Attitudes to Online Advertising: New Formats and New Perspectives
TESIS DOCTORAL
Título Attitudes to Online Advertising: New Formats and New Perspectives
Autor/es
María Elena Aramendía Muneta
Director/es
Cristina Olarte Pascual
Facultad
Facultad de Ciencias Empresariales
Titulación
Departamento Economía y Empresa
Curso Académico Attitudes to Online Advertising: New Formats and New Perspectives, tesis doctoral de María Elena Aramendía Muneta, dirigida por Cristina Olarte Pascual (publicada por la Universidad de La Rioja), se difunde bajo una Licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 3.0 Unported. Permisos que vayan más allá de lo cubierto por esta licencia pueden solicitarse a los titulares del copyright.
© El autor © Universidad de La Rioja, Servicio de Publicaciones, 2020 publicaciones.unirioja.es E-mail: [email protected]
Facultad de Ciencias Económicas y Empresariales Departamento de Economía y Empresa
Doctoral Thesis
ATTITUDES TO ONLINE ADVERTISING: NEW FORMATS AND NEW PERSPECTIVES
María Elena Aramendia Muneta Logroño, 2019
Facultad de Ciencias Económicas y Empresariales Departamento de Economía y Empresa
Doctoral Thesis
ATTITUDES TO ONLINE ADVERTISING: NEW FORMATS AND NEW PERSPECTIVES
PhD Candidate: María Elena Aramendia Muneta
Supervised by: PhD Cristina Olarte Pascual
Logroño, 2019
A Isabel, mi madre, porque sin ella, esta tesis nunca habría sido posible.
“ Don't let anyone rob you of your imagination, your creativity, or your curiosity. It's your place in the world; it's your life. Go on and do all you can with it, and make it the life you want to live.”
— Mae Jemison —
Agradecimientos
Después de este largo proceso doctoral, donde he puesto toda mi alma y pasión como “marketiniana” que soy, llega el momento de acordarme de todas las personas que han estado presentes en este tiempo. Espero que estas palabras sirvan de reconocimiento por su ayuda en esta tesis doctoral.
A mi directora de tesis, Dra. Cristina Olarte Pascual por su inestimable ayuda y apoyo incondicional, al departamento de Economía y Empresa y a la Universidad de la Rioja por todo lo que han aportado y me han enseñado como investigadora.
A mi madre, Dña. Isabel Muneta Salinas, que ha sido el mayor apoyo a lo largo de toda la tesis doctoral y que digamos que ella misma, también es doctora de la vida. Realmente, sin su ayuda en todo momento, esta tesis nunca habría sido posible.
A mi hermano José Félix por seguir con detalle la tesis y en especial, a mi sobrina, Maitane “Mai”, por enseñarme a ser una “desolentida” para afrontar esta tesis.
A mi tío Rafael por estar día y noche disponible para ayudarme, te estaré eternamente agradecida y a mi prima Zuriñe por aportar su creatividad de diseño que ha aprendido en su grado, a ellos que forman parte de mi equipo de tesis, eskerrik asko zuen baldintzarik gabeko laguntzagatik.
También tengo que recordar a mi padre, D. Jesús Aramendia Oroquieta y a mi abuela Dña. María Salinas Aramendia que aunque no están presentes, me enseñaron a disfrutar de los pequeños momentos de la vida y a que el trabajo tenaz siempre tiene sus frutos. Además, estoy convencida que allí donde se encuentren, han seguido con todo detalle este proceso doctoral, apoyándome y del cual, están muy orgullosos.
A mis amigos, en especial a Elena, Iñaki, Celine y Marta que estarán contentos que acabe esta tesis para verme un poco más y poder tomar unos pintxos juntos. Sobre todo, porque ya puedo cambiar el tema de conversación de ser monotemática sobre la tesis y los resultados que obtenía a cualquier otro tema de interés.
A la música que me ha acompañado en todo momento para ser fuente de inspiración y bien, como sustento en las largas horas sentada delante del ordenador.
A mis alumnos locales y extranjeros que supieron enseñarme cuál es la realidad actual y darme nuevas perspectivas y a la Universidad Pública de Navarra que me ha proporcionado apoyo indirecto y logístico.
A todos aquellos que me han apoyado y ayudado de una forma u otra. Quizás me he dejado a alguien, pero espero que estas palabras sirvan para recordarles que reconozco que han estado ahí en todo momento.
I would like to express my gratitude to all the researchers around the world who invited me to visit them to improve my research knowledge or just were willing to help me in the hardest moments, no matter what time or day it was.
# Abstract / Resumen
Abstract
Technological advances such as the mobile phone and the Internet have reshaped the advertising industry, where society, businesses and consumers have transformed their behaviour profiting from the new media of communication and advertising. In this context, engaging and creating long-term emotional bonds with consumers are essential as technologies enable a rapid two-way information exchange, which is the key factor in advertising. This doctoral thesis seeks to delve deeper into advertising formats and media under present-day conditions during the communication process. It also looks at advertisements from three perspectives that reflect the continuously changing and improving technologies, which requires that advertisers and researchers collaborate and work hand in hand.
