The Effects of Lighting Temperature and Complexity on Hotel Guests' Perceived Servicescape, Perceived Value, and Behavioral Intentions Jing Yang Iowa State University

Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations

2015 The effects of lighting temperature and complexity on hotel guests' perceived servicescape, perceived value, and behavioral intentions Jing Yang Iowa State University

Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Art and Design Commons, Business Administration, Management, and Operations Commons, Management Sciences and Quantitative Methods Commons, Recreation Business Commons, and the Recreation, Parks and Tourism Administration Commons

Recommended Citation Yang, Jing, "The effects of lighting temperature and complexity on hotel guests' perceived servicescape, perceived value, and behavioral intentions" (2015). Graduate Theses and Dissertations. 14694. https://lib.dr.iastate.edu/etd/14694

This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected].

The effects of lighting temperature and complexity on hotel guests' perceived servicescape, perceived value, and behavioral intentions

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

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

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

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

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.

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 xi 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.

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).

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).

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.

Organism (O) - Cognition: Perceived Environmental servicescape Response (R) Stimuli (S) - Consciousness: Fantasy, - Approach/Avoidance - Lighting imagery, creative play, and - Behavioral temperature memories intentions - Level of - Affect: Multi-attribute - Satisfaction, loyalty complexity attitude, global attitude - Emotion: Pleasure, arousal, dominance

- Mood: Good/bad mood - Value: Perceived value

Figure 1. The S-O-R Paradigm with Modification from Fiore and Kim (2007)

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 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.

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.

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.

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).

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 Atmospherics 1. Visual Dimensions: Conceptual Atmospherics (1973) color, brightness, size, in general shapes

2. Aural dimensions: volume, pitch

Olfactory dimensions:

Scent, Freshness 3. Tactile dimensions: softness, smoothness, temperature

Table 1. (continued)

Baker Atmospherics 1. Ambient factors: air Conceptual Atmospherics (1986) quality (temperature, in general 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

Bitner Servicescape 1. Ambient conditions: Conceptual Servicescape (1992) temperature, air in general 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.

Table 1. (continued)

Baker et Atmospherics 1. Ambient factors: Empirical Retail store al. (1994) 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/uncoopera tive

Wakefield Servicescape 1. Layout accessibility Empirical Five major and 2. Facility aesthetics college Blodgett football (1996) 3. Seating comfort stadiums, two 4. Electronic minor league equipment/displays baseball games, three 5. Facility cleanliness casinos

Wakefield Tangible 1. Building design & Empirical Professional and service factors decor hockey Blodgett 2. Equipment games, a (1999) family 3. Ambience recreation, center, and movie theaters

Table 1. (continued)

Turley and Atmospherics 1. External variables Conceptual Atmospherics Milliman 2. General interior in general (2000) variables

3. Layout and design variables

4. Point of purchase and decoration variables 5. Human variables

Lucas Servicescape 1. Ambience Empirical Casino (2003) 2. Navigation

3. Seating comfort

4. Interior décor 5. Cleanliness

Countrym Servicescape 1. Architectural style Empirical Hotel an and 2. Layout Jang (2006) 3. Colors

4. Lighting

Ryu and Dinescape 1. Facility aesthetics Empirical Restaurant Jang 2. Ambience (2008) 3. Lighting

4. Table settings 5. Layout 6. Service staff

Jang and Atmospherics 1. Layout Empirical Upscale Namkung 2. Interior design restaurant (2009) 3. Lighting

4. Background music

Table 1. (continued)

Ballantine Servicescape 1. Attractive stimuli: Empirical Retail store et al. Sound, space, color, (2010) layout, design features 2. Facilitating stimuli: Crowding, Employees Lighting and product display features an be both attractive and facilitating stimuli

Lam et al. Servicescape 1. Ambience Empirical Casino (2011) 2. Navigation

3. Seating comfort

4. Interior décor 5. Cleanliness

Ariffin et Servicescape 1. Facility aesthetics Empirical Hotel al. (2013) 2. Lighting

3. Ambience 4. Layout

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.

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.

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 nonmonetary 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.

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:

Intended H1 Perceived H6 Positive Complexity Servicescape WOM

H2 H7 H5 H3 H8

Intended Perceived Intention to Lighting Value Revisit Temperature H4 H9

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. 49

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:

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 Cool Treatment 11 Treatment 12 Treatment 13 Temperature Warm Treatment 21 Treatment 22 Treatment 23

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.

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 Pillows on Each Curtains Area Rug Frames Bed

1 No frames One white pillow Solid brown Solid brown Brown with one Brown with one Two exactly the One white and one 2 horizontal white vertical white same frames brown stripe stripe Brown with two Brown with two Four exactly the Two white and 3 horizontal white vertical white same frames two brown stripes stripes Six frames, same Six patterned Brown with three Brown with three color, different 4 pillows of the horizontal white vertical white shapes but similar same set stripes stripes sizes Eight frames, Eight pillows with Brown with four Brown with four 5 random shapes, mismatch random horizontal white diagonal stripes colors, and sizes patterns stripes Ten frames, Twelve pillows Brown with five Five crossing 6 random shapes, with mismatch horizontal white diagonal stripes colors, and sizes random patterns stripes Eleven frames, Twelve pillows Six crossing Brown with white 7 random shapes, with mismatch diagonal pixelated grid colors, and sizes random patterns 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

5 6

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 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.

