TURKISH TV DRAMAS AS A MARKETING TOOL TO PROMOTE IMAGE OF TURKEY AS A TOURIST DESTINATION WITHIN THE AMERICAN CONTINENT

By

ALI ISKENDER

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

2018

© 2018 Ali Iskender

To my Mom

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ACKNOWLEDGMENTS

First of all, I am very thankful to Dr. Stepchenkova who accepted to be my

advisor and the committee chair when I was in a situation trying hard but in vain and

sort of disoriented academically. Her small touches made big differences in my

academic journey.

Also, I would like to thank Dr. Kaplanidou and Dr. Onel to accept to be on my

committee. I am thankful to them for their valuable contribution.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES ........................................................................................................ 11

LIST OF TERMS ........................................................................................................... 12

CHAPTER

1 INTRODUCTION .................................................................................................... 15

Overview ................................................................................................................. 15 Why Turkish TV Dramas Loved by the Americans ................................................. 16

The Journey of Turkish TV Series .......................................................................... 18 Need and Rationale for the Study ........................................................................... 19 Purpose of the Study .............................................................................................. 22

Research Questions ............................................................................................... 23 Theoretical Framework ........................................................................................... 26

Limitations and Delimitations .................................................................................. 28 Limitations ........................................................................................................ 28 Delimitations ..................................................................................................... 28

Defining the Population ........................................................................................... 29

Organization of the Study ....................................................................................... 29

2 REVIEW OF THE RELEVANT LITERATURE ........................................................ 30

Push Pull Factor Tourist Motivations....................................................................... 30

Audience Involvement............................................................................................. 33 Place Familiarity ..................................................................................................... 36 Destination Image in Film Tourism ......................................................................... 38

Film Tourism ........................................................................................................... 41 Gaps in the Literature ............................................................................................. 47 Summary and Hypotheses ...................................................................................... 48

Mode of Watching (Dubbed or Subtitled).......................................................... 48 Platform of Watching (on TV or on the Internet) ............................................... 49

Genre ............................................................................................................... 50 Audience Involvement, Place Familiarity, and Destination Image .................... 53

3 METHODS .............................................................................................................. 57

Research Design .................................................................................................... 57 Data Collection ....................................................................................................... 57

Instrumentation-Measurement ................................................................................ 60

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Audience Involvement ...................................................................................... 60

Place Familiarity ............................................................................................... 64

Destination Image ............................................................................................ 65 Behavioral Intentions ........................................................................................ 65

Data Analysis (Statistical) Procedures .................................................................... 67

4 RESULTS ............................................................................................................... 68

Preliminary Descriptive Statistical Analyses ........................................................... 68

RQ-1: MANOVA Results ......................................................................................... 94 Mode and Platform of Watching ....................................................................... 94 Genre ............................................................................................................... 97

RQ-2: Multiple Regression Results ....................................................................... 105 Model 1: Place Familiarity (DV) and Audience Involvement (IV) .................... 107

Model 2: Affective Image (DV) and Aud. Inv. (IVs) and Place Familiarity (IV) 109 Model 3: Cognitive Image (DV) and Audience Inv. (IVs) and Place

Familiarity (IV) ............................................................................................. 111 Model 4: Visitation Interest (DV) and Aud. Inv. (IVs), Place Familiarity (IV),

and Destination Image (IVs). ....................................................................... 113 Model 5: WoM (DV) and Aud. Inv. (IVs), Place Familiarity (IV), and

Destination Image (IVs) ............................................................................... 116

Model 6: Willingness to Pay and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs) ............................................................................... 119

5 DISCUSSION ....................................................................................................... 122

Theoretical Implications ........................................................................................ 122

Practical Implications ............................................................................................ 133 Limitations ............................................................................................................. 135

Future Research ................................................................................................... 135 APPENDIX

A SUPPLEMENTARY ANALYSES .......................................................................... 138

Country of Origin ................................................................................................... 138 Actors .................................................................................................................... 143

Actresses .............................................................................................................. 148 Conclusion ............................................................................................................ 154

B ENGLISH VERSION OF THE SURVEY ............................................................... 156

C SPANISH VERSION OF THE SURVEY ............................................................... 177

D PORTUGUESE VERSION OF THE SURVEY ...................................................... 194

E LETTER OF IRB APPROVAL ............................................................................... 215

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F DONATION ........................................................................................................... 216

G A PICTURE OF A TRAVEL TO TURKEY ............................................................. 218

H A PICTURE OF LEARNING TURKISH ................................................................. 219

LIST OF REFERENCES ............................................................................................. 220

BIOGRAPHICAL SKETCH .......................................................................................... 231

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LIST OF TABLES Table page 4-1 Primary Demographics of Respondents ............................................................. 68

4-2 Secondary Demographics of Respondents ........................................................ 72

4-3 Versions of survey preferred, mode and platform of watching ............................ 73

4-4 Turkish TV Series watched ................................................................................. 74

4-5 Favorite Turkish TV Series ................................................................................. 76

4-6 Descriptive Statistics of Audience Involvement .................................................. 78

4-7 Audience Involvement: Exploratory Factor Analysis with Varimax Rotation (n=561) ............................................................................................................... 79

4-8 Descriptive Statistics of Personal and Interpersonal Audience Involvement ...... 81

4-9 Descriptive Statistics of Personal Bhv. Aud. Inv. After Variable Transformation ................................................................................................... 81

4-10 Descriptive Statistics of Place Familiarity Items .................................................. 82

4-11 Descriptive Statistics of Single Place Familiarity Variable .................................. 83

4-12 Descriptive Statistics of Destination Image ......................................................... 83

4-13 Descriptive Statistics of Affective Destination Image .......................................... 84

4-14 Descriptive Statistics of (transformed) Affective Destination Image .................... 85

4-15 Descriptive Statistics of Cognitive Destination Image ......................................... 85

4-16 Descriptive Statistics of (transformed) Cognitive Image ..................................... 86

4-17 Descriptive Statistics of Behavioral Intentions .................................................... 87

4-18 Exploratory Factor Analysis of Behavioral Intentions .......................................... 89

4-19 Descriptive Statistics of Visitation Interest .......................................................... 91

4-20 Descriptive Statistics of (transformed) Visitation Interest .................................... 91

4-21 Descriptive Statistics of WoM recommendation.................................................. 91

4-22 Descriptive Statistics of (transformed) WoM recommendation ........................... 92

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4-23 Descriptive Statistics of Willingness to Pay ........................................................ 93

4-24 Descriptive Statistics of (transformed) Willingness to Pay .................................. 93

4-25 Pearson Correlations between the Dependent Variables ................................... 94

4-26 Descriptive Statistics of Behavioral Aud. Inv. on Mode and Platform of Watching ............................................................................................................ 95

4-27 Multivariate Tests of Mode and Platform of Watching on Personal and Interpersonal Aud. Inv. ....................................................................................... 96

4-28 One-way ANOVA's with Audience Involvement Subscales as Dependent Variables ............................................................................................................ 96

4-29 Pearson Correlations between Eight Dependent Variables ................................ 97

4-30 Turkish TV Series According to Genre ............................................................... 98

4-31 Descriptive Statistics of Eight DVs According to Genre .................................... 100

4-32 Multivariate Tests of Eight DVs According to Genre ......................................... 101

4-33 One-way ANOVA's with Eight DVs According to Genre ................................... 102

4-34 Post-hoc Test of Eight DVs According to Genre ............................................... 103

4-35 Standard Multiple Regression between Place Familiarity (DV) and Audience Involvement (IV) ............................................................................................... 107

4-36 Standard Multiple Regression between Affective Image (DV) and Aud. Inv. (IVs) and Place Familiarity (IV) ......................................................................... 109

4-37 Standard Multiple Regression between Cognitive Image (DV) and Audience Inv. (IVs) and Place Familiarity (IV) .................................................................. 111

4-38 Standard Multiple Regression between Visitation Interest (DV) and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs) ................................. 113

4-39 Standard Multiple Regression between WoM (DV) and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs) .................................................... 116

4-40 Standard Multiple Regression between Willingness to Pay and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs) ................................. 119

A-1 Pearson Correlations between Eight Dependent Variables .............................. 138

A-2 Descriptive Statistics of Dependent Variables According to Countries ............. 139

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A-3 Multivariate Tests on Eight DVs According to Country ..................................... 140

A-4 One-way ANOVA's with Eight DVs According to Country ................................. 140

A-5 Post-hoc Test of DVs based on country of origin .............................................. 141

A-6 Pearson Correlations Among Eight DVs ........................................................... 143

A-7 Frequency Table of Actors appearing in Favorite Turkish TV Series ................ 144

A-8 Descriptive Statistics of Eight DVs According to Actors .................................... 145

A-9 Multivariate Tests of Eight DVs According to Actors ......................................... 146

A-10 One-way ANOVA's with Eight DVs According to Actors ................................... 146

A-11 Post-hoc Test of DVs According to Actors ........................................................ 147

A-12 Pearson Correlations between Eight Dependent Variables .............................. 148

A-13 Frequency of Actresses Appearing in the Favorite TV Series .......................... 149

A-14 Descriptive Statistics of Eight DVs According to Actresses .............................. 150

A-15 Multivariate Tests of Eight DVs According to Actresses ................................... 151

A-16 One-way ANOVA's with Eight DVs According to Actresses ............................. 152

A-17 Post-hoc Tests of Eight DVs According to Actresses ....................................... 153

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LIST OF FIGURES Figure page

1-1 Demonstration of Relations regarding RQ-1 ....................................................... 25

1-2 Demonstration of Relations regarding RQ-2 ....................................................... 26

1-3 Dimensions of Film Tourism ............................................................................... 27

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LIST OF TERMS AUDIENCE INVOLVEMENT

A widely accepted definition of the concept “involvement” in media and communication studies is the degree to which an audience engages with or relates to a particular medium in the communication process. Sood’s (2002) audience involvement scale from media studies was taken as reference in this study.

DESTINATION IMAGE

One of the comprehensive definitions of the destination image is "representing the sum of beliefs, ideas, and impressions that a person has about a place or location based on information processed from a variety of sources" (Baloglu and McCleary,1999; Crompton, 1979). Destination image has at least two dimensions, cognitive and emotional (Baloglu and Brinberg, 1997; Baloglu and McCleary, 1999).

PLACE FAMILIARITY

Place familiarity is the perception of how much an individual knows about a destination (Moorthy, Ratchford, and Talukdar, 1997). Familiarity has a vital role in consumer decision-making (Kim and Richardson, 2003).

FILM TOURISM Film tourism refers to “visitation to destinations where movies and TV programs have been filmed and which can be a production studio, a district, a town and a country featured within products.” Throughout the literature, film tourism is also named as film-induced tourism (Beeton, 2005), movie-induced tourism (Riley et al., 1998) media-related tourism (Busby and Klug, 2001). Some studies have sought a more inclusive term to name it. Screen tourism is one of them offered (Connell and Meyer, 2009, Kim et al., 2009).

SOAP OPERA Soap operas are one kind of mediatized products. Collins Dictionary defines soap opera as a serialized drama, usually dealing with domestic themes and characterized by sentimentally, broadcast on radio or television. The name, "soap" refers to the soap and detergent commercials originally broadcasted during shows, which were aimed at women who were cleaning their houses when viewing, and "opera" refers to the melodramatic characters of the shows.

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

TURKISH TV DRAMAS AS A MARKETING TOOL TO PROMOTE IMAGE OF TURKEY AS A TOURIST DESTINATION WITHIN THE AMERICAN CONTINENT

By

Ali Iskender

May 2018

Chair: Svetlana Stepchenkova Major: Recreation, Parks and Tourism

Recently, Turkish TV series have become popular within the American continent.

To illustrate, in 2016, SILA, Turkish TV drama, broadcast in Chile, received as high a

rating as the Copa America qualifying soccer-game between Brazil and Chile. Also,

more than 12 million people in Argentina alone watch the other Turkish TV drama,

Fatmagul.

Given these, the overall purpose of the study is to examine the effectiveness of

Turkish TV series as a marketing tool to promote image of Turkey as a tourist

destination within the American continent. The current study examines (1) if audience

involvement differs based on mode of watching (dubbed or subtitled) and platform of

watching (TV or the Internet) (2) if audience involvement, place familiarity, destination

image, and behavioral intentions differ based on genre and (3) the relationship between

audience involvement, place familiarity, destination image, and behavioral intentions.

The data is analyzed using factor analysis, multiple regression, Factorial MANOVA, and

two-way MANOVA.

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Sample consists of 561 Turkish TV series viewers from the American continent. It

is noted that Turkish TV series are capable of catching wide range of audiences

demographically in contrast to American and Korean TV series. It is found that platform

of watching (on TV or on the Internet) and mode of watching (dubbed or subtitled) do

not make any statistically significant difference on audience involvement with Turkish

TV series. It is found that audience involvement, affective destination image, visitation

interest, and willingness to pay show statistically difference based on genre. In order,

action, romance, and drama are found influential on certain variables while comedy and

history are found ineffective. Place familiarity remains constant for each genre.

Furthermore, audience involvement is approached from a behavioral aspect. Two

underlying dimensions are found and named as personal and interpersonal. It is found

that audience involvement is influential on certain destination marketing constructs

(place familiarity, destination image, visitation interest, WoM recommendation). Place

familiarity is found the distinctive predictor of cognitive destination image. Cognitive

image is found as the leading predictor of behavioral intentions (visitation interest, WoM

recommendation, and willingness to pay).

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CHAPTER 1 INTRODUCTION

Overview

The impact of mass media products on tourists’ decision-making has been

studied since 1990s (Urry, 1990; Butler, 1990; Riley and Van Doren, 1992). Movies, TV

shows, TV dramas, series, soap operas, even songs and video clips turned out to be

influential motivators for travelers (Tooke and Baker, 1996). The term “film tourism” has

gradually been shaped as a concept over time (Hudson and Ritchie, 2006a, 2006b).

Destination Marketing Organizations (DMOs) have jumped ahead to use this niche

market opportunity as a vehicle to promote image of their destinations (Connell, 2012).

Lately an unexpected soap opera success is achieved by Turkish TV series

across the American continent mainly in Latina America. However, about 25 or 30 years

ago, Latin American TV series' stormed Turkish TV channels, and their actors became

well-known figures in Turkey. After private TV broadcasting had been introduced in the

1990s, Turkish audiences began to watch more local content. Today it is evident that

the roles have reversed. Turkish TV series are watched with keen interest in the region.

To portray, in 2016, Turkish TV series "Sıla," which is aired in Chile, received as high a

rating as the Copa America qualifying game between Brazil and Chile (Varol, 2016).

Also, more than 12 million people in Argentina alone watch the other Turkish TV drama

“Fatmagul” recently (Tali, 2016). It is reported that some charity fundraising events have

had the themes “Turkish TV dramas” and Turks living in Latina American countries have

been asked how to travel Turkey. When Turkish President has visited those countries

lately, the opening talks were about the Turkish soap operas. It seems that these TV

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series have achieved something that most diplomacy tactics would not have (Kaplan,

2016).

Why Turkish TV Dramas Loved by the Americans

Juan Vicente, CEO of Megavision Co., pointed that watching Novellas (Soap

operas) is a huge part of leisure culture of Latin Americans (“Turkey Country of Honor,”

2015). Nevertheless, it is still interesting how the Americans fall in love with Turkish

dramas regardless of cultural and geographical distance.

The success of Turkish soap operas has become the subject of a number of

news reports released by the global mainstream media outlets such as BBC, NPR,

International Business Times, and Aljazeera. In a BBC news report penned by Tali

(2016), a Chilean Turkish soap opera viewer indicates “they are easier to connect to

than US TV series. I enjoy the old-fashioned romance in Turkish TV dramas more than

over-sexualisation and violence of Hollywood products.” The other big fan of Turkish TV

dramas from Peru stresses that she comes together with her friends and host Turkish

TV nights and they find that the plots are clever not including Hollywood clichés and

stereotypes and the products are excellent.

In the same news report, Burhan Gun (2016), president of the Turkish TV and

Cinema Producers, highlights the number of reasons why Turkish TV series are

embraced warmly by the Americans. One of the leading reasons is that Turkey is a very

multicultural country and actors and actresses represent a lot of different ethnic

backgrounds and it is easy for audiences to affiliate themselves with Turkish actors and

actresses. As parallel, in a news report from “Haberler.com”, Erdogan (2007), the

President of Turkey, asserted that Turkey consists of 36 ethnic groups in one of his

public discourses.

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In the same BBC news report penned by Tali, Omar Al-Ghazzi (2016), a lecturer

in journalism at the UK's University of Sheffield, who has written a number of academic

papers about Turkish popular culture, suggests that Turkish TV dramas "offer a

seductive modernity." He also adds “Turkish TV series showcase very saleable ideas

about a comfortable middle-class life that is accessible and culturally relevant for many

people." Pinto, head of Turkish distributor Global Agency, pointed the other reason

which could be that those developing countries have been going through the similar

societal changes with Turkey (“Turkish TV Series,” 2015).

Lately, as well as being broadcast on Netflix, the American streaming media, the

Turkish TV series have begun to be aired on Telemundo and Mundo Fox, the American

entertainment TV channels in Spanish language, which targets Hispanic-American

audiences in the US, nearly 60 million people. As well as Hispanic Americans, 630

million Latin Americans in twenty developing countries in South and Central America

plus Mexico consist of an enormous tourism market and where the population has been

enjoying the solid economic growth in the first two decades of this century, with a rise of

its middle class who are more eager to travel internationally (Ferreira and et al., 2013).

The statement “close in distance, far in mind” used to define current situation

between Korea and Japan, having controversial relationship despite of their

geographical proximity (The Korea Times, 2004b). It inspires to name this connection

established over a cultural phenomenon, TV series, between two geographically far

destinations as “far in distance, close in heart”, which can lead this phenomenon to

create economic opportunities in long-run beyond tourism activities.

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The Journey of Turkish TV Series

The first move into South America began in 2014 when Turkey's hit series "1,001

Nights" was aired on Chilean TV (Kaplan, 2016). It quickly became Chile's most-viewed

series of the year. After its success there, it is aired in Brazil, Argentina, Peru, Uruguay,

Bolivia, Paraguay, Ecuador, Colombia and Costa Rica and is scheduled to soon

broadcast in the Dominican Republic, Nicaragua, Guatemala, El Salvador, Panama,

and Honduras and the others.

Only a decade ago, Turkish exports of television series were about $1 million per

year. In 2016, the exports reached the minimum $350 million; it is reported that export

revenue has increased 25 percent in 2015 alone. Turkey has become the second

largest exporter of TV series after the US in the world. Turkish TV series were

previously sold for between $35 and $50 per episode. Today, these prices range from

$500 to $200,000 per episode (Kaplan, 2016).

Turkish TV dramas are regarded as a “soft power” of promoting image of Turkey

positively, Celik (2014), the former Cultural and Tourism Minister of Turkey, signified,

which is cited from a news report released by Turkish Premiership Public Diplomacy

Coordination. Almost 130 Turkish TV series have been exported and watched by more

than 400 million people in 140 countries around the world, Akman, chair of the

Association of Television Broadcasters, suggested (“Turkish Dramas,” 2014). The

Middle East, South Asia, the Balkans and Russia rank among major importers,

redefining a sort of Ottoman Empire territorial expansion through the airwaves (Varol,

2016).

MIPCOM 2015, the world’s entertainment content market, took place on 5

October and run through 8 October 2015 at the Palais des Festivals, in Cannes, France.

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As the country of Honor, Turkey was at the center of MIPCOM 2015 through dedicated

events such as matchmaking sessions, conferences, Fresh TV and much more.

MIPCOM 2015 brought the full richness of Turkey’s media and entertainment industry to

Cannes. It was an opportunity for the world to meet and do business with the largest

delegation ever of Turkish TV executives. The agreements signed at this meeting

brought Turkish TV products to the US market after the rest of the world.

Need and Rationale for the Study

Film tourism is not something new although the subject has become popular

within the academic research for slightly over last two decades. It could trace back to a

hundred years ago when the first movies were filmed (Beeton, 2005). Even the film

tourism phenomenon can be considered as a continuum of the tourism motivated by

literary. People read the novels, poems and some other literary works and these

vicarious experiences -in the imagination through the feelings or actions of another

person- induce them to travel places featured within the art pieces (Beeton, 2006).

Because film industry tourism research is a relatively new area, the theoretical

base of the field is still weak, and the body of knowledge is not well structured. Two

stream studies exist in film tourism research. The one stream attempts to construct a

theoretical framework (Beeton, 2005; Busby and Klug, 2001; Croy and Heitmann, 2011)

and the other stream aims to empirically test the concept with quantitative research

(Hudson and Ritchie, 2006; Fernandez-Young and Young, 2008). The earlier studies

were conducted to suggest whether film tourism phenomenon exist (Riley and Van

Doren, 1992, Tooke and Baker 1996). The visitor numbers assisted in proving the fact

that films increase the number of visitors at the destinations featured in the films

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although few scholars such as Macionis and Sparks (2006) have kept their skeptical

position on the issue.

As next step, the studies have focused on the reasons why and how some

movies and TV shows attract audiences to destinations (Buchmann, Moore and Fisher,

2010; Karpovich, 2010; Kim, 2012). The focus areas have become the storylines,

genres, and the level of audience involvement or the landscapes featured in the movies

(Kim and Richardson, 2003; Shani et al., 2009, Kim and et al., 2007). These constructs

are utilized in the present study to find out how they impact the destination images,

awareness, place familiarity, and behavioral intentions such as visitation interest and

word of mouth recommendation, willingness to pay. As the final step, DMOs realized

this opportunity to promote their destinations and nurture tourism in destinations in order

to increase the economic benefits of tourism (Connell, 2012).

The conducted film tourism studies have overwhelmingly focused on movies.

Very few TV series have become a subject of research such as the Korean TV series

“winter sonata” (Kim et al., 2007) within film tourism studies. In the same time, Beeton

(2010) brought the fact that the film tourism concept is only discussed within a western

paradigm; the products are Westerns, host destinations are Westerns, and audiences

are Westerns. There is a need to implement non-Western origin research in order to

diversify and deepen the body of knowledge as well as justify film tourism phenomenon

in a wider cultural and geographical senses. The studies regarding Korean TV series

were one of the very first attempts in film tourism area beyond a Western paradigm, but

the nature of the studies bases on the cultural and geographical proximity because the

Korean TV series gained the popularity in neighboring countries. On the other hand,

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Iwashita (2008) and Shani et al. (2009) assert that foreign destinations represent the

most unfamiliar locations. Therefore, conducting research beyond cultural and spatial

proximity eradicates the limitations created by cultural and spatial proximity.

Connell (2012), the scholar who has the only meta-analysis work regarding film

tourism, welcomes studies that focus on the parapsychological dimension of film

tourism. Beeton (2010), the scholar who has the only book dedicated to film tourism,

highlights the lack of interdisciplinary research between tourism and media studies.

There is a need of adding social psychology component into film tourism concept to

have a better understanding of film tourism phenomenon. Shani et al. (2009) offered

one of the few studies to investigate genre on destination image and visitation interest.

There is no single study utilizing audience involvement to inquire effects of movies or

TV dramas on place familiarity, destination image, and behavioral intentions including

visitation interest, word of mouth (WoM) recommendation, and willingness to pay.

Turkish soap operas phenomenon has been winding Arabic geography, Eastern

Europe, Balkan Region, Russia, Ukraine and more surprisingly throughout the American

continent, particularly, Latin American countries including Argentina, Brazil, Chile, Costa

Rica, México, Cuba, Puerto Rico, Uruguay and the others, provides a practical room to

fill some significant gaps remained by the main stream literature in tourism studies from

various aspects such as film tourism, destination image, and behavioral intentions. As it

is seen, the study aims to be the first actual attempt to investigate the implications of TV

dramas between culturally and geographically distant locations on the destination

image, in particular, the image of country, place familiarity and behavioral intentions

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toward a foreign destination through audience involvement, in particular, behavioral

audience involvement.

In brief, all these recognized gaps are needed to be narrowed down to inform

DMOs well and benefit from the marketing opportunities created by TV shows to

promote their destinations to mass audiences.

Purpose of the Study

The purpose of this study is to examine the effectiveness of Turkish TV series as

a marketing tool to promote image of Turkey as a tourist destination throughout the

American continent. The present study aims to fulfill the noted objective due to adding

the socio-psychological dimension with applying audience involvement scale to produce

more in-depth explanations of the relation between TV dramas and the concepts of

destination image, place familiarity, and behavioral intentions.

The study has four distinctive attributes. It has a cross-cultural attribute focusing

on outbound tourism opportunities created by TV dramas. The TV dramas, which are

the subject of the present study, and the destinations featured through the TV dramas,

are Turkish and the audiences are the Americans (including the US and Canada),

majority is Latin Americans. Furthermore, the body of film tourism knowledge has been

overly built on the Western origin studies (Cynthia and Beeton, 2009). The TV dramas,

the destinations featured, and the audiences are Westerns; at least one of these three

pillars. The present study is an attempt on understanding of film tourism beyond a

Western paradigm. The TV dramas, the destinations featured, and the audiences are

non-Westerns. In addition, the present study examines film tourism phenomenon

beyond a cultural and spatial (geographical) proximity. The audiences are located in

mainly South and Central America. TV dramas have been filmed in the destinations in

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Turkey, the Eurasian country. Besides, culturally, these two locations do not have solid

close ties or common grounds. Different languages are spoken in those two places,

Turkey and the American continent. People affiliate themselves with different religions

and they have different ethnic backgrounds in these two locations. Last but not least,

the study aims to be contribution to the discussion on the influence of TV shows on

destination image as a promoter and as a motivator of the travel decisions with utilizing

audience involvement concept as a first study, which consists of the major significance

of the study to tourism studies literature.