The first perspective analyses the effect of SMS advertising messages through mobile phones and smartphones working under the constraints of permission-based marketing. The replication of the well-known and widely cited paper by Tsang, Ho, and Liang in 2004 obtained some contradictory results; specifically, the only variable found to actually positively affect attitude was entertainment. The proposed model of attitudes, intentions and behaviour is interconnected. At the same time, permission-based mobile marketing is on the rise due to high exposure to advertisements. Mobile advertising plays a significant role in the digital world, where consumers respond and react according to the content of the message.
The second perspective focuses on the gender encoding process through the use of original digital video format in online advertising. Content analysis is performed on 324 original digital videos that have won awards from professional marketers. The results show that there is no significant association between gender and any of the ten studied attributes (mode of presentation, credibility, role, age, argument type, reward type, product type, background, setting, and end comment). Hence, women and men are equally portrayed in non-stereotypical activities and roles. However, central figures are more likely to be men than women. It is worth highlighting the change in women’s role according to advertisers’ and marketers’ criteria, especially regarding original digital videos.
The third perspective considers the process of communication with images and user feedback specifically on Instagram. Based on the stimulus-organism-response model, a content analysis is conducted of 1,094 pictures from 69 countries. Two different studies have been conducted, one by ordinary least squares and the other one by cross-country cluster. The results are consistent with the model, where attributes in Instagram photographs are associated with the success of a country’s image or the image of a tourist destination. The presence of people, animals and water have a positive impact on the engagement of Instagrammers, while the lack of authenticity and the constant exposure to photographs of the same country have the opposite effect. In fact, both studies on tourism and Instagram spotlight the relationship between likes and comments and the content of tourism promotion photographs on Instagram. The results aim at understanding users’ behaviour, thus, helping destination management organizations in general, and more specifically, countries.
The doctoral thesis closes with discussions and main conclusions, contributions, managerial implications and recommendations for future lines of research.
Resumen
Avances tecnológicos como Internet y el teléfono móvil inteligente han remodelado la industria de la publicidad. Los nuevos medios de comunicación y publicidad digital han hecho que empresas, consumidores, y la sociedad en general, actualmente hiperconectados modifiquen sus comportamientos. En este contexto, la creación de vínculos emocionales a largo plazo con los consumidores resulta esencial, ya que las nuevas tecnologías permiten un intercambio rápido de información bidireccional, lo que es un factor clave en la publicidad. Esta tesis doctoral pretende profundizar en los nuevos formatos, soportes y medios publicitarios, que interactúan durante el proceso de comunicación en las condiciones actuales. Para abordar dicho objetivo, se plantean cuatro estudios interrelacionados desde tres perspectivas que reflejan el constante cambio y mejora de las tecnologías.
La primera perspectiva estudia el efecto de la publicidad basada en el envío de mensajes cortos (SMS) a teléfonos móviles y smartphones, que opera con las restricciones del llamado “marketing de permiso”. Para ello, se ha realizado una réplica del trabajo de Tsang, Ho y Liang de 2004. Los resultados de este trabajo muestran un cambio respecto al modelo de partida, ya que la única variable que afecta positivamente a la actitud es el entretenimiento. Sin embargo, se constata la interconexión entre actitudes, intenciones y comportamientos que se plantean en dicho modelo.
La segunda perspectiva se centra en el estudio de la presencia de estereotipos de género en el proceso de codificación a través del uso del vídeo digital original como nuevo formato de publicidad en Internet. Se ha realizado un análisis de contenido de 324 vídeos digitales originales que han sido galardonados con premios por parte de profesionales del marketing. Los resultados muestran que no existe una asociación significativa entre el género y ninguno de los otros diez atributos estudiados: forma de presentación, credibilidad, rol, edad, tipo de argumento, tipo de recompensa, tipo de producto, segundo plano, entorno y comentario final). Aunque hay una mayor presencia de hombres que de mujeres como protagonistas de los anuncios, ambos géneros se muestran de manera similar en actividades y roles no estereotipados.
La tercera perspectiva analiza el proceso de comunicación basado específicamente en imágenes y reacciones de usuarios en Instagram. Para ello, se ha llevado a cabo un análisis de contenido sobre 1.094 imágenes procedentes de 69 países, centrado en el modelo de estímulo-organismo-respuesta. Se han realizado dos estudios siguiendo dos metodologías diferentes: uno de ellos por mínimos cuadros ordinarios y el otro, por conglomerados entre países. Los resultados de ambos estudios son consistentes con el modelo estímulo-organismo-respuesta: los atributos de las fotografías de promoción turística en Instagram se relacionan con la popularidad de la imagen de un país o de un destino turístico. La presencia de elementos como personas, animales y agua tiene un impacto positivo en la implicación de los usuarios de Instagram, mientras que la falta de autenticidad y la exposición constante a fotografías del mismo país tienen el efecto contrario. De hecho, ambos estudios reflejan la relación entre los “me gusta” y los “comentarios” y el contenido de las fotografías de promoción turística en Instagram. Los resultados se encaminan a comprender el comportamiento de los usuarios, lo que resulta de gran ayuda para las entidades gestoras de destinos turísticos en general, y más específicamente, para orientar la imagen turística de cada país.