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 µˆ 11, µˆ 12, … µˆ 23 (Table

7), and all the possible pairs were denoted as µˆ 12- µˆ 11, µˆ 13- µˆ 12, µˆ 13- µˆ 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.

Table 7. The Treatment Conditions of the Means of Perceived Servicescape

Level of complexity

Low Medium High

Lighting Warm Treatment 11 Treatment 12 Treatment 13 Temperature µˆ 11 µˆ 12 µˆ 13

Cool Treatment 21 Treatment 22 Treatment 23

µˆ 21 µˆ 22 µˆ 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 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 Mean Std. Std. 95% CI of Means Complexity Deviation Error 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

Table 9. Confidence Intervals of the Mean Differences in Perceived Complexity

(I) (J) Mean Std. Sig. 95% CI of Mean Differences Differen Error ce (I-J) Lower Upper Bound 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 Warm Count 235 20 255 Lighting Temperature % 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.

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 Warm Treatment 11 Treatment 12 Treatment 13 235 Temperature n11=75 n12=79 n13=81 Cool Treatment 21 Treatment 22 Treatment 23 238

n21=81 n22=78 n23=79 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

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

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* 1 (0.032) SVSP -0.097* 0.020 1 (0.036) (0.674) PV -0.001 0.120* 0.506** 1 (0.980) (0.010) (<0.001) ITR -0.012 0.048 0.706** 0.744** 1 (0.790) (0.299) (<0.001) (<0.001) WOM -0.061 0.044 0.736** 0.625** 0.817** 1 (0.185) (0.340) (<0.001) (<0.001) (<0.001) 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

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.

0.565 (<0.001) SVSP WOM

0.505 0.440 0.338 0.501 (<0.001) (<0.001) (<0.001) (<0.001)

PV ITR 0.521 (<0.001) Significant Non-significant

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 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

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

Depend Variables

Predictor Perceived Perceived ITR WOM Variables Servicescape Value

Intended -0.020 -0.041 0.069 -0.027 Complexity (0.891) (0.745) (0.401) (0.764) Intended -0.066 -0.055 0.056 -0.041 Lighting Temperatureb (0.591) (0.609) (0.417) (0.594)

Interaction -0.052 0.181 -0.042 0.043 (0.780) (0.261) (0.686) (0.708) Servicescape 0.518** 0.452** 0.566** (<0.001) (<0.001) (<0.001) Perceived 0.512** 0.339** Value (<0.001) (<0.001)

R2 0.013 0.269** 0.698** 0.625** (0.216) (<0.001) (<0.001) (<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.

Table 24. Comparing the Means of the Confidence Intervals of Perceived Servicescape under the Same Lighting Temperature

Mean scores 95% CI

µˆ 11 5.661 µˆ 11 - µˆ 12 [-0.219, 0.237]

µˆ 12 5.653 µˆ 12 - µˆ 13 [-0.176, 0.353]

µˆ 13 5.564 µˆ 11 - µˆ 13 [-0.174, 0.369]

µˆ 21 5.397 µˆ 21 - µˆ 22 [-0.584, -0.060]*

µˆ 22 5.720 µˆ 22 - µˆ 23 [0.173, 0.779]*

µˆ 23 5.244 µˆ 21 - µˆ 23 [-0.148, 0.455] Note. Equal variances are not assumed; *CI that excludes 0.

Figure 6. Means of Perceived Servicescape 82

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

Table 26. Comparing the Means of the Confidence Intervals of Perceived Value under the Same Lighting Temperature

Mean scores 95% CI

µˆ 11 5.311 µˆ 11 - µˆ 12 [-0.554, 0.277]

µˆ 12 5.449 µˆ 12 - µˆ 13 [-0.381, 0.460]

µˆ 13 5.409 µˆ 11 - µˆ 13 [-0.568, 0.370]

µˆ 21 5.067 µˆ 21 - µˆ 22 [-1.034, -0.192]*

µˆ 22 5.680 µˆ 22 - µˆ 23 [-0.156, 0.663]

µˆ 23 5.426 µˆ 21 - µˆ 23 [-0.766, 0.047] Note. Equal variances not assumed; *CI that excludes 0.

Figure 7. Means of Perceived Value 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 85

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 µˆ 11 = 5.661 µˆ 21= 5.397 [0.016, 0.513]*

Medium Complexity µˆ 12 = 5.653 µˆ 22 = 5.720 [-0.310, 0.176]

High Complexity µˆ 13 = 5.564 µˆ 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 µˆ 11 = 5.311 µˆ 21= 5.067 [-0.198, 0.686]

Medium Complexity µˆ 12 = 5.449 µˆ 22 = 5.680 [-0.624. 0.162]

High Complexity µˆ 13 = 5.409 µˆ 23 = 5.426 [-0.452, 0.419]

Note. Equal variances not assumed.

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.

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.

REFERENCES

AAA. (2012). Approval Requirements and Diamond Rating Guidelines. Retrieved on August 2, 2013 from http://www.bestwesternsupply.com/docs/AAADiamondRatingGuidelinesJune201 2.pdf

Al-Sabbahy, H. Z., Ekinci, Y., & Riley, M. (2004). An investigation of perceived value dimensions: Implications for hospitality research. Journal of Travel Research, 42(3), 226-234.