Generally speaking, the present study aims to examine whether watching TV

series, a huge part of leisure culture especially in Latin America and relatively passive

leisure activity, has some potential to be converted to the other active form of leisure

activity, travelling.

In a nutshell, this study aims to respond many what-ifs left by the relevant

tourism literature and add a new brick on the wall.

Research Questions

The research is primarily focused on the Turkish TV series as a marketing tool to

promote image of Turkey as a tourist destination throughout the American continent.

The credible concept to accomplish this purpose is audience involvement, in particular,

behavioral audience involvement, given the results of Kim’s study (2011). The existing

relevant studies have never utilized audience involvement scale to investigate how

movies or TV shows affect destination image, place familiarity, and behavioral intentions

including visitation interest, WoM recommendations, and willingness to pay towards a

destination, in particular, a foreign destination. Audience involvement scales have been

utilized to inquire actual on-site visit experiences (Kim et al, 2007; Kim, 2011). Kim et al.

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(2007) suggested a brand-new audience involvement scale and used it in their

research. Kim (2011) borrowed Sood’s (2002) audience involvement scale generated

for media studies and implemented the modified form of it. Sood (2002) generated the

scale for a radio soap opera mainly targeting children and having education-

entertainment purposes. The scale has five dimensions (affectively oriented interaction,

cognitively oriented interaction, behaviorally oriented interaction, referential reflection,

and critical reflection). The uniqueness of the present study is to (1) develop an

audience involvement scale based on evidence of involvement with the Turkish soap

operas taken from social networks and (2) implement this scale to identify the influence

of TV dramas on destination image, place familiarity, and behavioral intentions.

The Turkish TV dramas with distinctive features have been viewed by audiences

from various countries. This diversity of features of Turkish TV dramas assists in

inquiring if the level of audience involvement is influenced by mode of watching

(subtitled or dubbed) and platform of watching (TV or the Internet), and audience

involvement, place familiarity, destination image, and behavioral intentions are

influenced by genre.

All those things considered, the two groups of research questions were

developed:

RQ 1 (a): Does audience involvement with Turkish TV Series differ based on

mode of watching?

RQ 1 (b): Does audience involvement with Turkish TV Series differ based on

platform of watching?

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RQ 1 (c) Do audience involvement, (d) place familiarity, (e) destination image,

and (f) behavioral intentions differ based on genre?

Figure 1-1. Demonstration of Relations regarding RQ-1

RQ 2 (a): Does audience involvement contribute to predicting place familiarity?

RQ 2 (b): Do audience involvement and place familiarity contribute to predicting

destination image of Turkey?

RQ 2 (c): Do audience involvement, place familiarity, and destination image

contribute to predicting behavioral intentions toward Turkey?

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Figure 1-2. Demonstration of Relations regarding RQ-2

In addition, if audience involvement, place familiarity, destination image, and

behavioral intentions differ based on main actors, actresses, and origin country of

audiences are examined as a supplementary study. The results are placed in the

appendix section.

Theoretical Framework

The conceptual framework is reinforced by multiple components. From marketing

aspect, the product placement is the one pillar. Product placement notion refers to the

idea that placing products or destinations into a movie or a TV show that influences

viewers favorably (Balasubramanian, 1994). From tourism studies aspect, tourism

motivation theories are the other pillar. Riley and Van Doren (1992) argued that movies

and TV shows are pull factors in tourist motivation within Crompton's (1979) The Push

and Pull Factor theory of motivation. From media studies aspect, audience involvement

is the other column. As the relation between audience or potential film tourist and the

destination is built through some form of media representation, it is inevitable to include

27

audience involvement within the theoretical framework of the present research. Kim

(2012) utilized an audience involvement scale in case of visitation experience on-site

within film tourism studies. It is found that some forms of audience involvement such as

emotional and behavioral involvements, especially, behavioral involvement are more

influential on visit experience at the destination. Audience involvement concept leads

the theoretical framework toward consumer involvement theory inevitably. Eventually,

audiences are potential tourism consumers; tourists. Consumer involvement theory is

set up on some form of involvements; basically the level of involvement refers to how

much resource such as energy, thought, and time they allocate for their purchase

decision process (“Consumer Involvement Theory,” 2017). These pillars consist of the

theoretical framework of the present research.

Connell (2012)

Figure 1-3. Dimensions of Film Tourism

The graph above is an appropriate representative of research dimensions of film

tourism within a social science paradigm (model). Tourism with its impacts, managerial,

and behavioral components; Film and Media with its cinematography, film theory,

28

mediatization components; Marketing with its consumer behavior, branding, image and

promotion components; Cultural geography with its art, landscape, culture, nature,

space, and place components. As last, psychology with its social, cognitive, and

neuropsychology components constitute dimensions of film tourism.

Limitations and Delimitations

Limitations

The past visits to Turkey and exposure to the information regarding Turkey

through media outlets, magazines, news reports, books, casual conversations, and

other screen products, such as movies, TV shows, and TV series are the distinct

variables considered as limitations. Also, the honesty and the level of commitment of

participants cannot be controlled. That is the other limitation. As with many human

responses, it is not possible to conduct the present study with all potentially confounding

variables. In addition, the accelerated research process is one of the other leading

projected delimitations as the present research is a master's thesis study, not a doctoral

dissertation. Also, the restricted budget to spend to reach out wider audiences

throughout the American continent is the other restriction. Besides, since the TV series

have been aired at different times, the audiences can have a different level of impacts

on themselves at the time study is carried out.

Delimitations

The data collection process is carried out on the social media platforms, not in

person. The online surveys are distributed within the noted social media Turkish TV

series fan groups. So it is not self-administered. As a natural result of it, Turkish TV

drama viewers from the American continent, who are not social media users are

automatically excluded. These are the considered delimitations.

29

Defining the Population

This study aims to obtain a convenience sampling of Turkish TV drama viewers

from the American continent, including the US and Canada as well as Latin American

countries. They watch almost all episodes of at least one Turkish TV dramas that they

are pretty confident to have an opinion about the TV series.

Organization of the Study

The present study is divided into five chapters. Firstly, the introduction provides

an overview of the research subject, drafting purpose of the study and the significance

of the study. The second chapter, literature review, begins with a review of the

theoretical framework. It combines audience involvement, destination image, and place

familiarity within film tourism. It also discusses film tourism research methods and gaps

in the literature. The third chapter outlines the methods employed by the researcher to

do the statistical analysis of the study. It encompasses research questions, research

design, and data collection plans, instruments and data analysis procedures. The fourth

chapter contains the data and tables delineating the findings results of statistical

analyses. The fifth chapter discusses the findings, limitations, and recommendations for

future research.

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CHAPTER 2 REVIEW OF THE RELEVANT LITERATURE

In this chapter, the relevant literature of push pull factor tourist motivations in

general and in terms of media products, audience involvement, place familiarity,

destination image as well as film tourism literature are reviewed.

Push Pull Factor Tourist Motivations

Tourism motivations are grouped as pull and push factors (Dann, 1977). The

examination of the concept over push pull factors is widely applied (Uysal and Hagan,

1993). Dann (1981) found this distinction functional theoretically as it clarifies logical

and temporal sequencing. Push factors represent intrinsic desire to travel and pull

factors stand for attributes of the destination influencing people’s destination choices. In

other words, push factors are internal forces and pull factors are external forces (Uysal

and Jurowski, 1994). In general, push factors are identified as escape, relaxation,

prestige, adventure, social interaction. Pull factors are identified as physical features of

a destinations such as beaches, cultural attractions, recreation opportunities (Uysal and

Jurowski, 1994).

Gnoth (1997) offered that push factors, which are inner-directed motivations,

depend on pull factors, which are outer-directed values. Uysal and Jurowski (1994)

conducted a study to examine the degree of the reciprocal relationship between push

and pull factors for pleasure travel. They suggested that simultaneous inspection on

motivations and destination attributes would aid in developing destination marketing

strategies. Baloglu and Uysal (1996) followed the same track to evaluate a technique

allowing synchronous exploration of push and pull factors of motivations. They found a

significant relationship between destination attributes and motives. It would help

31

segmenting tourists and DMOs would know why their destinations are demanded. This

would lead DMOs to establish better marketing strategies.

The previous studies suggest that destination features pull people to travel some

destinations (Crompton, 1979). Push factors are pertaining to whether to go; pull factors

are pertaining to where to go (Klenosky, 2002). Pull factors follow push factors (Dann,

1981, p.207). However, these two phenomena are seen interrelated by Uysal and

Jorowski (1994). Klenosky (2002) used means-end theory to examine push-pull factors

relationships. Klenosky (2002) defines push factors as a force to lead individuals to

travel while pull factors are defined as leading individuals to choose a destination over

another. Pull factors in tourist motivation theory is “attract the tourist to a destination and

whose value is seen to reside in the object of travel (Riley and Doren, 1992).

Some other empirical studies examined the relations between push and pull

factors. Dann (1977) identified push factors as anomie and ego enhancement with using

scale development as the research approach. Crompton (1979) identified push factors

as escape, self-exploration and evaluation, relaxation, prestige, regression,

enhancement of kinship relationships, and social interaction; pull factors as novelty and

education with using unstructured in-depth interviews as the research approach. Yuan

and McDonald (1990) identified push factors escape, novelty, prestige, enhancement of

kinship relationships, relaxation/hobbies; pull factors as budget, culture and history,

wilderness, ease of travel, cosmopolitan environment, facilities, hunting with using factor

analyses of 29 motivational push items and destination pull items.

The push-pull framework is also suitable to examine the role of media products

as a pull factor and their influence on perceptions of audiences on destination (Riley

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and Van Doren, 1992). Macionis (2004) suggested that the push and pull factor theory

of motivation suits best to frame film-induced tourism from a theoretical aspect to

examine the phenomenon from a consumer aspect. Three concepts are proposed to

make a distinction among motivations; place (location attributes, landscapes, scenery),

personality (cast, characters, celebrity), and performance (plot, theme, genre)

Macionis (2004) provided useful categorization and clarified the motivations

gained over media representations. This “3P” categorization aids in understanding

where audiences’ motivations come from. Does it come from remarkable destination

features of “Place”; or genre type or the plot of the show or; actors and actresses acting

in the show? This 3P framework facilitates to identify sources of motivations.

Place: Some physical environments are needed in order to film shows. The

filming locations have some attributes such as authentic scenery and remarkable

landscape or some spectacular historical places that attract audiences’ attentions. This

visibility of those places is such a good way of promoting a destination. Iwashta (2003)

signified the opportunity obtained popular cultural products for places to be advertised.

Performance: Macionis (2004) expressed that “People are not only drawn to the

places that form the settings and landscapes for feature films, but they may also be

drawn to particular stories and genres, that is the drama of the plot, the elements of the

theme and the experiences of the people in the film.” From the performance aspect,

storylines, plots, and genres can be a motivation for viewers to consider visiting that

destination. Frost (2004) posited that filming shows in attractive locations might not be a

sole pull factor. Storylines and genres such as action, historical, romance, and drama

may be influential on audiences in terms of attracting them for the destination.

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Personality: Characters and/or actors and actresses may be a pull factor

motivating audiences to visit besides destination attributes that are featured in media

products and storylines, genres and plots. Celebrities and icons are used as advertising

faces by DMOs. For instance, James Bond is associated with Monte Carlo as a pull

factor for the destination (Macionis, 2004).

To summarize, as it is mentioned earlier, media products are offered to be

examined as a pull factor under the roof of push pull factors tourist motivation theories

(Riley and Van Doren, 1992). However, in which ways (the way information perceived,

the way emotions prodded, the way involvement occurred etc.) media products are

influential and which elements of media products (storyline, actors, genre etc.) are

indicators of their influences on audiences regarding destination marketing constructs

(place familiarity, destination image, visitation etc.) remained unclear. To be able to

argue confidently, mainly the relationship between audience involvement and place

familiarity, destination image, and behavioral intentions (visitation interest, WoM

recommendation, and willingness to pay) is aimed to be examined as well as the

examination of the relationship between genre and audience involvement, place

familiarity, destination image, and behavioral intentions under the umbrella of push pull

factors tourist motivation theories. Other than that, the conditions of watching (dubbed

or subtitled, on TV or on the Internet) are investigated if they are influential on audience

involvement.

Audience Involvement

Involvement is a major element of a couple of scholarly fields such as

communication and consumer behavior. A widely accepted definition of the concept

“involvement” in media and communication studies is “the degree to which an audience

34

engages with or relates to a particular medium in the communication process.” The

concept “involvement” has been discussed in the consumer behavior literature

(Krugman, 1965) and even longer in psychology (Sherif and Cantril 1947). In time, it has

become an attractive subject within the consumer decision process.

Sood (2002) suggests that the concept of audience involvement is a mediator

leading to behavior change. It serves as a means to induce interpersonal

communications among the audiences. Within the media and communication studies

Lozano (1992); Singhal and et al. (1999); Storey (1998) argued that some changes

occurred on knowledge, attitudes, and behaviors of audiences due to media programs.

Piotrow and et al. (1997) assessed that those changes do not necessarily occur as

direct and tangible effects but also intermediate level effects. Planned entertainment-

education messages in media programs aimed to promote behavioral changes on

audiences such as gender equality, safe sex and family harmony (Piotrow and et al.;

1997). For example, Sood (2002) conducted a study about a 104-episode

entertainment-education drama “Tinka Tinka Sukh” (Happiness lies in small things)

estimated reaching about 40 million people. His study argued that audience involvement

with the radio soap opera carrying entertainment-education messages increased self-

efficacy among audiences, which is considered an indirect effect, in other words,

intermediate effect. Such impacts are not regarded as direct effects on behaviors but

they are placed in many behavioral models as antecedents (Ajzen, 1991).

Audience involvement is considered as parasocial interaction (Rubin and et al.,

1985). Parasocial interaction is composed by affective, cognitive, and behavioral

components. Sood (2002) defines these three components effectively in a clear-cut

35

way. According to him, affectively oriented interaction is the degree to which audiences

associate themselves with main actors or some other features of a TV show (for

example, a place, way of communication or lifestyle etc.). Cognitively oriented

interaction is the degree to which audiences follow an episode with high attention and

think about its messages and content once it ends. Behaviorally oriented interaction is

the degree to which audiences talk about characters of show, reschedule their daily or

weekly things to spare time for a media program, and keep their affiliation with a media

program beyond its broadcasting hours in some forms such as making a media program

a subject in a casual conversation and paying attention news reports regarding the

same media program.

Film tourism studies imported audience involvement concept from media and

communication studies. Sood’s (2002) audience involvement scale from media studies

was utilized by Kim (2012) in tourism studies. Kim (2012) suggests that audience's

emotional and behavioral involvement affects audiences’ film tourism experiences on

destinations and he indicates that the more emotional and behavioral involvement of

audience with TV dramas, the higher possibility of them having visitation interest to

destinations featured through screen products. Kim (2010) asserted that the film tourism

studies ignore audience involvement component. Moreover, Kim and Richardson (2003)

empirically investigated the influence of “vicarious experience” -in the imagination

through the feelings or actions of another person- with a movie on destination image

changes. In similar, Lee et al. (2008) conducted an empirical study on the impact of

celebrity fan involvement on destination image. However, according to Kim (2012),

“contrary to theoretical support, neither empathic involvement nor celebrity fan

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involvement impacted on destination images toward locations depicted in films.”

According to Kim and Richardson (2003), it is because the construct of vicarious

experience or the construct of audience involvement is not as closely tied to empathic

involvement as has been suggested in the marketing literature. Therefore, re-

conceptualized constructs and theoretically supported measurement scales of audience

involvement would aid in obtaining better understanding of the intermingled

relationships between popular media consumption and destination image, behavioral

intentions, tourist experiences, and overall film tourism concept. Also, the multi-

dimensionality of audience involvement should be taken into account when

conceptualizing and operationalizing its concept in the context of film tourism (Connell,

2012).

Place Familiarity

Place familiarity is the perception of how much an individual knows about a

destination (Moorthy, Ratchford, and Talukdar, 1997). Familiarity has a vital role in

consumer decision-making (Kim and Richardson, 2003). The familiarity concept first

took place in tourism literature with a measuring role of previous visitations on a

destination for future visitations (Fakeye and Crompton, 1991). Hu and Ritchie (1993)

do not deny that a key element of familiarity is the previous visitation, and suggest that

other several factors have impacts on place familiarity, which are geographic distance

and level of overall knowledge about a place. On the other hand, Olsen et al. (1986)

pointed out the importance of familiarity with a destination regarding feeling secure and

comfortable, which leads to confidence in destination choice.

Diversification of communication and information sources over time resulted in

destination marketing scholars to consider familiarity as an attitudinal variable not just

37

simply as a result of previous visits (Baloglu, 2001; Iwashita, 2008). Studies of Hu and

Ritchie (1993), Iwashita (2008), Tasci (2009) assist in accepting place familiarity widely

as a construct in film tourism, and their research suggests that exposure to a destination

through television or film can create perceived familiarity with destinations featured.

Spears and Dutta (2014) indicate that higher level of familiarity and attachment of

Bollywood audiences with Switzerland, UK, and France is as a result of long-term

exposure to blockbuster movies over the decades since 1960`s. In their study, 35% of

respondents had no image of Portugal or the Netherlands as tourism destinations,

which is stated as a result of no major movies filmed in these two destinations.

Tasci (2009) digs into movies to find out if social distance can be narrowed

through screen products featuring the everyday life of a place's inhabitants to provide

familiarity and as a result of that portraying better, positive image and increasing

visitation interest. Her research found that familiarity created by visual outlets has

impacts on destination image and visitation interest. Riley and Van Doren (1992) argued

that extended exposure to a destination via a film decrease worries of potential tourists

caused by little-known situations at the destination as well as helping potential tourists

getting knowledge regarding the destination.

The studies discussing negative reflections of filmed products also come into

place. Tasci claims negativity leads audiences to activate their defense mechanisms

towards a destination. On the other hand, Croy and Walker (2003) address that

negative exposure to geographically far and little-known places can be favorable as

they lower unfamiliarity of the destination. A Turkish saying "Reklamin iyisi kotusu

olmaz” meaning “there is no such thing as bad publicity” is supported by the study of

38

Shani et al. (2009). Their study about Motorcycle Diaries showed that the desire to visit

South America has increased despite its controversial plot. Nevertheless, claiming the

greater familiarity, the more positive affiliation with destination features is not convincing

enough.

The concept of optimal familiarity presented by MacKay and Fesenmaier (1997)

has been applied as a middle way to end arguments within tourism marketing literature.

After a certain level of familiarity, the visitation interest becomes less attractive.

Becoming too familiar and too safe can lower adventure, novelty, and newness in any

destinations.

Destination Image in Film Tourism

The dynamic and complex nature of destination image caused to have multiple

definitions of the concept. However, one of the comprehensive definitions of the

destination image is "representing the sum of beliefs, ideas, and impressions that a

person has about a place or location based on information processed from a variety of

sources" (Baloglu and McCleary, 1999; Crompton, 1979). Destination image has at

least two dimensions, rational and emotional (Baloglu and Brinberg, 1997; Baloglu and

McCleary, 1999). The rational, or cognitive, element refers to all knowledge,

perceptions, and beliefs that potential travelers hold about a destination and interprets

the image as a set of relevant attributes. Emotional or affective, the element of DI refers

to consumers' feelings about a destination, which can be favorable, unfavorable, or

neutral. Baloglu and McCleary (1999) provide a review of research, which supports the

view that cognitive and affective elements are interrelated, with affect being largely

dependent on cognition.

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The destination image is a subjective construct, and it is not an easy task to

determine its components (Stepchenkova and Mills, 2010). Destination image plays a

role in the selection of the destination (Mayo and Jarvis, 1981). As a result of dynamic

and relative nature of destination image, perceptions of destination image change

person to person, with time, the physical distance between destination and potential

travelers (Gallarza et al., 2002). The more knowledge people have about a destination,

the more positive image is instilled in their minds (Baloglu, 2001; Crompton, 1979). After

Baloglu had proved it, she concluded that place familiarity is a critical component of

destination marketing. Gartner (1993) assessed that greater distance leads to distorted

reality about destination.

The other important factor on destination image is a source of information. Much

research has investigated destination image shaped through media such as TV, film,

literature, etc. (Frost, 2006; Hudson and Ritchie, 2006; H. Kim and Richardson, 2003).

Frost (2006) has studied on the use of film to promote heritage tourism. Hudson and

Ritchie (2006) presented a multifactor conceptual framework for understanding the film

tourism phenomenon, which has been investigated with case studies later on. Kim and

Richardson (2003) studied the motion picture impacts on DIs; the conceptual framework

introduced the notion of vicarious experience through empathy.

The primary purpose of movies and TV dramas is not to attract people to a

destination, but they influence audiences. Destination image is held from marketing

aspect in film tourism studies to date. Croy (2010) indicates the use of films in a

destination image management in the case of Lord of the Rings. Frost (2006) attempted

to identify the effect of historical movies on destination image from heritage tourism

40

aspect. It is concluded that films do not create a new destination image; rather they

contribute to an existing one.

Schofield (1996) identified that TV and films have huge impact on organic images

of destinations. The image of Rome is depicted as the city of sin and pleasure in the

movie La Dolce Vita (Gundle, 2002). Kim and Richardson (2003) found that the

cognitive and affective images of Vienna were affected by the movie Before Sunrise.

Croy and Walker (2003) indicated that even unfavorable depiction of a destination could

induce audiences to visit.

On the other hand, a brand-new disagreement voice with the extant destination

image literature has been raised by Kock and et al. (2016). They criticize the present

body of knowledge of destination image in terms of being too theoretical and not being

meticulously operationalized. They came up with a new destination construct. They

introduce a destination content model and offer that;

Like / Dislike

Pleasant / Unpleasant

Attraction/ Repulsion

Comfortable/ Uncomfortable

Should be used as an affective item construct.

Good / Bad

Positive / Negative

Favorable / Unfavorable

Worthwhile / Not Worthwhile

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Should be used as a cognitive item construct. They argue that these scales are

more effective to obtain overall evaluative tendency of an individual toward a

destination.

Film Tourism

Film tourism is presented as a subsequent of literary tourism (Beeton, 2005)

because film tourism is also a medium through which a range of cultural meanings and

values are communicated as well as being a function of media (Busby and Klug, 2001).

Film tourism refers to “visitation to destinations where movies and TV programs have

been filmed and which can be a production studio, a district, a town and a country

featured within products.” Throughout the literature, film tourism is also named as film-

induced tourism (Beeton, 2005), movie-induced tourism (Riley et al., 1998) media-

related tourism (Busby and Klug, 2001). Some studies have sought a more inclusive

term to name it. Screen tourism is the one of them offered (Connell and Meyer, 2009,

Kim et al., 2009).

Some studies have been conducted and demonstrating that screen products are

motivational pull factors for destinations (Riley and Van Doren, 1992; Tooke and Baker,

1996; Couldry, 1998; Beeton, 2001; Kim and Richardson, 2003). Macionis (2004)

suggests that Dann’s (1977) and Crompton's (1979) The Push and Pull Factor Theory

of Travel Motivation provides an appropriate theoretical framework to examine film

tourism from the consumer behavior perspective. Lord of the Rings in New Zealand and

the long-running American TV drama, Dallas, are well-known examples attracting

international tourists to the destinations as a pull factor (Riley and Van Doren, 1992).

42

Cohen (1986), Urry (1990), Butler (1990) are the very first scholars who

dedicated their studies to explore film tourism phenomenon. The Second wave was

actualized by Riley and Van Doren (1992), Tooke and Baker (1996) and Riley, Baker

and van Doren (1998). They attempted to frame the film tourism concept with distinct

clarity. Beeton (2001), Busby and Klug (2001) Connell (2005) explored the tourism

opportunities created by TV shows and movies. The first book dedicated to the film

tourism is published by Beeton in 2005.

Early studies attempted to find out the effects of the movies and TV series on

increase of tourism in destinations. After Hudson and Ritchie (2006a, 2006b) indicated

some TV series and movies are useful to promote destinations and induce tourism,

studies have been directed to find the features of successful movies concerning

promoting destinations and boosting tourism. As parallel, some marketing tools are

applied by DMOs to market their destinations and leverage tourism economy in their

places. Eventually, “Product Placement” concept has been brought on table. Widely

used definition of product placement is that “a planned entries of products into a film

that influence viewers favorably (Balasubramanian, 1994). The contemporary definition

of product placement is that “a hybrid communication form that offers an often captive

audience access to a brand that is presented in a discrete, non-argued and financed

manner in a movie, a TV series, a video game, or a literary or musical work.” (Delattre

and Colovic, 2009, p. 808). It is alternative to traditional advertising to deliver messages

through mediated means. The objective is to increase awareness and promote brands

or products with placing them into mass media platforms. The traditional advertising has

been losing ground lately; in oppose, product placement initiatives have been gaining

43

ground (Hudson and Ritchie, 2006b). Product placement is assumed to be perceived

positively and influence consumer behaviors and create loyalty with brand (Hart, 2003).

Hong and et al. (2008) suggested that brand-recall occurs regardless of a positive or

negative context. From tourism studies perspective, product placement is a brand-new

concept that is utilized as a theoretical framework within film tourism literature.

Obviously, in film tourism studies, destinations are the products that need to be

advertised. Product placement into film tourism is offered to be named as "place

placement" (Kim and Richardson, 2003). Kim and Richardson (2003) argued that “just

as product placements will influence a viewer’s attitude toward a brand, so too will films

have an impact on destination image if the location plays a part in a film.”