Finalmente, esta tesis doctoral aporta importantes conclusiones académicas, implicaciones prácticas para la gestión y recomendaciones para futuras líneas de investigación.
# Index of Contents
Content
# Chapter 1. Introduction ...... 1
1.1. Research justification ...... 3 1.2. Research objective and perspectives ...... 5 1.3. Research framework ...... 7 1.3.1. Mobile phone and advertising in hindsight ...... 7 1.3.2. A journey through gender stereotypes in the last decades ...... 8 1.3.3. S-O-R model applied to tourism and Instagram ...... 11 1.4. Structure of the thesis ...... 12
# Chapter 2. Consumer Attitudes towards Mobile Advertising: An Updated Vision ...... 17
2.1. Introduction ...... 19 2.2. Consumer perceptions of advertising ...... 20 2.3. Concept of SMS advertising ...... 20 2.4. Consumer attitudes towards SMS advertising ...... 22 2.5. Research framework and hypotheses ...... 23 2.6. Empirical study ...... 26 2.7. Data analysis and findings ...... 29 2.7.1 Data reliability ...... 29 2.7.2 Factors affecting attitudes ...... 29 2.7.3 Relationship between attitudes and intention ...... 31 2.7.4. Relationship between intention and behaviour ...... 32 2.7.5. Structural equation modelling ...... 33 2.7.6. Similarities and differences between the two studies ...... 35 2.8. Discussion and conclusions ...... 36
# Chapter 3. Gender Stereotypes in Original Digital Video Advertising ...... 39
3.1. Introduction ...... 41 3.2. Perceptions and bias generated through digital video advertising ...... 42 3.3. Online gender stereotypes ...... 44 3.4. Hypotheses ...... 45 3.5. Methodology ...... 49 3.5.1. Method ...... 49
3.5.2. ODVA sample ...... 49 3.5.3. Coding procedure ...... 50 3.5.4. Central measures and attributes ...... 51 3.6. Results ...... 52 3.7. Discussion and conclusions ...... 57
# Chapter 4. Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram ...... 61
4.1. Introduction ...... 63 4.2. Literature review ...... 65 4.2.1. Instagram as a major source of information about tourism destinations ...... 65 4.2.2. The power of images in the tourism industry ...... 66 4.2.3. Destination images and content analysis ...... 68 4.3. Formulation of the research questions ...... 69 4.3.1. S-O-R model for Instagram and DMOs ...... 69 4.3.2. Tourism destination image attributes (stimulus) ...... 70 4.3.3. Research questions ...... 73 4.4. Methodology ...... 74 4.4.1. Research design ...... 74 4.4.2. Sampling and coding process ...... 74 4.4.3. Statistical analysis ...... 75 4.4.4. Sample description ...... 76 4.5. Results ...... 78 4.6. Discussion and conclusions ...... 81
# Chapter 5. “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations ...... 85
5.1. Introduction ...... 87 5.2. Instagram and Tourism Destinations ...... 88 5.3. Methodology ...... 91 5.3.1. S-O-R model for Instagram and country image ...... 91 5.3.2. Qualitative content analysis and Attributes of tourism image destination (stimulus)...... 92 5.3.3. The sample ...... 94
5.4. Results ...... 95 5.4.1. Images, Likes and Comments by continent ...... 95 5.4.2. Images, Likes and Comments by country ...... 96 5.4.3. Perceptual Maps ...... 98 5.4.4. Cluster analysis ...... 100 5.4.5. Cluster characteristics ...... 101 5.5. Discussion and conclusions ...... 104
# Chapter 6. Conclusion ...... 109
6.1. Contributions and conclusions ...... 111 6.2. Managerial implications ...... 115 6.3. Limitations and future research lines ...... 118
# References ...... 123
# Index of Figures
Figure 1.1. TRA - Research framework for mobile phone ...... 8 Figure 1.2. Gender stereotypes for women and men ...... 11 Figure 1.3. S-O-R model for Instagram and tourism ...... 12 Figure 1.4. Structure framework of the dissertation ...... 13 Figure 2.1. Research Framework ...... 26 Figure 4.1. S-O-R model for Instagram and DMOs ...... 70 Figure 5.1. S-O-R model for Instagram and country image ...... 92 Figure 5.2. Perceptual Map ...... 99
# Index of Tables
Table 1.1. New perspectives on advertising ...... 7 Table 2.1. Questionnaire – Part A ...... 27 Table 2.2. Questionnaire – Part B ...... 27 Table 2.3. Questionnaire – Part C ...... 28 Table 2.4. Data reliability ...... 29 Table 2.5. Statistics on consumer attitudes ...... 30 Table 2.6. Results of the correlation analysis ...... 30 Table 2.7. Principal component analysis ...... 31 Table 2.8. Intention to receive mobile advertisements ...... 32 Table 2.9. Extent of message reading ...... 32 Table 2.10. Time taken to read the message ...... 32 Table 2.11. Fit indices ...... 33 Table 2.12. Parameter estimates of the general model ...... 34 Table 2.13. Differences and similarities between the two studies ...... 36 Table 3.1. Technical details of the research ...... 50 Table 3.2. Results for mode of presentation, credibility, role, age, argument type, and reward type ...... 55 Table 3.3. Results for product type, background, setting, and end comment ...... 56 Table 4.1. Attribute categories ...... 73 Table 4.2. Technical details of the research ...... 76 Table 4.3. Main descriptive data of dependent and independent variables ...... 77 Table 4.4. Relationship between attributes and likes and comments ...... 80 Table 5.1. Technical details of the research ...... 95 Table 5.2. Sample characteristics by continent ...... 96 Table 5.3. Sample characteristics by country ...... 97 Table 5.4. Characterization of country cluster ...... 103
# Chapter 1. Introduction
# Introduction 3
1.1. Research justification