Anuwichanont, J., & Mechinda, P. (2009). The impact of perceived value on spa loyalty and its moderating effect of destination equity. Journal of Business & Economics Research, 7(12), 73-89.

Areni, C. S., & Kim, D. (1993). The influence of background music on shopping behavior- classical versus top-forty music in a wine store. Advances in Consumer Research Volume 20, 336-340.

Areni, C. S., & Kim, D. (1994). The influence of in-store lighting on consumer's examination of merchandise in a wine store. International Journal of Research in Marketing, 11(2), 117–125.

Ariffin, A. A. M., Nameghi, E. N., & Zakaria, N. I. (2013). The effect of hospitableness and servicescape on guest satisfaction in the hotel industry. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration, 30(2), 127-137. doi: 10.1002/cjas.1246

Ashton, A. S., Scott, N., Solnet, D., & Breakey, N. (2010). Hotel restaurant dining : The relationship between perceived value and intention to purchase. Tourism and Hospitality Research, 10(3), 206–218.

Bagozzi, R. P. (1986). Principles of Marketing Management. Chicago, IL: Science Research Associates.

Baker, J. (1986). The role of the environment in marketing services: The consumer perspective. In J. A. Cepeil (Ed.), The services challenge: Integrating for competitive advantage (pp. 79-84). Chicago, IL: American Marketing Association. Baker, J., Grewal, D., & Parasuraman, A. (1994). The influence of store environment on quality inferences and store image. Journal of the Academy of Marketing Science, 22(4), 328–339.

Baker, J., Parasuraman, A., Grewal, D., & Voss, G. B. (2002). The influence of multiple store environment cues on perceived merchandise value and patronage intentions. Journal of Marketing, 66(2), 120-141. 96

Ballantine, P. W., Jack, R., & Parsons, A. G. (2010). Atmospheric cues and their effect on the hedonic retail experience. International Journal of Retail & Distribution Management, 38(8), 641-653. doi: 10.1108/09590551011057453

Baron, R. A. (1990). Lighting as a source of positive affect. Progressive Architecture, 71(12), 123-124.

Berlyne, D. E. (1960). Conflict, arousal, and curiosity. New York: McGraw-Hill Book Company.

Berlyne, D. E. (1974). Studies in the new experimental aesthetics. New York: Wiley.

Bettman, J. R. (1979). An information processing theory of consumer choice. Reading, MA: Addison-Wesle.

Bexton, W. H., Heron, W., & Scott, T. H. (1954). Effects of decreased variation in the sensory environment. Canadian Journal of Psychology, 8, 70-76.

Bitner, M. J. (1990). Evaluating service encounters: The effects of physical surroundings and employee responses. The Journal of Marketing, 54(2), 69-82.

Bitner, M. J. (1992). Servicescapes: The impact of physical surroundings on customers and employees. Journal of Marketing, 56(2), 57–71.

Booms, B. H., & Bitner, M. J. (1981). Marketing strategies and organization structures for service firms. Marketing of Services, 47-51.

Booms, B. H., & Bitner, M. J. (1982). Marketing services by managing the environment. Cornell Hospitality Quarterly, 23(1), 35-40.

Boulding, W., Kalra, A., Staelin, R., & Zeithaml, V. (1993). A dynamic process model of service quality: From expectations to behavioral intentions. Journal of Marketing Research, 30, 7-27.

Boyce, P. R. (2003). Human factors in lighting (2nd Ed.). London: Taylor & Francis.

Breckler, S. J. (1989). Affect versus evaluation in the structure of attitudes Journal of Experimental Social Psychology (25), 253-271.

Bush, L. E. (1973). Individual differences multidimensional scaling of adjectives denoting feelings. Journal of Personality and Social Psychology, 25, 50-57.

Campbell, D. T., & Boruch, R. F. (1975). Making the case for randomized assigument to treatments by considering the alternatives: Six ways in which quasi-experimental evaluations in compensatory education tend to underestimate effects. In C. A. 97

Bennett & A. A. Lumsdaine (Eds.), Evaluation and experiments: Some critical issues in assessing social programs (pp. 195-296). New York: Academic Press.

Chen, C.-F. (2008). Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: Evidence from Taiwan. Transportation Research Part A: Policy and Practice, 42(4), 709-717. doi: 10.1016/j.tra.2008.01.007

Choi, K. S., Cho, W. H., Lee, S., Lee, H., & Kim, C. (2004). The relationships among quality, value, satisfaction and behavioral intention in health care provider choice: A South Korean study. Journal of Business Research, 57, 913-921.

Čivre, Ž., Knežević, M., Zabukovec, P. B., & Fabjan, D. (2013). Facial attractiveness and stereotypes of hotel guests: An experimental research. Tourism Management, 36, 57-65. doi: 10.1016/j.tourman.2012.11.004

Countryman, C. C., & Jang, S. (2006). The effects of atmospheric elements on customer impression: The case of hotel lobbies. International Journal of Contemporary Hospitality Management, 18(7), 534-545. doi: 10.1108/09596110610702968

Craik, K. H. (1970). The comprehension of the everyday physical environment. In H. M. I. Proshansky, W. H.; Rivlin, L. G. (Ed.), Environmental psychology: Man and his physical setting (pp. 646-658). New York: Holt, Rinehart & Winston, Inc.

Cronin, J. J., Jr., Brady, M. K., & Hult, G. T. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193-218.