The late 2000s has witnessed the explosion of the number of journals dedicated

to film tourism studies. However, all research initiatives have remained within a Western

paradigm in mainstream film tourism studies until Korean entered into the scope. The

first attempt to understand film tourism phenomenon beyond a Western paradigm within

mainstream film tourism literature occurred due to the studies having Korean TV

dramas as a research subject. In 2007, the statistical data demonstrate that the TV

drama Fireworks increased the number of Taiwanese tourists to Korea (Kim, Chen, and

Su, 2009). The other TV drama Winter Sonata stimulated primarily Japanese tourists

and some other tourists from Taiwan, Thailand, Singapore and China to Korea (Kim and

et al. 2007). This Korean Wave (Hallyu), the popular culture, induced the inter-regional

outbound tourism overall. It is worth to mention that a number of studies have

showcased that significance of film tourism should not be underestimated. Urry and

Larsen (2011) argue the effect of media products creating demand for destinations

44

within postmodern concept, which means travelers avoid restricting themselves with the

conventional motives to travel and traditional places to visit. They seek authentic and

novel incentives and places (Macionis, 2004).

There is some evidence to suggest an average increase in visitor numbers of 30-

60% for film destinations (Connell 2012). Busy and Kulg (2001) acknowledged that two-

thirds of respondents agreed with the fact that TV shows and films induce tourism to

certain locations. The existing literature states that film and TV product consumption are

effective in shaping the image of destination and to increasing visitation interest. The

study of Hudson and Ritchie (2006b) revealed that 80 percent of British people consider

the movies as a source of supplying destination options and 20 percent exactly visit the

locations depicted in their favorite TV shows and movies. Iwashita (2008) affirmed that

20 percent of Japanese tourists visiting the UK is motivated by film and TV dramas. An

interesting result is reported by D’Angelo et al. (2006). The results of the study showed

that responses were more favorable to the item "Films` influence on the past three

years travel purchase" than the item "Films` influence on travel destination choice." It is

inferred that tourists are not conscious of the fact that their travel behaviors are affected

by films or TV series.

It is recognized that the locations featured in TV series or movies gain the pulling

power of these visual products to boost tourism in location. Connell (2005), Iwashita

(2008), and Beeton (2005) are favorable of the argument that the places featured in TV

and films attract more tourists while few others have still been questioning it. Attracting

tourists to destination with TV series or films occurs in 2 distinct ways; the one way is to

visit film locations where the filming takes place and other associated film sites. The

45

other one is to visit locations featured as touristic places through TV series and films

where the film or TV drama may not have been experienced.

The countries enjoying film tourism are as primary UK, USA, Australia, New

Zealand as secondary some European countries and Korea (Connell, 2012). Some of

them have boosted this potential with strategic marketing approaches (Beeton, 2010).

Suni and Komppula (2012) argued that film tourism in the UK is worth nearly 1.6 billion

pounds. The movie “Close Encounters of the Third Kind” induced visitors to Devil

Tower National Monument 12 years after its release. More than 20 percent of the

visitors acknowledged that their knowledge of the Monument comes from the movie

(Workman, Zeiger, and Caneday, 1990).

According to Riley et al. (1998), 12 US locations attracted nearly 43 percent more

tourists five years after the movies filmed at those destinations were released. And the

data analysis indicated that at least four years of visitation increase. For example,

tourists still visit the bench on which Forest Gump (1994) was sitting whiling telling the

story through the movie many years after the movie release.

One of the interesting exploratory studies was conducted by Shani et al. (2009),

which is in the case of the movie The Motor Cycle Diaries featuring South America. The

study suggested that the content of film did not have impact on overall perception of the

destination image viewing it after and before.

Alfred Hitchcock, the late American film director, used remarkable settings

promoting cityscape, landscape, and landmarks of film locations. His movie Vertigo

(1958) became a milestone to promote touristic icons of San Francisco worldwide

(Wexman, 1986). Crofts (1989) evaluates this Australian movie, Crocodile Dundee,

46

success as the first movie to develop tourism to Australia. Lord of the Rings trilogy is

another success story. Figuratively speaking, it put New Zeeland on the map. New

Zealand had not been as known as once the trilogy-movies were filmed there. Lord of

the Rings trilogy should be categorized in series in terms of longevity. Three

independent movies were released, but it took over three years like TV series.

In the case of Notting Hill, the heritage site experienced a 10 percent increase in

visitors in August 1999 alone after the launch of the movie (Busby and Klug, 2001). The

movie “Braveheart” and its impact on tourism and image of Scotland were studied by

Frost (2006). Im and Chon (2008) indicated that the classical musical “The Sound of

Music” still contributes to tourism in Salzburg, Austria many years after it released.

Munshi (2012) reported that after the Bollywood movie “Zindagi Na Milegi Dobara” was

released the arrivals to Spain from India increased 12 800 to 115 000 in 2011. Almost

1000 percent increase. It is learned from the study of Spears and Dutta (2014) that

Swiss Alps became a tourist attraction for Bollywood audiences after a number movies

have been filmed in Switzerland.

TV series may have an advantage by nature to be a tourism booster, which is

their longevity. Their longevity could aid in a long-term audience involvement with TV

dramas (Hudson and Ritchie, 2006b). Hawaii is a long-run success story in terms of

long-term ties between the audiences and the destination. Hawaii Five-O in 1968

introduces Hawaii to the world. It is learned from Bly (2004) that people still email

Hawaiian Tourism office and request information about the locations they saw in the TV

series. The newer TV series, Lost helped the Hawaii re-promote itself. The old long-

running TV show "Dallas" brought 500 000 tourists, in particular, Western Europeans,

47

each year to Dallas those years (Riley et al. 1992). Kim (2012), Kim and et al. (2009),

and Kim and et al. (2007) studied Korean TV dramas and their attracting foreign tourists

to Korea as well.

Soap Opera: Soap operas are one kind of media products. Collins Dictionary

defines soap opera as a serialized drama, usually dealing with domestic themes and

characterized by sentimentally, broadcast on radio or television. The origin of the word

derives from so called because manufacturers of soap were typical sponsors. The main

characteristics that define soap operas are "an emphasis on family life, personal

relationships, sexual dramas, emotional and moral conflicts; some coverage of topical

issues” (Bowles, 2000). The first serial considered being a "soap opera" was Painted

Dreams, which debuted on October 20, 1930, on Chicago radio station WGN. First

televised soap operas goes back to the late 1940s (Cox, 1999). The name, "soap"

refers to the soap and detergent commercials originally broadcasted during shows,

which were aimed at women who were cleaning their houses when viewing, and "opera"

refers to the melodramatic characters of the shows.

Gaps in the Literature

Film tourism literature is full of empirical studies: case studies, surveys, and lately

experiments. As it is a relatively young field, it is not regarded to have a widely accepted

theoretical and/or conceptual framework. The researchers in the field still stress that

more quantitative studies are in need to construct theoretical and conceptual structures

for film tourism studies (Beeton, 2010).

The future studies should assist in diversifying the type of products (not only

movies but also TV dramas, soap operas), destinations (not only the US, UK, Europe

and Australia but also Asia, Latin America, Balkans, the Middle East), and producers

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(not only Hollywood, Bollywood but also other national and regional producers, sectors)

to have more solid data to conceptualize film tourism realistically (Beeton, 2010). Also,

socio-psychological components such as audience involvement need to be utilized to

understand the relationship between TV shows and tourism with a deeper sense

(Connell, 2012).

The studies are narrowly focused; therefore, academicians and practitioners are

still relying on assumptions and speculation to some extent, as it is believed that some

other dimensions need to be explored and added in the concept (Connell, 2012).

Summary and Hypotheses

Mode of Watching (Dubbed or Subtitled)

With mode of watching it is meant that products are either subtitled (audio-visual)

or dubbed (over-voice). The subject has taken interest of both academic and business

circles. The film industry became interested because they invest a lot of financial

resources to translate products in other languages. Academic circles became interested

because they want to know influences of these two different methods on audiences

from different aspects.

Audiovisual translation or subtitling and dubbing are applied often for TV shows

and movies. Audiovisual text is received via two channels; the acoustic and the visual. It

includes two messages; verbal and non-verbal. These messages are delivered through

the screen. Dubbing is a matter of synchronization. Luyken and et al. (1991) defines

synchronization in dubbing as “the replacement of the original speech by a voice-track

which is a faithful translation of the original speech and which attempts to reproduce the

timing, phrasing and lip movements of the original”. The earliest strategies of film

makers to overcome the language problem were to produce a movie with multiple

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languages. At that time actors who were able to speak in multiple languages were

preferred (Kilborn, 1993).

Essentially, the general translation theory is applied to frame subtitling and

dubbing as it is a translation naturally and translation is defined as “a surface structure

entities by keeping the meaning invariable and as understanding the relationship

among language, thought, and objects and identifying the interaction among language,

culture, and communication” (Dizdar, 2012, pg. 52). Based on the definition, it can be

argued that subtitles and dubs could have influences on audience involvement.

From tourism studies’ aspect, since the present study is the exploratory research

examining influences of audience involvement with Turkish TV series on destination

marketing constructs (place familiarity, destination image, visitation interest, WoM

recommendation, and willingness to pay), it is aimed to examine if subtitling and

dubbing have different influences on audience involvement.

RQ 1 (a): Does audience involvement with Turkish TV Series differ based on

mode of watching?

Platform of Watching (on TV or on the Internet)

As Gambier (2003) stressed that “we are surrounded by screens” and

consumption of media products has been still increasing. All the aspects of consuming

media products are under investigation lately. One of them is conditions of reception.

Conditions of reception has been seen as one of the fundamental factors with

engagement of screen products (Agost, 1999). Some other scholars mentioned the

influence of the size of the screen in cinema or on TV on audiences (Agost, 1999).

When Simons (2014) asked the participants what can lead them to have a

stronger involvement with TV shows, most participants pointed the conditions that are

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related to the way of watching TV episodes such as size of the TV screen (big or small)

and the comfort of the sofa they are sitting on. A study conducted by Lombard and et

al. (2000) about the role of screen size on audiences found that the participants did not

report more enjoyment with the large screen; however, it was reported that the

participants watching on the large screen were more favorable to perceived quality of

the sets and the images featured within. The current study aims to examine if audience

involvement with Turkish TV series differ based on platform of watching (on TV or on

the Internet).

RQ 1 (b): Does audience involvement with Turkish TV Series differ based on

platform of watching?

Genre

Genre is a word meaning a style, especially in the arts, that involves a particular

set of characteristics. Its origin comes from French language. It begun to be used in

early 19th century (Cambridge Dictionary- online version). The Internet Movie Database,

the IMDb, classifies genre of products based on general mood of products and in this

type of classification feelings of audiences are taken as references. They are grouped in

two general categories; feel-good movies, such as comedy and romance, and dark

movies, such as horror and crime. Russel (1998) suggested that products featured in

shows are affiliated with same feelings that shows are dominated. So if shows are in a

dark category of genre, products that are featured within shows have negative

emotional attachments. When shows are in a feel-good category, products that are

featured within have positive emotional attachments.

On the other hand, Lozano (1992); Singhal and Rogers (1999); Storey (1998)

suggested that media programs cause some changes on knowledge, attitudes, and

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behaviors. Sood (2002) found that audience involvement is a mediator on behavioral

change. At this point, it becomes important to know the relationship between genre and

audience involvement with media products. Some studies found behavioral changes on

gender equality, safe sex, and family harmony due to shows watched by people.

(Piotrow and et al., 1997). In film tourism studies, Kim (2012) utilized audience

involvement to examine tourist experiences on film locations. However, there is a lack of

literature examining the relationship between genre and audience involvement.

To better understand the relationship between genre and audience involvement,

the current study aims to examine if audience involvement with Turkish TV series differ

based on genre.

In film tourism studies, a very limited number of empirical studies on how genre

influences destination marketing constructs and audiences’ attitudes toward

destinations. Place familiarity, destination image, and behavioral intentions including

visitation interest, WoM recommendation, and willingness to pay. It is a need to know

which components of media products such as genre, plot, storyline etc. and in what way

they influence those tourism destination marketing variables.

RQ-1 (c) Does audience involvement with Turkish TV series differ based on

genre?

Place familiarity is one of the primary pillars of the destination marketing

construct. Because as Olsen, McAlexander, and Roberts (1986) pointed the more

knowledge people have about a place, the more security and comfort they feel with the

destination and the more comfort and security they feel, the more tendency they show

in destination choice. Iwashita (2008) and Tasci (2009) with a positive scenario of a

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promotional video about Turkey found some increases in familiarity with the destination

after the exposure. Kim and Richardson (2003) did not find statistically meaningful

results in place familiarity with Vienna between the experiment and the control groups.

However, they were not a typical genre based studies. There is a need to inquire place

familiarity differs based on genre. The current study aims to examine if genre influences

place familiarity with Turkey.

RQ-1 (d) Does place familiarity with Turkey differ based on genre?

Destination image is the other crucial destination marketing construct. It is

approached from the evaluative aspect in this study. Cognitive and affective

components are generally used when destination image is handled within a destination

marketing framework. Some previous research found exposure to a destination over

movies influence destination image sometimes positively sometimes negatively (Kim

and Richardson, 2003; Shani et al., 2009; Hahm and Wang, 2011). Remembering

Russell’s argument (1998), the dominant emotion of the media products is

affiliated with the product featured within. Basically, it can be concluded that feel-good

shows produce affirmative impacts on destination image while dark movies or shows

fabricate unfavorable impacts on destination image. Also, remembering the fact that

genre classification is based on feelings audiences experience with media products.

Even though those studies are not purely genre studies, they can be considered prior

research of genre studies. The current study relies on the generally used classification

of genre such as action, romance, drama, and comedy to make it more understandable

for wider crowds and it investigates if destination image of Turkey with affective and

cognitive components differs based on genre.

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RQ-1 (e) Does destination image of Turkey differ based on genre?

The most important destination marketing component is behavioral intentions

toward a destination. “Behavioral intentions” is used as an umbrella term including

visitation interest, WoM recommendation, and willingness to pay, in general, in tourism

studies. Kim and Richardson (2003) found that the movie “Before Sunrise” increased

the interest visiting Vienna. They suggested that the exposure was solely enough to

induce audiences’ visitation interest regardless genre. Shani et al. (2009) conducted a

study on the movie “Motorcycle Diaries” featuring South America. Again, the exposure

created interest to visit. The interesting point is that some other destination marketing

constructs like place familiarity, cognitive and affective images were not consistent with

visitation interest. Some of them were affected negatively due to the content of the

movies. These nuances lead us to know if genre is influential on the subsequent

destination marketing constructs such as visitation interest, WoM recommendation, and

willingness to pay.

RQ-1 (f) Does genre influence behavioral intentions –visitation interest, WoM

recommendation, and willingness to pay- toward Turkey?

Audience Involvement, Place Familiarity, and Destination Image

Involvement is considered as a pre-communication status and a motivational

condition (Rubin and Perse, 1987). It impacts peoples’ perceptions, reactions to

messages (Sherif et al., 1965). Their involvement level shows how much importance

they put on the informational elements. They process messages more intensely when

they are more involved (Petty and Cacioppo, 1984). Involvement is classified as

affective, cognitive, and behavioral (Rubin and Perse, 1987). The current study focuses

on behavioral aspect of involvement with Turkish TV series. Rubin and Perse (1987),

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basically, defined behavioral involvement as “talking about messages”. Tan (1980)

pointed interpersonal discussions in political campaigns as an example of behavioral

involvement. Stanford (1984) found that those who talk back to the TV while watching

are more involved. Stanford (1984) named these viewers as personally guided. On the

other hand, Lemish (1985) found that the most involved audiences are the ones who

talk about program content with others. As audience involvement is a sign of level of

paying attention to messages and information received from TV shows, it would also be

an indicator for destinations featured within in terms of how they are perceived. During

the literature review, there is no study found examining the relationship between

audience involvement and destination in terms of destination marketing aspect in

tourism research. To explore the relationship between audience involvement and

places portrayed in TV shows, the widely used destination marketing constructs (place

familiarity, destination image, visitation interest etc.) in tourism studies are applied for

the current study.

H1-a: Personal Audience Involvement contributes to predicting Place Familiarity.

H1-b: Interpersonal Audience Involvement contributes to predicting Place Familiarity.

H2-a: Personal Audience Involvement contributes to predicting Affective Destination

Image.

H2-b: Interpersonal Audience Involvement contributes to predicting Affective Destination

Image.

H3-a: Personal Audience Involvement contributes to predicting Cognitive Destination

Image.

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H3-b: Interpersonal Audience Involvement contributes to predicting Cognitive

Destination Image.

H4-a: Personal Audience Involvement contributes to predicting Visitation Interest.

H4-b: Interpersonal Audience Involvement contributes to predicting Visitation Interest.

H5-a: Personal Audience Involvement contributes to predicting WoM Recommendation.

H5-b: Interpersonal Audience Involvement contributes to predicting WoM

Recommendation.

H6-a: Personal Audience Involvement contributes to predicting Willingness to Pay.

H6-b: Interpersonal Audience Involvement contributes to predicting Willingness to Pay.

In business studies, familiarity with products are found impactful on consumers’

purchase decisions (Laroche and et al., 1996). As parallel, media products create

familiarity with destinations (Riley and Van Doren, 1992: 269). Familiarity is an

information-based notion. The more knowledge people have about a place, the more

security, comfort, and confidence they have with this place. As it is seen place familiarity

influences people’s feelings and thoughts (Olsen and et al., 1986). As supporting,

audiences’ place familiarity level should influence not only visitation interest but also

destination image (Yang, 2011).

H2-c: Place Familiarity contributes to predicting Affective Destination Image.

H3-c: Place Familiarity contributes to predicting Cognitive Destination Image.

H4-c: Place Familiarity contributes to predicting Visitation Interest.

H5-c: Place Familiarity contributes to predicting WoM Recommendation.

H6-c: Place Familiarity contributes to predicting Willingness to Pay.

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As discussed earlier, it is approached the destination image from an evaluative

aspect. Because evaluation or interpretation of information is more important than

information itself (Russel, 1980; Kim and Richardson, 2003). In tourism context,

evaluation of physical features of a destination is superior to attributes of a destination

itself (Russel and Snodgrass, 1987). All these are used to argue significance of affective

component of destinations. The other destination image component besides affective

image is cognitive destination image. Early studies focused on cognitive image with

tangible attributes of destinations. Gartner (1993) emphasized the connection between

cognitive and affective image constructs and suggested that both play a role on

behavioral intentions toward a destination, especially, on visitation.

H4-d: Affective Destination Image contributes to predicting Visitation Interest.

H4-e: Cognitive Destination Image contributes to predicting visitation Interest.

H5-d: Affective Destination Image contributes to predicting WoM Recommendation.

H5-e: Cognitive Destination Image contributes to predicting WoM Recommendation.

H6-d: Affective Destination Image contributes to predicting Willingness to Pay.

H6-e: Cognitive Destination Image contributes to predicting Willingness to Pay.

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CHAPTER 3 METHODS

This chapter (1) demonstrates (1) identifies the research design; (2) explains the

data collection procedures; (3) elaborates the instrumentation and measurement; and

(4) provides an explanation of the proposed statistical procedures that will be utilized to

analyse the data.

Research Design

This study employs a survey-based design consisting a five-section

questionnaire including audience involvement, place familiarity, destination image, and

behavioural intentions and demographic profile. The survey-based design is in line with

other film tourism research (Kim et al., 2007; Kim, 2011) and is an appropriate method

for understanding the relationship between audience involvement and place familiarity,

destination image, and behavioural intentions as well as exploring the impacts of genre,

mode of watching (dubbed/subtitled), platform of watching (on TV/ the Internet) on

audience involvement, place familiarity, destination image, and behavioral intentions.

Data Collection

The population of interest is audiences from the American continent who have

viewed a reasonable number of episodes of at least one of the Turkish TV series.

The sample population is collected via surveying some members of the most

populous groups of Turkish TV series fans such as “Novelas Turcas, Series Turcas, and

Turkish TV Drama Appreciation Group” that have thousands of members on Facebook,

the social media platform. The sample consists of around 561 respondents.

Implementation

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The type of survey: The self-administered survey is implemented. The

Qualtrics, online survey software, is utilized. The questionnaire is initially prepared in

English and for the initial and back translation of the survey into Spanish and

Portuguese, it is applied to the help of the PhDs and PhD candidates from Spanish and

Portuguese studies department in Linguistics Faculty at the University of Florida. The

translators are native speakers of Spanish and Portuguese languages, from Brazil and

Colombia. As those languages, especially Spanish, are spoken in a wide geographical

area throughout the American continent, it is requested from the translators to pay

particular attention on word choices that can be understood by the diverse participants.

Facebook, the social media platform was used to reach participants. The Turkish

TV series fans have a number of groups, pages, and accounts on Facebook. The most

populous groups, pages, and accounts on those social media platforms such as

Novelas Turcas that has over thirteen thousand members, Series Turcas that has over

nine thousand followers, and Turkish TV Dramas Appreciation Group that has almost

six thousands members. First, the admins of those groups were informed about the

study. The researcher became the member of those groups with his personal Facebook

account. The IRB approval was received on 31st May 2017 and the questionnaire

posting on social media platforms began on the same day, 31st May, and it continued by

24th June 2017. The web link of the survey with the three language options; English,

Spanish, and Portuguese, was posted on the main pages of these Turkish TV series fan

groups on Facebook. The post included a small introduction text about the survey and a

picture including several actors and actresses from some prominent Turkish TV series.

To provide the credibility of the web link, not being considered as a spam by the

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members, the admins were asked to assure the credibility of the post with commenting

in the comment section of the post. Since the members post other stuff within the

groups throughout a day, every day a “Thank You” note was written in the comment

section to bring the survey web link post upfront of the main page in the groups in order

to make it more visible by more members.

In order to obtain response rate as high as possible, an incentive procedure was

implemented. The respondents were informed that each questionnaire that is filled out

by them becomes a meal for a child from underdeveloped countries. To establish this

purpose, it was cooperated with “ShareTheMeal”, the initiative of the United Nations

World Food Programme (WFP). WFP is the world’s largest humanitarian agency

fighting hunger. Each year, WFP reaches 80 million people with food assistance in

around 80 countries. WFP is 100% voluntarily funded. To illustrate, between January

and April 2016, “ShareTheMeal” initiative raised funds to support 2,000 mothers and

their babies in Homs, Syria, for a full year and starting August 2016, the initiative

supported the emergency food relief operation in Malawi, Africa, following one of the

strongest El Nino events on record. It is believed that the idea enabling respondents to

make a small contribution to endeavors fighting hunger in the world makes them more

willing to participate. On average, the donation for each full day meal costs to US$0.50

including foods, transport and preparation of meals, regular monitoring as well as all

other relevant costs. Returning to the noble subject, the survey was responded by 561

participants. US$280.50 (561 X US$0.50) was donated through the “ShareTheMeal”.

The receipt was attached in the appendix section.

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Instrumentation-Measurement

First of all, previous studies in tourism (Kim and et al, 2007; Kim, 2011) and the

other relevant studies from media studies (Rubin and Perse, 1987; Sood, 2002; Perse,

1990) were reviewed in order to determine scale and identify items, which could be

utilized or modified to measure the major variables of the study; audience involvement,

place familiarity, destination image, and behavioral involvement.

Audience Involvement

Sood’s (2002) audience involvement scale, which is generated for a radio soap

opera mainly targeting children and having education-entertainment purposes, is taken

as reference, the modified form of which was also used by Kim (2011) to measure

relationship between audience involvement and on-site film tourism experiences at the

locations featured in Jewel in the Palace, the Korean TV drama. Kim et al. (2007) and

Kim (2011) did not utilize the audience involvement scale to identify the effects of the

Korean TV dramas on destination image or behavioral intentions but these studies are

valuable as they are the very first research implementing audience involvement in

tourism research even though the scale was used for measuring travel experiences on

locations. Sood (2002) developed the scale with its five dimensions (affectively oriented

interaction, cognitively oriented interaction, behaviorally oriented interaction, referential

reflection, and critical reflection) for entertainment-education media programs, in

particular for monitoring “targeted education messages” in entertainment media

programs. However, generally speaking, TV series are entertainment products but they

do not contain education messages, especially targeted ones. For these reasons, some

dimensions and items are useless to apply for the proposed study. For instance,

referential reflection dimension completely aims to identify whether or not audiences

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think the media program reflects real life such as “I felt that TV show presented things

as they really are in life”. On the other hand, some items seem emotional by nature but

they were included in behavioral involvement dimension such as “I felt sad when bad

things happened to my favorite actors.” or vice versa; for example, the face validity of

the item “I scheduled my day/week around the soap opera.” seems more compatible

with behavioral involvement but it was placed in emotional involvement. In addition,

when Sood’s (2002) developed this scale the social media platform was not widely used

and its utilization was disregarded. In the present study, social media activities of

participants are considered as reliable indicators to measure level of audience

involvement. As the one of ultimate purposes of the study is to examine the relationship

between audience involvement and behavioral intentions, the scale built upon sense of

behavioral involvement is an easy-to-measure and a more credible structure.

Some other ways are applied in order to diversify the items used to build a scale

to measure level of audience involvement with Turkish TV series as well as taking

Sood`s (2002) and Kim’s (2011) studies as a trigger. The news reports, the content of

which consist of Turkish TV series, and including experts’ and audiences’ views, from

global media outlets such as BBC and NPR and local mainstream media outlets such

as Hurriyet Daily News and Daily Sabah are one of the ways to come up with new

items. For instance, a Turkish TV series fan from Peru cited that she comes together

with her friends and host Turkish TV nights. They share their thoughts and feelings

about some scenes and the whole episode while watching and after they watched.

Moreover, the comments and posts of the audiences on social media were

carefully monitored to have a wiser approach to produce items to measure behavioral

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involvement. For example, some fans that have missed an episode have been looking

for the recommendation where they could watch it or they have been asking if anyone

has recorded the episode and could share it with them. The other example, some fans

have been seeking gossip about the next episode or have opened a discussion on the

expected events in the next episode.