Innovation and communication technologies have gained attention among researchers, above all due to the birth of the Internet and the mobile phone. The Internet is an important source of information for consumers and a formidable channel of communication for advertisers (Faber, Lee, & Nan, 2004). Moreover, Internet and mobile phone advertising are viable media that are constantly on the increase (Silk, Klein, & Berndt, 2001). In light of these technological advances, society, businesses and consumers have changed their behaviour implementing proactive applications of innovations, profiting from the benefits they bring and transforming our lives and ways of working, consuming and, above all, communicating. While the art of advertising in communication among businesses and consumers had meant just sending a message about the features of the products or services, in the 1950s advertisements turned into selling the sizzle, the purpose of which was to sell what consumers wanted or would want and not what they needed (Pollay, 1985). Since then, the evolution of new technologies and deeper insights into advertising have been linked to create new forms of communication, new technologies that enable the engagement with consumers, who are able to provide immediate feedback and create long-term emotional bonds by providing information constantly and attracting consumers’ attention. In this scenario, researchers try to find cohesion in order to help marketers and advertisers.
Until the end of the twentieth century and more specifically, until the 1980s, advertising was based on the formats of printed media, radio, television and cinema (traditional marketing) and consistently used these supports with the rise of managerial marketing and consumer-orientation (Schwarzkopf, 2016). After the 1980s, post-modern advertising experienced one of the most important transitions, with the introduction of mobile phones and then the Internet (Beard, 2016). In the twenty-first century, the advertising world is marked by the co-existence of traditional marketing and new forms of marketing that are evolving every day and offer different perspectives to businesses and researchers and challenge them to stay up to date. In this scenario, mobile advertising and online marketing have excelled as news fields of research given that they encompass all these new formats and new prospects.
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With the proliferation of mobile phones and above all of smartphones, m- advertising has emerged in several formats, one of them is Short Messaging Service (SMS) (De Canio, Pellegrini, & Aramendia-Muneta, 2016). According to Ovum (2016), the number of SMS messages worldwide increased from 956 billion in 2013 to 1,292 billion in 2018, and will continue growing until 2020 due to the spread of the messaging services from private conversations to e-business and e-government in that period of time. Despite the increasing complexity of mobile advertising, information is still considered a very valuable incentive that affects positively the perception of mobile adverting in consumers. Mobile users could react in various ways, loving the information and appreciating what they receive, or even hating it. In fact, in order to deal with these reactions and ensure consumers’ acceptance of mobile advertising, the European Union is at the forefront of applying the idea of user-consent for receiving SMS advertisements. Under these new conditions and as the evolution of the use of mobile phone changes from year to year, there is a need to update the view of consumer attitudes to mobile advertising through SMS.
Since McLuhan and Fiore’s (1967) assertion that the medium is the message, technology has shaped and restructured the media. Television, newspapers, magazines, and radio are no longer the only media to encourage and influence consumers to purchase a specific product or service. Rapid changes in technology, especially with the introduction of the Internet, have altered the meaning of these traditional media, introducing updated processes of message interconnection in line with current cultural and social structural patterns. Consumers receive information via multiple platforms, including smart devices. Moreover, to reach new consumers, brands have to use new technologies and accept that if they wish to send a message, they must do so using not only new platforms but also new formats, from mobile applications to online campaigns. These new emerging formats, including original digital video (ODV), are more prevalent than older formats. Original digital video advertising (ODVA) refers to advertising using original digital video, which implies showing videos solely online. 84% of marketers and agencies surveyed by the Interactive Advertising Bureau (IAB) stated that ODVA is more engaging than television commercials, and 80% said that ODV is more effective than other digital video content, as the format enables more prominent placing and branding (Advertiser Perceptions, 2018). # Introduction 5
As regards the new ways of communication, online advertising has significantly reshaped advertisers and marketers’ behaviour (Aramendia-Muneta, 2012). In fact, the Internet is efficient in reaching users and getting feedback from them. In this process, advertisements can be more adapted and personalized to each target audience as a response to the information received from users’ feedback. However, with the appearance of Social Networks (SNs), the Internet and mobile phone have moulded the experiences they offer to adjust themselves to the new needs. SNs such as Instagram can be considered as a broadcast medium (Aramendia-Muneta, 2017) due to their enormous popularity. Thus, it seems obvious that SNs should not miss the opportunity and potential to get involved in the online advertising business. In fact, SNs have a deep impact on industries selling a service such as tourism, where technologies are the basis of success (Aramendia-Muneta & Ollo-López, 2013). SNs serve as a medium to show an appropriate image and engage potential or present customers.