Cronin, J. J., Jr., & Taylor, S. (1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56(3), 55–68.

Clynes, M. (1977). Sentics: The touch of emotions. Garden City, NY: Anchor Press.

Davis, J. M., McCourt, W. F., & Solomon, P. (1958). Sensory Deprivation: (1) Effect s of social contact, (2) Effects of random visual stimulation. Paper presented at the Paper read at American Psychiatric Association. Philadelphia.

Day, H. I. (Ed.) (1981). Advances in intrinsic motivation and aesthetics. New York: Plenum.

De Ruyter, J. K., Wetzels, M., & Bloemer, J. (1998). On the relationship perceived service quality, service loyalty and switching costs. International Journal of Service Industry Management, 9(5), 436–453.

De Ruyter, J. K., Wetzels, M., Lemmink, J., & Mattson, J. (1997). The dynamics of the service delivery process: A value-based approach. International Journal of Research in Marketing, 14(231–243).

Dember, W. N., & Earl, R. W. (1957). Analysis of exploratory, manipulatory, and curiosity behaviors. Psychological Review, 64(2), 91-96.

Demoulin, N. T. M. (2011). Music congruency in a service setting: The mediating role of emotional and cognitive responses. Journal of Retailing and Consumer Services, 18(1), 10-18. doi: 10.1016/j.jretconser.2010.08.007

Dollard, J., & Miller, N. E. (1950). Personality and psychotherapy: An analysis in terms of learning, thinking, and culture. New York: McGraw-Hill Book Company.

Donovan, R. J., & Rossiter, J. R. (1982). Store atmosphere: An environmental psychology approach. Journal of Retailing, 58(1), 34-57.

Donovan, R. J., Rossiter, J. R., Marcoolyn, G., & Nesdale, A. (1994). Store atmosphere and purchasing behavior. Journal of Retailing, 70, 283-294.

Dubé, L., & Renaghan, L. M. (2000). Creating visible customer value. Cornell Hotel and Restaurant Administration Quarterly, 41(1), 62-72.

Eysenck, H. J., Götz, K. O., Long, H. Y., Nias, D. K. B., & Ross, M. (1984). A new visual aesthetic sensitivity test: IV. Cross-cultural comparisons between a Chinese sample from Singapore and an English sample. Personality and Individual Differences, 5(5), 599–600.

Fiore, A. M. (2010). Understanding aesthetics for the merchandising and design professional (2nd ed.). New York, NY: Fairchild Books.

Fiore, A. M., & Kim, J. (2007). An integrative framework capturing experiential and utilitarian shopping experience. International Journal of Retail & Distribution Management, 35(6), 421-442. doi: 10.1108/09590550710750313

Fiore, A. M., Yah, X., & Yoh, E. (2009). Effects of a product display and environmental fragrancing on approach responses and pleasurable experiences. Psychology and Marketing, 17(1), 27-54.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Fisk, R. P., Rosenbaum, M. S., & Massiah, C. (2011). An expanded servicescape perspective. Journal of Service Management, 22(4), 471-490. doi: 10.1108/09564231111155088

Fiske, D. W., & Maddi, S. R. (1961). Functions of varied experience. Homewood, Illinois: Dorsey Press.

Flynn, J. E. (1976). Studies of the subjective influence of light and color. Color in the health care environment: Proceedings of a special workshop held at the National Bureau of Standards (pp. 13-18). Gaithersburg, MD: U.S. Department of Commerce, National Bureau of Standards.

Flynn, J. E., & Spencer, T. J. (1977). The effects of light source color on user impression and satisfaction. Journal of Illuminating Engineering Society, 6(3), 167–179.

Flynn, J. E., Spencer, T. J., Martyniuk, O., & Hendrick, C. (1973). Interim study of procedures for investigating the effect of light on impression and behavior. Journal of the Illuminating Engineering Society, 3(1), 87-94.

Fornell, C., Johnson, M., Anderson, E., Cha, J., & Bryant, B. (1996). The American customer satisfaction index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7-18.

Gale, B. (1994). Managing customer value: Creating quality and service that customers can see. New Tork: The Free Press.

Gaski, J. (1984). Theory of power and conflict in channels of distribution. Journal of Marketing, 48(3), 9-29.

Geissler, G. L., Zinkhan, G. M., & Watson, R. T. (2006). The influence of home page complexity on consumer attention, attitudes, and purchase intent. Journal of Advertising, 35(2), 69–80.

GE Lighting (2013, Sept. 24). What’s driving hotel lighting? Top trends for 2014. GE Lighting Newroom. Retrieved on May, 6, 2015 from http://pressroom.gelighting.com/news/whats-driving-hotel-lighting-top-trends-for- 2014 - .VXJr_GRViko

Gerard, R. M. (1957). Differential effects of colored lights on psychophysiological functions. Unpublished doctoral dissertation, University of California, Los Angeles.

Gifford, R. (1988). Light, decor, arousal, comfort, and communication. Journal of Environmental Psychology (8), 177-189.

Glanzer, M. (1958). Curiosity, exploratory drive, and stimulus satiation. Psychological Bulletin, 55, 302-315.

Gordon, G. (2003). Interior lighting for designers (4th ed.). Hoboken, NJ: John Wiley & Sons, Inc. 100

Gordon, G., & Nuckolls, J. L. (1995). Interior lighting for designers. New York: John Wiley & Sons, Inc.

Grewal, D., Monroe, K. B., & Krishnan, R. (1998). The effect of pice-comparison advertising on buyers' perception of acquisition value, transaction value, and behavioral intentions. Journal of Marketing, 62(2), 46-59.

Grönroos, C. (1997). Value-driven relational marketing: From products to resources and competencies. Journal of Marketing Management, 13(5), 407–420.

Gunderson, E. K. (1963). Emotional symptoms in extremely isolated groups. Archives of General Psychiatry, 9, 362-368.

Gunderson, E. K. (1968). Mental health problems in Antarctica. Archives of Environmental Health, 17, 558-564.

Ha, J., & Jang, S. (2012). The effects of dining atmospherics on behavioral intentions through quality perception. Journal of Services Marketing, 26(3), 204-215. doi: 10.1108/08876041211224004

Hazzard, F. W. (1930). A descriptive account of odors. Journal of Experimental Psychology, 13(4), 297-331.

Heide, M., Lærdal, K., & Grønhaug, K. (2007). The design and management of ambience—Implications for hotel architecture and service. Tourism Management, 28(5), 1315-1325. doi: 10.1016/j.tourman.2007.01.011

Hendrick, C., Martyniuk, O., Spencer, T. J., & Flynn, J. E. (1977). Procedures for investigating the effect of light on impression: Simulation of a real space by slides. Environment and Behavior, 9(4), 491-510.

Heron, W. (1961). Cognitive and physiological effects of perceptual isolation. In P. Solomon, J. H. Mendelson, P. E. Kubzansky, R. Trumbull, P. H. Leiderman & D. Wexler (Eds.), Sensory deprivation. Cambridge, Massachusetts: Harvard University Press Cambridge.

Hightower, R., Brady, M. K., & Baker, T. L. (2002). Investigating the role of the physical environment in hedonic service consumption: An exploratory study of sporting events. Journal of Business Research, 55, 697-707.

Hilliard, T. W., & Baloglu, S. (2008). Safety and security as part of the hotel servicescape for meeting planners. Journal of Convention & Event Tourism, 9(1), 15-34.

Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92-101.

101

Holbrook, M. B. (1981). Integrating compositional and decompositional analyses to represent the intervening role of perceptions in evaluative judgments. Journal of Marketing Research, 18(1), 13-28.

Holbrook, M. B., & Hirschman, E. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132- 140.

Huang, M.-H. (2003). Modeling virtual exploratory and shopping dynamics: An environmental psychology approach. Information & Management, 41(1), 39-47. doi: 10.1016/s0378-7206(03)00024-7

Hwang, J., & Ok, C. (2013). The antecedents and consequence of consumer attitudes toward restaurant brands: A comparative study between casual and fine dining restaurants. International Journal of Hospitality Management, 32, 121-131. doi: 10.1016/j.ijhm.2012.05.002

Hunt, J. M. (1960). Experience and the development of motivation: Some reinterpretations. Child Development, 31, 489-504.

Jain, R., & Bagdare, S. (2011). Music and consumption experience: A review. International Journal of Retail & Distribution Management, 39(4), 289-302. doi: 10.1108/09590551111117554

Jang, S., & Namkung, Y. (2009). Perceived quality, emotions, and behavioral intentions: Application of an extended Mehrabian–Russell model to restaurants. Journal of Business Research, 62(4), 451–460.

Jani, D., & Han, H. (2015). Influence of environmental stimuli on hotel customer emotional loyalty response: Testing the moderating effect of the big five personality factors. International Journal of Hospitality Management, 44, 48-57. doi: 10.1016/j.ijhm.2014.10.006

Jen, W., & Hu, K. (2003). Application of perceived value model to identify factors affecting passengers’ repurchase intentions on city bus: A case of the Taipei metropolitan area. Transportation, 30(3), 307-327.

Jen, W., Tu, R., & Lu, T. (2011). Managing passenger behavioral intention: An integrated framework for service quality, satisfaction, perceived value, and switching barriers. Transportation, 38(2), 321–342.

Johnson, M., & Nilsson, L. (2003). The importance of reliability and customization from goods to services. The Quality Management Journal, 10(1), 8-20.

Karlen, M., & Benya, J. (2004). Lighting design basics. Holboken, New Jersey: J. Wiley & Sons. 102

Kasmar, J. V. (1970). The development of a usable lexicon of environmental descriptors. Environment and Behavior, 2, 153-169.

Kenz, I., & Kers, C. (2000). Effects of indoor lighting, gender, and age on mood and cognitive performance. Environment and Behavior, 32(6), 817-831.

Kim, W., Jin-Sun, B., & Kim, H. (2008). Multidimensional customer-based brand equity and its consequences in midpriced hotels. Journal of Hospitality & Tourism Research, 32(2), 235-254.

Kim, W. G., & Moon, Y. J. (2009). Customers’ cognitive, emotional, and actionable response to the servicescape: A test of the moderating effect of the restaurant type. International Journal of Hospitality Management, 28(1), 144-156. doi: 10.1016/j.ijhm.2008.06.010

Kotler, P. (1973). Atmospherics as a marketing tool. Journal of Retailing, 49(4), 48–64.

Kubzansky, P. E. (1961). The effects of reduced environmental stimulation on human behavior: A review. In A. D. Biderman & H. Zimmer (Eds.), The manipulation of human behavior (pp. 51-95). New York: John Wiley & Sons.

LaBarbera, P. A., & Mazursky, D. (1983). A longitudinal assessment of consumer satisfaction/dissatisfaction: The dynamic aspect of the cognitive process. Journal of Marketing Research, 20(4), 393-404.

Lam, L. W., Chan, K. W., Fong, D., & Lo, F. (2011). Does the look matter? The impact of casino servicescape on gaming customer satisfaction, intention to revisit, and desire to stay. International Journal of Hospitality Management, 30(3), 558-567. doi: 10.1016/j.ijhm.2010.10.003

Lapierre, J., Filiatrault, P., & Chebat, J. C. (1999). Value strategy rather than quality: A case of business-to-business professional service. Journal of Business Research, 45(2), 235–246.

Lee, E. (2011). Adapting to cultural differences in residential design: The case of Korean families visiting the United States. Journal of Interior Design, 36(2), 1-19.

Lee, S., & Jeong, M. (2012). Effects of e-servicescape on consumers' flow experiences. Journal of Hospitality and Tourism Technology, 3(1), 47-59. doi: 10.1108/17579881211206534

Lévy, C. M., MacRaeb, A., & Kösterc, E. P. (2006). Perceived stimulus complexity and food preference development. Acta Psychologica, 123(3), 394-413. doi: 10.1016/j.actpsy.2006.06.006

103

Lichtenstein, D. R., Bloch, P. H., & Black, W. C. (1988). Correlates of price acceptability. Journal of Consumer Research, 15(2), 243-252.

Lichtenstein, D. R., Netemeyer, R. G., & Burton, S. (1990). Distinguishing coupon proneness from value consciousness: An acquisition-transaction utility theory perspective. The Journal of Marketing, 54(54-67).

Lin, I. Y. (2004). Evaluating a servicescape: The effect of cognition and emotion. International Journal of Hospitality Management, 23(2), 163-178. doi: 10.1016/j.ijhm.2003.01.001

Lin, I. Y., & Mattila, A. S. (2010). Restaurant servicescape, service encounter, and perceived congruency on customers’ emotions and satisfaction. Journal of Hospitality Marketing & Management, 19(8), 819-841. doi: 10.1080/19368623.2010.514547

Lindsley, D. B. (1951). Emotion. In S. S. Stevens (Ed.), Handbook of Experimental Psychology. New York: John Wiley & Sons.

Liu, Y., & Jang, S. (2009). The effects of dining atmospherics: An extended Mehrabian– Russell model. International Journal of Hospitality Management, 28(4), 494-503. doi: 10.1016/j.ijhm.2009.01.002

Lucas, A. F. (2003). The determinants and effects of slot servicescape satisfaction in a Las Vegas hotel casino. UNLV Gaming Research & Review Journal, 7(1), 1-19.

Mattila, A. S., & Wirtz, J. (2001). Congruency of scent and music as a driver of in-store evaluations and behavior. Journal of Retailing, 77, 273–289.

Maute, M. F., & Forrester, W. R. (1993). The structure and determinants of consumer complaint intentions and behavior. Journal of Economic Psychology, 14(3), 219- 247.

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Cambridge, MA: MIT Press.

Miller, N. E. (1944). Experimental studies of conflict. In J. M. Hunt (Ed.), Personality and the behavior disorders (pp. 431-465). New York: Ronald Press.

Miller, N. E. (1964). Some implications of modern behavior theory for personality change and psychotherapy. In P. Worchel & D. Byrne (Eds.), Personality change (pp. 149-175). New York: Wiley.

Milliman, R. (1986). The influence of background music on the behavior of restaurant patrons. Journal of Consumer Research, 13(2), 286-289.

104

Monroe, K. (1991). Pricing, marking profitable decision (2nd ed.). New York: McGraw- Hill.

Monroe, K. B., & Chapman, J. D. (1987). Framing effects on buyers’ subjective product evaluations. Advances in consumer research, 14(1), 193-197.

Morrison, M., Gan, S., Dubelaar, C., & Oppewal, H. (2011). In-store music and aroma influences on shopper behavior and satisfaction. Journal of Business Research, 64(6), 558-564. doi: 10.1016/j.jbusres.2010.06.006

Myszkowski, N., Storme, M., Zenasni, F., & Lubart, T. (2014). Is visual aesthetic sensitivity independent from intelligence, personality and creativity? Personality and Individual Differences, 59, 16-20. doi: 10.1016/j.paid.2013.10.021

Naqshbandi, M. M., & Munir, R. S. (2011). Atmospheric elements and personality: Impact on hotel lobby impressions. World Applied Sciences Journal, 15(6), 785- 792.

Nelson, K. (2009). Enhancing the attendee's experience through creative design of the event environment: Applying Coffman's dramaturgical perspective. Journal of Convention & Event Tourism, 10(2), 120-133.

Newman, J. W., & Werbel, R. A. (1973). Multivariate analysis of brand loyalty for major household appliances. Journal of Marketing Research, 404-449.

Ng, S., David, M. E., & Dagger, T. S. (2011). Generating positive word-of-mouth in the service experience. Managing Service Quality, 21(2), 133-151. doi: 10.1108/09604521111113438

Nunnally, J. C. (1978). Psychometric theory. New York, NY: McGraw-Hill.

Oliver, R. L., & Swan, J. E. (1989). Consumer perceptions of interpersonal equity and satisfaction in transactions: A field survey approach. Journal of Marketing, 53(2), 21-35.

Osgood, C. E. (1960). The cross-cultural generality of visual-verbal synesthetic tendencies. Behavioral Science, 5(2), 146–169.

Pae, J. Y. (2009). The effects of hotel guestroom lighting on consumers' emotional states, preferences and behavioral intentions (Master's). Retrieved on April 10, 2012 from http://etd.fcla.edu/UF/UFE0024814/pae_j.pdf

Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67(4), 420-450.

105

Parasuraman, A., & Grewal, D. (2002). The impact of technology on the quality-value- loyalty chain: A research agenda. Journal of the Academy of Marketing Science, 28(1), 168-174.

Parasuraman, A., Zeithaml, V., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.

Park, N., & Farr, C. A. (2007). The effect of lighting on consumers' emotions and behavioral intention in a retail environment: A cross-cultural comparison. Journal of Interior Design, 33(1), 17-32.

Park, N.-K., Pae, J. Y., & Meneely, J. (2010). Cultural preferences in hotel guestroom lighting design. Journal of Interior Design, 36(1), 21-34. Petrick, J. F. (2004a). First timers' and repeaters' perceived value. Journal of Travel Research, 43(1), 29-38.

Petrick, J. F. (2004b). Are loyal visitors desired visitors? Tourism Management, 25, 463– 470.

Petrick, J. F. (2002). Development of a multi-dimensional scale for measuring the perceived value of a servicet. Journal of Leisure Research, 34(2), 119-134.

Petrick, J. F., & Backman, S. J. (2002). An examination of the construct of perceived value for the prediction of golf travelers' intentions to revisit. Journal of Travel Research, 41(1), 38-45. doi: 10.1177/004728750204100106

Postrel, V. (2003). The substance of style. New York: Harper Collins.

Proshansky, H. M., Ittelson, W. H., & Rivlin, L. G. (1970). Environmental psychology: Man and his physical setting. New York: Holt, Rinehart & Winston.

Rea, M. S. (2000). The IESNA lighting handbook: Reference and application (9th ed.). New York: Illuminating Engineering Society of North America Publications Department.

Rechinhheld, F., & Sasser, W. J. (1990). Zero defections: Quality comes to service. Harvard Business Review, 68(Sepember-October), 105-111.

Rust, R. T., & Zahorik, A. J. (1993). Customer satisfaction, customer retention, and market share. Journal of Retailing, 69(2), 193-215.

Rutes, W. A., Penner, R. H., & Adams, L. (2001). Hotel design: Planning and development. New York, NY: W. W. Norton & Company.

106

Ryu, K., & Han, H. (2009). Influence of the quality of food, service, and physical environment on customer satisfaction and behavioral intention in quick-casual restaurants: Moderating role of perceived price. Journal of Hospitality & Tourism Research, 34(3), 310-329. doi: 10.1177/1096348009350624

Ryu, K., Han, H., & Kim, T.-H. (2008). The relationships among overall quick-casual restaurant image, perceived value, customer satisfaction, and behavioral intentions. International Journal of Hospitality Management, 27(3), 459-469. doi: 10.1016/j.ijhm.2007.11.001

Ryu, K., & Jang, S. (2008). DINESCAPE: A scale for customers' perception of dining environments. Journal of Foodservice Business Research, 11(1), 2-22.

Ryu, K., & Jang, S. (2007). The effect of environmental perceptions on behavioral intentions through emotions: The case of upscale restaurants. Journal of Hospitality & Tourism Research, 31(1), 56-72. doi: 10.1177/1096348006295506

Sánchez, J., Callarisa, L., Rodríguez, R. M., & Moliner, M. A. (2006). Perceived value of the purchase of a tourism product. Tourism Management, 27(3), 394-409. doi: 10.1016/j.tourman.2004.11.007

Sheth, J. N., Newman, B. I., & Gross, L. G. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22, 159–170.

Shilpa, B., & Rajnish, J. (2013). Music and shopping experience. IIMS Journal of Management Science, 4(2), 152-167.

Siguaw, J. A., & Enz, C. A. (1999). Best practices in hotel architecture. Cornell Hotel and Restaurant Administration Quarterly, 40(5), 44-49. doi: 10.1177/001088049904000508

Simpeh, K. N., Simpeh, M., Abdul-Nasiru, I., & Amponsah-Tawiah, K. (2011). Servicescape and customer patronage of three star hotels in Ghana’s metropolitan city of Accra. European Journal of Business and Management, 3(4), 119-131.

Singh, J. (1988). Consumer complaint intentions and behavior: Definitional and taxonomical issues. Journal of Marketing, 52(1), 93-107.

Sinha, I., & DeSarbo, W. S. (1998). An integrated approach toward the spatial modeling of perceived customer value. Journal of Marketing Research, 35(2), 236–249.

Siu, N. Y., Wan, P. Y., & Dong, P. (2012). The impact of the servicescape on the desire to stay in convention and exhibition centers: The case of Macao. International Journal of Hospitality Management, 31, 236-246.

Skinner, B. F. (1961). Cumulative record. New York: Appleton-Century-Crofts. 107

Solnick, S. J., & Hemenway, D. (1992). Complaints and disenrollment at a health maintenance organization. Journal of Consumer Affairs, 26(1), 90-103.

Spies, K., Hesse, F., & Loesch, K. (1997). Store atmosphere, mood and purchasing behavior. International Journal of Research in Marketing, 14, 1-17.

Suh, M., Moon, H., Han, H., & Ham, S. (2014). Invisible and intangible, but undeniable: Role of ambient conditions in building hotel guests’ loyalty. Journal of Hospitality Marketing & Management. doi: 10.1080/19368623.2014.945223

Summers, T. A., & Hebert, P. R. (2001). Shedding some light on store atmospherics: Influence of illumination on consumer behavior. Journal of Business Research, 54(2), 145–150.

Swartz, T., & Iacobucci, D. (1999). Handbook of services marketing and management. Thousand Oaks, CA: Sage Publications.

Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77, 203-220.

Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1997). Retail service quality and perceived value: A comparison of two models. Journal of Retailing and Consumer Services, 4(1), 39-48.

Sweeney, J. C., & Wyber, F. (2002). The role of cognitions and emotions in the music- approach-avoidance behavior relationship. Journal of Services Marketing, 16(1), 51-69.

Taylor, R., & Shanka, T. (2002). Attributes for staging successful wine festivals. Event Management, 7(3), 165–175.

Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4, 199- 214.

The Nielsen Company. (2013, September 17). Nielsen: Earned advertising remains most credible among consumers; trust in owned advertising on the rise. Nielsen Press Room. Retrieved on June 10, 2015 from http://www.nielsen.com/us/en/pressroom/2013/nielsen--earned-advertising-remains-most-credible-among- consumer.html

Tuch, A. N., Bargas-Avila, J. A., Opwis, K., & Wilhelm, F. (2009). Visual complexity of websites: Effects on users’experience, physiology, performance, and memory. International Journal of Human–Computer Studies, 67(9), 703–715.

108

Tuch, A. N., Presslaber, E. E., Stöcklin, M., Opwis, K., & Bargas-Avila, J. A. (2012). The role of visual complexity and prototypicality regarding first impression of websites: Working towards understanding aesthetic judgments. International Journal of Human-Computer Studies, 70(11), 794-811. doi: 10.1016/j.ijhcs.2012.06.003

Turley, L., & Milliman, R. (2000). Atmospheric effects on shopping behavior: A review of the experimental evidence. Journal of Business Research, 49, 193-211.

Urbany, J. E., & Bearden, W. O. (1988). Reference price effects on perceptions of perceived offer value, normal prices, and transaction utility. Proceedings of the 1989 Educators’ Conference, 45-49.

Wakefield, K., & Blodgett, J. (1994). The importance of servicescapes in leisure service settings. Journal of Service Marketing, 8(3), 66-76.

Wakefield, K., & Blodgett, J. (1996). The effect of the servicescape on customers’ behavioral intentions in leisure service settings. Journal of Services Marketing, 10(6), 45-61. doi: 10.1108/08876049610148594

Wakefield, K., & Blodgett, J. (1999). Customer response to intangible and tangible service factors. Psychology and Marketing, 16(1), 51-68.

Wang, C. Y., & Mattila, A. S. (2013). The Impact of Servicescape Cues on Consumer Prepurchase Authenticity Assessment and Patronage Intentions to Ethnic Restaurants. Journal of Hospitality & Tourism Research. doi: 10.1177/1096348013491600

Wang, Y., Lo, H., Chi, R., & Yang, Y. (2004). An integrated framework for customer value and customerrelationship-management performance: A customer-based perspective from China. Managing Service Quality, 14(2/3), 169–182.

Ward, L. M., & Russell, J. A. (1981). Cognitive set and the perception of place. Environment and Behavior, 13(5), 610-632.

Ward, P., Davies, B. J., & Kooijman, D. (2009). Ambient smell and the retail environment: Relating olfaction research to consumer behavior. Journal of Business and Management, 9(3), 289-302.

Wardono, P., Hibino, H., & Koyama, S. (2012). Effects of interior colors, lighting and decors on perceived sociability, emotion and behavior related to social dining. Procedia - Social and Behavioral Sciences, 38, 362-372. doi: 10.1016/j.sbspro.2012.03.358

109

Warshaw, P. R., & Davis, F. D. (1985). Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology, 21(3), 213- 228.

West, A., & Hughes, J. (1991). An evaluation of hotel design practice. The Service Industries Journal, 11(3), 362–380.

West, A., & Purvis, E. (1992). Hotel Design: The need to develop a strategic approach. International Journal of Contemporary Hospitality Management, 4(1), 15-22.

Williams, P., & Naumann, E. (2011). Customer satisfaction and business performance: A firm-level analysis. Journal of Services Marketing, 25(1), 20-32.

Woodruff, R. B. (1997). Customer value: The next source for competitive advantage. Journal of the Academy of Marketing Science, 25(2), 139-153.

Woodruff, R. B., & Gardial, S. F. (1996). Know your customer: New approaches to understanding customer value and satisfaction. Cambridge, MA: Blackwell Publishers.

Yoo, M., & Bai, B. (2013). Customer loyalty marketing research: A comparative approach between hospitality and business journals. International Journal of Hospitality Management, 33, 166-177.

Zeithaml, V. (1988). Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.

Zeithaml, V., Berry, L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46.

Zubek, J. P., Welch, G., & Saunders, M. G. (1963). Electroencephalographic changes during and after 14 days of perceptual deprivation. Science, 139, 490-492.