In addition, all the sessions where The CEOs of the importer and exporter

companies, the Turkish actors and actresses, scriptwriters, directors, and managers of

the production companies were hosted as speakers at the MIPCOM 2015, the world’s

entertainment content market took place in October 2015 in Cannes France, where

Turkey was the country of Honor, were watched meticulously to collect ideas to produce

an appropriate research instrumentation to measure behavioral involvement of

audiences. For instance, Juan Vicente, who is the CEO of the importer company of

Turkish TV dramas in Latin America and who also has the PhD degree from Temple

University in the US in media studies cited that during the TV dramas being aired, he

has followed the hashtags associated with Turkish TV series on social media. He has

coincided with some comments such as “Oh! I wish my husband treated me like Kerim,

the actor in a Turkish TV drama, treated his partner.” or some sarcastic ones such as

“Look at the guy sitting next to me and look at the guy in the show. How

unfortunate!”(“Turkey Country of Honor”, 2015)

Furthermore, personally, living in Florida, where Latin American population is

substantial, and being enrolled at the University of Florida (UF), where a number of

international students study at from other American countries, gave a chance to collect

information and listen to their anecdotes regarding Turkish TV series in person. It

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assisted me in collecting some ideas from casual conversations with Turkish TV drama

viewers on campus and around town. Besides, attending the festival “Downtown Latino

Gainesville 2016” hosted by Chamber of Hispanic Affairs Gainesville, which took place

on October 1st, 2016 created an opportunity to talk to wider population at once. For

instance, a graduate student at the UF from Ecuador indicates that Turkish TV dramas

in her country are like what the Games of Thrones is in the US. The other Cuban

student shares that the whole family, male and female members, watch Turkish TV

series with enthusiasm. She also notes that some family members live in Cuba and

some others live in Miami and they facetime one another to discuss the events in the TV

drama sometimes before sometimes after the episodes have been aired. She also

shares that when her mom gets pissed off at her for some reasons her mom calls her

“you are such a Hurrem”, Hurrem is a female character in the Turkish drama, the

Magnificent Century, who has reputation with her bitchy plans and actions. The other

Puerto Rican living in Ocala, Florida says that in summer 2016, she visited Puerto Rico

for a wedding event. The week she visited overlapped the week of the final episode of

the Turkish TV series, Fatmagul, broadcast. She expresses that everyone at the

wedding talked about the final episode of the drama. She highlights that the wedding

ceremony and the wedding dress of the bride, which are the regular conversation

subjects at a wedding event, were shadowed by the talks on the Turkish TV drama.

All those things considered, the modified 9-item 7-point Likert scale is

implemented to measure behavioral audience involvement with asking the degree to

which respondents agree with nine statements: (1) I voiced my thoughts and feelings

towards some scenes while watching Turkish TV drama;(2) I discussed the episodes

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with friends after I viewed them, (3) I looked forward to watching the next episode; (4) I

sought information or gossip about the upcoming episodes before they were

released;(5) I did not respond phone calls while watching Turkish TV drama; (6) I liked

pages, followed groups or posted something regarding Turkish TV drama or

actors/actresses on social media (Facebook, Twitter, Instagram, etc.); (7) If I coincides

with anything about Turkish TV drama on TV, in a newspaper, on the social media, or

magazine, I watch/read it, (8) I arranged my daily/weekly schedule around Turkish soap

opera. (9) I looked for ways to watch the episodes I missed. Even though Sood’s (2002)

audience involvement scale from media studies, which was utilized to measure

audience involvement with a radio soap opera carrying entertainment education

messages, was taken as reference to design the items and Factor Analysis is

performed to have a statistically more reliable and valid audience involvement scale.

Place Familiarity

To measure place familiarity it is benefited from using an existing 4-item 7-point

Likert Scale developed by Kim and Richardson (2003). This scale was produced

through a discussion with expert panel members comprised of researchers conducting

scale development. Yang (2011) also used the same scale in her research. Place

familiarity is measured ranging from “not at all familiar” to “extremely familiar,” with

physical environment and local lifestyle by the scale. In this study, the scale includes

three questions (1) How familiar are you with the culture of people in

Turkey? (Think about Turkish food, their entertainment, the Turkish wearing style; the

Turkish customs such as how they greet each other or how they treat their elder people

and youths, etc.; their traditions such as wedding ceremonies, etc.), (2) How familiar

are you with the cultural/historical attractions in Turkey? (Think about the

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places attracting tourists in Turkey; museums, palaces, mosques, ancient ruins, etc.),

(3) How familiar are you with the geographical features and natural

environment in Turkey? (Think about mountains, seas, beaches, cities, towns, etc.).

Destination Image

Destination image, from evaluative aspect, with its both cognitive and affective

components, is measured using the brand-new multi-item scale developed by Knock et

al. (2016) for both cognitive and affective constructs. The both scale is the 4-item 7-

point bipolar, single dimensional, and reflective construct.

The affective destination image is measured by four-item scales below;

How do you feel about Turkey as a tourism destination after watching the TV

series (7-point scale)

(1) like/dislike

(2) pleasant/unpleasant

(3) attractive/unattractive

(4) comfortable/uncomfortable

The cognitive destination image was measured by four-item scales below;

After watching the TV series, taking a holiday to Turkey is …

(1) positive/negative

(2) good/bad

(3) favorable/unfavorable

(4) worthwhile/not worthwhile

Behavioral Intentions

Behavioral intentions consist of three dimensions; visitation interest, WoM

recommendation, and willingness to pay. The all three were measured using the 5-item

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7-point scale. The two scales were taken as references to build up a scale to measure

visitation interest. One of them is outlined by Oberecker and Diamantopoulos (2011),

the other scale is produced by Shani et al. (2009). To measure WoM recommendation,

the scale that is developed by Arnett et al. (2003) and also utilized by Kock et al. (2016)

is taken as a reference. To measure willingness to pay, the scale that is employed by

Zeithaml et al. (1996), Baker and Crompton (2000), and Kock et al. (2016).

In this study, it is attempted to have more specific statements such as “I

searched flights to Turkey from my country.” than the generic statements such as “I

looked for information about Turkey” that are used in other studies. In addition, the

social media activities are placed in the scale, which is more commonly used compared

to the time at which the scales developed.

The scale that is used to measure behavioral intentions asks respondents about

their agreement with fourteen statements: The fourteen statements are; (1) I searched

flights to Turkey from my country, (2) I searched travel packages to Turkey, (3) I looked

for people who already visited Turkey to get informed about traveling Turkey, (4) I

began to subscribe some social media accounts posting pictures from Turkey, (5) I

googled tourist attractions in Turkey, (6) I would comment on pictures of touristic

attractions from Turkey on social media positively, (7) I would post pictures of tourist

attractions from Turkey through my social media accounts, (8) I would recommend

Turkey as a tourist destination to other people when asked, (9) I would invite my friends

to subscribe social media accounts posting pictures of touristic attractions from Turkey,

(10) I would talk about Turkey positively in a conversation about holiday destinations,

(11) I would be willing to save money for a holiday in Turkey, (12) I will likely book a

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vacation to Turkey at some point in future, (13) I would be willing to spend a little more

for a holiday in Turkey than a similar holiday in other destinations, (14) I would be willing

to spend $2000 for 10 day trip to Turkey.

Data Analysis (Statistical) Procedures

First of all, preliminary statistics are performed. Afterwards, to explore underlying

components of audience involvement and behavioral intentions, Exploratory Factor

Analyses are performed. Then some variable transformations are performed in order to

prepare variables to use in parametric analyses.

For the RQ-1, a two-way between-subjects factorial MANOVA and multiple one-

way between groups MANOVA are performed.

For the RQ-2, Multiple Regressions are performed.

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CHAPTER 4 RESULTS

The purpose of this study is to explore the effects of mode of watching and

platform of watching on audience involvement, and the effects of genre on audience

involvement, place familiarity, destination image, and behavioral intentions and the

relationships among audience involvement, place familiarity, destination image, and

behavioral intentions. This chapter presents the results of the research, directly

addressing the research questions proposed in the previous chapter. The result

reporting begins with presenting of demographics of participants, version of survey,

mode of and platform of watching, frequencies of Turkish TV series watched,

frequencies of favorite Turkish TV series. Following reporting continues with presenting

descriptive statistics of each continuous variable. Some of them are subject to factor

analysis to identify underlying components. Some of them are exposed to

transformation to increase normality of the data. The further reporting is followed by

presenting a two-way between-subjects factorial MANOVA and multiple one-way

between groups MANOVA results regarding RQ-1. The results chapter is finalized by

reporting multiple regression analyses related to the hypotheses of RQ-2.

Preliminary Descriptive Statistical Analyses

Table 4-1. Primary Demographics of Respondents

Variable Levels Frequency Percentage %

Gender Female 541 96.4 Male 19 3.4

Not responded 1 0.2

Total 561

100

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Table 4-1. Continued

Variable Levels Frequency Percentage %

Age 18-29 92 16.4 30-39 105 18.7 40-49 104 18.5 50-59 126 22.5 60+ 133 23.7

Not responded 1 0.2

Total 561 100

Education High school or lower

88 15.7

Some college 271 48.3

Bachelor’s or Higher

201 35.8

Not responded 1 0.2

Total 561 100

Countries Argentina 120 21.4 Brazil 77 13.7 Chile 74 13.2 Mexico 67 11.9 USA 43 7.7

Puerto Rico 35 6.2

Colombia 34 6.1 Peru 26 4.6 Uruguay 18 3.2 Spain 10 1.8 Cuba 8 1.4 Canada 7 1.2

Dominican Rep. 6 1.1

Paraguay 6 1.1 Nicaragua 6 1.1 Ecuador 3 0.5 Panama 3 0.5 Bolivia 3 0.5

70

Table 4-1. Continued

Variable Levels Frequency Percentage %

Countries Venezuela 3 0.5

Costa Rica 2 0.4

El Salvador 2 0.4

Bonaire, Dutch Antilles

1 0.2

Caribbean 1 0.2 Honduras 1 0.2 Portugal 1 0.2

Not responded 4 0.7

Total 561 100

Table 4-1 demonstrates the total number of respondents who have watched

Turkish TV dramas in the last five years is 561. Demographic profile of respondents are

organized as primary and secondary. The primary demographic profile’s items consist

of the responses of the questions asked about their (1) gender, (2) age, (3) education,

and (4) country; the secondary demographic profile table of the participants includes

answers of the questions that are (1) whether they speak any foreign language, (2)

whether they travel overseas, (3) whether they travel a country language of which they

do not speak and the language of survey they chose among three options; Portuguese,

Spanish, and English.

Among the sample of respondents who have watched Turkish TV dramas in the

last five years, the overwhelming majority are female viewers (541 individuals; 96.4

percent), the rest is male (19 individuals, 3.4 percent), and one participant avoided

responding the gender question (Table 4-1). Age is asked and reported as an ordinal

variable of 5 levels (Table 4-1). Even though the frequency spreads out equally among

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the age levels, there is a parallel increase between the age levels and the frequencies.

The age level between 18-29 has the lowest frequency with 92 cases equivalent to 16.4

percent. The age levels between 30-39 and 40-49 share very close values; 105 cases

equivalent to 18.7 percent and 104 cases equivalent to 18.5 percent in order. The age

level between 50-59 has the second most frequently reported age level with 126 cases

equivalent to 22.5 percent. The age level “60+” has the highest frequency with 133

cases equivalent to 23.7 percent. One case is available that did not answer the age

question. With respect to education, three education levels are reported and presented

to the respondents as an ordinal variable. 88 participants equivalent to 15.7 percent

reported that they have “high school or lower” education level. 271 participants

equivalent to 48.3 percent reported that they have some college level education

background without obtaining a bachelor diploma. 201 respondents equivalent to 35.8

percent reported that they have bachelors or higher degree. As of origin of the

respondents, they are mainly from South American countries. Other that, there are

some from North American countries. The sample includes a few participants from

Europe. In order of; the sample consists of 120 subjects (21.4 percent) from Argentina,

77 subjects (13.7 percent) from Brazil, 74 subjects (13.2 percent) from Chile, 67

subjects (11.9 percent) from Mexico, 43 subjects (7.7 percent) from USA, 35 subjects

(6.2 percent) from Puerto Rico, 34 subjects (6.1 percent) from Colombia, 26 subjects

(4.6 percent) from Peru, 18 subjects (3.2 percent) from Uruguay. Percentage of other

countries are less than 2 percent. There are 4 subjects not specifying where they are

from.

72

Table 4-2. Secondary Demographics of Respondents Variable Levels Frequency Percent %

Have you ever been to Turkey? Yes 64 11.4

No 492 87.7

Not responded 5 .9

Total 561 100

Have you ever traveled

overseas?

Yes 376 67

No 180 32.1

Not responded 5 .9

Total 561 100

Have you ever traveled a

country

Yes 295 52.6

language of which you don’t

speak?

No 262 46.7

Not responded 4 .7

Total 561 100

Do you speak any foreign

languages?

Yes 283 50.4

No 274 48.9

Not responded 4 .7

Total 561 100

Since international travel attitudes of respondents are an important part of the

research project, some other questions regarding their travel background are asked and

reported as a secondary demographic profile table (Table 4-2). The respondents are

asked whether they have ever been to Turkey: 64 participants (11.4 percent) answer

“yes”, 492 (87.7 percent) participants say “no” to this question, 5 participants leave

blank this question. The participants are also asked whether they have traveled

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overseas; 376 participants (67 percent) respond “yes”, 180 participants (32.1) say “no”,

5 participants avoid responding that question. The subjects are asked whether they

have ever travelled a country the language of which they do not speak; 295 subjects

(52.6 percent) answer “yes”, 262 subjects (46.7 percent) say “no”, 4 subjects refrain

from responding that question. As a secondary demographic profile question, the

Turkish TV drama viewers are also asked whether they speak any foreign languages;

283 viewers (50.4 percent) say “yes”, 274 viewers (48.8 percent) answer “no”, 4 viewers

leave that question blank.

Table 4-3. Versions of survey preferred, mode and platform of watching

Variable Levels Frequency Percent %

Version of Survey Portuguese 61 10.9%

Spanish 424 75.6%

English 76 13.5%

Total 561 100%

Mode of Watching Dubbed 260 46.3%

Subtitled 294 52.5%

Not responded 7 1.2%

Total 561 100%

Platform of Watching on TV 150 26.7%

on the Internet (+ Netflix) 410 73.1%

Not responded 1 .2%

Total 561 100%

Meanwhile, the survey is prepared in three different languages. 61 participants

(10.9 percent) fill out the survey in Portuguese; 76 participants (13.5 percent) fill out the

questionnaire in English, 424 (75.6 percent) participants respond the survey in Spanish.

260 participants (46.3 percent) have watched them dubbed. 294 participants (52.5

74

percent) have watched them with subtitles. 7 people (1.2 percent) do not respond this

question. 150 respondents (26.7 percent) have watched Turkish TV series on TV, 410

respondents (73.1 percent) have watched them on the Internet outlets (+ Netflix). 1

person (.2 percent) leaves it unanswered (Table 4-3).

Table 4-4. Turkish TV Series watched

Variable Frequency Percentage

1 Fatmagul 434 77%

2 Dirty Money and Love 355 63%

3 1001 Nights 330 59%

4 Sila 310 55%

5 Forbidden Love 266 47%

6 Ezel 261 47%

7 Endless Love 241 43%

8 Feriha 230 41%

9 Until Death 229 41%

10 Sultan Suleyman 218 39%

11 Brave and Beautiful 206 37%

12 MedCezir (Tide) 198 35%

13 Inside (Icerde) 186 33%

14 Beyond the Clouds 182 32%

15 Sura and Seyit 182 32%

16 Karadayi 158 28%

17 Kuzey Guney 140 25%

18 Silver (Gumus) 133 24%

19 The Day My Destiny written 132 24%

20 Elif 127 23%

21 Kosem Sultan 122 22%

22 Bidding Farewell 117 21%

23 Asi 115 20%

24 Selin 103 18%

25 Little Bride 99 18%

26 Lovebird 99 18%

27 Maral 98 17%

28 My Fair Lady 95 17%

29 Rental Love 94 17%

30 Love Again 91 16%

31 High Society 84 15%

32 That Life is Mine 77 14%

33 Twenty Minutes 76 14%

75

Table 4-4. Continued

Variable Frequency Percentage

34 Lost 73 13%

35 Black Rose 71 13%

36 Matter of Respect 69 12%

37 Cherry Season 68 12%

38 Love and Punishment 67 12%

39 As Time Goes By 65 12%

40 Love 64 11%

41 In Between 55 10%

42 Mercy 53 9%

43 Intersection 49 9%

44 Broken Pieces 48 9%

45 Bitter Life 47 8%

46 Adanali 45 8%

47 Mother 45 8%

48 Gonul 43 8%

49 Other Turkish TV Dramas 39 7%

50 Revenge 37 7%

51 Game of Silence 37 7%

52 Daughters of Sun 36 6%

53 Resurrection: Ertugrul 18 3%

54 Promise 17 3%

55 Love Not Listen to Reason 17 3%

56 Wounded Love 15 3%

57 Fi 14 2%

58 Bride of Istanbul 13 2%

59 Black Snake 12 2%

60 The End 11 2%

61 Kacak 8 1%

Table 4-4 presents each Turkish TV drama has been watched by how many

participants. In the study, 60 Turkish TV series are used by their names. Besides these

60 TV series, the TV series with very low frequency and percentage are brought

together under the title “Other Turkish TV series”. Returning to the noble subject,

Fatmagul is the most watched Turkish TV series among 561 participants with 434 times

(77 percent). It is followed by Dirty Money and Love with 355 times (63 percent) viewed,

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1001 Nights with 330 times (59 percent) viewed, Sila with 310 times (55 percent)

viewed, Forbidden Love with 266 times (47 percent) viewed, Ezel with 261 times (47

percent) viewed, Endless Love with 241 times (43 percent), Feriha with 230 times (41

percent) viewed, Until Death with 229 times (41 percent) viewed, Sultan Suleyman with

218 times (39 percent) viewed. The first ten most watched TV series are named. The

others are presented in Table 4-4.

Table 4-5. Favorite Turkish TV Series

Variable Frequency Percent %

Fatmagul 97 17.3

Dirty Money and Love 86 15.3

Endless Love 37 6.6

Karadayi 35 6.2

MedCezir 31 5.5

Inside 29 5.2

Sila 28 5.0

Feriha 20 3.6

Brave and Beautiful 20 3.6

Sultan Suleyman 18 3.2

Asi 16 2.9

1001 Nights 15 2.7

Forbidden Love 14 2.5

Ezel 12 2.1

Sura and Seyit 11 2.0

As Time Goes by 9 1.6

Kuzey Guney 7 1.2

Until Death 6 1.1

Bitter Life 6 1.1

Others 6 1.1

Rental Love 5 0.9

77

Table 4-5. Continued

Variable Frequency Percent %

My Fair Lady 3 0.5

Little Bride 3 0.5

Silver 3 0.5

The Day My Destiny written 2 0.4

Twenty Minutes 2 0.4

Kosem Sultan 2 0.4

Beyond the Clouds 2 0.4

That Life is Mine 2 0.4

Bidding Farewell 2 0.4

Resurrection 2 0.4

Wounded Love 2 0.4

Black Snake 2 0.4

Black Rose 1 0.2

Broken Pieces 1 0.2

Gonul 1 0.2

Mother 1 0.2

Lost 1 0.2

Lovebird 1 0.2

Bride of Istanbul 1 0.2

Love Not Listen to Reason 1 0.2

Not responded 18 3.2

Total 561 100

Table 4-5 presents favorite Turkish TV series among participants. Fatmagul and

Dirty Money and Love lead the list with 97 times (17 percent) and 86 times (15.3

percent) chosen as favorite Turkish TV dramas by 561 participants. They are followed

by Endless Love with 37 times (6.6 percent), Karadayi with 35 times (6.2 percent),

MedCezir with 31 times (5.5 percent), Inside with 29 times (5.2 percent), Sila with 28

78

times (5 percent) chosen as favorite Turkish TV drama by the respondents. The other

Turkish TV series chosen favorite TV series are listed in Table 4-5.

Table 4-6. Descriptive Statistics of Audience Involvement

Variable N Mean Std. Deviation

Skewness Kurtosis

I voiced my thoughts and feelings toward some scenes while watching.

558 6.050 1.513 -1.644 1.977

I discussed the episodes with friends after watching. 561 5.770 1.853 -1.438 .901

I looked forward to watching the next episode. 561 6.710 .925 -3.950 17.006

I sought information or gossip about the upcoming episodes before they were released.

561 5.960 1.907 -1.735 1.590

I ignored responding phone calls while watching. 558 4.970 2.242 -.685 -.975

I liked pages or followed groups about the Turkish TV drama or actors/actresses on social media

559 6.420 1.394 -2.627 6.231

I arranged my daily or weekly schedule around the Turkish TV drama

559 4.720 2.328 -.514 -1.265

I searched websites to watch the episodes I had missed 559 6.490 1.340 -3.026 8.532

When I see anything about the Turkish TV drama on TV, in newspaper, in a magazine, or on the soci…

561 6.400 1.363 -2.505 5.599

Table 4-6 displays descriptive statistics of 9 items of audience involvement scale.

It presents the number of participants responding each statement, mean scores,

standard deviations, skewness, and kurtosis values. The participants are asked to score

from 1 to 7 on a Likert-scale according to their agreements with the statements. The

statement “I looked forward to watching the next episode.” has the highest mean score

(6.710) with the lowest standard deviation value (.925) while the statement “I arranged

my daily or weekly schedule around the Turkish TV drama” has the lowest mean score

with the highest standard deviation value (2.328). Considering skewness and kurtosis

values of the items, it is seen that they have deviated scores. Skewness and kurtosis

are concerning the distribution of scores on continuous variables. The skewness value

provides an indication of the symmetry of the distribution. Kurtosis provides information

about the peakedness of the distribution. Obtaining value of 0 for both means the

79

distribution is purely normal, which is an unusual case in the social sciences. With

reasonably large samples, skewness does not make a substantive difference in the

analysis. Kurtosis can also underestimate the variance, but this risk is also reduced with

a large sample (200+ cases) (Tabachnick and Fidell 2013, p.80).

Having skewed data, either positively or negatively is common in social science.

This does not necessarily indicate a problem with the scale, but rather reflects the

underlying nature of the construct being measured. The thing suggested is that

transforming data. Before that, it is necessary to collapse items into groups.

Table 4-7. Audience Involvement: Exploratory Factor Analysis with Varimax Rotation (n=561)

Factor Loadings Com.

Scales Factor

1 Factor

2

Factor 1 (Personal Involvement – 6 items)

I searched websites to watch the episodes I had missed .790 .627

When I see anything about the Turkish TV drama on TV, in newspaper, in a magazine, or on the soci .779 .653

I looked forward to watching the next episode. .765 .620

I liked pages or followed groups about the Turkish TV drama or actors/actresses on social media .608 .464

I voiced my thoughts and feelings toward some scenes while watching. .523 .362

I sought info/gossip about the upcoming episodes .503 .476

Factor2 (Interpersonal Involvement – 3 items)

I arranged my daily or weekly schedule around the Turkish TV drama .816 .682

I ignored responding phone calls while watching. .816 .693

I discussed the episodes with friends after watching. .549 .434

Eigenvalue 3.934 1.078

Explained variance by factors (%) 32.076 23.611

KMO .837

Barlett's test of significance .000

Reliability coefficients .783 .672

Total variance extracted by the two factors is 55.687%. Item loading less than 0.5 were omitted.

Items measured on a 7-point Likert scale

80

Table 4-7 shows results of exploratory factor analysis of audience involvement.

The 9 items of audience involvement with the Turkish TV Series is subjected to

exploratory principle component factor analysis with ‘varimax’ rotation method that aims

to obtain maximum variances of loadings. As demonstrated in Table 4-7, two-factor

model is estimated with the 9 items. The factor solution accounts for 55.687 percent of

the total variance. Bartlett’s test of sphericity is significant (p < .000) and KMO measure

of sampling adequacy (.837) exceeds the minimum value (.60), which indicates

validation of the factor model. In the screeplot test, the Eigenvalue for the two factors

are greater than 1.0. These values are indicators of validity of the scale (Churchill,

1979). The two dimensions are named “personal audience involvement” and

“interpersonal audience involvement” based on sense of the items grouped. The

reasoning behind naming fashion of the factors is that the most prominent distinctive

feature between two dimensions is based on interaction with others and individual

behavioral patterns of items. The reliability test scores represent internal consistency of

the factors with a coefficient of .783 and .672. For exploratory studies, alpha greater

than 0.60 is acceptable (Hair et al. 1998). That is, the statistical results indicate

reliability and validity of the personal and interpersonal audience involvement scales to

measure the phenomenon.

Factor loadings that measure correlation between the items and the factors

range from .503 to .816. In addition, communalities for each dimension pointing out the

amount of variances account for by the factors were from .362 to .693. It shows each

variable contribute to establishing the factor structure. According to Hair et al. (1998),

thresholds for factor loadings +- .50; for communalities at least .40. These

81

recommended thresholds could be loosened for exploratory studies. From this point of

view, the study seems to satisfy the minimum requirements.

Table 4-8. Descriptive Statistics of Personal and Interpersonal Audience Involvement

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Personal B. Aud. Inv. 555 1 7 6.345 .990 -2.334 6.638

Interpersonal B. Aud. Inv. 556 1 7 5.158 1.672 -.669 -.573

After determining two factor solutions as audience involvement scale with two

variables; personal and interpersonal components, these two variables are computed.

Descriptive statistics are run to see distribution of scores of these variables. Table 4-8

displays that descriptive statistics of personal and interpersonal audience involvement

variables. Sample size of personal involvement is 555 while interpersonal has 556

samples. Mean score of personal involvement is 6.345 with .990 standard deviation

while interpersonal involvement has 5.158 as mean score with 1.672 standard

deviation. Skewness (.669) and kurtosis (-.573) values of interpersonal audience

involvement fall within acceptable level (-+ 2.5). On the other hand, skewness (-2.334)

and kurtosis (6.638) values of personal audience involvement indicate requirement of

transformation of variables.

Table 4-9. Descriptive Statistics of Personal Bhv. Aud. Inv. After Variable Transformation

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Personal B. Aud. Inv. (transformed) 555 .140 1 .745 .266 -.545 -1.107

The variable transformation is performed based on the recommendation of

(Tabachnick and Fidell 2013, p.80) concerning the shape of distribution of scores on

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histogram. Reflect and Inverse transformation is performed on personal audience

involvement.

(Formula: new variable= 1 / (K – old variable) where K = largest possible value +

1).

After the transformation of personal audience involvement (see Table 4-9), better

skewness and kurtosis values are obtained, which indicates more normalized data

distribution. In this way, audience involvement variables (Personal and Interpersonal)

are prepared to be utilized in parametric statistical analyses.

The next variable is place familiarity. Place familiarity variable is the other

variable that is projected to be used in multiple regression and MANOVA analyses later

on.

Table 4-10. Descriptive Statistics of Place Familiarity Items

Variable N Mean Std. Deviation

Skewness Kurtosis

How familiar are you with the culture of people in Turkey? 560 4.650 1.794 -.470 -.545

How familiar are you with the cultural/historical attractions in Turkey?

561 4.880 1.823 -.277 -.535

How familiar are you with the geographical features and natural environment in Turkey?

561 4.700 1.779 -.362 -.670

Table 4-10 shows descriptive statistics of Place familiarity. Place familiarity has 3

items. Each place familiarity item has a pretty good skewness and kurtosis values. With

adding up these 3 items, a new “global” place familiarity variable is created.

The formula used to compute new variable is;

Place Familiarity = (Item1 + Item2 + Item3) / 3

After the new place familiarity variable is computed descriptive statistics of the

single place familiarity variable is run.

83

Table 4-11. Descriptive Statistics of Single Place Familiarity Variable

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Place Familiarity 560 1 7 4.725 1.582 -.441 -.322

Table 4-11 displays mean score (4.725) with standard deviation (1.582) of place

familiarity variable. Skewness and kurtosis values are within acceptable levels. There is

no need to transform the variable. Internal reliability of place familiarity is also very

good. Cronbach’s Alpha value is 0.871.

Place familiarity is also prepared for further analyses. The next variable is

destination image.

Table 4-12. Descriptive Statistics of Destination Image Variable N Mean Std. Deviation Skewness Kurtosis

Affective Image

How do you feel about Turkey as a tourist destination?

Dislike (1) Like (7) 558 6.800 .605 -3.723 16.235

Unpleasant (1) Pleasant (7) 535 6.650 .816 -2.898 9.808

Unattractive (1) Attractive (7) 539 6.730 .845 -4.340 22.147

Uncomfortable (1) Comfortable (7) 530 6.350 1.146 -2.025 4.310

Cognitive Image

The idea taking a holiday to Turkey is…

Negative (1) Positive (7) 547 6.690 .849 -3.310 12.428

Bad (1) Good (7) 525 6.700 .828 -3.451 13.939

Unfavorable (1) Favorable (7) 523 6.630 .909 -3.131 11.429

Unworthy (1) Worthy (7) 526 6.690 .825 -3.336 12.950

Table 4-12 shows descriptive statistics of cognitive and affective components of

destination image. This division is made based on the literature regarding destination

image. 7 point Likert scale is utilized. To measure affective destination image, the

question “How do you feel about Turkey as a tourist destination?” is asked. The item

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“dislike – like” has the highest mean score (6.800) with standard deviation (.605). The

item “uncomfortable – comfortable” has the lowest mean score (6.350) with (1.146). To

measure cognitive destination image, the statement “the idea taking a holiday to Turkey

is …” is asked to be completed with “Negative – Positive”, “Bad – Good”, “Unfavorable –

Favorable”, “Unworthy – Worthy”. There is no big mean difference among four cognitive

image items. Even so, “Bad – Good” has the highest mean score (6.7) with .828

standard deviation; “Unfavorable – Favorable” has the lowest mean score (6.630) with

.909 standard deviation. Skewness and kurtosis values of both affective and cognitive

destination image components are presented in Table 4-12. The distribution of each

item is much skewed. Transforming the items is inevitable to eradicate or at least to

decrease the skewness and kurtosis values of the items in order to normalize

distribution of data.

With adding up these 4 items, a new “global” affective variable is created.

The formula is used to compute new variable is;

Affective Image = (Item1 + Item2 + Item3 + Item4) / 4

After the new variable is computed descriptive of the single affective variable is

run.

Table 4-13. Descriptive Statistics of Affective Destination Image

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Affective Image 524 2 7 6.630 .723 -2.700 8.643

Table 4-13 shows descriptive statistics of affective destination image. Sample

size is 524. Minimum score is 2 while maximum score is 7. The mean score is 6.63 with

.723 standard deviation. Skewness value would be considered acceptable if kurtosis

value had been smaller. In fact, considering values of both; the transformation of the

85

affective destination image variable is beneficial to the competence of the rest of

analyses. The type of transformation is decided based on distribution shape of the data

according to recommendation of (Tabachnick and Fidell 2013, p.80). Reflect and

inverse transformation is performed on affective image variable.

The formula that is used to transform affective variable:

(Formula: new variable= 1 / (K – old variable) where K = largest possible value +

1).

Transformed Affective Destination Image = 1 / ((7+1) - Affective DI variable)

Table 4-14. Descriptive Statistics of (transformed) Affective Destination Image

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Affective Image (transformed) 524 .17 1 .845 .239 -1.179 -.083

Table 4-14 shows descriptive statistics of transformed affective destination

image. After the transformation is performed affective image’s skewness and kurtosis

values are reversed into acceptable values: skewness values from -2.700 to -1.179;

kurtosis values from 8.643 to -.083.

On the other hand, internal reliability of affective image is tested. Cronbach’s

alpha value is .840 with 4 items. Affective image becomes prepared for further

parametric analyses.

Table 4-15. Descriptive Statistics of Cognitive Destination Image

N Minimum Maximum Mean Std. Dev Skewness Kurtosis

Cognitive Image 507 2.25 7 6.675 .790 -3.000 9.630

Table 4-15 shows descriptive statistics of cognitive destination image. 4 items of

cognitive image are computed to obtain a “global” cognitive image.

86

The formula used to compute new variable is;

Cognitive Image = (Item1 + Item2 + Item3 + Item4) / 4

It has 6.675 mean score with .790 standard deviation. Skewness and kurtosis

values are found high. They are warnings of the abnormal distribution. “Reflect and

inverse transformation” is conducted for cognitive image as well in order to normalize

the distribution according to recommendation of Tabachnick and Fidell (2013, p.80)

based on shape of data distribution. The formula used to transform the cognitive

variable is:

(Formula: new var.= 1 / (K – old variable) where K = largest possible value + 1).

Transformed Cognitive Destination Image = 1 / ((7+1) - Cognitive DI variable)

Table 4-16. Descriptive Statistics of (transformed) Cognitive Image

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Cognitive Image (transformed) 507 .17 1 .884 .235 -1.732 1.399

Table 4-16 shows descriptive statistics of transformed cognitive destination

image. It has .884 mean score with .235 standard deviation. After transforming cognitive

destination image variable, skewness and kurtosis obtain smaller values, which are

signs of fixing distribution of scores.

The other package of variables is behavioral intentions with its 3 commonly used

umbrella variables in tourism studies; visitation interest, WoM recommendation, and

willingness to pay. They are utilized in the present study. These three components are

used often. Sometimes individually sometimes two of them together but there is few

studies utilize all at once. The present study implements all the three. In addition, most

of the items that are utilized by the other studies found too generic such as “I get

informed about the destination” and the present study attempts to use more specific

87

items such as “I searched flights to Turkey from my country” and “I began to subscribe

some social media accounts posting pictures from Turkey” as well as utilizing social

media activities as indicators of behavioral intentions and the potential travel behavior

itself inherently, which can safely be proposed as a brand new research attempt. As a

result, modifications on these behavioral scales occurred to some extent. For these

reasons, exploratory factor analysis is conducted to inspect how all the three scales

stand together. Before that, descriptive statistics of items are run and presented.

Table 4-17. Descriptive Statistics of Behavioral Intentions Variable N Mean Std.

Deviation Skewness Kurtosis

I googled tourist attractions in Turkey. 560 5.890 1.713 -1.557 1.507

I looked for people who already visited Turkey to get informed about traveling Turkey.

560 5.010 2.201 -.677 -.978

I began to subscribe some social media accounts posting pictures from Turkey.

556 5.190 2.216 -.814 -.865

I searched flights to Turkey from my country. 554 4.520 2.502 -.343 -1.568

I searched travel packages to Turkey. 555 4.450 2.510 -.284 -1.602

I will likely book a vacation to Turkey at some point in future.

555 5.730 1.911 -1.308 .401

I would talk about Turkey positively in a conversation about holiday destinations.

559 6.330 1.220 -2.015 3.941

I would recommend Turkey as a tourist destination to other people when asked.

557 6.260 1.301 -1.949 3.608

I would comment on pictures of touristic attractions from Turkey on social media positively.

557 6.300 1.330 -2.173 4.508

I would post pictures of tourist attractions from Turkey through my social media accounts.

553 5.690 1.720 -1.260 .666

I would invite my friends to subscribe social media accounts posting pictures of touristic attrac

554 5.030 2.093 -.769 -.737

I would be willing to save money for a holiday in Turkey. 554 6.280 1.475 -2.253 4.361

I would be willing to spend a little more for a holiday in Turkey than a similar holiday in other destinations.

555 6.040 1.604 -1.710 2.077

I would be willing to spend $2000 for 10-day trip to Turkey. 552 6.270 2.055 -.271 1.320

Table 4-17 shows descriptive statistics of behavioral intentions. It has 14 items.

The statement “I searched travel packages to Turkey.” has the lowest mean score

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(4.450) with 2.510 standard deviation while the statement “I would talk about Turkey

positively in a conversation about holiday destinations.” has the highest mean score

(6.330) with 1.220 standard deviation. Skewness and kurtosis values not severely

exceed the acceptable thresholds overall. As following, exploratory factor analysis is

performed to investigate underlying components of behavioral intentions statistical-wise.

89

Table 4-18. Exploratory Factor Analysis of Behavioral Intentions

Factor Loadings Com.

Scales Factor 1

Factor 2

Factor 3

Factor 1 (Visitation Interest - 5 items)

I searched flights to Turkey from my country.

.890

.840

I searched travel packages to Turkey

.878

.818

I looked for people who already visited Turkey to get informed about traveling Turkey.

.760

.703

I began to subscribe some social media accounts posting pictures from Turkey.

.668

.627

I googled tourist attractions in Turkey.

.630

.571

Factor 2 (WoM Recommendation 5 items)

I would comment on pictures of touristic attractions from Turkey on social media positively.

.806

.684

I would post pictures of tourist attractions from Turkey through my social media accounts.

.753

.667

I would recommend Turkey as a tourist destination to other people when asked.

.748

.746

I would invite my friends to subscribe social media accounts posting pictures of touristic attrac

.723

.646

I would talk about Turkey positively in a conversation about holiday destinations.

.721

.732

Factor 3 (Willingness to Pay - 4 items)

I would be willing to save money for a holiday in Turkey.

.705 .766

I will likely book a vacation to Turkey at some point in future

.608 .602

I would be willing to spend a little more for a holiday in Turkey than a similar holiday in other destinations

.599 .683

I would be willing to spend $2000 for 10 day trip to Turkey

.548 .315

Eigenvalue 6.861 1.410 1.130

Explained variance by factors (%) 26.671 26.666 13.819

KMO .898

Barlett's test of significance .000

Reliability coefficients .887 .861 .663

Total variance extracted by the two factors is 67.155%. Item loading less than 0.5 were omitted.

Items measured on a 7-point Likert scale

90

The 14 items of behavioral intentions is subjected to exploratory principle

component factor analysis with varimax rotation method that aims to obtain maximum

variances of loadings. As demonstrated in Table 4-18, three-factor model is estimated

with the 14 items. The factor solution accounted for 67.155 percent of the total variance.

Bartlett’s test of sphericity is significant (p < .000) and KMO measure of sampling

adequacy (.898) exceeds the minimum value (.60), which indicates validation of the

factor model. In the screeplot test, the Eigenvalue for the three factors are greater than

1.0. These values are indicators of validity of the scale (Churchill, 1979). The three

dimensions are named “visitation interest, WoM recommendation and willingness to

pay” based on compatibility of the face value of items with the factors’ concepts. The

reliability test scores represent internal consistency of the factors with a coefficient of

.887, .861, .663. That is, the statistical results indicate reliability and validity of

behavioral intentions scales to measure the phenomenon.

Factor loadings that measure correlation between the items and the factors

range from .548 to .890. In addition, communalities for each dimension pointing out the

amount of variances account for by the factors were from .315 to .840. It shows that

each variable contributes to establishing the factor structure. According to Hair et al.

(1998), thresholds for factor loadings +- .50; for communalities at least .40. These

recommended thresholds could be loosened for exploratory studies. From this point of

view, the study seems to satisfy the minimum requirements.

After determining three factor solutions as behavioral intentions scales with three

variables; visitation interest, WoM recommendation, and willingness to pay, these three

variables are computed.

91

Table 4-19. Descriptive Statistics of Visitation Interest

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Visit Interest 549 1 7 5.019 1.860 -.622 -.884

Table 4-19 shows descriptive statistics of visitation interest variable. It has 549

samples. Its mean score is 5.019 with 1.860 standard deviation. Skewness and kurtosis

values are -.622 and -.884. Based on shape of distribution of scores as it is

recommended by Tabachnick and Fidell (2013, p.80), the variable is transformed.

Reflective and inverse transformation is operationalized as it is recommended by

Tabachnick and Fidell (2013, p.80),

Formula that is used is:

New variable = 1 / (K-old variable) where K= largest possible value + 1

Transformed Visitation Interest = 1/ ((7+1) - Visit Interest)

Table 4-20. Descriptive Statistics of (transformed) Visitation Interest

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Visit Interest (transformed) 549 .14 1 .511 .319 .539 -1.263

Table 4-20 shows transformed values of visitation interest variable. The sample

size is 549. Minimum score is .14 and maximum score is 1. Mean score is .511 with

.319 standard deviation. Skewness value is .539 and kurtosis value is -1.263. Skewness

and kurtosis values indicate normalized distribution of data. Visitation interest variable is

ready for further analyses.

Table 4-21. Descriptive Statistics of WoM recommendation

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

WoM Rec 546 1 7 5.921 1.259 -1.232 .871

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The second behavioral intention variable is WoM recommendation. Table 4-21

shows descriptive statistics of WoM recommendation. The sample size is 546. Minimum

score is 1 maximum score is 7. Mean score is 5.921 with 1.259 standard deviation.

Skewness is -1.232 and kurtosis value is .871. Based on the shape of distribution of

scores of WoM Recommendation variable, reflective and inverse transformation is

performed as it is recommended by Tabachnick and Fidell (2013, p.80).

Formula that is used is for transformation:

New variable = 1 / (K-old variable) where K= largest possible value + 1

Transformed WoM Rec= 1/ ((7+1)- WoM Rec)

Table 4-22. Descriptive Statistics of (transformed) WoM recommendation

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

WoM Rec (transformed) 546 .140 1 .643 .297 -.046 -1.522

Table 4-22 shows descriptive statistics of transformed WoM recommendation

variable. The sample size is 546. Minimum score is .140 while maximum is 1. Mean

score is .643 with .297 standard deviation. Skewness value is -.046 and kurtosis value

is -1.522. Skewness and kurtosis values indicate normalized distribution of data.

Visitation interest variable is ready for further analyses.

The third behavioral intensions variable is willingness to pay. To increase internal

reliability, the item “I would be willing to spend $2000 for 10 day trip to Turkey” is

removed. The internal reliability obtains higher value from 0.663 to 0.816.

Having a high internal reliability aids in having sound results in parametric

analyses being performed later on.

93

Table 4-23. Descriptive Statistics of Willingness to Pay

N Min Max Mean Std. Dev. Skewness Kurtosis

Willingness to Pay 546 1 7 6.018 1.430 -1.736 2.544

Table 4-23 shows the descriptive statistics of the variable, willingness to pay.

Sample size is 546. Minimum score is 1 while maximum score is 7. Mean score is 6.018

with 1.430 standard deviation. Skewness and kurtosis values are -1.736 and 2.544.

Based on shape of distribution of scores of willingness to pay variable, reflective and

inverse transformation is performed as it is recommended by Tabachnick and Fidell

(2013, p.80).

Formula used for transformation is:

New variable = 1 / (K-old variable) where K= largest possible value + 1

Transformed Will to Pay = 1/ ((7+1) - Will to Pay)

Table 4-24. Descriptive Statistics of (transformed) Willingness to Pay

N Min Max Mean Std. Deviation

Skewness Kurtosis

Will to Pay (transformed)

546 0.14 1 .706 .316 -.383 -1.505

Table 4-24 shows descriptive statistics of transformed willingness to pay variable.

Mean score is .706 with .316 standard deviation. After transformation of the variable,

skewness (-.383) and kurtosis (-1.505) values are brought within acceptable ranges to

be utilized in a parametric analysis.

All the variables that are going to be applied as independent and dependent

variables in further analyses, Regression and MANOVA, are prepared.

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RQ-1: MANOVA Results

RQ 1 (a): Does audience involvement with Turkish TV Series differ based on

mode of watching?

RQ 1 (b): Does audience involvement with Turkish TV Series differ based on

platform of watching?

As it is considered as an exploratory examination, it is refrained from using

hypotheses.

Mode and Platform of Watching

Table 4-25. Pearson Correlations between the Dependent Variables

Variables Interpersonal Aud. Inv.

Personal Aud. Inv. 0.228

Prior to conducting the MANOVA, a Pearson correlation is performed between

dependent variables in order to test the MANOVA assumption that the dependent

variables would be correlated with each other in the moderate range (Meyer, Gampst,

and Guarino, 2006). As can be seen in Table 4-25, a meaningful pattern of correlations

is detected among the dependent variables, suggesting the appropriateness of a

MANOVA. Box’s M test for homogeneity of dispersion matrices produces F (9, 47183) =

1.000, p = .437 (> .005), supporting the conclusion of homogeneity of variance matrices

(Huberty and Petoskey’s, 2000).

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Table 4-26. Descriptive Statistics of Behavioral Aud. Inv. on Mode and Platform of Watching

Mode Platform Mean Std. Dev. N

Personal Aud. Inv. Dubbed on TV .732 .266 122

on the Internet .761 .270 135

Total .747 .268 257

Subtitled on TV .827 .220 24

on the Internet .745 .265 261

Total .752 .262 285

Total on TV .747 .261 146

on the Internet .751 .266 396

Total .750 .265 542

Interpersonal Aud. Inv. Dubbed on TV .503 .296 122

on the Internet .543 .331 135

Total .524 .315 257

Subtitled on TV .563 .294 24

on the Internet .499 .301 261

Total .504 .300 285

Total on TV .513 .295 146

on the Internet .514 .311 396

Total .514 .307 542

Table 4-26 shows descriptive statistics of audience involvement on mode and

platform of watching. Participants are divided into two groups, the ones who watched

the TV series with subtitle and the ones who watched the TV series with dubbed,

according to the mode of watching. On the other hand, respondents are divided into two

groups: the ones who watched the TV series on TV and the ones who watched the TV

series on the Internet according to platform of watching. Total number of sample size

(N) is 542. Sample sizes are 257 dubbed, 285 subtitled, 146 on TV, 396 on the Internet.

The sample size of 542 includes enough cases for each cell of the 2 x 2 between

subjects design. There are far more cases than DVs (2) in the smallest cell (24).

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Skewness and Kurtosis values for DVs are not extreme, within acceptable values:

Personal Audience Involvement values are skewness: -.545, kurtosis: -1.107 (see Table

4-10). Interpersonal Audience Involvement values are skewness: .612 and kurtosis: -

1.142. No univariate outliers were found using a criterion z = |3.3|, (p = .001) with the

minimum and maximum values. Cook distance is applied to detect multivariate outliers.

No multivariate outliers are found. For no DV does the ratio of largest to smallest

variance approach 10:1.

Table 4-27. Multivariate Tests of Mode and Platform of Watching on Personal and Interpersonal Aud. Inv.

Value F H df E df Sig. Partial n2

Mode of Watching Wilks' Lambda .997 .861 2 537 .423 .003

Platform of Watching Wilks' Lambda .999 .330 2 537 .719 .001

Mode * Platform of Watching Wilks' Lambda .994 1.612 2 537 .200 .006

A two-way between-subjects factorial multivariate analysis of variance analysis is

conducted to explore impacts of the platform of watching and mode of watching on

levels of audience involvement with its two components, personal and interpersonal to

test hypotheses. With the use of Wilks’ criterion, the DVs are not significantly affected

by both mode of watching, F (2, 537) = .861, p (.423) > .001, and platform of watching,

F (2, 537) = .330, p = (.719) >.05, and by their interaction, F (2,537) = 1.612, p (.200)

>.05. The results reflect that there is no statistically significant MANOVA effect.

Table 4-28. One-way ANOVA's with Audience Involvement Subscales as Dependent Variables

Levene's Mode Platform Mode * Platform

F (3, 538) p F (1, 541) p n2 F (1, 541) p n2 F (1, 541) p n2

Personal Aud. Inv. 1.829 .141 .482 .224 .003 .648 .421 .001 2.900 .089 .005

Interpersonal Aud. Inv. 3.098 .026 .042 .838 .000 .100 .752 .000 1.873 .172 .003

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Prior to conducting a series of follow-up ANOVAs, the homogeneity of variance

assumption is tested for both audience involvement subscales. Based on a series of

Levene’s F Tests, the homogeneity of variance assumption is considered satisfied, even

though interpersonal audience involvement’ Levene’s F tests (p = .026) is statistically

significant (p > .05). Specifically, although the Levene’s F test suggests that the

variances associated with interpersonal audience involvement is not homogenous, an

examination of the standard deviation reveals that none of the largest standard

deviations are more than four times the size of the corresponding smallest, suggesting

that the ANOVA would be robust in this case (Howell, 2009) (see Table 4-26). A series

of one- way ANOVA’s on each of two DVs is conducted as a follow-up tests to the

MANOVA. As can be seen in Table 2, none of the ANOVA’s is statistically significant,

with ineffective effect size (partial n2).

RQ 1 (c) Do audience involvement, (d) place familiarity, (e) destination image,

and (f) behavioral intentions differ based on genre?

Genre

Table 4-29. Pearson Correlations between Eight Dependent Variables

Variables 1 2 3 4 5 6 7 8

1. Personal Aud. Inv. 1.000

2. Interpersonal Aud. Inv. .228 1.000

3. Place Familiarity .069 .256 1.000

4. Affective Dest. Image .266 .202 .276 1.000

5. Cognitive Dest. Image .301 .146 .222 .704 1.000

6. Visit Interest .244 .218 .363 .384 .381 1.000

7. WoM Rec. .213 .346 .350 .483 .498 .540 1.000

8. Will to Pay .205 .199 .281 .495 .566 .573 .530 1.000

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Prior to conducting the MANOVA, a series of Pearson correlations are performed

between among all the eight dependent variables are reported in order to test the

MANOVA assumption that the dependent variables would be correlated with each other

in the moderate range (Meyer, Gampst, and Guarino, 2006). As can be seen in Table 4-

29, a meaningful pattern of correlations is detected among the dependent variables,

suggesting the appropriateness of a MANOVA. Box’s M test for homogeneity of

dispersion matrices produces F (144, 50759) = 1.346, p = .004, supporting the

conclusion of homogeneity of variance matrices slightly as well (Huberty and

Petoskey’s, 2000).

Table 4-30. Turkish TV Series According to Genre

TV Series Genre

Action Romance Drama Comedy History

Dirty Money and Love 86

Ezel 12

Karadayi 35

Twenty Minutes 2

Until Death 6

Lost 1

Bitter Life 6

Inside 29

Fatmagul 97

Forbidden Love 14

Silver 3

Brave and Beautiful 20

Endless Love 37

Sila 28

1001 Nights 15

Black Rose 1

Kuzey Guney 7

Feriha 20

Little Bride 3

The Day my Destiny written 2

Asi 16

Broken Pieces 1

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Table 4-30. Continued

TV Series Action Romance Drama Comedy History

Mother 1

As Time Goes by 9

Beyond the Clouds 2

That Life is Mine 2

Bidding Farewell 2

Bride of Istanbul 1

My Fair Lady 3 MedCezir 31 Rental Love 5 Love not listen to reason 1 Sultan Suleyman 18

Kosem Sultan 2

Sura and Seyit 11

Lovebird 1

Ressurection 2

Wounded Love 2

Black Snake 2

Total 177 169 112 40 38

First of all, genre of TV dramas are determined based on the classifications

made on the websites; on IMDB.COM, the Internet Movie Database (abbreviated IMDb)

which is an online database of information related to films, television programs and

video games, including cast, production crew, fictional characters, biographies, plot

summaries, trivia and reviews, operated by IMDb.com, Inc., a subsidiary of Amazon,

and on TURKISHTVDRAMA.COM including basic information about Turkish TV series.