In this context, where new technologies appear and directly affect advertising, the advertising industry needs to respond according to the new formats, new context and trends. This dissertation considers advertising from updated perspectives with the aim of responding to the new requirements of society, businesses and present- day conditions.
1.2. Research objective and perspectives
This dissertation looks at advertisements from three perspectives that reflect the continuously changing and improving technologies and therefore, both advertisers and researchers are required to go in the same direction. Technologies affect not only advertising but also the elements in the communication process, which creates the circle of communication. Stemming from the sender’s field of experiences, the sender encodes a message, decoded by the receiver, which in turn becomes part of the receiver’s field of experience. Once the message is decoded, the receiver might react with a response and even send feedback to the sender. The process is sender- encoding-message-decoding-receiver-response-feedback-sender (Kotler &
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Armstrong, 2010). The general objective is to delve deeper into advertising formats and media under present-day conditions during the communication process.
Nevertheless, due to the various media and formats in the current market and to enrich the understanding of the numerous contexts, the dissertation addresses three perspectives, which are connected to three aspects: the circle of communication, format, and medium, all of them acting in the new present-day context.
The first perspective analyses the effect of sending SMS advertisements through mobile phones and smartphones as new media in the present-day contexts constrained by permission-based marketing. In the process of communication, how do mobile phone and smartphone users decode the SMS messages and what is their attitude to this kind of advertisements and what might their response be in terms of intention and behaviour?
The second perspective focuses on the encoding process on the sender’s end through the use of ODV format in online advertising in our present-day context. The complex encoding process encompasses not only the message about the brand, product or service that it is intended to promote, but also other intrinsic information that might affect society’s values.
The third perspective considers the last process of communication (response- feedback-sender) with images specifically in Instagram in our present-day context. Through responses in the form of likes and comments in Instagram, this feedback could affect the image of a tourist destination. That way, destinations might reformulate their message in the future.
Table 1.1 shows how this dissertation considers the three perspectives.
# Introduction 7
Table 1.1. New perspectives on advertising
New Circle of New New New Perspectives communication Format Medium Context
1 Decoding-Receiver- Up-to-date Up-to-date Response SMSs Mobile Phone & Smartphone
2 Sender-Encoding- ODVs Online Present-day Receiver
3 Response-Feedback- Up-to-date SNs- Sender Images Instagram
1.3. Research framework
As this dissertation has embraced three different perspectives and three different advertising formats, the theoretical framework differs in each of the three perspective axes. Therefore, the theoretical framework is presented as follows: a) mobile phone and advertising in hindsight; b) a journey through gender stereotypes in the last few decades; c) S-O-R model applied to tourism and Instagram.
1.3.1. Mobile phone and advertising in hindsight
Mobile advertising still uses the tool of Short Messaging Service (SMS) as the industry assesses its value for promoting their products or services. Researchers have applied Fishbein and Ajzen’s (1975) theory of reasoned action (TRA) as a framework to predict behaviour using attitudinal variables, where people’s intentions are influenced by attitudes towards behaviour. This model has been applied to mobile phone research since Tsang, Ho and Liang et al. (2004) publication. The attitudinal variables were: entertainment as a part of the joy and pleasure of receiving SMS advertisements for mobile phone users; informativeness as a source of information that an SMS message can contain; irritation as the opposite of entertainment; and credibility as a reference for purchasing a mobile phone through trust. The TRA model for mobile phone can be seen in Figure 1.1.
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Figure 1.1. TRA - Research framework for mobile phone
In hindsight and according to Eisend, Franke and Leigh (2016) the research world should replicate studies in order to test their findings, even those studies that are widely cited. Therefore, the framework of the TRA model adapted to the context of the mobile phone by Tsang et al. (2004) would be worth replicating in our present- day context, with an up-to-date format and new medium, allowing a comparison of the results with the original study. Above all, because for continuing learning and further research, it is necessary to go back to the past (Witkowski, 1989).
1.3.2. A journey through gender stereotypes in the last decades
A sizeable number of studies since 1970 have focused on the portrayal of women and men in advertising and ever since, the gender stereotype issue has gained momentum among researchers. This part is divided into two time periods: twentieth century overview and twentieth-first century overview.
In one of the first research works on the topic, in the early 1970s, Courtney and Lockeretz (1971) attempted to survey women’s roles in advertising and found that women were presented in the actual roles they had in real life at the time. Women were most often seen as either in a decorative role (sex object) or in the traditional family role (housewives and mothers), but unfortunately not as professional or working women (Dominick & Rauch, 1972). McArthur and Resko (1975) also noted the importance of studying traditional gender roles. In that way, men were presented as qualified, independent and authoritative when presenting an idea or product. Women were depicted as product users, dependent and never outside the home. Goffman (1979) was a pioneer in this field and introduced the variable of the use of # Introduction 9
body and pose in the creation of stereotypes. He also found that women were portrayed as being weak, passive and subordinate, and men as powerful, experts and leaders.
The United States was the focus of attention of the main researchers, but in 1981, Manstead and McCulloch looked at the situation in the UK and then more researchers followed their innovative approach. While the major studies linked depiction of women to product categories reinforcing the material aspect of the product (Courtney & Whipple, 1983), gender was considered an essential variable in the use of a specific product (Debevec & Iyer, 1986). In the 1980s, research studies took into account not only the structural aspect of the advertisement, but also the emotional states of the viewers, such as perception, beliefs, values and attitudes (Pollay, 1986). Advertisements can be a source of cultural information for viewers (Bretl & Cantor, 1988). At the end of the 1980s, researchers started to consider cultural differences between countries by making cross-cultural comparisons (Gilly, 1988). Television commercials had a deep impact on viewers and these commercials reinforced sex-role stereotypes (Lovdal, 1989).
While in the studies of the 1970s and 1980s women appeared in advertisements as sex objects and housewives, in the 1990s Michell and Taylor (1990) and Ferguson, Kreshel and Tinkham (1990) perceived that advertisers were more sensitive to the portrayal of women in their advertisements and showed women in a less stereotyped role. Women were depicted in non-traditional roles (Zotos & Lysonski, 1994). In their studies, researchers introduced the idea of masculinity score (Hofstede, 1980) and found that a high masculinity country is expected to be more gender stereotyped than a low masculinity country (Huang, 1995; Wiles, Wiles, & Tjernlund, 1995). Advertisers strengthened and activated gender stereotypes in the audience (Lavine, Sweeney, & Wagner, 1999).
On the whole, the twentieth century saw the emergence of a real framework in gender stereotypes, creating variables to measure them, mainly through content analysis (Craig, 1992; Elasmar, Hasegawa, & Brain, 1999; Lundstrom & Sciglimpaglia, 1977). However, at the end of the century, Furnham and Mak (1999) implemented new methodologies like meta-analysis.
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The new century raised new hopes of non-stereotypical gender roles, as shown in Bresnahan and Inoue (2001), where the results reflected an equal number of females and males as primary characters. However, there were persistent differences in product type, settings and voice-over attributes. Women started to play a main role in the world of advertising. Women needed specific advertisements for them because they responded differently to marketing communication campaigns (Wolin, 2003). Advertisements should be created according to the specific audience, as not all messages should be the same for the different types of target audience (Sheehan, 2014).
The new trends focused on audience response based on the use of different images (Hogg & Garrow, 2003; Orth & Holancova, 2004). Ganahl, Prinsen and Netzley (2003) confirmed the unbalanced use of women and men in advertisements for gender specific products. However, advertisers continued using retro-sexist images in the female environment (Gill & Arthurs, 2006). The attempts to change this appraisal mentioned by Michell and Taylor (1990) never actually materialized. It seems that in online advertising, gender stereotypes are still prevalent (Plakoyiannaki, Mathioudaki, Dimitratos, & Zotos, 2008). In 2010, Eisend applied meta-analysis to analyse gender roles and found that gender stereotyping is predominant in advertising. Although stereotyping has decreased over the years, especially in high masculinity countries, the expected reduction in stereotyping is progressing very slowly with hardly noticeable changes.
Findings by Wallis (2011) about stereotypes in music videos revealed that women are still presented as sexual objects and men with aggressive attitudes. In 2011, Paek, Nelson and Vilela’s research confirmed that females were portrayed in stereotypical attitudes. There is not much difference as compared with previous studies; gender stereotypes in the 1970s were still the norm in the twenty-first century. Although more than 30 years had passed, women and men were still presented in the same ways as in the early 70s. Figure 1.2 summarises the prototype models of women and men. # Introduction 11
Figure 1.2. Gender stereotypes for women and men
1.3.3. S-O-R model applied to tourism and Instagram
Laroche (2010) finds that the stimulus-organism-response (S-O-R) paradigm is the most useful for explaining online consumer behaviour, as the Internet is a universal medium and there is more experience and research in this field. That author adds that the S-O-R model is the most likely to provide productive solutions with regard to how online consumers behave.
To use this model, it is necessary first to look deeper into the theory of S-O-R. Mehrabian and Russell’s (1974) S-O-R paradigm suggests that there is a sequential correlation between stimulus, organism and response and asserts that environmental psychology is one of the hardest areas for researchers to explore due to the subjective nature of the data (e.g., reactions to colour). Stimuli are factors that trigger internal emotional and cognitive reactions (Kim, Lee, & Jung, 2019). Drawing on this framework, this study is an initial attempt to begin to understand which stimuli (i.e., image attributes) are the most appropriate and have the greatest impact on Instagrammers’ behaviour in the form of likes and comments (response). If destination marketing organizations (DMOs) act on these findings, the resulting measures could elicit a response from potential tourists, not only enhancing the destination’s image, but also increasing the number of tourists. Other researchers (e.g., Gatautis, Vitkauskaite, Gadeikiene, & Piligrimiene, 2016; Kim et al., 2019) have adapted the Mehrabian and Russell (1974) model to the online environment.
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In the present study, the S-O-R model is adapted from Mehrabian and Russell (1974) to Instagram and DMOs, where the stimulus is the image attributes, the organism refers to Instagrammers’ reaction and the expected response involves the Instagrammers’ likes and comments (Figure 1.3).
Figure 1.3. S-O-R model for Instagram and tourism
How to attract more tourists is a key issue for DMOs, especially how to control the social-media environment due to its increasingly central role in the tourism industry (Fatanti & Suyadnya, 2015). Unlike traditional environments, the social- media environment enables interactivity between the information provider and users (Gatautis et al., 2016) and fosters new forms of interaction with and relating to customers (Sawhney, Verona, & Prandelli, 2005). Hence, if they are early adopters of this approach, DMOs will be pioneers in improving the destination image and in attracting new consumers (Aramendia-Muneta, 2012).
1.4. Structure of the thesis
This dissertation aims to provide new insights into formats in advertising as a present-day issue and phenomenon. To this purpose, the dissertation is divided into six chapters, each one focusing on the advertising theme involved. The first chapter introduces the dissertation. From chapter two to five, four studies are conducted. The first study looks at SMS advertising from a replication perspective. The second deals with gender stereotypes in the new format of original digital video advertising as an up-to-date format. SNs, and Instagram in particular, are researched in the third and fourth studies, but while in the third one the research focuses on finding the key attributes for succeeding in tourism, the research in the fourth seeks to identify # Introduction 13
clusters of countries with similar success in likes and comments. Figure 1.4 summarizes the structure of the thesis.
Figure 1.4. Structure framework of the dissertation
Chapter 2, ‘Consumer Attitudes towards Mobile Advertising: An Updated Vision’, takes a new approach towards the theory of reasoned action in the present- day new context of mobile phones. Mobile advertising plays a significant role in the digital world, where consumers respond and react according to the content of the message. Short Messaging Service is one of the mobile phone communication tools marketers use. This research examines the well-known and widely cited paper by Tsang et al. (2004), replicating their research with some contradictory results; specifically, the only variable found to actually positively affect attitude was entertainment. The proposed model of attitudes, intentions and behaviour is interconnected. At the same time, permission-based mobile marketing is on the rise due to high exposure to advertisements. These findings open up a new scenario for mobile advertising.
Chapter 3, ‘Gender Stereotypes in Original Digital Video Advertising’ looks at this new format as an independent trend in the advertising industry and as a new avenue of research. Content analysis is performed on 324 original digital videos that
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have won awards from professional marketers. The results show that there is no statistically significant association between gender and any of the ten studied attributes (mode of presentation, credibility, role, age, argument type, reward type, product type, background, setting, and end comment). Hence, women and men are equally portrayed in non-stereotypical activities and roles. However, central figures are more likely to be men than women. This chapter highlights the change in women’s role according to advertisers’ and marketers’ criteria for original digital videos.
Chapter 4, ‘Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram’ highlights the relationship between likes and comments and the content of tourism photographs on Instagram with the aim of understanding user behaviour and, thus, helping destination management organizations. Based on the stimulus-organism-response model, a content analysis was conducted of 1,094 pictures that received 131,116,800 likes and 2,859,448 comments. The results show that Instagrammers’ responses are influenced differently by different picture attributes, resulting in dissimilar behaviour with regard to likes and comments. Specifically, likes, as immediate reactions, tend to be driven by content featuring people, views, or common habits. In contrast, comments, which require greater effort on the part of the Instagrammer, are elicited by the topic of festivals or hotels, colours such as cream, green, orange, or yellow, images of water or animals, and images featuring tourist activities, mostly at night. Repetitive or fake pictures negatively impact likes. By analysing the content of the information provided by the uploaded photographs, a typology of photographic attributes is developed to offer clues for destination management organizations to enhance engagement with potential customers and Instagram users.
Chapter 5, ‘“The best” and “The least”: Cross-country Cluster Analysis of Instagram and Tourism Destinations’, provides an analysis of the key destination image attributes influencing the number of likes and comments by cross-country cluster on the base of stimulus-organism-response. For each of the 1,094 pictures of 69 countries obtained from Instagram and using content analysis, seven attributes are measured (main topic, centricity, time of the day, people, water and as dysfunction variables, photomontage and repetitive country). Two main clusters of # Introduction 15
countries have been found ‘the best’ and ‘the least’, whose difference is the success in obtaining more or fewer likes or comments from Instagrammers. Photographs taken during the day that show tourism or entertainment facilities as well as panoramic views of nature connected with sightseeing are prevalent in the successful country cluster group. The form of presentation of a picture which describes situations where tourists interact with the destination by taking part in the normal life of the place or by presenting tourist activities such as biking or partying take pride of place for being successful. The presence of people and water in the picture has a positive impact, and are common features for becoming successful, while the high frequency use of a country image and the lack of authenticity of the destination have the opposite effect. Overall, the results are consistent with the idea that attributes in the photographs on Instagram are associated with the successful image of a country as a tourism destination.
The last chapter considers the main conclusions, limitations, managerial implications and future lines of research.
# Chapter 2. Consumer Attitudes towards Mobile Advertising: An Updated Vision
# Consumer Attitudes towards Mobile Advertising: An Updated Vision 19
2.1. Introduction
Short Messaging Service (SMS) is still a very useful tool for mobile advertising. According to a study by ComScore (2010) conducted in five European countries (the UK, France, Germany, Spain and Italy), more than 100 million mobile phones received SMS advertisements. Coupons, discounts, promotions, contests and messages soliciting donations to charities and non-profit organisations were the SMS messages to receive the highest user response, whilst SMS messages advertising products, services or brands received the lowest.
Millward Brown Digital (2013) encouraged marketers to continue using this tool, noting that almost 3 out of 5 consumers prefer marketing texts over other mobile marketing formats (videos or banners) and 68% find marketing messages very useful. Likewise, Cleff (2007) highlighted the need to get users’ consent to receive SMS messages in accordance with EU regulations (Directive 95/46/EC, Directive 97/66/EC, Directive 2002/58/EC). This is because when Europeans give their consent, they express an implicit willingness to receive SMS advertisements.
As SMS advertisements are a live issue, and since Tsang et al.’s (2004) paper on the subject is well-known and widely cited by researchers working on consumer attitudes towards them, replicating that research with an updated vision could be beneficial for the discipline of mobile advertising. More than 1,000 studies have been based on Tsang et al.’s work, and their conclusions based on their structural equation modelling are still used by researchers to draw connections between attitudes, intention and behaviour in relation to mobile phones today. Furthermore, according to Eisend et al. (2016, pp. 1), ‘To trust the findings of seminal studies, no matter how carefully conducted and widely cited, replications are needed.’ Accordingly, this research aims at replicating the original study and comparing the results. To this end, the remainder of the chapter is divided into three parts. The first reviews the literature on consumer perceptions of advertising, the concept of SMS advertising and attitudes towards SMS advertising. The second explains the research methodology, sample data and data analysis and reports the main findings. Finally, the third includes the discussion, implications and suggestions for further research.
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2.2. Consumer perceptions of advertising
According to Eze and Lee (2012), the concept of advertising plays a significant role in determining a business’s growth prospects, which are closely related to consumer attitudes towards, and perceptions of, particular products and/or services along with the brand as a whole. The variables most often considered to assess consumer attitudes towards advertising are the hedonic dimension, consumer manipulation, the economic condition, product information, materialism and social integration. Furthermore, according to Friman (2010), the primary purpose of an advertisement is to persuade consumers, thereby influencing their behaviour in the long run. It has also been argued that certain aspects, such as purchase intention and brand attitudes, can be used to measure advertising effectiveness.
Similarly, according to Raluca and Ioan (2010), consumer attitudes towards advertisements refer to the reactions and responses that target and potential customers show to an advertisement based on its content, setting and the message conveyed. Customer engagement with a given brand also largely depends on these factors, as an advertisement is considered effective only when it has the potential to influence customers’ purchasing decisions. Fatima and Lodhi (2015) and Ling, Piew and Chai (2010) found that advertisements must have certain features to impact consumer attitudes and perceptions. Of these features, the most important were for the advertisement to be informative, appeal to consumers’ emotions, and be reliable and trustworthy from the consumers’ perspective.
2.3. Concept of SMS advertising
Mobile phone use spread amongst consumers along with the exponential growth of the Internet. Advertisers found that mobile phones can be used as a new medium for delivering advertisements. Such wireless marketing first became possible with the advent of Short Messaging Service technology. Wireless marketing allowed marketers to connect consumers to brands through the wireless Internet (Zoller, Matthews, & Van Housen, 2001). This was the early stage of SMS advertising; however, attitudes towards mobile phones changed with the emergence of # Consumer Attitudes towards Mobile Advertising: An Updated Vision 21
smartphones. The rise of smartphones has transformed everyday life in innumerable ways, positioning them as a priority for attaining desired outcomes in all domains. In the field of business and marketing, smartphones have proved to be of immense significance, not only enabling marketers to promote their products faster and more simply, but also largely helping them reach every corner of the world via the wireless interface (Grewal, Bart, Spann, & Zubcsek, 2016; Kumar, 2013).
Today, people largely depend on their mobile phones not only to communicate with others, but also to gather information about news and events around the world (PricewaterhouseCoopers LLP, 2015). