................................. 28 Recent Servicescape Research in Hospitality ........................................................... 34
Perceived Value ............................................................................................................ 35 The Concept of Perceived Value .............................................................................. 36
iii
Hirschman and Holbrook’s definition of value .................................................... 36 Acquisition value and transaction value ............................................................... 37
Determinants of Perceived Value ............................................................................. 39 Perceived costs and perceived benefits ................................................................. 39
Measurements of Perceived Value ............................................................................ 39 Unidimensional measurements ............................................................................. 39 Acquisition value and transaction value measurements ....................................... 40 Experiential measurements ................................................................................... 41 Combined approach .............................................................................................. 42
The Linkage between Servicescape and Perceived Value ........................................ 42 Behavioral Intentions .................................................................................................... 43
Conceptualization of Behavioral Intentions .............................................................. 43 The Linkage between Servicescape and Behavioral Intentions ................................ 44 The Linkage between Perceived Value and Behavioral Intentions .......................... 46
Chapter Summary ......................................................................................................... 48
CHAPTER 3 METHODS ............................................................................................ 49
Introduction ................................................................................................................... 49 Treatment Conditions .................................................................................................... 50 Stimuli Development .................................................................................................... 51 Questionnaire Development .......................................................................................... 53 Definitions and Measurement of Variables .................................................................. 55
Perceived Servicescape ............................................................................................. 55 Perceived Value ........................................................................................................ 58 Behavioral Intentions ................................................................................................ 58
Data Analysis Method ................................................................................................... 60 Reliability and Validity ................................................................................................. 61 Structural Equation Modeling ....................................................................................... 61 Chapter Summary ......................................................................................................... 61
CHAPTER 4 ANALYSIS AND RESULTS ................................................................ 63
Introduction ................................................................................................................... 63 Data Collection ............................................................................................................. 63 Data Analysis ................................................................................................................ 64
Manipulation Checks ................................................................................................ 64 Demographics ........................................................................................................... 67 Reliability .................................................................................................................. 70 Factor Analysis and Validity .................................................................................... 70 Hypothesis Testing – Hypotheses 5 to 9 ................................................................... 75 Hypothesis Testing – Hypothesis 1 to 4 ................................................................... 76
The interaction and the main effects ..................................................................... 77 The effects of intended complexity and intended lighting temperature ............... 79
Chapter Summary ......................................................................................................... 86
iv
CHAPTER 5 DISCUSSION AND CONCLUSIONS ................................................. 87
Introduction ................................................................................................................... 87 Discussion of Findings .................................................................................................. 87
The Effects of Intended Complexity ......................................................................... 87 The Effect of Intended Lighting Temperature .......................................................... 88 The Relationship between Perceived Servicescape, Perceived Value, and Behavioral Intentions ................................................................................................ 89
Theoretical Implications ............................................................................................... 90 Managerial Implications ............................................................................................... 91 Limitations and Future Research .................................................................................. 92
REFERENCES ................................................................................................................. 95
v
LIST OF FIGURES
Page
Figure 1 The S-O-R Paradigm with Modification from Fiore and Kim (2007)…………..7
Figure 2 Theoretical Model ............................................................................................ 48
Figure 3 The Design of the Virtual Hotel Room ............................................................ 51
Figure 4 The Six Experimental Conditions of the Virtual Guestroom ........................... 56
Figure 5 Structural Diagram with Standardized Parameter Estimates ............................ 76
Figure 6 Means of Perceived Servicescape .................................................................... 81
Figure 7 Means of Perceived Value ................................................................................ 83
vi
LIST OF TABLES
Page Table 1 Dimensions of Servicescape/Atmospherics ....................................................... 30 Table 2 Experimental Conditions ................................................................................... 50 Table 3 The Designs of the Seven Complexity Levels Used in the Pilot Test ............... 54 Table 4 Measurement of Perceived Servicescape ........................................................... 57 Table 5 Measurement of Perceived Value ...................................................................... 58 Table 6 Measurement of Behavioral Intentions .............................................................. 60 Table 7 The Treatment Conditions of the Means of Perceived Servicescape ................ 60 Table 8 Means of Confidence Intervals of Perceived Complexity ................................. 65 Table 9 Confidence Intervals of the Mean Differences in Perceived Complexity ......... 65 Table 10 The Manipulation Check of Intended Lighting Temperature ...................... …66 Table 11 Useful Sample Size of Each Treatment Condition ......................................... .67 Table 12 Gender and Age ............................................................................................... 68 Table 13 Ethnicity, Education, and Annual Household Income ..................................... 68 Table 14 Cronbach's Alpha Values …………………………………………………….70
Table 15 Confirmatory Factor Analysis (CFA) of Individual Items …………………...70 Table 16 Confirmatory Factor Analysis (CFA) with Averaged Perceived Servicescape Dimensions ................................................................................ 72 Table 17 Correlations among All of the Dimensions ………………………………......73 Table 18 Correlations among the Variables with Perceived Servicescape Overall ........ 74 Table 19 R-squared Values ............................................................................................. 76 Table 20 Factorial ANOVA with Dependent Variable Being Perceived Servicescape .................................................................................................... 77
vii
Table 21 Factorial ANOVA with Dependent Variable Being Perceived Value ............. 77 Table 22 The Main and the Interaction Effects of Intended Complexity and Intended Lighitng Temperature ...................................................................... 79 Table 23 ANOVA of Intended Complexity on Perceived Servicescape ........................ 80 Table 24 Comparing the Means of the Confidence Intervals of Perceived Servicescape under the Same Lighting Temperature ..................... 81 Table 25 ANOVA of Intended Complexity on Perceived Value ................................... 82 Table 26 Comparing the Means of the Confidence Intervals of Perceived Value under the Same Lighting Temperature ........................................................... 82 Table 27 ANOVA of Intended Lighting Temperature on Perceived Servicescape ........ 84 Table 28 ANOVA of Intended Lighting Temperature on Perceived Value ................... 84 Table 29 Comparing the Means and the Confidence Intervals of Perceived Servicescape
under the Same Complexity ……………………………………………..…..85 Table 30 Comparing the Means and the Confidence Intervals of Perceived Value under the Same Complexity ............................................................................ 85
viii
ACKNOWLEDGMENTS
I would like to give very special thanks my committee chair, Dr. Thomas Schrier,
and my committee members, Dr. Ann-Marie Fiore, Dr. Frederick Lorenz, Dr. Anthony
Townsend, and Dr. Tianshu Zheng, for their guidance and support throughout my study
and the completion of my dissertation. I appreciate all the help of Dr. Schrier, in
particular his patience in answering my endless questions and in improving my poor
writing skills at the beginning of this process. I would also like to thank Dr. Fiore. Her
class in aesthetics helped me find my research interests, and her encouragement
motivated me to pursue higher standards, which I thought I was not capable of. Thank
you Dr. Lorenz for answering my questions in statistics and for helping me with my
methods and data analysis. I appreciate Dr. Zheng for his guidance in research and in
preparing me to be a better candidate. I also thank Dr. Townsend for his helpful feedback
in refining my research and in expanding my knowledge. This study could not have been
accomplished without the guidance of my committee members.
I would also like to give deep thanks to my family for their support during my
Ph.D. program, especially my parents. They supported me when I had doubts in myself.
In addition, I appreciate the help and support of my friends, colleagues, and the
department faculty and staff. I appreciate the kindness and friendship of Yuyang Chen,
Songtao Lu, Wenyu Wang, and Weitao Zhang. I also enjoyed working closely with Dr.
Linda Niehm, Dr. SoJung Lee, Yu-Chih Karen Chiang, Mai Wu, KaEun Luna Lee, and
Xiaowei Xu. I also appreciate the opportunities and support of the Department of
Apparel, Events, and Hospitality Management. I would also like to give a special thank to
ix
Dr. Douglas Bonett, whose classes changed my dislike of math and inspired me to pursue
a minor in statistics. Additionally, it is a great pleasure to work with my former
colleagues in Michigan and Florida. Also, thank you to my professors at Michigan State
University. I could not have started my Ph.D. program without their help.
Obtaining a Ph.D. degree is not an easy process. I am very fortunate to have been
assisted by so many wonderful people. I apologize to those who helped my in the process
but I have forgotten to listed here, your influence is very importance to my achievement
and I greatly appreciate it. I wish all of you the best in your future endeavors.
x
ABSTRACT
Previous studies have investigated the effects of environmental stimuli, such as
music, scent, and lighting. However, complexity has not been widely discussed,
particularly in three-dimensional spaces. The current study primarily examines the effects
of intended complexity and intended lighting temperature on perceived servicescape,
perceived value. A 2 (warm lighting, cool light) × 3 (low complexity, medium
complexity, high complexity) between-subject experiment was conducted. Six computer-
generated images with the same guestroom floor plan were utilized to represent the six
treatment conditions. An online-survey was distributed via Amazon Mechanical Turk. A
total of 473 responses were used to test the proposed hypotheses.
The results suggested several important findings. First, the effects of intended
complexity were examined by utilizing a consistent lighting temperature. Among the cool
light conditions, medium complexity generated the highest perceived servicescape among
the three complexity levels, and it also generated a higher perceived value than low
complexity. No significant differences in perceived servicescape and perceived value
were found among the complexity levels under warm light.
Second, the effects of intended lighting temperature were assessed by utilizing a
constant complexity level. Under the low and high complexity levels, warm light
generated a higher perceived servicescape than cool light, but no significant difference
was found under medium complexity. The differences in perceived value were not
significant. Third, no significant interactions between intended complexity and intended
lighting temperature were found. Finally, the data showed that perceived servicescape
positively influenced perceived value, intention to revisit, and intention to spread positive
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word-of-mouth. Perceived value positively influenced intention to revisit and intention to
spread positive word-of-mouth.
The current study has several major contributions. Theoretically, it expands the
knowledge in complexity in three-dimensional spaces. In addition, it captures the
inverted U-shape relationship between intended complexity and perceived servicescape.
Furthermore, it develops a multi-item measurement for perceived complexity. Practically,
it provides valuable information for managers who deal with similar demographics. Hotel
managers could choose either to change lighting temperature or to change complexity
level to generate high perceived servicescape and/or perceived value, which increases
guests’ intention to revisit and intention to spread positive word-of-mouth.
1
CHAPTER 1
INTRODUCTION
The purpose of the current study is to examine how the level of complexity and
lighting temperature in a hotel room influence perceived servicescape, perceived value,
and behavioral intentions of the guest. This will be done by conducting a 2 (warm
lighting, cool light) × 3 (low complexity, medium complexity, high complexity) between-
subject experiment with images of a virtual hotel guestroom. This chapter will first
provide an overview of the S-O-R paradigm, also referred as the Mehrabian-Russell
Model, which serves as the theoretical framework for the current study. Then the
theoretical and practical contributions of this study to the hotel industry will be discussed.
The S-O-R Paradigm (The Mehrabian-Russell Model)
Overview
Early studies in environmental psychology were centered on two basic concerns:
“The direct impact of physical stimuli on human emotions” and “the effect of the
physical stimuli on a variety of behaviors, such as work performance or social
interaction” (Mehrabian & Russell, 1974, p.4). The survey study of Proshansky, Ittelson,
and Rivlin (1970) was an important step forward in exploring the effects of the
environmental design, as the authors proposed an operational definition of environmental
psychology and covered diverse interests (Mehrabian & Russell, 1974). However, they
could not define environmental psychology conceptually due to the lack of adequate
theory (Mehrabian & Russell, 1974). Based on these concerns, Mehrabian and Russell
2
(1974) proposed the stimulus-organism-response (S-O-R) paradigm, also called “the
Mehrabian-Russell model”, which suggested that environmental stimuli (S) elicit
organism (O), and organism drives consumers' behavioral responses (R) that includes
approach and avoidance.
The development of environmental stimuli
The development of environmental stimuli (S) was based on the concept of
environmental displays. Environmental displays refer to describing “units of the everyday
physical environment” (Craik, 1970, p. 647). For instance, a classroom can be described
in terms of the chairs, the desks, the amount of lighting in it, and the arrangement of the
desks (Craik, 1970).
The development of organism: Pleasure, arousal, and dominance
People share some similar reactions to environmental stimuli despite language
and cultural differences because humans are equipped biologically to generate these
autonomic emotional reactions (Osgood, 1960). Physiological studies contributed to the
connection by refining the basic organism dimensions. Bush (1973) selected 264
adjectives to describe feelings and summarized three dimensions: Pleasantness-
unpleasantness, level of activation, and level of aggression. Based on Bush (1973) and
other studies in physiology (Berlyne, 1960; Lindsley, 1951), Mehrabian and Russell
(1974) used pleasure, arousal, and dominance (PAD) as the three basic types of internal
reactions of the organism to stimuli. Pleasure, arousal, and dominance could be traced
back to the three dimensions of emotion in the study of Bush (1973): Pleasure
3
corresponds to the pleasantness-unpleasantness dimension, arousal corresponds to the
level of activation, and dominance corresponds to the level of aggreession (Mehrabian &
Russell, 1974). In addition, the valance of pleasure, arousal, and dominance constitute the
essential element of people’s emotional responses to all situations (Mehrabian & Russell,
1974).
Pleasure, arousal, and dominance are basic components of emotions in repsonse
to stimuli (Mehrabian & Russell, 1974). As examples, the feeling of boredom is low on
pleasure, arousal, and dominance. The feeling of excitement is high on all of the three
(Mehrabian & Russell, 1974). The feeling of relaxiation is high on pleasure and
dominance but low on arousal (Mehrabian & Russell, 1974).
The development of response: Approach-Avoidance
Pleasure and arousal result in two contrasting behaviors: Approach or avoidance
(Mehrabian & Russell, 1974), while dominance was found not to be a significant
predictor of behaviors (Donovan & Rossiter, 1982; Ward & Russell, 1981). Approach-
avoidance behaviors are considered to have four aspects (Mehrabian & Russell, 1974):
Desire to stay or not to stay, desire to explore or not to explore, desire to work or not to
work, and desire to affiliate (social) or not to affiliate. Approach behaviors refer to all the
positive aspects, whereas avoidance behaviors refer to the opposite behaviors (Mehrabian
& Russell, 1974).
4
The linkage between environmental stimuli and organism
The first linkage in the S-O-R paradigm is the linkage between environmental
stimuli and the organism. This linkage was built upon research in synesthesia and
semantic differential (Mehrabian & Russell, 1974). First, experiments in synesthesia
show that stimulation in one sense influences perception in another sense because people
response to stimuli biologically (Mehrabian & Russell, 1974). For example, Hazzard
(1930) asked the participants to describe odors, a large proportion of the adjectives used
by them were from modalities other than olfactory, such as light and bright. This finding
demonstrated that stimulation in one sense did not remain within the same sense; the
stimulation actually affected perceptions in other senses, which support the transfer from
stimuli to organism variables (Mehrabian & Russell, 1974).
Second, studies in semantic differential also supported the linkage between
environmental stimuli and organism. Kasmar (1970) developed a list with a total of 500
pairs of adjectives describing architectural spaces. Mehrabian and Russell (1974) adapted
66 of them and conducted factor analysis and regression analysis. Nine factors were
found: Pleasant, bright and colorful, organized, ventilated, elegant, impressive, large,
modern, and functional. They further found that all of these factors could be described in
terms of regression equations that constituted of pleasure, arousal, and dominance. In
other words, pleasure, arousal, and dominance are the three fundamental elements that
constitute various kinds of internal emotional responses of the organism (Mehrabian &
Russell, 1974).
5
The linkage between organism and responses
The second linkage in the S-O-R model, between the organism and responses,
was build upon findings in positive reinforcement and information rate (Mehrabian &
Russell, 1974). First, positive reinforcement results when a stimulus is followed by the
increasing likelihood of the behavior, and negative reinforcement refers to the opposite
response to kind of stimulus (Skinner, 1961). Positive reinforcement leads to approach
responses, whereas negative reinforcement leads to avoidance responses, as approach
behaviors are maximized when the level of pleasantness is maximized and the level
arousal is moderate (Dollard & Miller, 1950; Miller, 1944, 1964). This model is also
referred to as Miller’s approach-avoidance model (Mehrabian & Russell, 1974).
In addition to positive reinforcement, the concept of information rate also bridges
stimuli and approach-avoidance (Mehrabian & Russell, 1974). Information rate refers to
(1) spatial complexity and (2) the rate and the volume of information changing (i.e.,
temporal factors) in an environment (Huang, 2003; Mehrabian & Russell, 1974). In the
current study, although six different guestroom designs are used, each layout remained
the same, therefore temporal factors are beyond the scope of the current study.
Information rate is calculated as the following: “If n independent events occur and
each of these events is one of the k equally likely alternatives, then the amount of
information (H) is given by H=nlog2k…For instance, of two paintings, one of which
contains two, and the other eight, equally distributed colors, the latter has three times
(log28=3) the information of the former (log22=1)…When the alternatives of an outcome
are not equally probable, the amount of information is less than when the alternatives are
equally probable…Within a spatial or temporal distribution of events, the total amount of
6
information is simply the sum of information from each event or component, provided
that the component events are independent” (Mehrabian & Russell, 1974, pp. 77-78).
Information rate directly correlates with arousal, and arousal correlates with
approach-avoidance with an inverted U-shape curve relationship. Approach is maximized
at a moderate level of arousal, while extremely high or low arousal lead to avoidance
(e.g., Dember & Earl, 1957; Fiske & Maddi, 1961; Glanzer, 1958; Hunt, 1960). In other
words, people avoid extremely high and extremely low levels of arousal conditions.
(Bexton, Heron, & Scott, 1954; Davis et al., 1958). When an environment contains an
extremely low information rate, such as prison cells, long sea voyages, and exploration of
the polar regions (Gunderson, 1963, 1968), a person experiences a low level of arousal
(or boredom) and would try to avoid the environment (Heron, 1961; Zubek, Welch, &
Saunders, 1963). Alternately, when an environment contains extremely a high
information rate, a person would be overwhelmed as human organisms are unable to
handle persistently high-arousal situations. As a result, the environment would also be
avoided (Mehrabian & Russell, 1974).
Extending the S-O-R paradigm
When the S-O-R paradigm was first introduced in 1974, it only included three
types of organism and two types of responses. Fiore and Kim (2007) expanded the
paradigm by introducing more constructs.
Bagozzi (1986) indicated that organism is internal processes and structures
intervening between stimuli external to the person and the final actions. Thus, the
organism variable is not limited to emotional responses including pleasure, arousal, and
7
dominance; it also includes other internal responses including cognitions (e.g., thoughts
about the stimuli) and perceived value (Fiore & Kim, 2007) associated with the response
to the stimuli. In the current study, perceived servicescape is individuals’ judgments
regarding the design of a space, thus it is a type of cognition under the organism variable.
In addition, perceived value reflects value, thus is also an organism variable.
Behavioral response (R) also includes behavioral intentions, satisfaction, and
loyalty (Fiore & Kim, 2007). In this study, the author focused on intention to spread
positive word-of-mouth and intention to revisit. The extended S-O-R paradigm,
illustrating variables central to the present study, is shown in Figure 1.
Note. Adapted from “An approach to environmental psychology,” by Mehrabian, A. and Russell, J. A., 1974, Cambridge, MA: MIT Press and “An integrative framework capturing experiential and utilitarian shopping experience,” by Fiore, A. M. and Kim, J. , 2007, International Journal of Retail & Distribution Management, 35(6), p. 424. Constructs in bold are the focuses of the current study.
The current study adapted the extended S-O-R paradigm to examine the effects of
lighting temperature and level of complexity. According to the extended S-O-R paradigm
(Fiore & Kim, 2007), lighting temperature and the level of complexity are environmental
Environmental Stimuli (S)
- Lighting temperature
- Level of complexity
Organism (O) - Cognition: Perceived servicescape - Consciousness: Fantasy, imagery, creative play, and memories - Affect: Multi-attribute attitude, global attitude - Emotion: Pleasure, arousal, dominance - Mood: Good/bad mood - Value: Perceived value
Response (R) - Approach/Avoidance - Behavioral
intentions - Satisfaction, loyalty
Figure 1. The S-O-R Paradigm with Modification from Fiore and Kim (2007)
8
stimuli that affect the organism and lead to behavioral responses toward the guestroom.
The organism variables in this study include guests’ perceived servicescape and
perceived value. The following sections discuss the constructs in the current study.
Intended Servicescape versus Perceived Servicescape
Servicescape refers to the environment in which a service encounter happens
(Booms & Bitner, 1981). There is a difference between intended servicescape and
perceived servicescape (Kotler, 1973). Intended servicescape are sensory indicators that
are intentionally built into an environment (i.e., environmental stimuli), while perceived
servicescape is people’s perception of these sensory indicators, which is subjective and
may differ from one person to another (Kotler, 1973). In the current study, the author
focused on two particular environmental stimuli or intended servicescape elements,
which are intended lighting temperature and intended complexity. These two elements
are intentionally added to spaces by architectures, thus they are environmental stimuli (S),
whereas perceived servicescape is what people think about a guestroom that is shown to
them. Perceived servicescape is perceptual and thus an organism variable (O).
The Importance of Servicescape
Servicescape plays important roles in services organizations including hotels.
First, servicescape may help hotels to create a competitive advantage (Kotler, 1973;
Morrison, Gan, Dubelaar, & Oppewal, 2011) because guests may choose a service
provider due to the superior servicescape (Kotler, 1973). At the present time customers
look for aesthetic (design) aspects to differentiate products or brands (Postrel, 2003).
9
Therefore hotel servicescape can be a special characteristic that guests can use to identify
a hotel from others (West & Hughes, 1991). With a competitive advantage, managers can
charge a higher price, thus servicescape can also improve the financial performance of a
company (Hightower, Brady, & Baker, 2002).
Second, servicescape can be an efficient marketing tool that bridges consumers
and companies. A service provider can communicate their organizational and marketing
objectives through deliberately designed servicescape (Bitner, 1992). Hotel servicescape
can also effectively deliver messages that the hotel wishes to communicate to their guests
(West & Purvis, 1992).
Third, servicescape elements can also improve perceptions (Sweeney & Wyber,
2002) and increases approach behavior (Ballantine et al., 2010; Fiore, Yah, & Yoh, 2009;
Mehrabian & Russell, 1974; Ryu & Jang, 2007; Shilpa & Rajnish, 2013). It has been
reported that in a store that had a well-organized servicescape, customers showed a
higher satisfaction and spent more time in the self-service area than customers who were
in a store that had a disorganized servicescape (Spies, Hesse, & Loesch, 1997). Another
study found that fast tempo classical music increases perceived service quality, and liking
of music increases perceived merchandise quality (Sweeney & Wyber, 2002). It has also
been found that guests at a wine store tended to select more expensive products when
classical music was playing, thus the store’s revenue was increased (Areni & Kim, 1993).
In addition, congruent1 music and scent increased the overall satisfaction and customers’
intention to return (Mattila & Wirtz, 2001). The reason why these servicescape elements
1 Congruent refers to elements (i.e. music and scent) that are both high arousal or both low arousal. For instance, slow tempo music congruent with lavender scent, fast tempo music congruent with grapefruit scent (Mattila & Wirtz, 2001).
10
work well is that they create high levels of pleasure and moderate levels of arousal that
enhance the customers’ hedonic experience, therefore increase approach behavior.
Fourth, hotel servicescape creates value for the guests and is one of the top
attributes guests consider when making hotel purchase decision (Dubé & Renaghan,
2000). The high importance of the servicescape in hotel purchase decision, as apposed to
public space and property size, is likely because guests spend a long time in guestrooms
(Lin, 2004).
Finally, although offering outstanding servicescape is beneficial for a company,
managing certain aspects of servicescape is relatively simpler than others. Servicescape
contains ambient, design, and social factors (Baker, Grewal, & Parasuraman, 1994). The
current study manipulates the wall canvases, curtains, pillows, area rugs, and lighting
temperature in a virtual guestroom, which all belong to the categories of ambient and
design factors. Compared to managing social factors that involve employees and
customers, managing the ambient and design factors are easier (Fisk, Rosenbaum, &
Massiah, 2011; Swartz & Iacobucci, 1999). Hotel management, either those of
independent hotels or at a corporate level, can manipulate the ambient and design factors
of servicescape. Examples include the relative easiness of changing temperature, music
(Demoulin, 2011), and scent. However, they do not have total control over employees or
other things such as constructions nearby, football game schedules, or weather. Therefore,
managing ambient and design factors of servicescape leads to a better “value” for
practitioners; practitioners have more control over it, it is easy to manipulate, and the
costs are lower than changing other aspects such as the social factors.
11
The Importance of Positive Word-of-mouth and Intention to Revisit
The current study discusses behavioral intentions by focusing on two dimensions:
Intention to spread positive word-of-mouth and intention to revisit. Both of these two
dimensions are crucial for hotel managers, as they are indicators of hotel guests’ future
behaviors (Fishbein & Ajzen, 1975): Would the guests recommend a hotel to their family
and friends or do so online, and would they come back to the hotel again?
In today’s society, word-of-mouth messages from family and even strangers are
both highly persuasive. In a study conducted by Nielsen Global Survey in 2013, 84% of
people indicated that they trust word-of-mouth messages from their family and friends,
and 70% of the participants indicated that they trust word-of-mouth messages posted
online. The trust of word-of-mouth messages from family and friends ranked the highest
among all forms of advertising (The Nielsen Company, 2013).
In the lodging industry repeat customers (i.e., loyal customers) are extremely
important. First, retaining current customers costs six times cheaper than attracting new
customers (Petrick, 2004b). Second, compared to non-loyal customers, loyal customers
tend to be less price-sensitive (Williams & Naumann, 2011), spend more, and are less
likely to consider other hotels (Yoo & Bai, 2013). As such, many hotels, such as Marriott,
Starwood, and Hyatt, have developed loyalty programs to attract customers to revisit their
properties.
Both word-of-mouth and revisitation are important for a company’s success, but
tracking the actual behaviors of customers can be challenging. It is relatively easy to
track the number of times a customer has visited a hotel, assuming the hotel has a well-
maintained loyalty program database. However, it is difficult to obtain information on the
12
actual number of times a customer has mentioned a company to friends, family members,
or online and what did they said about the company. As such research will commonly
utilize behavioral intentions as an indicator of actual behaviors (Fishbein & Ajzen, 1975).
The current study focuses on intention to spread positive word-of-mouth and intention to
revisit, because these two dimensions indicate the degree to which a guest would spread
appraisals and stay with a hotel in the future, which are important determinants of a
hotel’s success.
Research Contribution
The current study has both academic and industrial contributions. Academically, this
study reveals the impact of lighting temperature and complexity on perceived servicescape
and perceived value, which provides empirical support for the extended S-O-R paradigm
(Fiore & Kim, 2007). In particular, there is a limited amount of complexity literature
available and many of them focus on webpage complexity (Geissler, Zinkhan, and Watson,
2006; Tuch, Bargas-Avila, Opwis, & Wilhelm, 2009; Tuch, Presslaber, Stöcklin, Opwis,
& Bargas-Avila, 2012). The current study contributes to obtaining a better understanding
regarding complexity in three-dimensional spaces.
In addition, the current study expands the body of literature and can be utilized in
the areas of environmental psychology, interior design, and lodging operations. Third,
due to the experimental design, the results of this study provide solid evidence of the
effect of lighting temperature and the level of complexity. Fourth, as an exploratory
attempt in researching level of complexity in a three-dimensional space, this study
explores what would be the appropriate to be placed in a hotel guestroom, which would
promote further discussion in preventing information overload.
13
Practically, the current study provides helpful information to interior designers
and hotel managers. There has been an increasing trend of replacing incandescent light
bulbs with compact fluorescent light (CFL) or light-emitting diode (LED) bulbs, which
are more energy efficient, require less maintenance, and generate pleasant lighting for
guests (GE Lighting, 2013). When switching to CFL or LED bulbs, managers need to
decide the lighting temperature (warm versus cool). Thus, it is important to understand if
one lighting temperature generates more positive effects among guests than another that
the investment would be well worth. The current study indicates the type of lighting
temperature that is perceived more positively so that practitioners can better design a
hotel guestroom.
While chain hotels generally have the resources to design guestrooms with
excellent servicescape, individual hotels, motels, or bed-and-breakfast inns might find
such task to be very challenging, especially small properties. The owners of small
properties often have a great deal of control over the design of the guestrooms; however,
they might feel confused regarding methods to decorate their guestrooms which are
comfortable for the guests and show the uniqueness of the hotel. Two common mistakes
are either to decorate rooms in a manner that are complex and appear overwhelming, or
to decorate rooms in a way that lacks decoration and appears boring. The current study
developed a design plan that provide an appropriate level of complexity and is simple to
adapt for small or independent hotels.
14
Definition of Terms
The following terms will be utilized in this study to discuss of environmental
stimuli and the consequential responses:
Affect: Emotional responses and feelings, such as love, hate, joy, and anxiety
(Breckler, 1989; Holbrook & Hirschman, 1982).
Arousal: “A feeling state varying along a single dimension ranging from sleep to
frantic excitement (Mehrabian & Russell, 1974, p.18)”.
Behavioral intentions: “The degree to which a person has formulated conscious
plans to perform or not perform some specified future behavior” (Warshaw & Davis,
1985, p. 214).
Dominance: “An individual’s feeling … based on the extent to which he feels
unrestricted or free to act in a variety of ways” (Mehrabian & Russell, 1974, p.19).
Intention to revisit: A customer’s tendency of repatronage a business (Lam, Chan,
Fong, & Lo, 2011).
Intention to spread positive word-of-mouth (WOM): The intention of saying
positive comments regarding a business.
Organism: “Internal processes and structures intervening between stimuli external
to the person and the final actions, reactions, or responses emitted, consisting of
perceptual, psychological, feeling, and thinking activities” (Bagozzi, 1986, p. 46).
Perceived value: “Customer’s overall assessment of the utility of a product based
on perceptions of what is received and what is given” (Zeithaml, 1988, p. 14).
Pleasure: The degree to which a person feels good, joyful, happy (Donovan &
Rossiter, 1982).
15
Servicescape: “The environment in which the service is assembled and in which
the seller and customer interact, combined with tangible commodities that facilitate
performance or communication of the service” (Booms & Bitner, 1981, p. 36).
Chapter Summary
This chapter discusses the purpose of the current study and the theoretical
foundation, which is the S-O-R paradigm. In addition, it also explains the importance of
servicescape, positive word-of-mouth, and intention to revisit. Furthermore, it discusses
the research and practical contributions of the current study. Finally, the definitions of
some important constructs are provided. The following chapter reviews the literature
related the constructs and the hypotheses.
16
CHAPTER 2
LITERATURE REVIEW
Introduction
This chapter discusses the previous research that provided evidence for the
development of the hypotheses of this study. The literature review is organized as four
major sections. The first section discusses previous studies in complexity and lighting
temperature, including those focused on complexity, on lighting temperature, and on the
interaction between the two constructs. The second section discusses existing literature in
servicescape, starting with the development of the concept, its dimensions, and recent
studies in hospitality servicescape. The third section reviews the conceptualization, the
determinants, and the measurement of perceived value as well as how it is related to
perceived servicescape. The final section examines the conceptualization of behavioral
intentions and the influences of servicescape and perceived value on behavioral
intentions.
The Effects of Complexity and Lighting Temperature
The Effects of Complexity
It is critical to create guestrooms that generate a moderate level of arousal so that
guests feel comfortable when staying in these rooms. As the S-O-R paradigm indicates,
the relationship between information rate and approach behavior is a inverted U-shape
curve (Mehrabian & Russell, 1974): People avoid situations with an extremely high
information rate or extremely low information rate, because an extremely high level of
17
informaiton rate leads to a high level of arousal that is overwhelming, while an extremely
low level of informaiton rate leads to a low level of arousal that is boring and
uncomfortable (Mehrabian & Russell, 1974). The following paragraphs discuss how
extremely low and high complexity generate negative effects on individuals.
The reason why people would not enjoy being in an extremely low complexity
enviornment can be demostrated via sensory deprivation. Sensory deprivation includes
reduced input to the senses, restricted movement in an environment, and partially limited
social contact (Kubzansky, 1961), such as being in a prison cell or taking a sea voyage
(Gunderson, 1963, 1968). These environments contain an extremely low information rate,
which leads to extremely low arousal. It causes declined thinking, feelings, and
perceptions (Mehrabian & Russell, 1974) and lead to avoidance behaviors (Heron, 1961;
Zubek et al., 1963). Bexton et al. (1954) found that the lower the information rate during
an experiment, the higher the number of participants there were that abandoned the study.
During an experiment conducted by Davis et al. (1958), participants in the experimental
group which experienced sensory deprivation also abandoned the experiment more
quickly than those in the control group.
Although extremely low arousal feeling causes avoidance behaviors, humans are
unable to handle persistently high-arousal situations as well (Bexton et al., 1954; Davis et
al., 1958). A large variety of arousing stimuli lead to an extremely high arousal feeling,
which causes General Adaption Syndrome (GAS). GAS consists of three stages: Alarm
reaction, resistance, and cease to function. The initial stage “alarm reaction” includes the
generation of adrenaline and corticoid that lead high arousal and low pleasure feelings
such as nervousness and anxiety. If the stimulation continues, a person experiences the
18
second stage “resistance”, in which his or her body’s effort of adapting the stimuli
becomes harmful. Some examples of this include depression, headaches, insomnia,
gastric issues, or high blood pressure. Once the stimulation outranges a person’s
capability to handle it, the final stage “cease to function” occurs and the person feels
overwhelmed and collapses (Mehrabian & Russell, 1974).
Beddings, curtains, and decorations in a room can all be considered as different
types of stimuli that work together to generate a certain level of information rate.
Compared to a moderate level of information rate, an extremely high level or an
extremely low level of information rate are perceived less positively (Mehrabian &
Russell, 1974). However, as the design of a hotel room is determined by numerous
stimuli, researchers have not reached the conclusion regarding when the information rate
would be too high or too low.
Although it is difficult to quantify how much information is too much or too little
for a three-dimensional environment, some researchers have approached the topic by
examining perceived complexity in two-dimensional settings such as webpage design.
Complexity “relates to the degree of stimulation from the number and physical quality of
units, the degree of dissimilarity of units, and the level of organization in the arrangement
of units” (Day, 1981, p.33). Complexity contains three components: Number of units,
degree of interest of the units, and cohesion among the units (Day, 1983, p.33). First,
number of units is “the number of identifiable parts of the form” (Fiore, 2010, p. 357).
Second, units may have different degree of interest, as some units contain a higher
amount of stimulation to the nervous system thus appear to be more interesting (Fiore,
2010). For instance, assuming two coats, a red one and a grey one, have exactly the same
19
style. The red coat is more stimulating than the grey coat and appears to be more complex
(Fiore, 2010). This difference is possibly due to that red is intrinsically more exciting to
the human brain (Clynes, 1977), and such excitement can cause an increase in blood
pressure, respiratory rate, or eye blink frequency (Gerard, 1957). Third, cohesion refers to
the similarity among units and the regularity of the unit layout (Fiore, 2010). Complexity
increases when units share little cohesion or the arrangement of units is irregular (Fiore,
2010). To summarize the above, complexity can be increased by increasing the number
of units, by increasing the degree of interest, and/or by decreasing the cohesion among
units (Fiore, 2010).
Research in web page design indicates that complexity is a major determinant of
viewers’ perceptional responses and behaviors (Tuch et al., 2012). For example, Geissler
et al. (2006) suggests that web pages with moderate level of complexity enable effective
communication and generate more positive feedback among viewers. Tuch et al. (2012)
reports that compared to websites with low or medium visual complexity, websites of
high visual complexity receive more negative aesthetical judgments. Tuch et al., (2009)
indicates that a further increase in complexity leads to increases in facial muscle tension
and negative valence appraisal. Therefore, complexity is believed to be an important
factor in the context of the current study because complexity of visual stimuli has a
strong influence on viewers’ responses (Geissler et al., 2006; Tuch et al., 2009; Tuch et
al., 2012).
Although the studies above indicated that complexity influences people’s
responses, there is limited evidence that complexity influences perceived servicescape
20
and perceived value. However, previous studies in the related fields provide evidence
supporting such connections.
First, researchers found when comparing two stores of the same brand, customers
had higher satisfaction in the self-service area in the organized store than those in the
disorganized store (Spies et al., 1997). In addition, music and scent that are congruent
enhanced overall satisfaction and customers’ intention to return (Mattila & Wirtz, 2001).
These effects are likely to be that the two stores provide moderate complexity: The
organized store and the store with congruent music and scent both have cohesive units, so
that the complexity is not too high to be overwhelming. In addition, as these two stores
also have customers, employees, and product displays the information rate is also
unlikely to be boringly low.
Second, even though the two studies discuss satisfaction and intention to revisit
instead of perceived servicescape and perceived value, it has been well discussed that
perceived servicescape (Lam et al., 2011; Wakefield & Blodgett, 1994, 1996, 1999) and
perceived value (Fornell, Johnson, Anderson, Cha, & Bryant, 1996; Jen, Tu, & Lu, 2011;
Johnson & Nilsson, 2003) are positively associated with satisfaction, and perceived value
is positively associated with intention to revisit (Cronin, Brady, & Hult, 2000; Jen et al.,
2011). In other words, when satisfaction and intention to revisit are at a high level,
perceived servicescape and perceived value would also be at a high level. Therefore, it is
reasonable to make the inference that the moderate complexity in these two studies also
generates a high level of perceived servicescape and perceived value.
21
Based on the rationale above, an environment with a moderate complexity is
expected to be perceived more positively than those with an extremely low or extremely
high complexity. Thus the following hypotheses are proposed:
Hypothesis 1: Perceive servicescape is more positive under a medium level of
complexity than under low or high levels of complexity.
Hypothesis 2: Perceived value is more positive under a medium level of
complexity than under low or high levels of complexity.
The Effects of Lighting Temperature
Lighting temperature describes the color of a lamp when lighted (Gordon, 2003,
p.45). Lighting temperature is often measured by correlated color temperature (CCT)
with the unit of Kelvin (K), such as 3000K and 4200K (Gordon, 2003). The larger the
number, the cooler the color of the light appears; the smaller the number, the warmer the
color of the light appears (Gordon, 2003).
The hotel industry is showing an increased interest in lighting because of raising
energy costs and customer demand especially from women and business travelers (Pae,
2009). In addition, previous lighting research indicated that good hotel guestroom
lighting should be designed to create an inviting, homelike atmosphere (Rea, 2000).
Therefore, the visual aesthetic of lighting temperature is very important to the hotel
industry.
Although few hospitality studies have investigated lighting temperature, many
researchers in other fields has examine such topics. Studies of the psychological effects
of lighting mostly focused on lighting preference and visual perception. Some studies
22
have employed the S-O-R paradigm to examine the impacts of lighting on customers’
behaviors in the retail industry (Areni & Kim, 1994; Park & Farr, 2007; Summers &
Hebert, 2001). Many of the studies focus on the quantity of light (Areni & Kim, 1994;
Baker, Parasuraman, Grewal, & Voss, 2002; Summers & Hebert, 2001) or the lighting
temperature (Kenz & Kers, 2000; Park & Farr, 2007; Park, Pae, & Meneely, 2010).
Research regarding the influence of lighting temperature on visual perception has
found that individuals’ impressions of an environment can be changed by the
characteristics of lighting (Flynn & Spencer, 1977; Flynn, Spencer, Martyniuk, &
Hendrick, 1973; Hendrick, Martyniuk, Spencer, & Flynn, 1977). Flynn (1976)
investigated several lighting variables including the light temperature (warm versus cool)
and found that the impression of relaxation versus tension as well as the impression of
pleasantness were cued or modified by lighting design, which aligned with the S-O-R
paradigm. More specifically, cool light (4100K) strengthens the impressions of visual
clarity, while warm light (3000K) strengthens the impression of pleasantness, particularly
when a feeling of relaxation is desirable (Gordon & Nuckolls, 1995).
Park and Farr (2007) conducted a 2 (color rendering index1 of 79 versus color
rendering index of 95) x 2 (CCT of 3000 K versus CCT of 5000K) x 2 (Caucasian-
Americans versus South Koreans) within-subject experiment to test effect of color
rendering index (CRI) and correlated color temperature (CCT) on emotional states and
approach-avoidance intentions in the retailing setting. The emotional states were pleasure
and arousal, and the behavioral intentions were approach and avoidance intentions. The
1 Color rendering refers to “how colors appear under a given light source”, and Color Rendering Index (CRI) measures “the color rendering ability of a light source” (Gordon, 2003, p.45).
23
results showed that warm light (3000K) was more pleasurable and more preferred, while
cool light (5000K) was more arousing, provided more visual clarity, and generated more
approach intentions. In addition, cultural background showed some significant effects.
American participants preferred warm (3000K) light whereas Korean participants
preferred cool (5000K) light.
In addition, lighting was also found to influence task performance in three ways:
First, it can improve the visibility for doing the task (the visual system); second, it can
change the mood of people and motivate them (the perceptual system); third, it can
increase alertness (the circadian system) (Boyce, 2003). Baron (1990) compared cool
white, warm white, natural white, and fluorescent lamps, and found that the participants
exposed to warm white light showed greater risk-taking willingness than participants
exposed to any other lamps. The explanation is that warm white light increased arousal as
well as positive affect, therefore increasing participants’ willingness to take risks. As for
lighting temperature of hotel guestrooms specifically, Park et al. (2010) conducted an
experimental study on the influence of understand guestroom lighting temperature. Based
on the previous findings in lighting combined with the S-O-R paradigm, Park et al.
(2010) designed a 2 (cool, warm) × 2 (bright, dim) between-subject experiment using
pictures of a virtual guestroom to examine the interactions among lighting temperature,
lighting intensity, and culture background (Americans and South Koreans). The results
suggested that the type of lighting perceived as the more arousing or pleasure depends on
participants’ cultural background. Participants from North America perceived dim
lighting as more arousing, whereas participants from South Korea perceived bright
lighting as more arousing. In addition, participants from North America perceived
24
warm/dim lighting as more pleasure while participants from South Korea perceived
warm/bright lighting as more pleasure (Park et al., 2010), which is likely due to the
common usage of bright light in Korean homes (Lee, 2011).
As the extended S-O-R paradigm (Fiore & Kim, 2007) indicates environmental
stimuli (S) influences organism (O) that includes cognition and value, thus lighting
temperature is expected to influence perceived servicescape and perceived value. The
current study was conducted in the United States, as such most of the participants were
expected to be Americans. As warm light was perceived as more pleasant than cool light
among American participants (Park & Farr, 2007), and perceived servicescape is
positively associated with pleasure (Lin & Mattila, 2010), thus warm light is expected to
generate a higher level of perceived servicescape than cool light.
Park and Farr (2007) also indicated that warm light created a higher approach
intention than cool light. Approach intention was considered as a type of behavioral
intentions (Park & Farr, 2007), and behavioral intentions are positively influenced by
perceived value (Donovan, Rossiter, Marcoolyn, & Nesdale, 1994; Liu & Jang, 2009).
Given that warm light generated a higher level approach intention than cool light, it is
reasonable to expect that warm light would also generate a higher level of perceived
value than cool light. Based on the discussion above, the following hypotheses are prosed:
Hypothesis 3: Perceive servicescape is more positive under warm lighting
conditions than under cool lighting conditions.
Hypothesis 4: Perceived value is more positive under warm lighting conditions
than under cool lighting conditions.
25
The Interaction Effect of Lighting and Complexity
Gifford (1988) examined the effect of lighting and room décor style on
interpersonal communication. The participants were paired with friends and were asked
to write two letters to one another in bright versus soft lighting and office-like versus
home-like décor. The results showed that the participants spent more time to write longer
letters and made more intimate communication with bright light and home-like
decoration conditions. Thus, it was concluded that soft lighting lowers an individual’s
arousal level. However, to the author’s knowledge, no study has examined the interaction
effect between lighting temperature and the level of complexity.
Servicescape
From Atmospherics to Servicescape: An Overview
A hotel’s servicescape plays an important role in forming customers’ impression
(Bitner, 1992). However, before the 1970s, servicescape was neglected by business
owners, mainly due to its silent nature and the functional thinking of owners (Kotler,
1973). Kotler (1973) introduced atmospherics to the retailing industry. From this work
the definition of atmospherics developed for application in retail was “the effort to design
buying environments to produce specific emotional effects in the buyer that enhance his
purchase probability” (Kotler, 1973, p. 50). It was further suggested that atmosphere
might serve as an attention-creating, message-creating, and affect-creating medium
(Kotler, 1973).
26
By the early 1980s, managers and researchers in the retailing industry started
recognizing the importance of store atmospherics in influencing customers’ quality
perceptions and purchase decision (Baker et al., 1994). Booms and Bitner (1981)
indicated that the marketing mix of services contained four traditional elements and three
new elements. The four traditional elements were product, price, place, and promotion,
and the three new elements were physical evidence, participants, and process. Physical
evidence refers to the physical surroundings. Participants are all humans within the
service encounters, including employees and customers. Process refers to the procedures
and flow of activities.
Later, Booms and Bitner (1982) further discussed the managerial implications of
atmospherics. The authors recommended the usage of atmospherics as a marketing tool,
which could be particularly effective for service providers. In addition, atmospherics
plays the same role for services as packaging does for tangible products. The reason is
that atmospherics constitutes the “package” that helps customers in knowing what the
service is and what the service provider can do. Finally, it was also indicated that
atmospherics would inspire customers’ approach or avoidance behaviors, which linked
atmospherics to the S-O-R paradigm.
While Booms and Bitner (1982) discussed the importance of atmospherics from a
managers’ perspective, Donovan and Rossiter (1982) tested the S-O-R paradigm in the
retail industry to understand how atmospherics influences consumers. The results
indicated that pleasure and arousal were significant predictors of consumers’ approach
and avoidance behaviors, while dominance was not.
27
Similar to Donovan and Rossiter (1982), Baker (1986) also studied the effects of
atmospherics from consumers’ perspective, because many studies at that time approached
the topic from marketers’ perspectives and the role of atmpherics was not clear (Baker,
1986). Base on the S-O-R paradigm and the results of Donovan and Rossiter (1982), the
author proposed several propositions regarding the effects of the environment on
consumers, such as the probability of avoidance and approach behaviors, time spend in a
service facility, and the importance of atmospherics for consumers under different
circumstances.
Bitner (1990) took a step further by discussing the relationship among
atmospherics, service quality, and satisfaction. A 3 (internal explanation vs. external
explanation vs. no explanation) x 2 (offer vs. no offer) x 2 (organized environment vs.
disorganized environment) between-subject experiment was conducted. Pictures of
organized or disorganized desks of a travel agent and description of a service failure
incident were given to the participants. The results indicated that atmospherics of a
company can influence how customers perceive the service failure: Participants who saw
the picture of an organized travel agency atmosphere were less likely to expect the failure
to occur again than those who saw a picture of a disorganized agency.
Later, Bitner (1992) started to use the term “servicescape” to describe
atmospherics in service settings. Based on the S-O-R paradigm, a conceptual framework
was presented for a better understanding of the impacts of physical surroundings on
employees and customers. Bitner (1992) also indicated that there was a major lack of
empirical studies or theoretical frameworks regarding the role of atmosphere in
marketing literature. This research gap inspired later studies in developing servicescape
28
frameworks (e.g., Jain & Bagdare, 2011; Lee & Jeong, 2012; Ward, Davies, & Kooijman,
2009).
The Dimensions of Servicescape
Many studies in the 1980s focused on store atmosphere as a general concept
(Baker et al., 1994). One of the early attempts of breaking down atmosphere was done by
Baker (1986), which broke down the general concept of environment into three basic
components: Ambient factors, design factors, and social factors. Ambience factors are
“background conditions that exist below the level of our immediate awareness”, design
factors are “stimuli that exist at the forefront of our awareness”, and social factors are
“people in the environment” (p. 80).
After Baker (1986), studies began to discuss the dimensions of servicescape.
Bitner (1992) considered ambient as one dimension but did not include social factors.
Baker et al. (1994) followed the three main dimensions suggested by Baker (1986),
selected some elements from each of the main dimension, and conducted an empirical
study using video tape of a real store. They found ambient factor influenced merchandise
quality and service quality; social factors influenced merchandise quality. Merchandise
quality and service quality further influenced store image.
The studies above are conceptual or focused on the retailing industry. During the
1990s researchers began to examine servicescape in leisure and hospitality settings.
Wakefield and Blodgett conducted studies in servicescape of leisure settings (Wakefield
& Blodgett, 1996, 1999). Wakefield and Blodgett (1994) focused on the tangible aspects
of servicescape in sport complexes and did not examine ambience, while Wakefield and
29
Blodgett (1999) included ambience as a dimension of servicescape. The research of
Wakefield and Blodgett provided valuable information for supplementary studies (e.g.,
Hwang & Ok, 2013; Kim & Moon, 2009; Ryu & Han, 2009) in the hospitality setting.
Turley and Milliman (2000) reviewed the previous literature and proposed a
conceptual framework that categorized findings of previous works. Similar to Bitner
(1992), Turley and Milliman (2000) also discussed organism and responses of both
customers and employees.
Lucas (2003) was the first paper that investigated casino servicescape. The items
used to measure navigation, seating comfort, and interior décor were adapted from
(Wakefield & Blodgett, 1996). Some special characteristics of a casino, such as coin
sound and machine sound were also considered. The relationship among satisfaction with
servicescape, overall satisfaction, and behavioral intentions were examined.
In addition to casinos, hotels and restaurants also have special characteristics that
some dimensions of retail store servicescape might not be applicable. Therefore,
Countryman and Jang (2006) developed a measurement tailored to hotel lobbies, Ryu and
Jang (2008) developed “DINESCAPE” designated to restaurants. Jang & Namkung
(2009) also studies restaurant servicescape but each dimension was measured with one
item. Lam et al. (2011) followed the casino servicescape dimensions of Lucas (2003),
however focused on cognitive satisfaction and affective satisfaction. The study of Ariffin,
Nameghi, and Zakaria (2013) treated servicescape as a moderator with four dimensions
and found the more attractive the servicescape, the stronger the effect of hotel hospitality
on guests’ satisfaction. These studies provided major contributions for investigating
servicescape in the hospitality and tourism industry. In particular, the measurement
30
developed by Countryman and Jang (2006) offered a methodological foundation for the
current study in hotel servicescape.
Different from most of the servicescape literature that are conceptual or empirical,
Ballantine, Jack, and Parsons (2010) conducted qualitative research and asked retail store
customers what they thought about of store servicescape. Based on the responses, the
authors classified environmental stimuli into two categories: Attractive and facilitating.
Attractive stimuli attracted attention and approach behaviors, whereas facilitating stimuli
were those that are needed to complete product engagement. Some stimuli can be both
facilitating and attractive.
An overview of servicescape dimensions is presented in Table 1.
Table 1. Dimensions of Servicescape/Atmospherics
Authors Term used Dimensions Type Topic
Kotler (1973)
Atmospherics
1. Visual Dimensions: color, brightness, size, shapes
2. Aural dimensions: volume, pitch
Olfactory dimensions: Scent, Freshness
3. Tactile dimensions: softness, smoothness, temperature
Conceptual
Atmospherics in general
31
Table 1. (continued)
Baker (1986)
Atmospherics 1. Ambient factors: air quality (temperature, humidity, circulation/ventilation), noise (level, pitch), scent, cleanliness
2. Design factors: (1). Aesthetic: architecture, color, scale, materials,
texture, pattern, shape, style, accessories
(2). Functional: layout, comfort, signage
3. Social factors: (1). Audience, number, appearance, behavior
(2). Service personnel: number, appearance, behavior
Conceptual
Atmospherics in general
Bitner (1992)
Servicescape 1. Ambient conditions: temperature, air quality, noise, music, odor, etc. 2. Space and function: layout, equipment, furnishings, etc.
3. Sign, symbol and artifacts: signage, personal artifacts, style of décor, etc.
Conceptual Servicescape in general
32
Table 1. (continued)
Baker et al. (1994)
Atmospherics 1. Ambient factors: music, lighting, smell
2. Design factors: floor covering, wall covering, displays/fixtures,
color, cleanliness, ceilings, dressing room size, aisles width, signs
3. Social factors: nicely/sloppily dressed, cooperative/uncooperative
Empirical Retail store
Wakefield and Blodgett (1996)
Servicescape 1. Layout accessibility
2. Facility aesthetics 3. Seating comfort
4. Electronic equipment/displays
5. Facility cleanliness
Empirical Five major college football stadiums, two minor league baseball games, three casinos
Wakefield and Blodgett (1999)
Tangible service factors
1. Building design & decor 2. Equipment
3. Ambience
Empirical Professional hockey games, a family recreation, center, and movie theaters
33
Table 1. (continued)
Turley and Milliman (2000)
Atmospherics
1. External variables 2. General interior variables 3. Layout and design variables 4. Point of purchase and decoration variables
5. Human variables
Conceptual
Atmospherics in general
Lucas (2003)
Servicescape
1. Ambience
2. Navigation 3. Seating comfort
4. Interior décor 5. Cleanliness
Empirical
Casino
Countryman and Jang (2006)
Servicescape
1. Architectural style 2. Layout
3. Colors 4. Lighting
Empirical
Hotel
Ryu and Jang (2008)
Dinescape 1. Facility aesthetics
2. Ambience 3. Lighting
4. Table settings 5. Layout
6. Service staff
Empirical
Restaurant
Jang and Namkung (2009)
Atmospherics
1. Layout
2. Interior design 3. Lighting
4. Background music
Empirical
Upscale restaurant
34
Table 1. (continued)
Ballantine et al. (2010)
Servicescape 1. Attractive stimuli: Sound, space, color, layout, design features
2. Facilitating stimuli: Crowding, Employees
Lighting and product display features an be both attractive and facilitating stimuli
Empirical Retail store
Lam et al. (2011)
Servicescape
1. Ambience 2. Navigation
3. Seating comfort 4. Interior décor
5. Cleanliness
Empirical
Casino
Ariffin et al. (2013)
Servicescape 1. Facility aesthetics 2. Lighting
3. Ambience 4. Layout
Empirical
Hotel
Recent Servicescape Research in Hospitality
Recently, hospitality researchers have looked at the effects of servicescape in
restaurants (e.g., Ha & Jang, 2012; Kim & Moon, 2009; Ryu & Jang, 2008; Ryu & Jang,
2007), festivals (Taylor & Shanka, 2002), casinos (Lam et al., 2011), and convention
centers (Nelson, 2009; Siu, Wan, & Dong, 2012). However, the effects of guestroom
servicescape have not been widely examined, although some recent studies have
discussed servicescape of other areas in the lodging industry (e.g., Ariffin et al., 2013;
Heide, Lærdal, & Grønhaug, 2007; Hilliard & Baloglu, 2008; Lucas, 2003; Naqshbandi
35
& Munir, 2011; Simpeh, Simpeh, Abdul-Nasiru, & Amponsah-Tawiah, 2011). Hilliard
and Baloglu (2008) discussed safety and security components of hotel servicescape from
the perspective of meeting planners. Heide et al. (2007) examined servicescape from the
standpoint of hotel managers and design experts. Ariffin et al. (2013) investigated the
influence of a hotel’s overall servicescape on guests’ satisfaction. Suh, Moon, Han, and
Ham (2014) found that air quality, temperature, music and noise/sound level positively
affected a luxury hotel’s overall image; air quality, odor/aroma, music, noise/sound level,
and overall image also positively affected customer satisfaction. Naqshbandi and Munir
(2011) focused on the relationship between hotel lobby servicescape and the impression
of a hotel lobby. The above studies expand the knowledge in hotel servicescape, however
they are all non-experimental studies, which has the limitation of not providing solid
evidence for causal effects between servicescape dimensions and subsequent responses.
To the knowledge of the author, only one study (Park et al., 2010) was experimental,
however it investigated how guestroom servicescape influences pleasure and arousal and
did not examine other variables.
Perceived Value
Although the theory of perceived value can be traced back to the late 1970s (Al-
Sabbahy, Ekinci, & Riley, 2004), empirical approaches are fairly recent (Sweeney,
Soutar, & Johnson, 1997). Hirschman, Holbrook, and Zeithaml approached the concept
of perceived value from different perspectives. The following sections discuss these
theories and ideas.
36
The Concept of Perceived Value
Hirschman and Holbrook’s definition of value
The Information processing model believes that consumers are logical thinkers
who process information rationally, solve problems using information, and purchase the
best choices they can find (Bettman, 1979). This perspective was widely used in the early
studies in consumer behavior. In the late 1970s, researchers started to question that this
perspective as it neglected phenomena including various playful leisure activities,
sensory pleasures, daydreams, esthetic enjoyment, and emotional responses, which are
results of emotionality rather than rationality (Hirschman & Holbrook, 1982).
Based on the integrative model of perception, which suggests that the effects of
product features on consumers’ evaluations are mediated by perceptions (Holbrook,
1981), Hirschman and Holbrook (1982) proposed the experiential perspecitve. They
indicated that not all shopping experiences are rational; some are rather results of
attempts to receive sensory or cognitive stimulation and to satisfy curiosity. Consumption
began to be seen as a process that involves fantasies, feelings, and fun (Hirschman &
Holbrook, 1982). Later, Sheth, Newman, and Gross (1991) introduced the experiential
perspective to perceived value research. The experiential perspective is a popular
perspective and has been utilized by many studies in perceived value (e.g., De Ruyter,
Wetzels, & Bloemer, 1998; De Ruyter, Wetzels, Lemmink, & Mattson, 1997; Sánchez,
Callarisa, Rodríguez, & Moliner, 2006; Sinha & DeSarbo, 1998; Sweeney & Soutar,
2001; Woodruff, 1997).
To further explore perceived value from the experiential perspective, Sheth et al.
(1991) suggested that perceived value has five dimensions: Functional value, conditional
37
value, epistemic value, social value, and emotional value. Function value is “the
perceived utility acquired from an alternative’s capacity for functional, utilitarian, or
physical performance. An alternative acquires functional value through the possession of
salient functional, utilitarian, or physical attributes” (Sheth et al., 1991, p.160). In other
words, functional value refers to the rational and economic value perceived by consumers
(Sánchez et al., 2006). Conditional value is “the perceived utility acquired as the result of
the specific situation or set of circumstances facing the choice maker” (Sheth et al., 1991,
p.162). Epistemic value is “the perceived utility acquired from an alternative’s capacity to
arouse curiosity, provide novelty, and/or satisfy a desire for knowledge” (Sheth et al.,
1991, p.162). Social value is defined as “the utility derived from the product’s ability to
enhance social self-concept” (Sweeney & Soutar, 2001, p. 211), relating to the social
impact of a person’s purchase (Sánchez et al., 2006). Finally, emotional value is “the
utility derived from the feelings or affective states that a product generates” (Sweeney &
Soutar, 2001, p. 211), it relates to a person’s internal emotions or feelings (Sánchez et al.,
2006). Sheth et al. (1991) also indicated three fundamental propositions: A consumer
decision is a function of multiple types of consumption value, the types of consumption
value contribute differently in any given decision, and the types of consumption value are
independent. Sheth et al. (1991) influenced many later studies in perceived value (e.g.,
De Ruyter et al., 1997; Grönroos, 1997; Sweeney & Soutar, 2001).
Acquisition value and transaction value
Based on the study of Zeithaml (1988), Grewal, Monroe, and Krishnan (1998)
expanded the realm of perceived value by proposing acquisition value and transaction
38
value. Acquisition value is similar to perceived value defined by Zeithaml (1988).
Transaction value refers to the psychological enjoyment achieved by taking advantage of
discounts or deals (Lichtenstein, Netemeyer, & Burton 1990; Monroe & Chapman 1987;
Thaler 1985; Urbany and Bearden 1989). Later, acquisition value and transaction value
were empirically proved to be two valid dimensions of perceived value (Petrick &
Backman, 2002). This approach was further developed by adding in-use value and
redemption value in a dynamic model (Parasuraman & Grewal, 2002). In-use value refers
to the utility value received from using a product/service, whereas redemption value
refers to the residual benefit obtained when the purchase happens or at the end of the
product’s life or termination of the service (Parasuraman & Grewal, 2002). “Dynamic”
refers to the importance of the four dimensions vary during the product/services life. For
example, acquisition and transaction value are more important when making the
purchase, however in-use value and redemption value are more important after making
the purchase (Parasuraman & Grewal, 2002).
Although the transaction and acquisition value approach is recognized by some
researhcers, some evidence suggests that it may have disadvantages. Al-Sabbahy, Ekinci,
and Riley (2004) adapted the scale of Grewal et al. (1998) in the hotel and restaurant
settings. However, the validity result indicated that only perceived acquisition value is a
valid dimension of perceived value, possibly due to the non-exclusiveness between the
two dimensions.
39
Determinants of Perceived Value
Perceived costs and perceived benefits
According to the Zeithaml’s (1988) definition of perceived value, perceived value
refers to the trade-off between perceived benefits and perceived costs, thus these two
constructs are important determinants of perceived value (Al-Sabbahy et al., 2004;
Monroe, 1991; Zeithaml, 1988). Perceived benefits are what customers can get from a
service provider (Jen & Hu, 2003; Lapierre, Filiatrault, & Chebat, 1999). Perceived costs
contain two elements, which are monetary price and nonmonetary price (Choi, Cho, Lee,
Lee, & Kim, 2004; Wang, Lo, Chi, & Yang, 2004; Zeithaml, 1988). Monetary price
refers to the actual amount of money consumers have to pay for a purchase, while non-
monetary price refers to the time, energy and psychological costs paid for a purchase (Jen
& Hu, 2003). The relation between perceived benefits and perceived value is positive,
whereas the relation between perceived costs and perceived value is negative. Therefore,
increasing perceived benefits and/or reducing perceived costs could improve customers’
perceived value (Jen, Tu, & Lu, 2011).
Measurements of Perceived Value
Unidimensional measurements
Perceived value can be analyzed unidimentionally or multi-dimensionally (Chen,
2008). In some early empirical studies, perceived value was measured by a self-reported,
unidimensional item, which asked participants to evaluate the value of their purchase
(Gale, 1994). The unidimensional measure has been criticized for lack of validity and
consumers might define value differently (Anuwichanont & Mechinda, 2009; Chen,
40
2008; Woodruff & Gardial, 1996). In addition, some researchers argued that perceived
value is a multi-dimensional construct, therefore multidimensional measures would be
more appropriate (Sweeney & Soutar, 2001). Furthermore, multidimensional
measurements allow marketers to compare the relative importance of each dimension and
to identify how well each of the dimensions works in improving perceived value (Petrick,
2004a; Woodruff, 1997). The following sections discuss the various multidimensional
measurements of perceived value.
Acquisition value and transaction value measurements
Given the definitions of acquisition value and transaction value, Grewal et al.
(1998) proposed and tested these two dimensions in a tangible product settings (bicycles)
by developing a twelve-item-measurement (nine items measuring acquisition value and
three measuring transaction value) with a seven-point Likert scale. The results showed
that acquisition value is positively impacted by the product benefits, and is negatively
impacted by the money paid to receive the product. Perceived transaction value, the
“expected” or “fair” price (Lichtenstein, Bloch, & Black, 1988), has a positive influence
on perceived acquisition value (Grewal et al., 1998). The two dimensions were later
verified by Petrick and Backman (2002) in the golf industry. The results proved the
reliability and validity of Grewal et al. (1998) scale and showed that although both
acquisition value and transaction value positively influence perceived value, transaction
value is a stronger indicator of perceived value.
Similar to the study of Petrick and Backman (2002), Al-Sabbahy et al. (2004) also
applied the measurement proposed by Grewal et al. (1998) but to the hospitality industry
41
(hotels and restaurants). They kept all of the twelve items and the seven-point Likert
scale, but changed the items from present to past tense to include post-consumption
evaluation and reworded some of the items to be applicable to the hospitality industry.
However, the empirical results showed that validity of the transaction value was too low,
thus only perceived acquisition value was considered as a valid construct in assessing
perceived value of hospitality services. Al-Sabbyhy et al. (2004) indicated that such a
result was likely caused by the non-exclusiveness and confusion between acquisition
value and transaction value. Thus, there is no agreement on whether these two
dimensions would both hold true in the hospitality industry.
Experiential measurements
Based on the study of Sheth et al. (1991), Sweeney and Soutar (2001) developed
PERVAL (perceived value) scale which contains 19 items. The items were grouped into
four dimensions: Emotional value, social value, and two functional values. Functional
value I referred to price/value for money, and functional value II referred to result of the
performance/ perceived quality. The authors successfully proved the reliability and
validity of the scale in product setting in both pre and post consumption stages. The
difference between Sheth et al. (1991) and Sweeney and Soutar (2001) is that Sheth et al.
(1991) suggested the value dimensions are independent, while Sweeney and Soutar
(2001) believed the value dimensions are interrelated.
The PERVAL scale is an major step forward in measuring perceived value
(Sánchez et al., 2006). Following the structure of the study of PERVAL, Sánchez et al.
42
(2006) proposed GLOVAL (global purchase perceived value) scale for tourism packages.
It is constructed by 16 functional items, 16 emotional items, and eight social items.
Combined approach
More recently, some researchers combined more than one approach mentioned
above to pursue a better coverage of the concept. For instance, Petrick (2002) developed
SERV-PERVAL (perceived value of a service) scale for the service setting and tested the
validity in fast food and cruise industry by combining the experiential and the traditional
approaches. The author reported that perceived value is composed of five dimensions:
Service quality, emotional response, monetary price, behavioral price (nonmonetary
price), and reputation. Similarly, Anuwichanont and Mechinda (2009) measured the same
dimensions in the spa industry. Ashton, Scott, Solnet, and Breakey (2010) measured
perceived brand image, perceived quality, and perceived sacrifice including monetary and
nonmonetary sacrifices, as dimensions of perceived value.
The Linkage between Servicescape and Perceived Value
Among the measurements mentioned previously, the experiential measurements
(Sheth et al., 1991; Sweeney & Soutar, 2001) and the combined approach (Petrick, 2002)
tend to be very detailed and multidimensional, and the validity of transaction value in the
hospitality industry is uncertain (Al-Sabbyhy et al., 2004). Thus, the author decides to
adapt Zeithaml’s definition that perceived value, which is “customer’s overall assessment
of the utility of a product based on perceptions of what is received [perceived benefits]
and what is given [perceived sacrifices]” (Zeithaml, 1988, p.14). Zeithaml’s definition is
43
relatively simple and general. It measures the overall perceived value without
differentiate any sub-dimensions and is easy for participants to answer.
Given the definition of overall perceived value adapted in this study, it is believed
that servicescape can influence overall perceived value. An environment influences
perceptions of perceived value (Donovan et al., 1994), because extrinsic attributes (an
environment) could serve as value signals for consumers when they are evaluating
perceived benefits and costs (Zeithaml, 1988). Thus, a favorable impression of a physical
setting may transfer to a positive perception of value (Mehrabian & Russell, 1974). Such
rationale has been supported by a study in restaurant servicescape, which found that the
perceived servicescape of the restaurant enhanced customers’ overall perceived value
(Liu & Jang, 2009). Thus, it is expected that a good servicescape would improve overall
perceived value. Therefore, the following hypotheses are proposed:
Hypothesis 5: There is a positive relationship between perceived servicescape and
perceived value.
Behavioral Intentions
Conceptualization of Behavioral Intentions
Customers stay with a company because they are pleased with the company’s
service and are more likely to purchase additional services and spread positive word-of-
mouth (Zeithaml, Berry, & Parasuraman, 1996). However, service failures lead to various
financial impacts. When customers leave a company, new customers must be attracted
but they come at a high cost as it involves advertising, promotion, and sales costs. In
44
addition, new customers do not generation much profit for a period of time (Zeithaml et
al., 1996).
As actual behaviors may be difficult to track, behavioral intentions have been
used as an indicator of actual behavior (Fishbein & Ajzen, 1975). The concept of
behavioral intentions is defined as “the degree to which a person has formulated
conscious plans to perform or not perform some specified future behavior” (Warshaw &
Davis, 1985, p. 214). Most early studies operationalize behavioral intentions as a
unidimensional construct (Zeithaml et al. 1996) and measured by a single item asking
purchase intention (Cronin & Taylor, 1992). Zeithaml et al. (1996) summarized that there
are favorable and unfavorable behavioral intentions. Favorable behavioral intentions
included intentions to spread positive word-of-mouth (Boulding, Kalra, Staelin, &
Zeithaml, 1993), recommend to others (Parasuraman, Berry, & Zeithaml, 1991;
Parasuraman, Zeithaml, and Berry, 1988; Rechinhheld & Sasser, 1990), paying a
premium price (LaBarbera & Mazursky, 1983), and remaining loyalty (Newman &
Werbel, 1973; Rust & Zahorik, 1993). Unfavorable behaiovral intentions included
intentions to complain (Maute & Forrester, 1993; Singh, 1988; Solnick & Hemenway,
1992), to switch to competiors, and a decrease in amount of business (Zeithaml et al.,
1996). The current study focuses on two dimension of behavioral intentions: Intention to
spread positive word-of-mouth and intention to revisit.
The Linkage between Servicescape and Behavioral Intentions
Human behavior is influenced by the servicescape in which it occurs. Many
managers utilize servicescape, for instance, cinnamon roll bakeries use the fragrance of
45
freshly backed rolls to entice customers into the store (Bitner, 1992). In academia,
servicescape influences consumer behavior has been widely accepted (Turley &
Milliman, 2000). Many studies (e.g. Bitner, 1992; Countryman & Jang, 2006; Donovan et
al., 1994; Jang & Namkung, 2009; Mattila & Wirtz, 2001) reported servicescape as a
determinant of behavioral intentions, such as word-of-mouth or repurchase intention. For
instance, Donovan and Rossiter (1982) tested the S-O-R paradigm on consumers and
found that servicescape influences their likelihood of returning to a store. Spies et al.
(1997) reported that store servicescape positively influences customers’ intention to
revisit. Wakefield and Blodgett (1996) found that leisure service providers’ servicescape
influences customers’ intention to repatronize.
Many studies in restaurant and hotel research also reported similar findings. Ryu
and Jang (2008) found that restaurant servicescape directly influenced behavioral
intentions. Wardono, Hibino, and Koyama (2012) conducted a 2 (monochromatic and
complementary colors) x 2 levels of lighting quality (bright and dim lighting) x 2 levels
of décor qualities (elaborate and plain decors) experiment using pictures of a virtual
restaurant. A total of eight treatment conditions were included. In their study, the
behavioral intentions variable was measured using three items including “want to revisit
several times, to linger long, and do not mind to wait”. The ANOVA results indicated
that of the behavioral intentions of the group with monochromatic colors, dim lighting
and plain décor was significantly higher than four out of eight treatment conditions1.
1 The four conditions were: (1) monochromatic colors, bright lighting, and elaborate décor, (2) complementary colors, bright lighting, and elaborate décor, (3) complementary colors, dim lighting and elaborate décor, and (4) complementary colors, bright lighting, and plain décor.
46
In the hotel industry, hotel design was one of the most significant factors driving
hotel purchase decision (Dubé & Renaghan, 2000). Simpeh et al. (2011) examined the
relationships between patronage intentions and the three dimensions of hotel servicescape
adapted from Bitner (1992): Ambient conditions, spatial layout and functionality, and
signs, symbols, and artifacts. All of the three dimensios were positively correlated with
customers’ patronage.
The support of the influence of each servicescape dimension on intention to
revisit can be found in previous studies as well. Interior décor of casinos, which included
perceptions of background colors, enhanced affective satisfaction, and affective
satisfaction further enhanced intention to revisit (Lam et al., 2011). Thus it is possible
that perception of hotel room colors also enhances intention to revisit a hotel. Ryu and
Han (2009) indicated that the attractiveness of interior design and décor, lighting, and
color are highly important to satisfaction of quick service restaurant guests, because their
satisfaction is positively linked to behavioral intentions. Therefore, it is expected that
servicescape dimensions influence behavioral intentions positively.
Hypothesis 6: There is a positive relationship between perceived servicescape
and intention to spread positive word-of-mouth.
Hypothesis 7: There is a positive relationship between perceived servicescape and
intention to revisit.
The Linkage between Perceived Value and Behavioral Intentions
Many studies that followed the definition of perceived value proposed by
Zeithaml (1988) have reported the positive relationship between perceived value and
47
behavioral intentions (Chen, 2008; Cronin et al., 2000; Jen et al. 2011; Ryu, Han, & Kim,
2008). When individuals believe what they have received from a product or service
exceeds what they have paid, the possibility that they would like to repeat the exchange
transaction in the future would increase (Liu & Jang, 2009), and they may also like to
recommend it to others so that others can benefit as well. Cronin et al. (2000) examined
the influences of perceived value on customers’ behavioral intentions including intention
to spread positive word-of-mouth. The results indicated that perceived value directly
influenced behavioral intentions. Chen (2008) investigated perceived value’s relationship
with behavioral intentions among airline passengers. Behavioral intentions included
repurchase intention and recommendation intentions. The results suggested that perceived
value positively influenced behavioral intentions. Ryu et al. (2008) examined perceived
value and behavior intentions in the restaurant setting. In their study, the definition of
perceived value was similar to Zeithaml (1988), and behavioral intentions were measured
with intention to revisit and intention to recommend. The results also suggested that
perceived value positively influenced behavioral intentions. Liu and Jang (2009)
examined the relationship between perceived value and behavioral intentions in a
Chinese restaurant. Behavioral intentions included repeat purchase, recommendation, and
spreading positive word-of-mouth. They found a positive relationship between perceived
value and behavioral intentions as well. Similarly, Jen et al. (2011) found that perceived
value was a predictor of both intention to spread positive word-of-mouth and intention to
re-use a service. Based on these findings, the following hypotheses are proposed:
Hypothesis 8: There is a positive relationship between perceived value and
intention to spread positive word-of-mouth.
48
Hypothesis 9: There is a positive relationship between perceived value positively
and intention to revisit.
Based on previous research, the following hypothesis were proposed:
Figure 2. Theoretical Model
Note. H1 and H2 propose inverted U-shape relationships. H3 and H4 propose that warm light generates a higher perceived servicescape and perceived value than cool light. H5 to H9 propose positive linear relationships.
Chapter Summary
This chapter reviews previous studies that support the development of the
proposed hypotheses. More specifically, this chapter first discusses the existing findings
regarding the effects of intended complexity and intended lighting temperature. The
second section examines the concept of servicescape, including the concept development,
its dimensions, and research in hospitality servicescape. The third section discusses the
concept of perceived value, the determinants, measurements, and its connection with
servicescape. The final section reviews the concept of behavioral intentions and its
relationships with servicescape and perceived value. The next chapter discusses the
methodology of the current study.
Intended Complexity
Intended Lighting Temperature
Perceived Servicescape
Perceived Value
Intention to Revisit
Positive WOM
H1
H2
H3
H4
H5
H6
H7
H8
H9
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CHAPTER 3
METHODS
Introduction
The current study will focus on two servicescape elements, lighting temperature
and level of complexity, as the effects of lighting temperature have not been widely
discussed in hospitality research and the effects of complexity in three dimensional
spaces have not been examine to the knowledge of the author. Images of a virtual
guestroom will be used in the experiment.
The reason of choosing guestroom servicescape is that guestrooms leave a more
lasting impression on guests than any other hotel space, such as the hotel lobby,
restaurants, or service space (Rutes et al., 2001). In addition, guestroom design was
found to be a critical attributes driving the hotel-booking decision (Dubé & Renaghan,
2000).
The reason of proposing an experimental design is that causal inference is
problematic when experiments are not used as many threats to internal validity remain
unexamined (Campbell & Boruch, 1975). With experimental design, differences in
endogenous variables would be caused by the treatments. Therefore, the combination of
guestroom servicescape and experimental design is expected to lead to valuable findings.
The purpose of the current study is to examine the effects of the level of
complexity and lighting temperature on guests’ perceptions and their behavioral
intentions. It will be accomplished through two objectives:
50
1. To compare the differences in means of perceived servicescape, perceived
value, and behavioral intentions among all the treatment groups
2. To test the strength of the associations among the two environmental stimuli
or servicescape elements (lighting temperature and level of complexity),
perceived servicescape, perceived value, and behavioral intentions.
The proposed methodology is presented in the following sections. The first
section introduces the sampling and data collection methods. The second section focuses
on questionnaire development. The third section discusses the definitions of the variables
and measurement. The fourth section describes the statistical analysis procedures.
Treatment Conditions
A 3 (low complexity, medium complexity, high complexity) × 2 (cool lighting,
warm lighting) between-subject experiment will be conducted. Participants were enrolled
via Amazon Mechanical Turk. Each treatment condition contained only one picture, and
one treatment will be randomly assigned to each participant. The treatment conditions are
illustrated in Table 2. The lighting temperature and the level of complexity were the only
two elements that varied across the images.
Table 2. Experimental Conditions
Level of complexity
Low Medium High
Lighting
Temperature
Cool Treatment 11 Treatment 12 Treatment 13
Warm Treatment 21 Treatment 22 Treatment 23
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Stimuli Development
A virtual guestroom was established. The floor plan and lighting plan were
adapted from Karlen and Benya (2004) (Figure 3). Only the living area was included in
the floor plan; bathroom, entry area, and closet were not be included. The segmentation
of the virtual hotel guestroom was three-diamond (upper mid-scale), which was
determined based on a pilot test, which suggests that more than 50% of the participants
preferred to stay at three-diamond hotels (upper mid-scale). Examples of brands that
belongs to this segment are many Marriott and Hilton properties. The room size of the
three-diamond virtual guestroom was determined based upon Rutes et al. (2001), which
suggested that minimum living area dimensions (excluding bathroom, closet, or entry) of
such brands was 15×20 feet. Thus the virtual hotel guestroom was designed to follow
such guideline (Figure 3).
Figure 3. The Design of the Virtual Hotel Room
Note. Adapted from “Lighting Design Basics,” by M. Karlen and J. C. Benya, 2004, Holboken, New Jersey: J. Wiley & Sons, p.111.
52
A company was hired to draw the interior design rendering images. The style of
the virtual hotel room was a home-like style. The reason is that Siguaw and Enz (1999)
suggested that the architectural style impacts the profitability of a hotel. Hotels should
attempt to adopt a “home-like style” in order to provide a relaxing environment for guests.
Some three diamonds Marriott and Hilton hotels as well as the Approval Requirements
and Diamond Guidelines for Lodging by AAA (2012) were used to develop the room
images.
Lighting temperature and the level of complexity were the only two factors that
varied across the six experimental treatments. The design of lighting temperatures (cool
versus warm) followed the methodology of Park et al. (2010): A color temperature of
approximately 3000K was selected as the warm light, a color temperature of
approximately 4200K was selected as the cool light.
The three complexity conditions were: Low complexity, medium complexity, and
high complexity. Based on the three components of complexity (Fiore, 2010), the level of
complexity was manipulated by changing the number of canvases hanging on the wall,
the numbers and the patterns of the pillows on the beds, the pattern of the curtains, and
the pattern of the area rugs. The increase in complexity was achieved by increasing the
number of units and decreasing the cohesion among the units (Fiore, 2010).
In the final data collection, participants were randomized into the six treatment
conditions (two lighting temperatures × three complexity levels) and the images were
shown to the participants. Each participant was instructed to view the image of one
experimental condition. They were asked to imagine that they were staying in this hotel
room when filling out the survey. The room rate was given at the beginning of the survey
53
and was shown again at the beginning of the perceived value section. Because visual
complexity starts to influence viewers evaluations after only 17 milliseconds of exposure
(Tuch et al., 2009), the methodology is believed to be effective.
Questionnaire Development
The survey instrument was developed based on previous research. The survey
contained three main sections. The first section included items measuring the four
endogenous variables: Perceived servicescape, perceived value, intention to revisit and
intention to spread positive word-of-mouth. The second section was the manipulation
check of the independent variables. This section contained questions asking the
participants’ perceptions of light temperature and level of complexity. The first question
measured their perceptions of lighting temperature, which was “I believe the lighting in
this room was”. The options were “warm lighting”, “cool lighting”, and “I am not sure”.
Following the lighting manipulation check was the complexity check, which contained
four questions: “The interior design of the hotel room looked complex”, “The interior
design of the hotel room looked simple”, “The interior design of the hotel room looked
like there was a lot going on”, “The interior design of the hotel room was composed as a
mixture of many patterns”. The last question was adapted from (Lévy, MacRaeb, &
Kösterc, 2006). The third section contained four (4) demographic questions including
gender, age group, ethnicity, and household income.
A pilot test was conducted to ensure that the design of complexity would work as
expected. A total of 14 pictures were created. Based on the study of Fiore (2010), the
indended complexity was changed by adding more pillows, wall canvases, stripes on the
54
carpet and on the curtains as well as decreasing cohension among these items. All other
elements and indices were the same across the pictures (Table 3). The 14 pictures were
coded as warm light 1 to 7 and cool light 1 to 7, where 1 represented the simplest design
and 7 represented the most complex design from the author’s perspective.
Table 3. The Designs of the Seven Complexity Levels Used in the Pilot Test
Wall Canvas Frames
Pillows on Each Bed Curtains Area Rug
1 No frames One white pillow Solid brown Solid brown
2 Two exactly the same frames
One white and one brown
Brown with one horizontal white stripe
Brown with one vertical white stripe
3 Four exactly the same frames
Two white and two brown
Brown with two horizontal white stripes
Brown with two vertical white stripes
4
Six frames, same color, different shapes but similar sizes
Six patterned pillows of the same set
Brown with three horizontal white stripes
Brown with three vertical white stripes
5 Eight frames, random shapes, colors, and sizes
Eight pillows with mismatch random patterns
Brown with four horizontal white stripes
Brown with four diagonal stripes
6 Ten frames, random shapes, colors, and sizes
Twelve pillows with mismatch random patterns
Brown with five horizontal white stripes
Five crossing diagonal stripes
7 Eleven frames, random shapes, colors, and sizes
Twelve pillows with mismatch random patterns
Brown with white grid
Six crossing diagonal pixelated stripes
Undergraduate and graduate students from a large Midwestern University were
invited to participate in the pilot test. A total of 211 students participated in the pilot test.
Confidence intervals were calculated to compare the mean differences of perceived
complexity. Among the 14 pictures tested in the pilot test, three complexity levels,
55
namely warm 1, 4, and 6 and cool light 1, 4, and 6, had significantly different perceived
complexity scores. Therefore theses six pictures were selected in the final data collection.
The six pictures are displayed in Figure 4.
Definitions and Measurement of Variables
All the variables were defined based on previous studies. The definitions and the
foundation for the measurement development for each construct were described in the
following sub-sections. Multiple items were used to measure perceived service quality,
perceived value, and the two dimensions of behavioral intentions (i.e., intentions to
revisit and intention to spread positive word-of-mouth).
Perceived Servicescape
Bitner (1992) started to use the term “servicescape” to refer to atmospherics in
service organizations, and atmospherics as “the effort to design buying environments to
produce specific emotional effects in the buyer that enhance his purchase probability”
(Kotler, 1973, p. 50). As discussed previously, intended servicescape is environmental
stimuli that are built into an environment, whereas perceived servicescape is the
perception of these stimuli which might vary from one person to another (Kotler, 1973).
In this study, the lighting temperatures and the level of complexity in the pictures
were the intended servicescape elements, whereas the questions asking the participants’
perceptions of servicescape in the survey measured perceived servicescape. Thus, the
56
Figure 4. The Six Experimental Conditions of the Virtual Guestroom
Note. From top to bottom: Warm light, cool light; From left to right: Low complexity, medium complexity, and high complexity
56
57
term “perceived servicescape” were be used to differentiate perceived servicescape in the
questionnaire from the intended servicescape in the pictures.
The questions asking perceived servicescape were adapted from the study of
Countryman and Jang (2006). The items were initially developed to measure guests’
perceived lobby servicescape, which has four dimensions: Style, layout, colors, and
lighting. All items were on a 7-point Likert scale (1= Strongly Disagree, 7= Strongly
Agree). Table 4 includes the items that were used in this study to measure perceived
servicescape.
Table 4. Measurement of Perceived Servicescape
Statements for Perceived Servicescape
The style of the hotel room was sophisticated.
The style of the hotel room was artful.
The style of the hotel room was beautiful.
The style of the hotel room was impressive.
The layout of the hotel room was graceful.
The layout of the hotel room was accommodating.
The colors of the hotel room were beautiful.
The colors of the hotel room were soothing.
The colors of the hotel room were pleasant.
The lighting of the hotel room was appropriate.
The lighting of the hotel room was inviting.
The lighting of the hotel room was positive.
58
Perceived Value
Perceive value is defined as “customer’s overall assessment of the utility of a
product based on perceptions of what is received and what is given” (Zeithaml, 1988, p.
14). Three (3) items adapted from Kim, Jin-Sun, Kim (2008), which were used to
measure Zeithaml’s definition of perceived value, were used in the current study These
three items were: (a) The hotel had very good value for money; (b) the price paid for the
hotel room was very acceptable; (c) the hotel appeared to be a bargain. The items were on
a 7-point Likert scale (1= Strongly Disagree, 7= Strongly Agree) (Table 5).
Table 5. Measurement of Perceived Value
Statements for Perceived Value
The hotel had very good value for money.
The price paid for the hotel room was very acceptable.
The hotel appeared to be a bargain.
Behavioral Intentions
The concept of behavioral intentions is defined as “the degree to which a person
has formulated conscious plans to perform or not perform some specified future
behavior” (Warshaw & Davis, 1985, p. 214). Items adapted from previous studies were
used to measure the two dimensions of behavioral intentions: Intention to revisit and
intention to spread positive word-of-mouth.
Intention to revisit refers to a customer’s tendency of repatronage a business (Lam
et al., 2011). Three items adapted from Kim and Moon (2009) were used to measure
intention to revisit. These three items were originally developed by Oliver and Swan
(1989) and Zeithaml et al. (1996). Oliver and Swan (1989) measured the construct
59
“intention” by asking the participants “If they would deal with this same salesperson
again on their next car purchase if he/she were still available”. Item descriptors included
"likely-unlikely," "very probable-not probable," "very possible-impossible," and "certain-
no chance” (Oliver & Swan, 1989, p.29). Zeithaml et al. (1996) used “Consider XYZ
your first choice to buy services” to investigate intention to revisit.
Based on Oliver and Swan (1989) and Zeithaml et al. (1996), Kim and Moon
(2009) used the following three (3) questions: (1) I would like to revisit this restaurant in
the near future, (2) I have a strong intention to bring my family and friends to visit this
restaurant again, and (3) This restaurant would be my first choice over other restaurants.
All items will be on a 7-point Likert scale from 1 (Strongly disagree) to 7 (Strongly
agree). In this study, “restaurant” was changed to “hotel”.
Positive word-of-mouth refers to “any positive communication about a service
firm’s offerings” (Ng, David,Dagger, 2011, p. 133). Intention to spread positive word-of-
mouth refers to a customer’s tendency to give such communication. Three (3) items
adapted from Ng et al. (2011) were used to measure intention to spread positive word-of-
mouth (WOM). The three items were first developed by Zeithaml et al. (1996). Although
they were originally designed to measure the loyalty dimension of behavioral intentions,
supplementary studies have used them to measure positive WOM (e.g. Choi et al., 2004;
Ha & Jang, 2012; Hightower et al., 2002).
The items measuring the two dimensions of behavioral intentions are listed in
Table 6. All the items were on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly
agree).
60
Table 6. Measurement of Behavioral Intentions
Statements for Behavioral Intentions
Statements for Intention to Revisit
I would like to revisit this hotel in the near future.
I have a strong intention to return with my family and friends to stay at this hotel.
This hotel would be my first choice over other hotels.
Statements for Intention to Spread Positive Word-of-Mouth
I will say positive things about this hotel to other people.
I will recommend this hotel to someone who seeks my advice.
I will encourage friends and relatives to do business with this hotel.
Data Analysis Method
Testing hypotheses 1 to 4 was conducted by calculating ANOVA and confidence
intervals of the mean differences of perceived servicescape and perceived value. As an
example, the means of perceived servicescape were denoted as 11, 12, … 23 (Table
7), and all the possible pairs were denoted as 12- 11, 13- 12, 13- 12, etc.
Confidence intervals were calculated for each pair of comparisons using Tukey-Kramer
adjustment. For the demographic information, descriptive statistics were used to examine
frequencies and percentages.
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂ µ̂ µ̂ µ̂
61
Table 7. The Treatment Conditions of the Means of Perceived Servicescape
Level of complexity
Low Medium High
Lighting Temperature
Warm Treatment 11
11
Treatment 12
12
Treatment 13
13
Cool Treatment 21
21
Treatment 22
22
Treatment 23
23
Reliability and Validity
For reliability, Cronbach’s alpha with the cutoff value of 0.70 was used (Nunnally,
1978). Standardized factor loadings were utilized to assessed convergent validity.
Discriminant validity was evaluated by comparing the correlations and reliability
estimates (Gaski, 1984).
Structural Equation Modeling
SPSS Statistics Version 20 was used to prepare data, to examine demographics, to
analyze Exploratory Factor Analysis, and to test hypotheses 1 to 4. Mplus version 6 was
utilized to test Confirmatory Factor Analysis (CFA), to conduct structural equation
modeling (SEM), to examine hypotheses 5 to 9, and to examine other possible models.
Chapter Summary
In conclusion, the purpose of the study is to investigate the effect of lighting
temperature and level of complexity on perceived servicescape, perceived value, and
behavioral intentions. A between-subject experimental design that examines guestroom
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
62
servicescape using picture of a virtual hotel guestroom is proposed. Definitions of the
variables, sampling procedures, measurement development, data collection method, and
plan of data analysis are discussed.
63
CHAPTER 4
ANALYSIS AND RESULTS
Introduction
The purpose of this chapter is to discuss the data analysis procedure and to present
to the results of the current study. This chapter is organized as five sections. The first
section introduces the data collection procedure and the manipulation checks. The second
section discusses the demographics and descriptive statistics of the participants. The next
section describes the results of the reliability, validity, and the factor analysis. The fourth
section discusses the results of the hypothesis testing results involving Structural
Equation Modeling (SEM), Analysis of Variance (ANOVA), and confidence intervals
(CI). The proposed positive relationships of Hypothesis 5 to Hypothesis 9 were first
examined using SEM, followed by the more complicated discussion of testing Hypothesis
1 to Hypothesis 4. The final section concludes the chapter with a brief chapter summary.
Data Collection
A between-subject experiment was conducted for the final data collection. The
survey was distributed via Amazon Mechanical Turk. The estimated time to complete the
survey was five minutes and the participants were paid $0.50 for each approved response.
Because the virtual hotel room was hypothetically located in the Midwest region of the
United States, only people whose location was within the United States were allowed to
take the survey. Each participant was randomly presented with one of the six pictures
selected based on the pilot test results. One attempt was allowed for each participant.
64
The survey was collected on April 9, 2015 and a total of 572 responses were
received. The average time to complete the survey was 5 minutes and 26 seconds, which
yielded an effective hourly rate of $5.52.
Data Analysis
Manipulation Checks
In the final data collection, intended complexity contained three different levels,
(low, medium, and high) and lighting temperature contained two different levels (warm
light versus cool light). To ensure these treatment conditions worked as expected,
perceived complexity and perceived lighting temperature were measured. Four questions
were used to measure perceived complexity: (1) The design of the hotel room looked
complex, (2) The design of the hotel room looked simple (reverse coded), (3) The design
of the hotel room looked like there was a lot going on, and (4) The design of the hotel
room was composed as a mixture of many patterns. Item 4 was adapted from Lévy et al.
(2006), whereas the other three items were developed based upon expert opinion and
validated via a pilot study. Each of the four questions used a 7-point Likert scale (1 =
strongly disagree, 7 = strongly agree). One question was used to measure perceived
lighting temperature, which was “I believed the lighting in the room was”. Three options
were provided: “Warm lighting”, “Cool lighting”, or “I am not sure”.
After reverse coding the second item of perceived complexity, an Exploratory
Factor Analysis (EFA) with Maximum Likelihood (ML) was first performed on the four
perceived complexity items, as most of them were not adapted from previous studies. All
of the four items loaded on the same dimension with loadings ranging from 0.51 to 0.84.
65
After confirming the loadings, an average was taken among all of the perceived
complexity items. To examine the successfulness of intended complexity manipulation, a
one-way ANOVA with the average perceived complexity being the dependent variable
was performed. The F-value was 196.511 (p < 0.001, df = 2). The mean of the averaged
scores (Table 8) showed an increase trend as intended complexity increased from low to
high. In addition, the confidence intervals of the mean differences, which were calculated
with Tukey-Kramer adjustment, all excluded 0 (Table 9). The mean of perceived
complexity under high complexity level was 5.022 out of 7. Although such score was not
as high as 6 or 7, it is still considered as adequate because when designing the complexity
levels, the pictures were designed in a way that they did not appear too complex to be
unrealistic for the hotel industry. Compared to the means of perceived complexity under
low (2.830) and medium complexity (3.825), the high complexity level was believed to
be an effective representation of highly complex hotel guestrooms. Based on these results,
it was concluded that the manipulation of intended complexity was successful.
Table 8. Means of Confidence Intervals of Perceived Complexity
Intended Complexity
Mean Std. Deviation
Std. Error
95% CI of Means
Lower Bound Upper Bound
Low 2.830 0.989 0.073 2.686 2.973
Medium 3.825 1.178 0.086 3.655 3.996
High 5.022 1.073 0.076 4.871 5.172
66
Table 9. Confidence Intervals of the Mean Differences in Perceived Complexity
(I) (J) Mean Difference (I-J)
Std. Error
Sig. 95% CI of Mean Differences
Lower Bound
Upper Bound
Low Medium -0.996 0.112 <.0001 -1.260 -0.731
High -2.192 0.111 <.0001 -2.452 -1.931
Medium High -1.196 0.111 <.0001 -1.456 -0.936
A chi-square test was performed to examine the successfulness of intended
lighting temperature manipulation. The chi-square value was 309.214 (p<0.001, df=2)
and most of the participants’ perceived lighting temperatures were consistent with the
intended lighting temperature shown in the pictures (Table 10). Based on these results, it
was concluded that the manipulation of intended lighting temperature was also successful.
Table 10. The Manipulation Check of Intended Lighting Temperature
Intended Lighting Temperature
Warm Cool Total
Perceived Lighting Temperature
Warm Count 235 20 255
% 79.4% 7.3% 44.7%
Cool Count 47 238 285
% 15.9% 86.5% 49.9%
Not Sure Count 14 17 31
% 4.7% 6.2% 5.4%
Total Count 296 275 571a
% 100% 100% 100.0%
Note. a One participant did not answer the perceived lighting temperature question.
67
In the responses to the perceived lighting temperature question, 31 participants
indicated that they were not sure what the lighting temperature of the room was and one
participant did not answer the perceived lighting temperature question. In addition, 67
participants’ perceived the lighting temperature were different than the intended lighting
temperature presented in the pictures. These 99 responses were removed before
conducting further analyses in order to avoid the results being confounded. Therefore a
total of 473 responses were used in the following analyses. The sample sizes of each
treatment condition after deleting the 99 responses are displayed below (Table 11).
Table 11. Useful Sample Size of Each Treatment Condition
Level of complexity
Low Medium High Total
Lighting Temperature
Warm Treatment 11
n11=75
Treatment 12
n12=79
Treatment 13
n13=81
235
Cool Treatment 21
n21=81
Treatment 22
n22=78
Treatment 23
n23=79
238
Total 156 157 160 473
Demographics
After removing the 99 responses, the data showed that there were more male
participants (58.6%) than female participants. Over 54% of these participants were 30
years old or younger. Approximately 40.6% of the participants were males between 21 to
35 years of age. More than 78% of them were Caucasians, and 40% of them have
received a Bachelor’s degree. Their annual household income ranged from under $30,000
to over $250,000, but mostly (80.6%) were under $90,000. Among these participants,
68
92.4% of them indicated that they stay at hotels at least once per year. A detailed
description of the demographics is displayed in Table 12 and Table 13.
Table 12. Gender and Age
Age Male Female Total
Number % Number % Number %
18-20 18 3.8 10 2.1 28 5.9
21-25 78 16.5 39 8.2 117 24.7
26-30 74 15.4 38 7.7 112 23.1
31-35 40 8.5 31 6.6 71 15.0
36-40 24 5.1 21 4.4 45 9.5
41-45 14 3.0 18 3.8 32 6.8
46-50 8 1.7 9 1.9 17 3.6
51-55 10 2.1 13 2.7 23 4.9
56-60 7 1.5 12 2.5 19 4.0
Over 60 4 0.8 5 1.1 9 1.9
Total 227 58.6 196 41.4 473 100%
Table 13. Ethnicity, Education, and Annual Household Income
Variables Frequency Percentage
Ethnicity
Caucasian 372 78.6%
African American 39 8.2%
Native American 6 1.3%
Hispanic 31 6.6%
Asian/Pacific Islander 51 10.8%
69
Table 13. (continued)
Other 1 0.2%
Total 473 100%
Highest Degree Received
High school / GED 142 30.0%
Associate 84 17.8%
Bachelor’s degree 192 40.6%
Master’s degree 33 7.0%
Professional degree 9 1.9%
Doctorate degree 10 2.1%
Other 1 0.4%
Total 473 100%
Household Income
Under $30,000 139 29.4%
$30,000-$59,999 151 31.9%
$60,000-$89,999 91 19.3%
$90,00-$119,999 38 8.0%
$120,000-149,999 17 3.6%
$150,000-$179,999 7 1.5%
$180,000-$209,999 4 0.8%
$210,000-$239,999 1 0.2%
$240,000 and above 2 0.4%
Would rather not say 4 0.8%
Missing 19 4.0%
Total 473 100%
70
Reliability
The questionnaire instrument’s reliability was examined by Cronbach’s alpha
(Table 14). The Cronbach’s alpha values ranged from 0.721 to 0.949, which satisfied the
cutoff value of 0.7, ensuring adequate internal consistency (Nunnally, 1978).
Table 14. Cronbach’s Alpha Values
Variables Cronbach’s alpha
Perceived complexity 0.813
Perceived servicescape 0.922
Style 0.861
Layout 0.721
Color 0.895
Lighting 0.898
Perceived value 0.903
ITR 0.934
WOM 0.949
Note. ITR = Intention to revisit, WOM = intention to spread positive word-of-mouth
Factor Analysis and Validity
Confirmatory Factor Analysis (CFA) was conducted to ensure the questionnaire
items measured the underlying constructs. All of the items loaded on their underlying
constructs and all loadings were greater than 0.50 (Table 15).
Table 15. Confirmatory Factor Analysis (CFA) of Individual Items
Items Standardized Factor Loadings
Complex1 0.805
Complex2 0.838
71
Table 15. (continued)
Complex3 0.723
Complex4 0.513
Style1 0.680
Style2 0.650
Style3 0.915
Style4 0.888
Layout1 0.869
Layout2 0.687
Color1 0.813
Color2 0.860
Color3 0.929
Light1 0.811
Light2 0.877
Light3 0.907
PV1 0.946
PV2 0.887
PV3 0.831
ITR1 0.927
ITR2 0.944
ITR3 0.881
WOM1 0.923
WOM2 0.958
WOM3 0.912
Note. Complex = perceived complexity, PV = perceived value, ITR = intention to revisit, WOM = intention to spread positive word-of-mouth
72
As the perceived servicescape items all loaded highly on the underlying
dimensions, an average was taken among the items measuring each dimension to
generate four averages: Averaged style, average layout, average color, and average
lighting scores. A second CFA was performed to examine how these four averages
loaded on perceived servicescape (Table 16). The correlations after calculating the four
averages are reported in Table 17.
Table 16. Confirmatory Factor Analysis (CFA) with Averaged Perceived Servicescape Dimensions
Items Standardized Factor Loadings
Complex1 0.814
Complex2 0.838
Complex3 0.714
Complex4 0.509
Stylea 0.839
Layouta 0.726
Colora 0.827
Lighta 0.653
PV1 0.946
PV2 0.887
PV3 0.832
ITR1 0.927
ITR2 0.945
ITR3 0.880
73
Table 16. (continued)
WOM1 0.923
WOM2 0.958
WOM3 0.912
Note. a Calculated by averaging the items that measured the same dimensions; Complex = perceived complexity, PV = perceived value, ITR = intention to revisit, WOM = intention to spread positive word-of-mouth
Table 17. Correlations among All of the Dimensions
Complex Style Layout Color Light PV ITR WOM
Complex 1
Style 0.132a 1
Layout -0.104b 0.759 1
Color -0.025c 0.790 0.678 1
Light -0.012d 0.588 0.561 0.628 1
PV 0.136 e 0.506 0.508 0.482 0.394 1
ITR 0.070f 0.731 0.644 0.667 0.540 0.788 1
WOM 0.058g 0.715 0.650 0.700 0.598 0.679 0.854 1
Note. P-value in a parenthesis. Complex = perceived complexity, PV = perceived value, ITR = intention to revisit, WOM = intention to spread positive word-of-mouth; a. p = 0.015, b. p = 0. 065, c. p = 0.638, d. p = 0.822, e. p = 0.010, f. p = 0.178, g. p = 0.261, all other correlations were significant with p values less than to 0.001
Convergent validity was assessed by the factor loadings and the correlations. The
results showed that servicescape dimensions had good standardized loadings (0.839,
0.726, 0.827, and 0.653 in Table 16). The standardized CFA loadings of the items
measured perceived value, itention to revisit, and intentiont to spread positive word-of-
74
mouth were at least 0.832, which showed that these items also measured the underlying
constructs they were intended to measure. Thus convergent validity was confirmed.
The standardized factor loadings of the four averaged scores loaded highly on
perceived servicescape (>0.50 in Table 16). However, the correlations among the four
dimensions of perceived servicescape were also high, ranging from 0.561 to 0.790 (p
values less than 0.001, Table 17). Thus, these dimensions were further combined by
taking an average of the four dimensions, and the correlations after taking the average are
reported in Table 18.
Table 18. Correlations among the Variables with Perceived Servicescape Overall
PTEMPa PCOMP SVSP PV ITR WOM
PTEMP 1
PCOMP 0.099*
(0.032)
1
SVSP -0.097*
(0.036)
0.020
(0.674)
1
PV -0.001
(0.980)
0.120*
(0.010)
0.506**
(<0.001)
1
ITR -0.012
(0.790)
0.048
(0.299)
0.706**
(<0.001)
0.744**
(<0.001)
1
WOM -0.061
(0.185)
0.044
(0.340)
0.736**
(<0.001)
0.625**
(<0.001)
0.817**
(<0.001)
1
Note. P-value in a parenthesis. a1 = warm light, 2=cool light; PTEMP = perceived lighting temperature, PCOMP = perceived complexity, SVSP = perceived servicescape, PV = perceived value, ITR = intention to revisit, WOM = intension to spread positive word-of-mouth; P-value in a parenthesis. *p ≤ 0.05. **p ≤ 0.001
75
Discriminant validity was then assessed by comparing the correlations with the
reliability values of perceived servicescape, perceived value, intention to revisit, and
intention to spread positive word-of-mouth. Discriminant validity is established if the
correlation between two variables is not higher than their individual reliability values
(Gaski, 1984). All pairs satisfied such recommendation. Although the highest correlation
was between intention to revisit and positive word-of-mouth (0.817), this correlation was
still lower than the reliability values of these two constructs (0.934 and 0.949 in Table
14). Thus it was concluded that the discriminant validity was confirmed.
Hypothesis Testing – Hypotheses 5 to 9
Hypotheses 5 to 9 are related to the relationships among perceived servicescape,
perceive value, intention to revisit (ITR) and intention to spread positive word-of-mouth
(WOM). These hypotheses were tested with the averages of the questionnaire items.
Mplus version 6 was used to test these hypotheses.
The model fits the data perfectly (χ2(0) = 0, p = 0, CFI = 1, TLI = 1, SRMR = 0,
RMSEA = 0). The hypothesis testing results are displayed in Figure 5 and the R-squared
values are presented in Table 19. Hypothesis 5 (β = 0.505, p <0.001) was supported;
perceived servicescape was positively associated with perceived value. Hypothesis 6 (β =
0.565, p <0.001) was supported, which indicated that perceived servicescape also
positively predicted intention to spread positive word-of-mouth. Hypothesis 7 was
significant (β = 0.440, p < 0.001), supporting the proposed relationship between
perceived servicescape and intention to revisit. Hypothesis 8 (β = 0.338, p < 0.001) and
76
hypothesis 9 (β = 0.521, p < 0.001) were also significant, thus perceived value positively
predicted intention to revisit and intention to spread positive word-of-mouth.
Figure 5. Structural Diagram with Standardized Parameter Estimates
Note. SVSP = perceived servicescape, PV = perceived value, ITR = intention to revisit, WOM=intention to spread positive word-of-mouth; p-value in a parenthesis, *p ≤ 0.05, **p ≤ 0.001
Table 19. R-squared Values
Depended variable R-squared values p-value
Perceived value 0.255 <0.001
Intention to revisit 0.696 <0.001
Intention to spread positive word-of-mouth 0.626 <0.001
Hypothesis Testing – Hypothesis 1 to 4
The following sections discuss the effects of intended complexity and intended
lighting temperature. In order to avoid misinterpretation of the results, the first section
examines the interaction effect between these two stimuli, in which perceived
PV
WOM
ITR
SVSP
0.565 (<0.001)
0.338 (<0.001)
0.521
(<0.001)
0.440 (<0.001)
0.505 (<0.001)
0.501 (<0.001)
Significant
Non-significant
77
servicescape and perceived value are the dependent variables. The second section tests
hypotheses 1 to 4. These hypotheses examine the effects of complexity and lighting
temperature on perceived servicescape and perceived value.
The interaction and the main effects
As all the measurement loaded on their designated factors, averages were
computed when examining the interaction and the main effects. Factorial ANOVA
analyses were first performed with the dependent variables being perceived servicescape
(Table 20) and perceived value (Table 21). The interactions were not significant.
Table 20. Factorial ANOVA with Dependent Variable Being Perceived Servicescape
Source df SS MS F Sig.
Model 5 13.022 2.604 3.472 0.004
ICOMP 2 6.050 3.025 4.033 0.018
ITEMP 1 3.604 3.604 4.805 0.029
ICOMO×ITEMP 2 3.366 1.683 2.243 0.107
Error 451 338.337 0.750
Total 456 351.359
Note. ICOMP = intended complexity, ITEMP = intended lighting temperature
78
Table 21. Factorial ANOVA with Dependent Variable Being Perceived Value
Source df SS MS F Sig.
Model 5 14.583 2.917 1.617 0.154
ICOMP 2 9.957 4.979 2.760 0.064
ITEMP 1 0.009 0.009 0.005 0.945
ICOMP×ITEMP 2 4.307 2.153 1.194 0.304
Error 451 813.494 1.804
Total 456 828.076
Note. ICOMP = intended complexity, ITEMP = intended lighting temperature
As the factorial ANOVA results did not provide detailed information regarding
how intended complexity and intended lighting temperature influenced perceived
servicescape and perceived value, multiple regression was also conducted. The dependent
variables not only included perceived servicescape and perceived value, intention to
revisit and intention to spread positive word-of-mouth were also included. The results are
displayed in Table 16.
First, the interaction effects between the two stimulus variables on both perceived
servicescape (p = 0.837) and perceived value (p = 0.257) were non-significant. In
addition, the interaction effect was also non-significant for intention to revisit (p = 0.540)
and intention to spread positive word-of-mouth (p = 0.678). Because all of analyses
performed to examine the interactions showed non-significant results, the main effects
were then examined.
Second, the main effect of intended complexity and intended lighting temperature
on perceived servicescape and perceived value were not significant. However, because
hypotheses 1 to 4 proposed differences in perceived servicescape and perceived value
79
among the multiple treatment conditions, the non-significant main effects could not be
considered as evidence to reject hypotheses 1 to 4. Third, the results also suggested that
after including intended complexity, intended lighting temperature, and the interaction
term, hypotheses 5 to hypothesis 9 were still supported.
Table 22. The Main and the Interaction Effect of Intended Complexity and Intended Lighting Temperature
Predictor Variables
Depend Variables
Perceived Servicescape
Perceived Value
ITR WOM
Intended Complexity
-0.020
(0.891)
-0.041
(0.745)
0.069
(0.401)
-0.027
(0.764)
Intended Lighting Temperatureb
-0.066
(0.591)
-0.055
(0.609)
0.056
(0.417)
-0.041
(0.594)
Interaction -0.052
(0.780)
0.181
(0.261)
-0.042
(0.686)
0.043
(0.708)
Servicescape 0.518**
(<0.001)
0.452**
(<0.001)
0.566**
(<0.001)
Perceived Value
0.512**
(<0.001)
0.339**
(<0.001)
R2 0.013
(0.216)
0.269**
(<0.001)
0.698**
(<0.001)
0.625**
(<0.001)
Note. a -1 = warm, 0 = not sure, 1 = cool; b1 = warm light, 2 = cool light; the p-value is in parentheses, *p ≤ 0.05. **p ≤ 0.001
The effects of intended complexity and intended lighting temperature
Intended complexity and intended lighting temperature are the two stimulus
variable in the current study. Hypotheses 1 and 2 were related to intended complexity.
80
Hypothesis 1 proposed that perceived servicescape would be evaluated as the most
preferred under medium level of intended complexity. In other words, when averaging
over all servicescape items, the averaged servicescape score of treatment 12 was expected
to be higher than those of 11 and 13, and the score of treatment 22 was expected to be
higher than those of 21 and 23.
As mentioned previously, the four dimensions of perceived servicescape, namely
style, layout, color, and lighting, were highly correlated with each other (Table 17).
Therefore, to test the proposed hypotheses, mean scores of perceived servicescape was
calculated by taking the average of all perceived servicescape items and the means are
compared using SPSS version 20. An Analysis of Variance (ANOVA) test was first
performed. The F-value was significant (Table 23).
Table 23. ANOVA of Intended Complexity on Perceived Servicescape
Sum of Squares df Mean Square F Sig.
Between Groups 6.075 2 3.038 4.030 0.018
Within Groups 346.745 460 0.754
Total 352.821 462
To further investigate the effect of intended complexity, confidence intervals (CI)
of the mean differences were then calculated to test hypothesis 1. The means and the
confidence intervals are displayed in and Table 24 and Figure 6.
81
Table 24. Comparing the Means of the Confidence Intervals of Perceived Servicescape under the Same Lighting Temperature
Mean scores 95% CI
11 5.661 11 - 12 [-0.219, 0.237]
12 5.653 12 - 13 [-0.176, 0.353]
13 5.564 11 - 13 [-0.174, 0.369]
21 5.397 21 - 22 [-0.584, -0.060]*
22 5.720 22 - 23 [0.173, 0.779]*
23 5.244 21 - 23 [-0.148, 0.455]
Note. Equal variances are not assumed; *CI that excludes 0.
Figure 6. Means of Perceived Servicescape
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
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The CI results showed that the mean of treatment 12 was not significantly higher
than the mean of treatment 11 or 13 (5.653 versus 5.661 and 5.564), while the mean of
treatment 22 was significantly higher than the means of treatments 21 and 23 (5.720
versus 5.397 and 5.244). Thus hypothesis 1 was only supported under the cool light
condition; given the cool light condition, medium complexity yielded the highest
perceived servicescape score.
Hypothesis 2 proposed that perceived value score is the highest under a medium
level of complexity. Thus the perceived value score of treatment 12 was expected to be
greater than those of 11 and 13, and the score of treatment 22 was expected to be greater
than those of 21 and 23.
Following the same procedure involved in testing hypothesis 1, an ANOVA was
first performed. Then the mean scores of perceived value and the confidence intervals (CI)
of the mean differences were calculated. The results are displayed in Table 25, 26, and
Figure 7.
Table 25. ANOVA of Intended Complexity on Perceived Value
Sum of Squares df Mean Square F Sig.
Between Groups 11.405 2 5.703 3.194 0.042
Within Groups 828.332 464 1.785
Total 839.737 466
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Table 26. Comparing the Means of the Confidence Intervals of Perceived Value under the Same Lighting Temperature
Mean scores 95% CI
11 5.311 11 - 12 [-0.554, 0.277]
12 5.449 12 - 13 [-0.381, 0.460]
13 5.409 11 - 13 [-0.568, 0.370]
21 5.067 21 - 22 [-1.034, -0.192]*
22 5.680 22 - 23 [-0.156, 0.663]
23 5.426 21 - 23 [-0.766, 0.047]
Note. Equal variances not assumed; *CI that excludes 0.
Figure 7. Means of Perceived Value
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
µ̂ µ̂ µ̂
84
The CI results showed that under the warm light condition, all the mean
differences of perceived value were not significant. The mean of treatment 22 was only
significantly higher than the mean of treatment 21, while the CI of the mean difference
between 22 and 23 did not exclude 0 thus was inconclusive. Thus, hypothesis 2 was not
supported. However, under the cool light condition medium complexity did yield a
significantly higher perceived value score than did low complexity.
As the effect of complexity on perceived servicescape and perceived value were
discussed, the next step was to examine hypotheses 3 and 4, which related to lighting
temperature. Hypothesis 3 and 4 proposed that the warm light condition generated higher
perceived servicescape and perceived value scores than the cool light condition. Two
ANOVA were first conducted, and then CI of the mean differences between the warm
and cool lightings on each complexity level were examined using t-tests with SPSS
version 20. The ANOVA results are reported in Tables 27 and 28.
Table 27. ANOVA of Intended Lighting Temperature on Perceived Servicescape
Sum of Squares df Mean Square F Sig.
Between Groups 3.338 1 3.338 4.403 0.036
Within Groups 349.483 461 0.758
Total 352.821 462
Table 28. ANOVA of Intended Lighting Temperature on Perceived Value
Sum of Squares df Mean Square F Sig.
Between Groups 0.001 1 0.001 0.001 0.980
Within Groups 839.736 465 1.806
Total 839.737 466
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The CI results are displayed in Table 29 and Table 30. For perceived servicescape,
the confidence interval under high complexity and low complexity excluded 0 (Table 29),
which suggests warm light leaded to a higher perceive servicescape score under both low
and high complexity. As for perceived value, none of the confidence intervals excluded 0
(Table 30). Therefore hypothesis 3 was supported under both high complexity and low
complexity, and hypothesis 4 was not supported.
Table 29. Comparing the Means and the Confidence Intervals of Perceived Servicescape under the Same Complexity
Complexity Level Meanwarm Meancool 95% CIwarm-cool
Low Complexity 11 = 5.661 21= 5.397 [0.016, 0.513]*
Medium Complexity 12 = 5.653 22 = 5.720 [-0.310, 0.176]
High Complexity 13 = 5.564 23 = 5.244 [0.0001, 0.641]*
Note. Equal variances not assumed; *CI that excludes 0.
Table 30. Comparing the Means and the Confidence Intervals of Perceived Value under the Same Complexity
Complexity Level Meanwarm Meancool 95% CIwarm-cool
Low Complexity 11 = 5.311 21= 5.067 [-0.198, 0.686]
Medium Complexity 12 = 5.449 22 = 5.680 [-0.624. 0.162]
High Complexity 13 = 5.409 23 = 5.426 [-0.452, 0.419]
Note. Equal variances not assumed.
µ̂ µ̂
µ̂ µ̂
µ̂ µ̂
µ̂ µ̂
µ̂ µ̂
µ̂ µ̂
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Chapter Summary
In this chapter, the effects of intended complexity and intended lighting
temperature on perceived servicescape and perceived value were examined. In addition,
the relationships among perceived servicescape, perceived value, and behavioral
intentions were also discussed. The results provided support to some of the proposed
effects. The next chapter will discuss the interpretation of the results and the implications.
Finally, the limitations of the current study and suggestions for future research will be
discussed.
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CHAPTER 5
DISCUSSION AND CONCLUSIONS
Introduction
This chapter provides a summary of the major findings and discusses the results
of the statistical analysis. In addition, this chapter also examines the implications from
theoretical and practical perspectives. Finally, this chapter reviews the limitations of the
current study and offers recommendation for future studies.
Discussion of Findings
The current study is multi-disciplinary; the study combined the S-O-R paradigm
in environmental psychology and perspectives from research in interior design, marketing,
and hospitality. By combining the knowledge in these fields, the current study provides a
comprehensive insight regarding the relationships among the variables of interest. It
revealed valuable findings that would help future studies, particularly those related to
intended complexity.
The Effects of Intended Complexity
The current study is mainly based upon two theories in Mehrabian and Russell
(1974): (1) The inverted U-shape between information rate and pleasure and approach
behavior and (2) the S-O-R paradigm. Hypotheses 1 to 2 proposed that perceived
servicescape and perceived value are the most positive under a medium level of
complexity, in which complexity is a type of environmental stimuli. The first theory
indicates that as the complexity of the room increases, the information rate in the room
88
also increases. As a result, individual’s perceptions of the room are expected to increase
at first because the room appears less boring, reach a optimal point under moderate
amount of information or a moderate level of complexity, then decrease as complexity
keeps on increasing because it is overwhelming (Mehrabian & Russell, 1974).
Previous studies have investigated complexity in website design. However, a
number of these studies did not catch the full inverted U-shape curve, but, showed either
a positive or a negative linear relationship, which is possibly because that the stimuli
were not simple or complex enough (Tuch et al., 2012). In the current study, a total of 14
rooms were designed that differed in complexity and lighting temperature. Six were
selected based on a pilot test to identify those best representing low, medium, and high
complexity with warm or cool lighting temperatures. Perceived servicescape and
perceived value were used as the dependent variables. The results found that with cool
light, perceived servicescape showed an inverted U-shape as complexity increases. This
finding is consistent with Berlyne (1974); Geissler et al. (2006), and Mehrabian and
Russell (1974).
The Effect of Intended Lighting Temperature
Similar to intended complexity, intended lighting temperature is also a type of
environmental stimuli (Mehrabian & Russell, 1974). Previous studies indicated that
American participants prefer warm light over cool light (Park & Farr, 2007), because
warm light generates pleasant feelings especially when people need to relax (Gordon &
Nuckolls, 1995). Thus, hypothesis 3 and 4 proposed that given the same complexity level,
perceived servicescape and perceived value are higher under the warm light condition
89
than under the cool light condition. The confidence intervals showed that given low and
high complexity levels, warm light generated a higher perceived servicescape than did
cool light, thus hypothesis 3 was supported under low and high complexity levels, but not
supported under medium complexity, which is likely due to that medium complexity
contained a near-optimal level of information rate that weakens the difference between
the warm and cool lighting conditions. Hypothesis 4 was not supported. The results
partially supported the finding of Park and Farr (2007). The reason for the difference
between warm and cool light under moderate complexity needs further examination. The
results did not show any significant interaction effect of intended complexity and
intended lighting temperature on any of the variables examined.
The Relationship between Perceived Servicescape, Perceived Value, and Behavioral
Intentions
The current study focused on two dimensions of behavioral intentions, which
were intention to spread positive word-of-mouth (WOM) and intention to revisit (ITR).
These two dimensions were analyzed separately. The results showed that perceived
servicescape enhanced perceived value, which supported hypothesis 5. This finding is
consistent with many previous studies (Liu & Jang, 2009; Mehrabian & Russell, 1974;
Zeithaml, 1988). Hypotheses 6 and 7 proposed the positive relationships between
perceived servicescape and WOM and between perceived servicescape and ITR. Both of
these two hypotheses were supported, which is consistent with previous studies as well
(Bitner, 1992; Countryman & Jang, 2006; Donovan & Rossiter, 1982; Donovan et al.,
1994; Jang & Namkung, 2009; Mattila & Wirtz, 2001). The results also supported
90
hypotheses 8 and 9, which proposed that perceived value positively affects WOM and
ITR, echoing Cronin et al. (2000) and Jen et al. (2011).
Theoretical Implications
The current study provides important contributions to servicescape research. First,
this study contributes to gaining a more in-depth understanding regarding complexity.
Designing the low, medium, and high complexity levels and measuring perceived
complexity were the main challenges of the current study. Complexity has not been
widely studied, and many studies in complexity examined the complexity of two-
dimensional websites (Geissler et al., 2006; Tuch et al., 2009; Tuch et al., 2012).
Complexity in a three-dimensional space has been rarely studied, and the existing
research focuses on increasing the number of pictures and changing the color scheme
(Wardono et al., 2012) due to the complex nature of decorating a room. Based on
Wardono et al. (2012), the current study takes a step forward by considering the cohesion
among the units and demonstrated designing three-dimensional spaces that differ
significantly in the level of perceived complexity.
In addition, many of the studies in complexity faced the challenge of capturing the
inverted U-shape (Tuch et al., 2012). The current study successfully captured the inverted
U-shape between intended complexity and perceived servicescape under the cool light
condition. Moreover, the study also developed a three-item measurement of perceived
complexity, which was proven to be reliable and valid by the data collected. Although
more tests are needed to ensure the robustness of such measurement, it would be a helpful
tool for future studies to further explore the topic.
91
In the field of hospitality research, although servicescape has been widely
discussed (Ariffin et al., 2013; Jang & Namkung, 2009; Jani & Han, 2015; Lam et al.,
2011; Lucas, 2003; Ryu & Han, 2009; Ryu & Jang, 2007; Simpeh et al., 2011; Suh et al.,
2014), most of them focused on the relationships among perceptual variables, only a
small number of research were experimental designs (Čivre, Knežević, Zabukovec, &
Fabjan, 2013; Mattila & Wirtz, 2001; Milliman, 1986; Naqshbandi & Munir, 2011; Wang
& Mattila, 2013), in which the effect of stimulus variables were manipulated and their
effects on perceptual variables were examined. The current study utilizes experimental
design, which revealed stronger evidence than non-experimental design regarding the
effects of intended complexity and intended lighting temperature.
Managerial Implications
In addition to academic implications, the current study also contributes to
managing hotel room servicescape. In general, the items that differ across the six
treatment conditions are lighting temperature, curtains, the number of canvases on the
walls, and the area rug. These items can be easily changed and are relatively inexpensive
to manipulate. Therefore, hotel managers who deal with a potential guest pool that is
similar to the sample of the current study could utilize the findings of the study with a
minimal budget and time investment. The results of the current study offers two options:
(1) change the lighting temperature or (2) change the decoration.
Some hotels might prefer to change the lighting temperature but to keep a certain
level of complexity in the room decoration. When comparing lighting temperature at the
same complexity levels, warm light created higher perceived servicescape under both low
92
and high complexity. Thus, for hotels that have guestrooms that are similar to the low or
high complexity conditions in Figure 4, it would be more beneficial to use warm light
than to use cool light. For hotels that have guestrooms that are similar to the medium
complexity condition, it is believed that both warm light or cool light work would
generate similar perceived servicescape. Costs would not be a major concern as warm
light and cool light bulbs of the similar type are generally similar in price.
Some other hotels might prefer to change the complexity via changing the
decoration but to keep a certain lighting temperature. When comparing complexity within
the same lighting temperature, the warm light conditions did not show significant
differences in perceived servicescape and perceived value. For the cool light conditions,
the medium intended complexity created the highest perceived servicescape among the
three complexity levels. In addition, the medium complexity also led to a higher
perceived value than low complexity. As perceive servicescape and perceived value
positively influence WOM and ITR, it is recommended for the hotels to have cool light in
the guestrooms to provide a medium level of complexity in the room, as illustrated in
Figure 4.
Limitations and Future Research
One limitation of the study is that the sample is people living within the United
States and is mostly male. Therefore, the results might not be applicable to other
demographic groups. Future research could assess how people with different cultural
backgrounds react to the same stimuli and whether cultural background could influence
how a room is perceived.
93
Another limitation is that the results could vary on the characteristics of the
sample. A person’s capability of recognizing harmony and excellent design (i.e., visual
aesthetic sensitivity) (Eysenck, Götz, Long, Nias, & Ross, 1984) depends on intelligence,
figural creativity, and openness to aesthetics (Myszkowski, Storme, Zenasni, & Lubart,
2014). Thus people who have higher aesthetic sensitivity might have more salient
responses to visual stimuli, while people who have lower aesthetic sensitivity might find
all of the treatment conditions acceptable. Future studies could consider these moderating
characteristics and examine the generalizability of the findings.
A third limitation is that the data was collected via an online survey with the
platform of Amazon Mechanical Turk. The manipulation check of intended lighting
temperature indicated that 67 people perceived the lighting temperature as the opposite as
what was actually presented. It is likely that people might not carefully read the questions
of the study, which could affect the effect sizes. Future studies could use hard copies to
collect data, which might lead to larger effect sizes.
Complexity can be changed by increasing number of units, increasing degree of
interest of the units, or decreasing cohesion among the units (Fiore, 2010). In the current
study, only the number of units and the cohesion among the units are manipulated. The
degree of the interest of the units remains unchanged in order to manage the number of
possible combinations. Future studies could increase the interest of units by changing the
color scheme and examine its influences on perceived complexity, on organism, and on
response variables.
As for intended lighting temperature, Park et al. (2010) measured the effect of
lighting temperature on pleasure and arousal and found a significant difference, while the
94
current study measures the effects of lighting temperature on perceived servicescape and
on perceived value, however the proposed differences under the warm light condition are
not supported. As Park and Farr (2007) have reported that lighting temperature influences
pleasure and arousal, it is likely that intended lighting temperature has a more salient
influence on pleasure and arousal but not on perceived servicescape and perceived value.
It is also likely that there are some moderators or it depends on the characteristics of the
sample. Future studies could further examine how warm and cool light influence different
organism variables.
95
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