Five different genres are identified; action (177), romance (169), drama (112), comedy

(40), and history (38). The dramas chosen as favorite by the participants are coded

based on these five genres and a new variable was created. Table 4-30 above shows

which TV series are coded under which genre and number of their frequency chosen as

favorite by the participants.

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Table 4-31. Descriptive Statistics of Eight DVs According to Genre

Genre

Personal Inv.

Interpersnl Inv

Place Affective Img

Cognitive Img

Visit Interest

WoM Rec Will to Pay Fam

N M SD M SD M SD M SD M SD M SD M SD M SD

Action 152 .78 .23 .53 .31 4.94 1.41 .86 .23 .91 .21 .57 .32 .67 .29 .76 .29

Romance 138 .76 .26 .52 .31 4.81 1.57 .87 .23 .91 .22 .54 .33 .69 .29 .72 .31

Drama 92 .76 .27 .50 .30 4.69 1.56 .85 .25 .86 .25 .50 .32 .61 .29 .71 .33

Comedy 33 .74 .27 .41 .26 4.38 1.70 .84 .22 .91 .20 .45 .31 .52 .27 .64 .32

History 32 .61 .29 .39 .21 4.08 1.88 .71 .29 .79 .30 .37 .26 .51 .32 .54 .34

Total 447 .76 .26 .50 .30 4.74 1.56 .85 .24 .89 .23 .52 .32 .64 .29 .71 .31

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Prior to MANOVA, descriptive statistics are reported as a part of MANOVA

results (see Table 4-31). TV series are placed into five different genres; action (152),

romance (138), drama (92), comedy (33), and history (32). Total N is 447. Mean scores

and standard deviations of each genre group on eight DVs are presented. The sample

size of 447 includes enough cases for each cell of the between subjects design. There

are far more cases than DVs (8) in the smallest cell (16). Skewness and Kurtosis values

for DVs are not extreme, within acceptable values. No univariate outliers are found

using a criterion z = |3.3|, (p = .001) with the minimum and maximum values. Cook

distance is applied to detect multivariate outliers. No multivariate outliers are found. For

no DV does the ratio of largest to smallest variance approach 10:1.

Table 4-32. Multivariate Tests of Eight DVs According to Genre

Varible Value F Hypothesis df Error df Sig. Partial Eta Squared

Wilks' Lambda .898 1.492 32 1605.796 .038 .027

A one-way between groups multivariate analysis of variance is performed on

eight dependent variables to investigate differences in audience involvement, place

familiarity, destination image, and behavioral intentions based on the single

independent variable, genre of TV dramas (see Table 4-32). IBM SPSS MANOVA is

used for the analyses. Total N is 447. There are no univariate and multivariate within-

cell outliers at p< .001. Results of evaluation of assumptions of normality, homogeneity

of variance-covariance matrices, linearity, and multicollinearity are satisfactory. The

results for the dependent variables are considered separately. There is statistically

significant difference in at least one of the eight DVs based on genre, F (32, 1605) =

1.492, p (.038) < .05; Wilks’ value = .898, partial n2 = .027.

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Table 4-33. One-way ANOVA's with Eight DVs According to Genre

Levene's Genre

F (4, 442) p F (4, 447) p n2

Personal A. I. 2.681 .031 2.994 .019 .026

Interpersonal A I 4.838 .001 2.515 .041 .022

Place Fam. 1.745 .139 2.605 .035 .023

Affective Image 3.348 .010 3.441 .009 .030

Cognitive Image 7.083 .000 2.445 .046 .022

Visit Interest 2.612 .035 3.439 .009 .030

WoM Rec. .579 .678 4.653 .001 .040

Will to Pay 1.659 .158 3.623 .006 .032

Prior to conducting a series of follow-up ANOVAs, the homogeneity of variance

assumption was tested for DVs. Based on a series of Levene’s F Tests, the

homogeneity of variance assumption was considered satisfied, even though some DVs’

Levene’s F tests is statistically significant (p > .05). Specifically, although the Levene’s F

test suggested that the variances associated with some DVs are not homogenous, an

examination of the standard deviation revealed that none of the largest standard

deviations were more than four times the size of the corresponding smallest, suggesting

that the ANOVA would be robust in this case (Howell, 2009) (see Table 4-31 for SDs). A

series of one- way ANOVA’s on each of eight DVs is conducted as a follow-up tests to

the MANOVA. As can be seen in Table 4-33, all the eight variables of the ANOVA’s are

statistically significant, with a decent effect size (partial n2): Personal audience

involvement (p = .019) with around 3% effect size, interpersonal audience involvement

(p = .041) with slightly over 2%. Place familiarity (p = .023) with over 2% effect size.

Affective image (p = .009) with 3% effect size, cognitive image (p = .046) with slightly

over 2% effect size, Visitation interest (p = .009) with 3% effect size. WoM

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recommendation (p = .001) with 4% effect size, willingness to pay (p = .006) with over

3% effect size.

Table 4-34. Post-hoc Test of Eight DVs According to Genre

Action vs History

Romance vs History

Drama vs History

Action vs Comedy

Romance vs Comedy

Post Hoc Test

Mean difference p

Mean difference p

Mean difference p

Mean difference p

Mean difference p

Personal Aud. Inv. Tukey .171 .006 .151 .023 .146 .043 .151 .052 .166 .025

Interpersonal Aud. Inv. Tukey

Place Fam. Tamhane

Affective Image Tukey .155 .007 .169 .003 .146 .023

Cognitive Image Tukey

Visit Interest Tukey .202 .010 .169 .053

WoM Rec. Tamhane .177 .063

Will to Pay Tamhane .213 .020

Finally, a series of post-hoc analyses are performed to examine individual mean

difference comparisons across all five different genres and all eight dependent variables

(see Table 4-34). The type of post-hoc test (Tukey or Tamhane) is determined

according to Levene’s test scores. Instead of reporting every single one of the post hoc

test results, only the ones that have statistically significant mean differences are

reported. Tukey is used for post-hoc test of personal audience involvement, as p value

is .031. It is found that the participants who chose the TV series as favorite genre of

which is action, romance, and drama have higher personal audience involvement than

the participants who chose the TV series as favorite genre of which is history. Action-

History; p value = .006 with .1705 mean difference. Romance-History; p value = .023

with .1509 mean difference. Drama-History; p value = .043 with .1462 mean difference.

Tukey is used for post-hoc test of affective destination image, as p value is .001. It is

104

found that the participants who chose the TV series as favorite genre of which is action,

romance, and drama are more favorable to affective destination image than the

participants who chose the TV series as favorite genre of which is history. Action-

History; p value = .007 with .1549 mean difference. Romance-History; p value = .003

with .1694 mean difference. Drama-History; p value = .023 with .1462 mean difference.

Tukey is used for post-hoc test of visitation interest as p value is .035. It is found that

the participants who chose the TV series as favorite genre of which is action have

higher Visitation Interest than the participants who chose the TV series as favorite genre

of which is history with p value = .010. An inspection of the mean scores indicated that

.2018 mean difference occurred in favor of the participants favorite genre of which is

action. It is also worth to note that the participants who picked the TV dramas as favorite

genre of which is romance have higher in Visitation interest than the participants who

picked the TV dramas as favorite genre of which is history too but p-value is slightly

over 5 percent (p = .053) with .1694 mean difference. Tamhane is used as post hoc test

of WoM recommendation, as p-value is .678. It is found that the participants who chose

the TV series as favorite genre of which is action and romance are more favorable to

WoM recommendation than the participants who chose the TV series as favorite genre

of which is comedy. Action- Comedy; p-value = .052, mean difference is .1509.

Romance-Comedy; p-value = .025, mean difference is .1658. Other than that, the

participants who chose the TV series as favorite genre of which is romance are more

favorable to WoM recommendation than the participants who chose the TV series as

favorite genre of which is history with .1773 mean difference but p-value is slightly over

.05, which is .063. Tamhane is used as post hoc test of willingness to pay, as p-value is

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.158. It is found that the participants who chose the TV series as favorite genre of which

is action have statistically significant higher scores in willingness to pay than the

participants who chose the TV series as favorite genre of which is history with p-value =

.020 and mean difference = .2131. The other variables do not have statistically

significant differences based on genre. About interpersonal audience involvement, place

familiarity, and cognitive image variables, any significant differences are not reported in

the post-hoc Table 4-34 even though they are reported with significant values in the

ANOVA Table 4-33. The reason is that the type of post-hoc test (Tukey or Tamhane)

chosen for these variables based on Levene’s scores do not give statistically significant

results in the post-hoc analysis.

RQ-2: Multiple Regression Results

RQ: 2 (a): Does audience involvement contribute to predicting place familiarity?

RQ: 2 (b): Do audience involvement and place familiarity contribute to predicting

destination image of Turkey?

RQ: 2 (c): Do audience involvement, place familiarity, and destination image

contribute to predicting behavioral intentions toward Turkey?

Multiple regressions are applied to respond this research question. The

mathematical expression of it is:

Y = A + B1X1 + B2X2 + ... +BkXk

Multiple regression has a number assumptions about the data. Very first of them

is sample size. Different authors recommend different required number of cases for

multiple regression. Stevens (1996, p.72) suggests 15 cases for each predictor.

Tabachnick and Fidell (2013, p. 123) recommend a simple formula: N> 50 + 8m (where

m= number of independent variables). Based on the number of IVs; the present study

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performs 6 regression models and the model that requires highest number of cases has

five predictors. That is, minimum 90 cases are needed to perform those 6 regression

models (50 +8 (5) = 90). As the present study has more than 500 participants, the

sample size assumption is met.

The other assumption is outliers. Outliers are checked as a part of the initial data

screening but additional outlier detection is carried in the multiple regression procedure.

Outliers on the dependent variables are detected from the standardized residual plot.

Tabachnick and Fidell (2013, p. 128) recommend treating those with standardized

residual values above 3.3 (or less than -3.3) as outliers. There are no heavy outliers

affecting the regression models.

The other assumption is multicollinearity and singularity. This refers to the

realtionship among the predictor variables. Multicollinearity is the indicator of high

corelation between independent variables (r=.9 and above). Singularity means one

predictor variable is a combination of other indepdent variables. The highest correlation

among the predictors is .703 between cognitive and affective destination image

variables. To consider the multicollinearity between two predictors have to have .9 and

above. (Pallant, p.153). That is, there is no multicollinearity and singularity among the

predictors utilized in the 6 regression models.

107

Model 1: Place Familiarity (DV) and Audience Involvement (IV)

Table 4-35. Standard Multiple Regression between Place Familiarity (DV) and Audience Involvement (IV)

Place Fam. Persona A. I. Interper A. I.

B p sr2

Personal Aud Inv .238 *** .520 .637 .026 .008

Interpersonal Aud Inv .309 .520 *** 1.33 .000 .047

R² = .104

Adjusted R² = .100

R = .322

Intercept = 3.590

Model F (2,547) = 31.660

Unique variability= .055, shared variability= .049; %95 confidence limits from .06 to .15

A standard multiple regression is performed between place familiarity as

dependent variable and audience involvement with its personal and interpersonal

components as independent variables. Analysis is performed using SPSS

REGRESSION. Table 4-35 displays the correlations between the variables, the

unstandardized regression coefficients (B), intercept, the semipartial correlations (sr2),

R2, and adjusted R2. R for regression is different from zero, F (2,547) = 31.660, with R2

at .104 and %95 confidence limits from .06 to .15. The adjusted R2 value of .100

indicates that 10 percent of variability in place familiarity is predicted by personal and

interpersonal audience involvements. For the both regression coefficients that differ

from zero, 95% confidence limits are calculated.

The two IVs in combination contribute almost 5% (.049) in shared variability.

Altogether, slightly over 10% (10% adjusted) of the variability in place familiarity is

108

predicted by two audience involvement variables. Between those two independent

audience involvement variables, however, interpersonal audience involvement has

higher importance on predicting the DV, place familiarity, as indicated by the squared

semipartial correlations: nearly 5 percent. Personal Involvement has nearly 1 percent

unique contribution to the model.

(ACCEPTED) H1-a: Personal Audience Involvement contributes to predicting Place

Familiarity. (α = .05)

(ACCEPTED) H1-b: Interpersonal Audience Involvement contributes to predicting Place

Familiarity. (α = .10)

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Model 2: Affective Image (DV) and Aud. Inv. (IVs) and Place Familiarity (IV)

H2-a: Personal Audience Involvement contributes to predicting Affective Destination

Image.

H2-b: Interpersonal Audience Involvement contributes to predicting Affective Destination

Image.

H2-c: Place Familiarity contributes to predicting Affective Destination Image.

Table 4-36. Standard Multiple Regression between Affective Image (DV) and Aud. Inv. (IVs) and Place Familiarity (IV)

Affective Image

Personal A. I.

Interpersonal A. I.

Place Familiarity

B β p sr2

Personal Aud Inv .360 *** .520 .238 .244 .272 .000 .054

Interpersonal A. I. .282 .520 *** .309 .065 .083 .086 .005

Place Familiarity .276 .238 .309 *** .028 .186 .000 .031

R² = .173

Adjusted R² = .168

R = .416

Intercept = .499

Model F (3,515) = 35.856

Unique variability= .090; shared variability= .083; 95% confidence limits from .12 to .23

A standard multiple regression is performed between affective destination image

as dependent variable, personal audience involvement, interpersonal audience

involvement, and place familiarity as independent variables. Analysis is performed using

SPSS REGRESSION. Table 4-36 displays the correlations between the variables, the

unstandardized regression coefficients (B), intercept, the standardized regression

110

coefficients (β), the semipartial correlations (sr2), R2, and adjusted R2. R for regression

is different from zero, F (3,515) = 35.856, with R2 at .173 and %95 confidence limits

from .12 to .23. The adjusted R2 value of .168 indicates that almost 17 per cent of

variability in affective destination image is predicted by the three independent variables;

place familiarity, personal and interpersonal audience involvement. For the two

regression coefficients that differ from zero, 95% confidence limits are calculated.

The three IVs in combination contribute another .083 in shared variability.

Altogether, slightly over 17% (R2= .173, adjusted R2= .168) of the variability in affective

destination image is predicted by the IVs. Between those independent variables,

however, personal audience involvement has highest importance on predicting the DV,

as indicated by the squared semipartial correlations (sr2): over 5 percent (.054). Place

familiarity has significant contribution to the regression model with 3 percent (.031) as

well. On the other hand, interpersonal involvement does not have significant

contribution to the regression model although its bivariate correlation with the DV (.282)

is higher than place familiarity (.276). It is because interpersonal audience involvement

has shared variability with the other two independent variables, which retains it

contributing the regression model solely.

(ACCEPTED) H2-a: Personal audience involvement contributes to predicting Affective

Destination Image. (α = .01)

(REJECTED) H2-b: Interpersonal Audience Involvement contributes to predicting

Affective Destination Image.

(ACCEPTED) H2-c: Place Familiarity contributes to predicting Affective Destination

Image. (α = .01)

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Model 3: Cognitive Image (DV) and Audience Inv. (IVs) and Place Familiarity (IV)

H3-a: Personal Audience Involvement contributes to predicting Cognitive Destination

Image.

H3-b: Interpersonal Audience Involvement contributes to predicting Cognitive

Destination Image.

H3-c: Place Familiarity contributes to predicting Cognitive Destination Image.

Table 4-37. Standard Multiple Regression between Cognitive Image (DV) and Audience Inv. (IVs) and Place Familiarity (IV)

Cognitive Img

Personal Aud Inv

Interpersonal Aud Inv

Place Familiarity

B β p sr2

Personal Aud Inv .353 *** .520 .238 .266 .302 .000 .066

Interpersonal Aud Inv .235 .520 *** .309 .027 .035 .484 .000

Place Familiarity .222 .238 .309 *** .020 .139 .002 .017

R² = .146

Adjusted R² = .140

R = .382

Intercept = .576

Model F (3,499) = 28.334

Unique variability= .083; shared variability= .063; 95% confidence limits from .09 to .20

A standard multiple regression is performed between cognitive destination image

as depend variable; personal audience involvement, interpersonal audience

involvement, and place familiarity as independent variables. Analysis is performed using

SPSS REGRESSION. Table 4-37 displays the correlations between the variables, the

unstandardized regression coefficients (B), intercept, the standardized regression

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coefficients (β), the semipartial correlations (sr2), R2, and adjusted R2. R for regression

is different from zero, F (3, 499) = 28.334, with R2 at .146 and %95 confidence limits

from .09 to .20. The adjusted R2 value of .140 indicates that 14 per cent of variability in

cognitive image is predicted by place familiarity, personal and interpersonal audience

involvement.

The three IVs in combination contribute another almost .063 in shared variability.

Altogether, nearly %15 (R2= .146, adjusted R2= .140) of the variability in cognitive image

is predicted by the independent variables. Between those independent variables,

however, personal audience involvement has highest importance on predicting the DV,

as indicated by the squared semipartial correlations (sr2): nearly 7 percent (.066). The

other independent variable that has significance in the model is place familiarity with its

2 percent (.017) unique contribution. On the other hand, interpersonal audience

involvement does not have significant contribution to the regression model although its

bivariate correlation with the DV (.235) is higher than place familiarity (.222). It is

because interpersonal audience involvement has the shared variability with the other

two independent variables, which retains it contributing the regression model solely.

(ACCEPTED) H3-a: Personal Audience Involvement contributes to predicting Cognitive

Destination Image. (α = .05)

(REJECTED) H3-b: Interpersonal Audience Involvement contributes to predicting

Cognitive Destination Image.

(ACCEPTED) H3-c: Place Familiarity contributes to predicting Cognitive Destination

Image. (α = .05)

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Model 4: Visitation Interest (DV) and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs).

H4-a: Personal Audience Involvement contributes to predicting Visitation Interest.

H4-b: Interpersonal Audience Involvement contributes to predicting Visitation Interest.

H4-c: Place Familiarity contributes to predicting Visitation Interest.

H4-d: Affective Destination Image contributes to predicting Visitation Interest.

H4-e: Cognitive Destination Image contributes to predicting visitation Interest. Table 4-38. Standard Multiple Regression between Visitation Interest (DV) and Aud.

Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs)

Visit Interest

Personal Aud Inv

Interpersonal Aud Inv

Place Fam

Affective Img

Cognitive Img

B β p sr2

Personal Aud Inv .376 *** .520 .238 .360 .353 .244 .204 .000 .028

Interpersonal Aud Inv .277 .520 *** .309 .282 .235 .026 .025 .593 .000

Place Familiarity .363 .238 .309 *** .276 .222 .047 .237 .000 .063

Affective Img .384 .360 .282 .276 *** .704 .163 .123 .028 .010

Cognitive Img .381 .353 .235 .222 .704 *** .222 .164 .003 .013

R² = .279

Adjusted R² = .272

R = .528

Intercept = -.242

Model F (5,490) = 37.940

Unique variability= .114; shared variability= .165; 95% confidence limits from .21 to .33

114

A standard multiple regression is performed between visitation interest as

depend variable; personal audience involvement, interpersonal involvement audience

involvement, place familiarity, affective destination image, and cognitive destination

image as independent variables. Analysis is performed using SPSS REGRESSION.

Table 4-38 displays the correlations between the variables, the unstandardized

regression coefficients (B), intercept, the standardized regression coefficients (β), the

semipartial correlations (sr2), R2, and adjusted R2. R for regression is different from

zero, F (5,490) = 37.940, with R2 at .279 and %95 confidence limits from .21 to .33. The

adjusted R2 value of .272 indicates that 27 per cent of variability in visitation interest is

predicted by the independent variables.

The five IVs in combination contribute another 16 percent (.165) in shared

variability. Altogether, almost %28 (R2= .279, adjusted R2= .270) of the variability in

visitation interest is predicted by the independent variables. Between those independent

variables, however, place familiarity has highest importance on predicting the DV, as

indicated by the squared semipartial correlations (sr2): slightly over 6 percent unique

contribution. Personal involvement follows it with 3 percent. Affective and cognitive

images contribute to the model with 1 percent each uniquely. Even though the bivariate

correlation of interpersonal audience involvement with the DV, visitation interest, is .277,

because of sharing variability with the other IVs, this does retain it to make unique

contribution to the regression model.

(ACCEPTED) H4-a: Personal Audience Involvement contributes to predicting Visitation

Interest. (α = .01)

115

(REJECTED) H4-b: Interpersonal Audience Involvement contributes to predicting

Visitation Interest.

(ACCEPTED) H4-c: Place Familiarity contributes to predicting Visitation Interest. (α =

.01)

(ACCEPTED) H4-d: Affective Destination Image contributes to predicting Visitation

Interest. (α = .05)

(ACCEPTED) H4-e: Cognitive Destination Image contributes to predicting Visitation

Interest. (α = .01)

116

Model 5: WoM (DV) and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs)

H5-a: Personal Audience Involvement contributes to predicting WoM Recommendation.

H5-b: Interpersonal Audience Involvement contributes to predicting WoM

Recommendation.

H5-c: Place Familiarity contributes to predicting WoM Recommendation.

H5-d: Affective Destination Image contributes to predicting WoM Recommendation.

H5-e: Cognitive Destination Image contributes to predicting WoM Recommendation.

Table 4-39. Standard Multiple Regression between WoM (DV) and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs)

WoM Rec

Personal Aud Inv

Interpersonal Aud Inv

Place Familiarity

Affective Img

Cognitive Img

B β p sr2

Personal Aud Inv .441 *** .520 .238 .360 .353

.186

.167

.000

.019

Interpersonal Aud Inv .415 .520 *** .309 .282 .235

.172

.178

.000

.022

Place Familiarity .350 .238 .309 *** .276 .222

.029

.158

.000

.021

Affectie Img .483 .360 .282 0.276 *** .704 .18

1 .14

6 .00

4 .01

0

Cognitive Img .498 .353 .235 0.222 0.704 ***

.329

.260

.000

.034

R² = .403

Adjusted R² = .396

R = .634

Intercept = -.165

Model F (5,490) = 66.032

Unique variability= .106; shared variability= .289; 95% confidence limits from .33 to .46

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A standard multiple regression is performed between “WoM Recommendation”

as depend variable; personal audience involvement, interpersonal audience

involvement, place familiarity, affective destination image, and cognitive destination

image as independent variables. Analysis is performed using SPSS REGRESSION.

Table 4-39 displays the correlations between the variables, the unstandardized

regression coefficients (B), intercept, the standardized regression coefficients (β), the

semipartial correlations (sr2), R2, and adjusted R2. R for regression is different from

zero, F (5, 490) = 66.032, with R2 at .403 and %95 confidence limits from .33 to .46 The

adjusted R2 value of .396 indicates that 40 per cent of variability in WoM

recommendation is predicted by the five independent variables utilized in the regression

model.

The five IVs in combination contribute another .289 in shared variability.

Altogether, 40% of the variability in WoM recommendation is predicted by IVs. Between

those independent variables, however, cognitive image has highest importance on

predicting the DV, as indicated by the squared semipartial correlations (sr2): over 3

percent unique contribution. Interpersonal involvement (.022), place familiarity (.021),

and personal involvement (.019) with around 2 percent unique contribution to the

regression model. Affective image has lowest unique contribution to the model with 1

percent. Apparently, all the five independent variables have significance contribution to

the regression model to some extend at various levels.

(ACCEPTED) H5-a: Personal Audience Involvement contributes to predicting WoM

Recommendation. (α = .01)

118

(ACCEPTED) H5-b: Interpersonal Audience Involvement contributes to predicting WoM

Recommendation. (α = .01)

(ACCEPTED) H5-c: Place Familiarity contributes to predicting WoM Recommendation.

(α = .01)

(ACCEPTED) H5-d: Affective Destination Image contributes to predicting WoM

Recommendation. (α = .01)

(ACCEPTED) H5-e: Cognitive Destination Image contributes to predicting WoM

Recommendation. (α = .01)

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Model 6: Willingness to Pay and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs)

H6-a: Personal Audience Involvement contributes to predicting Willingness to Pay.

H6-b: Interpersonal Audience Involvement contributes to predicting Willingness to Pay.

H6-c: Place Familiarity contributes to predicting Willingness to Pay.

H6-d: Affective Destination Image contributes to predicting Willingness to Pay.

H6-e: Cognitive Destination Image contributes to predicting Willingness to Pay.

Table 4-40. Standard Multiple Regression between Willingness to Pay and Aud. Inv. (IVs), Place Familiarity (IV), and Destination Image (IVs)

Will to Pay

Personal Aud Inv

Interpersonal Aud Inv

Place Familiarity

Affective Img

Cognitive Img

B β p sr2

Personal Aud Inv .306 *** .520 .238 .360 .353 .073 .053 .163 .003

Interpersonal Aud Inv .244 .52 *** .309 .282 .235 .039 .037 .389 .001

Place Familiarity .281 .238 .309 *** .276 .222 .025 .126 .001 .014

Affective Image .495 .36 .282 .276 *** .704 .183 .138 .008 .009

Cognitive Image .566 .353 .235 .222 .704 *** .552 .069 .000 .083

R² = .364

Adjusted R² = .357

R = .603

Intercept = -.129

Model F (5,488) = 55.797

Unique variability= .110; shared variability= .254; 95% confidence limits from .29 to .42

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A standard multiple regression is performed between willingness to pay as the

dependent variable, personal audience involvement, interpersonal audience

involvement, place familiarity, affective destination image, and cognitive destination

image as the independent variables. Analysis is performed using SPSS REGRESSION.

Table 4-40 displays the correlations between the variables, the unstandardized

regression coefficients (B), intercept, the standardized regression coefficients (β), the

semipartial correlations (sr2), R2, and adjusted R2. R for regression is different from

zero, F (5, 488) = 55.797, with R2 at .364 and %95 confidence limits from .29 to .42. The

adjusted R2 value of .357 indicates that percent of variability in is predicted by. For the

(IVs) regression coefficients that differ from zero, 95% confidence limits are calculated.

The two IVs in combination contribute another .25 in shared variability.

Altogether, 36% (R2= .364, adjusted R2= .357) of the variability in willingness to pay is

predicted by variables. Between those independent variables, however, cognitive image

has highest importance on predicting the DV, as indicated by the squared semipartial

correlations (sr2): over 8 percent. The other two variables, affective image with 1 percent

(.009) and place familiarity over 1 percent (.014), make unique contribution to the

model. Personal and interpersonal audience involvements do not have unique

significant contribution to the model even though they have a good amount of bivariate

correlations with the DV, willingness to pay. It is because these two IVs have a good

deal of shared variability in the DV with the other three IVs.

(REJECTED) H6-a: Personal Audience Involvement contributes to predicting

Willingness to Pay.

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(REJECTED) H6-b: Interpersonal Audience Involvement contributes to predicting

Willingness to Pay.

(ACCEPTED) H6-c: Place Familiarity contributes to predicting Willingness to Pay. (α =

.01)

(ACCEPTED) H6-d: Affective Destination Image contributes to predicting Willingness to

Pay. (α = .01)

(ACCEPTED) H6-e: Cognitive Destination Image contributes to predicting Willingness to

Pay. (α = .01)

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CHAPTER 5 DISCUSSION

This chapter discusses the findings of the study, their theoretical and practical

implications, as well as limitations and directions for the future research.

Theoretical Implications

The study is mainly conducted within the framework of push and pull factor

theory of travel motivation, audience involvement theory, and product placement. Within

the scope of audience involvement theory borrowed from media studies, it is

investigated if audiences’ level of involvement with TV series affect their level of

involvement with the destination featured with utilizing place familiarity, destination

image –cognitive and affective-, behavioral intentions -visitation interest, WoM

recommendation, and willingness to pay-. Within the framework of product placement,

the role of TV series to promote destinations depicted in is inquired with Turkish TV

series among audiences from the American continent. In addition, the phenomenon is

inquired between geographically, historically, and culturally distant locations, -Turkey

and the American continent-, which gives higher validity to the research as it limits the

influence of other information sources about the destination on participants besides TV

series. From scholarly perspective, the other importance of the study is to investigate

the phenomenon in non-Western paradigm; the TV series and overwhelming number of

participants are non-Western, which is in contrast to many studies existing in the

literature. Moreover, in light of push pull factor travel motivation theory; the present

study takes the TV series as a pull factor, which has more potential to keep their

impacts on audience longer compared to movies.

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On the other hand, the study investigates if genre, platform of watching (the TV

or the Internet), and mode of watching (dubbed or subtitled) make changes on audience

involvement, perceptions of destination image and behavioral intentions – visitation

interest, WoM recommendation, and willingness to pay-.

The other thing that draws the attention is that participants are overwhelmingly

female. As the sample was collected among Turkish TV drama viewers, it is fair to say

that Turkish TV series are watched by female audiences overwhelmingly which is not

surprising for TV dramas (Redfern, 2011). Some other studies prove this situation as an

advantage. For instance, a study conducted by Jenkins (1978) shows that wives have a

significant influence on travel decisions. The same study and the study conducted by

Gitelson and Kerstetter (1995) highlight the influence of relatives and/or friends on travel

decisions. The Turkish TV drama viewers, female or male, have families; they have

relatives; they are friends of some people. Considering all those, it is not a drawback

their being female overwhelmingly.

In terms of their ages the audiences are very diverse even though there is an

increasing trend in ages of audiences. It is a coherent case because the older people

get, the more they watch TV (Redfern, 2011). In terms of education, 85 percent of

audiences have had some college experiences by contrast the commonly belief that the

audiences of TV dramas are less educated and the study conducted by Kang and Kim

(2011), which found Chinese who watch Korean TV series have lower education. The

other interesting fact is that having a sample from 22 different countries from the

American continent proves the size of geography that is conquered by Turkish TV

series.

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11 percent of participants have been to Turkey before. A big portion of them has

not been to Turkey. From academic perceptive, small portion, only 11 percent, of them

visiting Turkey already increases the possibility of impacts of Turkish TV series solely

on audiences toward image of Turkey and Turkey itself. This situation increases

reliability of results of the current student in terms of theoretical aspect, because one of

the influential information sources about a destination is visiting that destination and its

influence is restricted in this case. On the other hand, the study of Jiang Mingqiu (2013),

which is made a news report by Peter Barefoot (2013), found that Chinese viewers with

high education and high income watch rational and light-hearted American TV shows,

the viewers with low education and low income watch Korean TV shows, plots of which

are found irrational sometimes. More than half of Turkish TV drama viewers speak at

least a foreign language and already traveled overseas. It is an indicator of how worldly

people they are and how much interest they have for knowing the things around the

globe. It can be concluded that Turkish TV series are capable of catching wide range of

viewers from different demographics.

Audience involvement is the major component of the study. It is focused on

behavioral aspect of audience involvement. Exploratory factor analysis was performed

to inquire underlying components of audience involvement from behavioral aspect. Two

factors are determined and they are named as “personal and interpersonal” because of

the nature of the items that fell in two factors (Table 4-7). Also, Stanford (1984) and

Lemish (1985) are taken as references for the naming fashion. Stanford (1984)

suggested that audiences who talk back to the TV are more involved and personally

guided whereas Lemish (1985) addressed that viewers who talk about the programs

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with others are more involved. This personal and interpersonal distinction of audience

involvement is offered to be utilized by future studies.

Six hypotheses are tested regarding RQ-1 to inquire if interpersonal and personal

audience involvement are affected by platform and mode of watching, and interaction

between both. The results show that watching Turkish TV series on TV or on the

Internet platforms or watching them with dubbed or subtitled or in any cases of

combination of TV-dubbed, TV-subtitled, the Internet-dubbed, and the Internet-subtitled

do not make any statistically significant difference on personal and interpersonal

audience involvement. It can be approached to this situation from two different aspects.

It could be thought that the audiences who watch the Turkish TV series on TV may have

higher involvement because the TV series are shown at a fixed time and a certain day

of the week and it means that they need to adjust their day or week according to the TV

show. On the other hand, it could be thought that watching them on the Internet may

require more effort because on the Internet most of Turkish shows are watched on

illegal platforms so the viewers need to put extra effort to find them. Sometimes they

find it without dubbed and subtitled or with an unprofessional subtitles even sometimes

some bilingual people make translations and create subtitles sell them to audiences. An

audience reported that she was paying $2 for each episode for the translation made by

a Turkish college student even though she did not find the subtitle a good one.

In parallel, watching Turkish series with subtitles or dubbed does not make any

difference on personal and interpersonal audience involvement. It could be approached

on it from different aspects as well. It could be thought that watching them dubbed

would make audiences more engaged with the TV series as they are in native language

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of audiences. On the other hand, it could be thought that audiences may enjoy more

hearing natural voices of their favorite actors and actresses. The result is compatible

with the study conducted by Koolstra and et al. (2002). In the European Union

countries, many TV shows are imported from foreign-language countries (Koolstra and

et al., 2002). Koolstra and et al. (2002) focus on the pros and cons of dubbing and

subtitling of TV shows in terms of information processing, aesthetics, and learning

effects. Koolstra and et al. (2002) suggest that neither dubbing nor subtitling overweight

one another. The present study also inquires differences on involvement of audiences

with Turkish TV series, their familiarity with Turkey, image of Turkey they hold on their

minds, their visitation interest, WoM recommendation, and willingness to pay based on

their origin of country after watching Turkish TV series. Generally speaking, the study

finds that Turkish TV series have more impact on audiences from South America than

audiences from North America. When it was asked why it is the case, several reasons

can be proposed: North American viewers are English speakers. They are more

exposed to news reports of global media outlets. Turkey is often in the news reports

because of some issues in the region where Turkey is located and they are not happy

news reports without doubt. For that reason, the audiences from North America (the US

and Canada) may already have an image of Turkey in their minds. However, Turkish TV

series may show them a different face of Turkey and may heal the image of Turkey in

their minds. On the other hand, it can be assumed that Spanish and Portuguese

speaking viewers may have a more blanket image of Turkey in their minds since they

are not exposed to high volume of news reports in which Turkey is mentioned in a

negative way. So their building image of Turkey bases more on the Turkish TV series

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that they have watched. Detailed speaking, the results of study show that personal

involvement with Turkish TV series of Chileans are statistically higher than viewers from

the North America (the US and Canada). Viewers from Mexico have statistically better

affective image of Turkey than the North American viewers (the US and Canada).

Argentinians have statistically significant cognitive image of Turkey than the other South

Americans and North Americans, and WoM recommendation and willingness to pay

than North Americans (the US and Canada). Overall, it would be inferred that

Argentinians are the most influenced nation by Turkish TV series in terms of building a

better image of Turkey.

Yang (2012) found that romantic drama did not have positive impacts on

destination marketing constructs; image and visitation interest. The Turkish TV series

genre of which is action, romance, and drama create statistically significant personal

audience involvement compared to history and comedy. In parallel, the Turkish TV

series genre of which are action, romance, and drama create a better affective image of

Turkey compare to the Turkish TV series genre of which is history. The Turkish TV

series genre of which action and romance create statistically significant visitation

interest compared to the Turkish TV series genre of which is history. The Turkish TV

series genre of which is action create statistically significant willingness to pay

compared to the Turkish TV series genre of which is history. Overall, the Turkish TV

series genre of which is action such as Dirty Money and Love, Inside, Karadayi, and

Ezel are the most influential TV series on audiences in terms of creating higher

audience involvement, building up a better affective image of Turkey, increasing

visitation interest and willingness to pay for visiting. The second influential genre type on

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audiences is romance. The leading TV series genre of which is romance are Fatmagul,

Forbidden Love, Endless Love, and Brave and Beautiful. They are influential on

audience involvement, affective image, visitation interest and, and WoM

recommendation. Drama-oriented Turkish TV dramas such as 1001 Nights, Sila, Feriha,

Asi, As Time Goes by, Kuzey Guney are influential on audience involvement and

affective image. Comedy and History-oriented Turkish TV series such as My Fair Lady,

Rental Love, MedCezir, Sultan Suleyman (Magnificent Century), and Sura and Seyit are

not found as influential as on audience involvement, image of Turkey, and behavioral

intentions; visitation interest, WoM recommendation, willingness to pay. Other than that,

any genre type could not make significant difference on place familiarity. It can be

concluded that all of them create some familiarity with Turkey to some extent, which is

compatible with the result that place familiarity increases over times regardless of the

genre found by (Rudowsky, 2013).

The second research question investigates if audience involvement can help

explaining place familiarity, destination image, and behavioral intentions. Six multiple

regression is performed to answer the research question. Regression Model 1 finds that

audience involvement with its personal and interpersonal components play a role on

explaining place familiarity. In other words, audience's’ level of involvement with Turkish

TV series, especially interpersonal involvement, is an indicator of their level of familiarity

with Turkey. It is an expected result by common sense. When audiences are so

involved with the TV series it is high possibility that they put more attention to catch

some interesting cultural motives, historical places, and landscape pieces in TV series.

129

Regression Model 2 displays roles of personal and interpersonal audience

involvement and place familiarity on explaining affective image. Again, audiences’ level

of involvement with Turkish TV series increase possibility of holding a better affective

image of Turkey in their minds. The result suggests a certain level of familiarity leads

people to build up some feelings towards the destination, which is parallel to the

findings of Tasci (2009).

Regression Model 3 showcases impacts of personal and interpersonal

involvement and place familiarity on cognitive image of Turkey held by audiences. All

the three have impacts on cognitive image. Having involvement with the TV series and

gaining familiarity with the destination aid in having a better cognitive image in

audiences’ minds. Cognitive image is considered as the one of the most important

abstract elements of the study. Even though it is an intangible phenomenon, it is

suggested that cognitive image plays crucial role on travel decisions and destination

choices (Baloglu and Brinberg, 1997; Baloglu and McCleary, 1999). Affective image is a

significant element to initiate very first sympathy with the destination (Baloglu and

McCleary, 1999). However, in order to evolve this sympathy to visitation interest and

ultimately actual visit, the cognitive image turns to be a more crucial component

(Baloglu and McCleary, 1999). Destination choice cannot be a solely feeling-based

decision at all. Traveling is the activity that people expect to have a good time and

collect good memories. They allocate two valuable things; the limited free time that they

have within a year and the money that they save. They are assumed to tend to make

reasonable travel decisions while using those sources. In short, the finding that

audience involvement with its personal and interpersonal involvement as well as place

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familiarity have impacts on cognitive image increases their significance in tourism

studies.

Regression Model 4 displays influences of personal and interpersonal audience

involvement, place familiarity, affective and cognitive images on visitation interest.

Besides interpersonal audience involvement, all the other variables have importance on

creating visitation interest. Place familiarity is found as a distinctive contributor on

explaining visitation interest. It completely makes sense. Because Turkey is a distant

destination for audiences from the American continent and it is assumed that it was not

very well known and Turkish TV series have built up familiarity with Turkey to some

extent. It seems that the portion of familiarity that Turkish TV series have built up is at

the optimum level that encourages the viewers to visit, which is compatible with the

concept of optimal familiarity presented by MacKay and Fesenmaier (1997). Personal

audience involvement, cognitive and affective images are also influential on creating

visitation interest. About interpersonal audience involvement’s having statistically

insignificant influence on visitation interest can be explained by having shared

contribution to the model with personal involvement and place familiarity.

Regression Model 5 inquiries penetration of personal audience involvement,

interpersonal audience involvement, place familiarity, affective and cognitive image on

WoM recommendation. It is found that all the variables have statistically significant

impacts on WoM recommendation. Recommending Turkey as a tourist destination

requires less effort than considering visiting and paying. That is the explanation of WoM

recommendation’s having higher scores compared to visitation interest and willingness

to pay. Also, cognitive image is the most distinctive element of predicting WoM

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recommendation. Audience involvement with its personal and interpersonal components

is almost equally influential on WoM recommendation. Place familiarity is also effective

on predicting WoM recommendation. Overall, if people show willingness to recommend

a country to visit to other people after watching TV series from this country, the study’s

results display that they must have a good level of involvement with TV series, a certain

level of familiarity with the country, and a good image about the country.

Regression Model 6 shows impacts of personal and interpersonal audience

involvement, place familiarity, affective and cognitive image on willingness to

pay. Willingness to pay is the strongest behavioral intentional component. The

statements that the respondents are asked to respond have more potential to consider

the phenomenon seriously. One of the items consisting of willingness to pay variable is

“I am willing to spend 2000 American Dollar for a trip to Turkey for 10 days” (The

average cost is taken by the Capacity Tour Company). The study’s results show that the

image, in particular cognitive image, plays main role on willingness to pay. Affective

image and place familiarity have impacts on that too. However, audience involvement is

not found as effective on willingness to pay as on WoM recommendation. Overall, it is

fair to conclude that audience involvement with its personal and interpersonal

components, place familiarity, affective and cognitive destination images are influential

on behavioral intentions; visitation interest, WoM recommendation, and willingness to

pay. When the statements get stronger about considering visiting seriously, cognitive

image becomes the distinctive variable. When the statements are relatively softer such

as about recommending, all the variables assist in explaining the phenomenon.

However, it cannot be ignored that audience involvement with TV series and place

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familiarity that is gained over watching Turkish TV dramas are significant components

since they also contribute to predicting destination image.

As a result, the study is carried out within the framework of product placement,

audience involvement, and push and pull factor theory of travel motivation. Within the

scope of audience involvement theory borrowed from media studies, it is found that

audiences’ level of involvement with TV series affect their level of involvement with the

destination featured with utilizing place familiarity, destination image –cognitive and

affective-, behavioral intentions -visitation interest, WoM recommendation, and

willingness to pay-. Within the framework of product placement theory, the role of TV

series to promote destinations depicted in are determined with Turkish TV series among

audiences from the American continent. Meanwhile, the concept “product placement”

can be renamed as “place placement” in this case. In addition, the phenomenon is

identified between geographically, historically, and culturally distant locations, -Turkey

and the American continent-, which gives higher validity to the research as it limits the

influence of other information sources about the destination on participants besides TV

series. From academic perspective, the other importance of the study is to investigate

the phenomenon in non-Western paradigm; the TV series and overwhelming number of

participants are non-Westerns. Moreover, in light of push pull factor travel motivation

theory, the findings suggest that the TV series as a pull factor, which have more

potential to keep their impacts on audiences longer compare to movies.

On the other hand, it is found that genre make changes on audience

involvement, perceptions of destination image and behavioral intentions – visitation

interest, WoM recommendation, and willingness to pay- while platform of watching (the

133

TV or the Internet) and mode of watching (dubbed or subtitled) do not make any

changes on them.

Practical Implications

From a practical implication aspect, the findings have brought a number of

opportunities created by Turkish TV series and to be utilized for marketing and

promotional purposes. As parallel, Turkish DMOs lately seek for new Tourist markets as

well as their current markets mainly Europe, the former Soviet region, and the other

neighboring countries. The popularity and image created by Turkish TV series within the

American continent can be used to expand the market and diversify tourist portfolio.

Turkish tourism authorities need to take the lead and promote Turkish touristic places,

encourage Turkish local travel agencies to cooperate with their counterparts in those

countries. For example, a map or app showing those TV series filming locations can be

produced to assist potential tourists in finding them easily.

Turkish Tourism Ministry has forty two Tourism Offices around the world. There

is no office in the Spanish speaking countries in the American continent. There are

some other Turkish governmental institutions such as “Yunus Emre Institute” promoting

Turkey, Turkish culture, and language. They have over forty offices around the world

but they do not have any offices in the American continent besides in the US. It might

be a sign of their unawareness or ignorance of this opportunity. They need to take

actions immediately to utilize this opportunity in benefit of Turkey. Other than that,

Turkish Airlines, which flies in more countries than any other airlines in the world (“300th

Flight Destination,” 2017) and which is a global brand that is semi-governmental

enterprise, may increase the number of flights and destinations of direct flies from the

countries in the American continent to Turkey.

134

From a marketing aspect, the genre-based classification among TV series can

aid in classifying audiences, potential tourists, which can facilitate diversifying

promotional activities of destination marketing organizations. Especially action and

romance genres are found more influence on audiences overall.

Without doubt, the social media touches every part of life. It changes the form of

watching TV series too. It converts this leisure activity to an interactive one with

providing platforms where people can create fan groups and share their opinions and

feelings and inform each other with posting some news reports or videos about TV

series or main actors/actresses or anything related to current topics in Turkey. It carries

the connection among audiences beyond being fans of the same TV series. They learn

Turkish language and create WhatsApp and Facebook groups to practice Turkish

(Appendix H). They post some facts about Turkish culture; they make Turkish friends

and make trip plans to Turkey together (Appendix G). Culture and Tourism Ministry of

Turkey should consider these platforms to organize more professional online language

courses, post short promotional videos about Turkey, its culture and touristic places,

which should not cost a lot to actualize. The ministry should also encourage people who

are in hospitality and tourism sector to learn Spanish and Portuguese languages with

some ways such as free courses or some tax exemptions for employees and employers

who put effort to learn.

Last but not least, based on the results of present study, it can be concluded that

the production companies do not need to invest money into dubbing, which costs way

more than subtitling. Because the study results show that either dubbed or subtitled

135

have no difference on audience involvement with the shows. Even hearing natural

voices of actors/actresses makes audiences enjoy it more.

There are some situations that have potential to ruin the tourism opportunities

created by Turkish TV series; problematic international relations and political instability.

The recent crisis between Russia and Turkey and the failed coup attempt in Turkey in

July 2016 damaged the image of Turkey and tourism in Turkey. An anecdote from a

participant who is Cuban-American from Texas who traveled Turkey in July 2016 to see

the places featured in the series Sultan Suleyman (Magnificent Century). The night that

the coup occurring she just landed in Istanbul Airport and was stuck at the airport and

all the passengers were sent back next day without stepping out of the airport because

of the security concerns.

Limitations

One of the limitations regarding the study are over 60 Turkish TV dramas’ that

are subjects of the study being watched or aired at different times, which may affect the

impacts of each TV drama on audiences. The other limitation is that the sample of the

study consists of the audiences who have access to social media. The audiences who

are not social media users are excluded by nature.

Future Research

The results can replicate with different TV products and samples and it can be

investigated why action and romance are more influential on audiences in terms of

travel behavioral intentions.

Even though the data of current study may be appropriate to investigate which

actor/actresses are more liked country by country; however, it is beyond the scope of

the present study. The other studies are very welcomed particularly on this topic.

136

In order not to miss the whole picture, it has to be stressed that the participants

are contacted over social media platforms. They are already social media or Internet

users. It may be ignored that the Turkish TV series viewers who are not social media or

internet users in the sample. Actually, if they are just TV drama viewers and not

interested in beyond this, they do not match the purpose of the work. Because they do

not likely have any interest to travel. At this point, another question becomes more

important, what is the percentage of internet users, supposedly worldly people, among

Turkish TV drama viewers? That is such a good research question that is brought to

table by the current study.

Turkish TV series have been watched by female audiences overwhelmingly.

However, they are watched by a wide range of audiences in terms of life stage (age)

and social status (mainly education). Future studies can be carried out how and why

Turkish TV series are capable of catching viewers such a wide range of demographics.

Movies and TV series are dealt with under same concept; film tourism. But they

have a significant difference that is their durations. TV series compare to movies are

aired in longer periods. Another study can be conducted on a movie and TV series

featuring same destination and examine if they have different level of impacts on

audiences in terms of destination image and behavioral intentions in short term and long

term.

After a few more years pass from now, the influence of Turkish TV dramas can

be monitored with the official number of tourists visiting Turkey from those countries

with a well-structured regression model over years. The other way doing that; going to

137

Istanbul airport and surveying people arriving from those countries with random

sampling.

To be able to speak more confidently, some hardcore scientific studies may be

conducted to inquire this phenomenon. People may be asked about their travel

decisions and destination choices and it can be observed what part of their brains

become activated while they make their decisions.

138

APPENDIX A SUPPLEMENTARY ANALYSES

The purpose of the supplementary analyses is to examine if audience

involvement, place familiarity, destination image, and behavioral intentions differ based

on country of audiences, actors and actresses. MANOVA is conducted to obtain the

results.

Country of Origin

RQ-A1: Do audience involvement, place familiarity, destination image, and

behavioral intentions differ based on country of audiences?

Table A-1. Pearson Correlations between Eight Dependent Variables

Variables 1 2 3 4 5 6 7 8

1. Personal Aud. Inv. 1.000

2. Interpersonal Aud. Inv. .228 1.000

3. Place Familiarity .069 .256 1.000

4. Affective Dest. Image .266 .202 .276 1.000

5. Cognitive Dest. Image .301 .146 .222 .704 1.000

6. Visit Interest .244 .218 .363 .384 .381 1.000

7. WoM Rec. .213 .346 .350 .483 .498 .540 1.000

8. Will to Pay .205 .199 .281 .495 .566 .573 .530 1.000

Prior to conducting the MANOVA, a series of Pearson correlations that are

performed between among all the eight dependent variables are reported in order to

test the MANOVA assumption that the dependent variables would be correlated with

each other in the moderate range (Meyer, Gampst, & Guarino, 2006). As can be seen in

Table A-1, a meaningful pattern of correlations is detected among the dependent

variables, suggesting the appropriateness of a MANOVA. Box’s M test for homogeneity

of dispersion matrices produces F (324, 49969) = 1.125, p = .061 (> .005), supporting

the conclusion of homogeneity of variance matrices (Huberty and Petoskey’s, 2000).

139

Table A-2. Descriptive Statistics of Dependent Variables According to Countries

Personal Inv.

Interprsnl Inv

Place Fam

Affective Img

Cognitive Img

Visit Interest

WoM Rec

Will to Pay

Country N M SD M SD M SD M SD M SD M SD M SD M SD

Argentina 99 .770 .262 .489 .311 5.034 1.611 .864 .224 .864 .224 .568 .336 .680 .296 .780 .302

Brazil 55 .732 .249 .531 .295 4.594 1.475 .833 .249 .833 .249 .555 .326 .702 .281 .676 .302

Chile 60 .842 .223 .515 .271 5.006 1.342 .865 .240 .865 .240 .554 .309 .706 .286 .719 .305

Mexico 64 .786 .233 .459 .276 4.771 1.470 .902 .199 .902 .199 .546 .314 .670 .264 .744 .302

Colombia 26 .720 .259 .542 .341 4.718 2.224 .901 .207 .901 .207 .493 .298 .723 .266 .743 .288

Uruguay 16 .681 .331 .441 .314 4.833 1.656 .876 .243 .876 .243 .519 .360 .563 .307 .773 .354

Puerto Rico 29 .758 .216 .577 .321 4.805 1.443 .821 .264 .821 .264 .511 .344 .594 .300 .728 .330

Peru 23 .752 .259 .521 .297 4.942 1.003 .816 .268 .816 .268 .446 .279 .545 .256 .684 .328

USA & Canada (NorthAmerica) 41 .670 .293 .455 .275 4.211 1.665 .748 .283 .748 .283 .430 .321 .477 .304 .553 .319

Other South Americans 48 .705 .283 .513 .323 4.438 1.640 .820 .248 .820 .248 .432 .297 .580 .311 .634 .323

Total 461 .754 .259 .501 .299 4.762 1.566 .848 .240 .848 .240 .520 .321 .642 .295 .709 .314

Table A-2 shows descriptive statistics of the dependent variables according to

countries. Ten groups are used for the origin country variable; Argentina (99), Brazil

(55), Chile (60), Mexico (64), Colombia (26), Uruguay (16), Puerto Rico (29), Peru (23),

North America (USA & Canada) (41), and Other South Americans (48). Total N is 461.

The sample size of 461 includes enough cases for each cell of the between subjects

design. There are far more cases than DVs (8) in the smallest cell (16). Mean scores

are included with standard deviation values. Skewness and Kurtosis values for DVs are

not extreme, within acceptable values. No univariate outliers are found using a criterion

z = |3.3|, (p = .001) with the minimum and maximum values. Cook distance is applied to

detect multivariate outliers. No multivariate outliers are found. For no DV does the ratio

of largest to smallest variance approach 10:1.

140

Table A-3. Multivariate Tests on Eight DVs According to Country

Variable Value F Hypothesis df Error df Sig. Partial Eta Squared

Country Wilks' Lambda .790 1.486 72 2708.326 .005 .029

A one-way between groups multivariate analysis of variance is performed on

eight dependent variables to investigate differences in audience involvement, place

familiarity, destination image, and behavioral intentions based on origin country of

participants (see Table A-3). Preliminary assumption testing is conducted to check for

normality, linearity, univariate and multivariate outliers, homogeneity of variance-

covariance matrices, and multicollinearity, with no serious violations noted. The results

for the dependent variables are considered separately. With the use of Wilks’ criterion, it

is found that participants from at least two different countries have statistically significant

scores on at least one DV, F (72, 2708) = 1.486, p (.005) < .05; Wilks’ value = .790,

partial n2 = .029.

Table A-4. One-way ANOVA's with Eight DVs According to Country

Levene's Country of Origin

F (9, 451) p F (9, 461) p n2

Personal Aud. Inv. 3.101 .001 1.846 .058 .036

Interpersonal Aud. Inv. 1.531 .134 .689 .719 .014

Place Fam. 2.213 .020 1.409 .182 .027

Affective Image 3.674 .000 1.592 .115 .031

Cognitive Image 12.058 .000 3.383 0 .063

Visit Interest 1.72 .082 1.364 .202 .027

WoM Rec. .911 .515 3.347 .001 .063

Will to Pay .732 .680 2.346 .014 .045

Prior to conducting a series of follow-up ANOVAs, the homogeneity of variance

assumption is tested for DVs. Based on a series of Levene’s F Tests, the homogeneity

141

of variance assumption is considered satisfied, even though some DVs’ Levene’s F

tests is statistically significant (p > .05). Specifically, although the Levene’s F test

suggests that the variances associated with some DVs are not homogenous, an

examination of the standard deviation reveals that none of the largest standard

deviations are more than four times the size of the corresponding smallest, suggesting

that the ANOVA would be robust in this case (Howell, 2009) (see Table A-2). A series of

one- way ANOVA’s on each of eight DVs is conducted as a follow-up tests to the

MANOVA. As can be seen in Table A-4, some of the ANOVA’s are statistically

significant, with a decent effect size (partial n2): Personal audience involvement (p =

.058) with around 4% effect size, Cognitive Image (p = .000) with over 6% effect size,

WoM recommendation (p = .001) with over 6% effect size, willingness to pay (p = .045)

with nearly 5% effect size.

Table A-5. Post-hoc Test of DVs based on country of origin

Chile vs

North America (USA&Canda)

Mexico vs

North America (USA&Canada)

Argentina vs

North America (USA&Canada)

Argentina vs

Other South Americans

Post Hoc Test

Mean differenc

e Std Error p

Mean differenc

e

Std Erro p

Mean

differe

Std Error p

Mean differe

nc Std Error p

Personal Aud. Inv. Tukey .172 .521 .035

Affective Image Tukey .154 .478 .044

Cognitive Image Tukey .147 .042 .020 .129 .040 .020

WoM Rec.

Tamhane .204 .056 .023

Will to Pay Tukey .227 .058 .004

Finally, a series of post-hoc analyses are performed to examine individual mean

difference comparisons across all ten groups of country and all eight dependent

142

variables (see Table A-5). The type of post-hoc test (Tukey or Tamhane) is determined

according to Levene’s test scores. Instead of reporting every single one of the post hoc

test results, only the ones that have statistically significant mean differences are

reported. Tukey is used as Post hoc test of personal audience involvement as

significant level is .001. It is found that Chileans have higher personal involvement

compare to North Americans (the US & Canada) (p = .035). An inspection of the mean

scores indicated that .1715 mean difference occurred in favor of Chileans. Tukey is

used as Post hoc tests of affective destination image as significant level is .000. It is

found that Mexicans are more favorable to affective destination image than North

Americans (the US & Canada) (p = .044). An inspection of the mean scores indicated

that .1541 mean difference occurred in favor of Mexicans. Tukey is used as Post hoc

test of cognitive destination image as significant level is .000. It is found that

Argentinians are more favorable to cognitive destination image than North Americans

(the US & Canada) with p = .020 and .1467 mean difference and the other South

Americans with p = .044 and .1292 mean difference. Tamhane is used as Post hoc test

of WoM recommendation as significant value is .202. It is found that Argentinians are

more favorable to WoM recommendation than North Americans (the US & Canada) with

p = .023 and .2036 mean difference. Tukey is used as Post hoc test of willingness to

pay as significant level is .014. It is found that Argentinians are more favorable to

willingness to pay than North Americans (the US & Canada) with p = .004 and .2271.

Besides the dependent variables that are detailed above, there is no statistically

significant difference among the other dependent variables and their groups based on

origin country of participants.

143

Actors

RQ-A2: Do audience involvement, place familiarity, destination image, and

behavioral intentions differ based on actors?

Table A-6. Pearson Correlations Among Eight DVs

Variables 1. 2. 3. 4. 5. 6. 7. 8.

1. Personal Aud. Inv. 1.00

2. Interpersonal Aud.

Inv. .228 1.00

3. Place Familiarity .069 .256 1.00

4. Affective Dest.

Image .266 .202 .276 1.000

5. Cognitive Dest.

Image .301 .146 .222 .704 1.000

6. Visit Interest .244 .218 .363 .384 .381 1.000

7. WoM Rec. .213 .346 .350 .483 .498 .540 1.000

8. Will to Pay .205 .199 .281 .495 .566 .573 .530 1.000

Prior to conducting the MANOVA, a series of Pearson correlations that are

performed among all the eight dependent variables are reported in order to test the

MANOVA assumption that the dependent variables would be correlated with each other

in the moderate range (Meyer, Gampst, & Guarino, 2006). As can be seen in Table A-6,

a meaningful pattern of correlations is detected among the dependent variables,

suggesting the appropriateness of a MANOVA. Box’s M test for homogeneity of

dispersion matrices produces F (252, 23833) = 1.237, p = .006, supporting the

conclusion of homogeneity of variance matrices (Huberty and Petoskey’s, 2000).

144

Table A-7 shows “actors” variable. To create “actors” variable, the main male

characters of the TV dramas that are chosen as favorite by the participants are

determined. Seven leading actors are identified: Engin Akyurek, Cagatay Ulusoy,

Kivanc Tatlitug, Kenan Imirzalioglu, Burak Ozcivit, Halit Ergenc, M. Ali Alakurt, and

Murat Yildirim. They are assigned as levels of the new “actors” variable. Table A-7

Table A-7. Frequency Table of Actors appearing in Favorite Turkish TV Series

TV Series Actors

Engin Akyurek

Cagatay Ulusoy

Kivanc Tatlitug

Kenan Imirzalioglu

Burak Ozcivit

Halit Ergenc

M. Ali Alakurt

Murat Yildirim

Fatmagul 97

Dirty Money & Love 86

Feriha 20

MedCezir 31

Inside 29

Forbidden Love 14

Kuzey Guney 7

Sura & Seyit 11

Silver 3

Brave & Beautiful 20

Ezel 12

Kardayi 35

Bitter Life 6

Endless Love 37

Lovebird 1

1001 Nights 15

Sultan Suleyman 18

Wounded Love 2

Sila 28

Asi 16

Total 183 80 55 53 38 35 28 16

145

above shows which actor is the main character of which TV series chosen one of the

favorite series by the participants.

Table A-8. Descriptive Statistics of Eight DVs According to Actors

Actors

Personal Inv.

Interpersnl Inv

Place Fam

Affective Img

Cognitive Img

Visit Interest

WoM Rec

Will to Pay

N M SD M SD M SD M SD M SD M SD M SD M SD

Engin Akyurek 156

.751

.253 .519 .313

4.910

1.450

.866

.235

.904

.227

.546

.312

.668

.280

.738

.298

Cagatay Ulusoy 67

.802

.231 .463 .261

4.483

1.687

.862

.218

.936

.176

.509

.312

.617

.269

.706

.314

Kivanc Tatlitug 44

.726

.283 .475 .297

4.667

1.423

.795

.275

.839

.273

.508

.330

.622

.316

.603

.324

Kenan Imirzaliogl 42

.811

.215 .571 .339

5.143

1.555

.885

.205

.901

.217

.639

.325

.759

.298

.769

.307

Burak Ozcivit 32

.762

.267 .548 .309

4.542

1.954

.912

.186

.974

.105

.500

.354

.671

.300

.768

.305

Halit Ergenc 30

.742

.297 .524 .328

4.678

1.891

.843

.259

.852

.271

.457

.323

.573

.332

.660

.356

M. Ali Alakurt 20

.691

.293 .342 .153

3.850

1.068

.831

.272

.884

.248

.448

.330

.575

.256

.665

.332

Murat Yildirim 13

.705

.236 .514 .332

5.256

1.115

.793

.286

.864

.261

.487

.320

.658

.291

.714

.309

Total 404 .75

9 .25

5 .504 .303 4.74

9 1.57

2 .85

7 .23

6 .90

1 .22

4 .52

8 .32

2 .65

2 .29

1 .71

4 .31

3

Prior to MANOVA, descriptive statistics are reported as a part of MANOVA

results (see Table A-8). The seven actors are identified: Total N is 404. Mean scores

and standard deviations of each actor on eight DVs are presented. The sample size of

404 includes enough cases for each cell of the between subjects design. There are

more cases than DVs (8) in the smallest cell (13). Skewness and Kurtosis values for

DVs are not extreme, within acceptable values. No univariate outliers are found using a

criterion z = |3.3|, (p = .001) with the minimum and maximum values. Cook distance is

applied to detect multivariate outliers. No multivariate outliers are found. For no DV

does the ratio of largest to smallest variance approach 10:1.

146

Table A-9. Multivariate Tests of Eight DVs According to Actors

Value F Hypothesis df Error df Sig. Partial n2

Wilks' Lambda .860 1.064 56 2100.14 .350 .021

The one-way between groups multivariate analysis of variance is performed on

eight dependent variables to investigate differences in audience involvement, place

familiarity, destination image, and behavioral intentions based on the actors who acted

in the TV dramas chosen as favorite by the participants. Seven leading actors are

determined based on their frequency in the favorite dramas. IBM SPSS MANOVA is

used for the analyses. Total N is 404. There are no univariate and multivariate within-

cell outliers at p< .001. Results of evaluation of assumptions of normality, homogeneity

of variance-covariance matrices, linearity, and multicollinearity are satisfactory. The

results for the dependent variables were considered separately. There is not a

statistically significant difference in dependent variables based on actors, F (56, 2100) =

1.064, p (.350) > .05; Wilks’ value = .860, partial n2 = .021.

Table A-10. One-way ANOVA's with Eight DVs According to Actors

Levene's Actors

F (7, 396) p F (7, 404) p n2

Personal Aud. Inv. 2.222 .032 .947 .470 .016

Interpersonal Aud. Inv. 4.721 .000 1.529 .156 .026

Place Fam. 2.664 .011 2.163 .037 .037

Affective Image 3.125 .003 .989 .439 .017

Cognitive Image 6.323 .000 1.496 .167 .026

Visit Interest .579 .773 1.306 .246 .023

WoM Rec. 1.859 .075 1.634 .124 .028

Will to Pay 1.382 .211 1.458 .181 .025

147

Prior to conducting a series of follow-up ANOVAs, the homogeneity of variance

assumption is tested for DVs. Based on a series of Levene’s F Tests, the homogeneity

of variance assumption is considered satisfied, even though some DVs’ Levene’s F

tests are statistically significant (p > .05). Specifically, although the Levene’s F test

suggested that the variances associated with some DVs are not homogenous, an

examination of the standard deviation reveals that none of the largest standard

deviations are more than four times the size of the corresponding smallest, suggesting

that the ANOVA would be robust in this case (Howell, 2009). A series of one- way

ANOVA’s on each of eight DVs is conducted as a follow-up tests to the MANOVA. As

can be seen in Table A-10, among eight variables, only place familiarity of the ANOVA’s

are slightly statistically significant, with a decent effect size (partial n2): Place Familiarity

(p = .037) with almost 4% effect size.

Table A-11. Post-hoc Test of DVs According to Actors

Kenan Imirzalioglu vs

M. Ali Alakurt

Post Hoc Test Mean difference p

Place Familiarity Tukey 1.2929 0.048

Finally, a series of post-hoc analyses are performed to examine individual mean

difference comparisons across all seven different actors and all eight dependent

variables (see Table A-11). The type of post-hoc test (Tukey or Tamhane) is determined

according to Levene’s test scores. Among all the eight dependent variables only place

familiarity score between the actors Kenan Imirzalioglu and Mehmeh Ali Alakurt shows

slightly statistically significant differences with p value = .048 and mean difference is

1.2929 in favor of Kenan Imirzalioglu although Wilks’ value was not significant. Tukey is

148

used as post hoc test of place familiarity, as significance level is .011. Instead of

reporting all the post-hoc results on each DV, only place familiarity is reported, as it has

statistically significant mean difference.

Actresses

RQ-A3: Do audience involvement, place familiarity, destination image, and

behavioral intentions differ based on actresses?

Prior to conducting the MANOVA, a series of Pearson correlations that are

performed between among all the eight dependent variables are reported in order to

test the MANOVA assumption that the dependent variables would be correlated with

each other in the moderate range (Meyer, Gampst, & Guarino, 2006). As can be seen in

Table A-12, a meaningful pattern of correlations is detected among the dependent

variables, suggesting the appropriateness of a MANOVA. Box’s M test for homogeneity

of dispersion matrices produces F (216, 33304) = 1.183, p = .035 (> .005), supporting

the conclusion of homogeneity of variance matrices (Huberty and Petoskey’s, 2000).

Table A-12. Pearson Correlations between Eight Dependent Variables

Variables 1 2 3 4 5 6 7 8

1. Personal Aud. Inv. 1

2. Interpersonal Aud. Inv. 0.228 1

3. Place Familiarity 0.069 0.256 1

4. Affective Dest. Image 0.266 0.202 0.276 1

5. Cognitive Dest. Image 0.301 0.146 0.222 0.704 1

6. Visit Interest 0.244 0.218 0.363 0.384 0.381 1

7. WoM Rec. 0.213 0.346 0.35 0.483 0.498 0.54 1

8. Will to Pay 0.205 0.199 0.281 0.495 0.566 0.573 0.53 1

149

Table A-13. Frequency of Actresses Appearing in the Favorite TV Series

TV Series Actresses

Tuba Buyukustun

Beren Saat

Berguzar Korel

Cansu Dere

Neslihan Atagul

Serenay Sarikaya

Hazal Kaya

Dirty Money & Love 86

My Fair Lady 3

Asi 16

Twenty Minutes 2

Brave & Beautiful 20

Fatmagul 97

Forbidden Love 14

Kosem Sultan 2

1001 Nights 15

Karadayi 35

Wounded Love 2

Sila 28

Ezel 12

Mother 1

Endless Love 37

MedCezir 31

Feriha 20

Total 127 113 52 41 37 31 20

Table A-13 above shows “actresses” variable. To create “actresses” variable, the

main female characters of the TV series that were chosen as favorite by the participants

are determined. Seven leading actresses are identified: Tuba Buyukustun, Beren Saat,

Berguzar Korel, Cansu Dere, Neslihan Atagul, Serenay Sarikaya, and Hazal Kaya. They

are assigned as levels of the new “actresses” variable. Table A-13 displays which

actress is the main character of which TV series chosen one of the favorite series by the

participants.

150

Table A-14. Descriptive Statistics of Eight DVs According to Actresses

Actresses Personal

Inv. Interpersonal

Inv Place Fam

Affective Img

Cognitive Img

Visit Interest WoM Rec

Will to Pay

N M SD M SD M SD M SD M SD M SD M SD M SD

Tuba Buyukustun 111 .751 .245 .520 .315 4.835 1.377 .848 .245 .895 .235 .512 .312 .609 .278 .716 .298

Beren Saat 89 .750 .262 .507 .314 4.978 1.487 .868 .238 .897 .228 .561 .313 .724 .275 .731 .305

Berguzar Korel 46 .823 .214 .623 .354 5.326 1.408 .909 .183 .911 .209 .616 .323 .737 .306 .812 .292

Cansu Dere 29 .730 .292 .401 .224 4.161 1.107 .828 .252 .856 .252 .515 .361 .595 .270 .658 .332

Neslihan Atagul 32 .762 .267 .548 .309 4.542 1.954 .911 .185 .974 .104 .500 .353 .671 .300 .768 .305

Serenay Sarikaya 25 .781 .254 .397 .241 4.253 1.841 .835 .232 .937 .186 .501 .332 .579 .276 .617 .330

Hazal Kaya 18 .786 .248 .515 .300 4.167 2.049 .916 .207 .931 .208 .398 .261 .580 .277 .724 .342

Total 350 .764 .252 .514 .311 4.777 1.553 .868 .229 .906 .219 .531 .322 .656 .286 .726 .308

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Prior to MANOVA, descriptive statistics are reported as a part of MANOVA

results (see Table A-14). The seven actresses are determined: Total N is 350. Mean

scores and standard deviations of each actress on eight DVs are presented. The

sample size of 350 includes enough cases for each cell of the between subjects design.

There are more cases than DVs (8) in the smallest cell (18). Skewness and kurtosis

values for DVs are not extreme, within acceptable values. No univariate outliers are

found using a criterion z = |3.3|, (p = .001) with the minimum and maximum values.

Cook distance is applied to detect multivariate outliers. No multivariate outliers are

found. For no DV does the ratio of largest to smallest variance approach 10:1.

Table A-15. Multivariate Tests of Eight DVs According to Actresses

Value F Hypothesis df

Error df Sig. sr2

Wilks' Lambda .818 1.438 48 1657.323 .027 .033

The other one-way between groups multivariate analysis of variance is

performed on eight dependent variables to investigate differences in audience

involvement, place familiarity, destination image, and behavioral intentions based on the

actresses who acted in the TV dramas chosen as favorite by the participants (see Table

A-15). Seven leading actresses are determined based on their frequency in the favorite

dramas, as can be seen in Table A-15. IBM SPSS MANOVA is used for the analyses.

Total N is 350. There are no univariate and multivariate within-cell outliers at p < .001.

Results of evaluation of assumptions of normality, homogeneity of variance-covariance

matrices, linearity, and multicollinearity are satisfactory. The results for the dependent

variables are considered separately. There is a statistically significant difference in DVs

based on actresses, F (48, 1657) = 1.438, p (.027) < .05; Wilks’ value = .818, partial n2

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= .033. Among the eight dependent variables, only two dependent variables show some

statistically significant differences between the actresses.

Table A-16. One-way ANOVA's with Eight DVs According to Actresses

DVs Levene's Actresses

F (6, 343) p F (6, 350) p n2

Personal Aud. Inv. 1.916 .078 .638 .700 .011

Interpersonal Aud. Inv. 5.516 .000 2.299 .034 .039

Place Fam. 3.914 .001 3.163 .005 .052

Affective Image 3.123 .005 .939 .467 .016

Cognitive Image 4.182 .000 .966 .448 .017

Visit Interest 2.029 .061 1.341 .238 .023

WoM Rec. .605 .727 2.764 .012 .046

Will to Pay 1.002 .424 1.488 .181 .025

Prior to conducting a series of follow-up ANOVAs, the homogeneity of variance

assumption is tested for DVs. Based on a series of Levene’s F Tests, the homogeneity

of variance assumption is considered satisfied, even though some DVs’ Levene’s F

tests are statistically significant (p > .05). Specifically, although the Levene’s F test

suggests that the variances associated with some DVs are not homogenous, an

examination of the standard deviation reveals that none of the largest standard

deviations are more than four times the size of the corresponding smallest, suggesting

that the ANOVA would be robust in this case (Howell, 2009) (see Table A-14 for SDs).

A series of one- way ANOVA’s on each of eight DVs is conducted as a follow-up tests to

the MANOVA. As can be seen in Table A-16, among the eight DVs, Interpersonal

Audience Involvement of the ANOVA’s are statistically significant with (p = .037) and

almost 4% effect size; Place familiarity of the ANOVA’s are statistically significant with

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(p = .005) and over 5% effect size; WoM recommendation of the ANOVA’s are

statistically significant with (p = .012) and almost 3% effect size.

Table A-17. Post-hoc Tests of Eight DVs According to Actresses

DVs

Berguzar Korel vs

Cansu Dere

Berguzar Korel vs

SerenaySarikaya

Beren Saat vs

Berguzar Korel

Post Hoc Test Mean difference p

Mean difference p Mean difference p

Interpersonal Aud. Inv. Tukey .2220 .040 .2267 .050

Place Familiarity Tukey 1.1652 .023

WoM Rec Tamhane .1150 .079

Table A-17 shows post-hoc tests of eight DVs according to actresses. Tukey is

used as post hoc test of interpersonal audience involvement, as significance level is

.000. It is found that the participants who picked the TV series as favorite in which

Berguzar Korel acted have higher interpersonal audience involvement scores than the

participants who picked the TV series as favorite in which Cansu Dere and Serenay

Sarikaya acted. In order, p-value = .040 and mean difference is .2220; p-value = .050

and mean difference is .2267. Tukey is used as post hoc test of place familiarity as p-

value is .001. It is also found that the participants who picked the TV series as favorite

in which Berguzar Korel acted have higher scores in place familiarity than the

participants who picked the TV series as favorite in which Cansu Dere acted. p-value =

.023, mean difference is 1.1652. On the other hand, Wilks’ value (p = .012) indicates

that WoM recommendation variable also has statistically significant difference based on

categories of actresses; however, in post-hoc test, it is found that Beren Saat and

Berguzar Korel’s WoM recommendation scores has .115 mean difference in favor of

Beren Saat with p = .079. Meanwhile, tamhane is used as post-hoc test for WoM

recommendation. Other statistical analyses do not have statically significant results.

154

Conclusion

Lee and et al. (2008) found that celebrity fun involvement affects people’s

perceptions of tourism destinations (familiarity, image, and visitation interest). Busby

and et al. (2013) found that appearances of Kivanc Tatlitug, Turkish actor, in Turkish TV

series influence destination image creation of Turkey, especially Istanbul in the Arab

world. The present study also inquires if audiences have significant differences in

involvement, place familiarity, image, and behavioral intentions in terms of actors.

Mainly eight actors acting in the favorite Turkish TV dramas are determined. Among

actors only the TV series in which Kenan Imirzalioglu have acted makes audiences

more familiar with Turkey than the TV series in which Mehmet Ali Alakurt have acted.

Having only statistically significant result for one variable “place familiarity” and between

only two actors among seven makes the result questionable. However, when it is looked

into it deeper, the result may come from the number of TV series those two actors have

acted in. The three of TV series that Kenan Imirzalioglu have acted in is chosen as

favorite, which are “Ezel, Bitter Life, and Karadayi” and only one TV series Mehmet Ali

Alakurt have acted in is chosen as favorite, which is “Sila”. So being acted in three

different TV series compared to single one may make him automatically more influential

on audiences. However, it is given less credits to this possibility by the researcher

because if the result was related to the number of TV series the actors acted in, Kivanc

Tatlitug, who have acted in five different favorite TV series, would be the most influential

actor on audiences. This fact, it would not be said that it refutes this argument, but it

would confidently be said that it decreases the credibility of this approach or it may

really be that Kenan Imirzalioglu, possibly because of his “charisma”, encourages

audiences to know about Turkey more.

155

Likewise, among seven actresses some significant differences are found as well.

The TV series in which Berguzar Korel has acted are found more influential on

audiences than the TV series in which Cansu Dere and Serenay Sarikaya have acted in

terms of audience involvement and place familiarity. Overall, the study finds the

prominent actor and actress who are more influential on audiences from the American

continent.

In addition, one day, If Turkish authorities, DMOs or some private enterprises

and corporations establish some promotional campaigns in Spanish and Portuguese

languages throughout the American continent, they may use those actors/actresses

who are more liked by audiences from the American continent as an advertising face of

Turkey. For this purpose, the results of the present study suggest Kenan Imirzalioglu as

an actor and Berguzar Korel as an actress. In the same time, the popularity of Engin

Akyurek and Beren Saat in Latin America and the growing popularity of Kivanc Tatlitug

and Tuba Buyukustun in North America (the US and Canada) cannot be denied.

156

APPENDIX B ENGLISH VERSION OF THE SURVEY

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APPENDIX C SPANISH VERSION OF THE SURVEY

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APPENDIX D PORTUGUESE VERSION OF THE SURVEY

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APPENDIX E LETTER OF IRB APPROVAL

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APPENDIX F DONATION

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APPENDIX G A PICTURE OF A TRAVEL TO TURKEY

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APPENDIX H A PICTURE OF LEARNING TURKISH

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BIOGRAPHICAL SKETCH

Ali Iskender was born in 1988 as a son of the dad from Macedonia and the mom

from Serbia/Kosovo and raised in Turkey. He went to school in Turkey mostly. He

enrolled a European Union exchange student program in Poland for an academic year

in 2011 and 2012. He obtained double degrees; one in Economics from Anatolia

University, the other one in Business from Uludag University. He also studied English at

Mediterranean University and University of Texas at Austin for almost two academic

years in total. He graduated from the master’s program in the Department of Tourism,

Recreation, and Sport Management at the University of Florida in 2018.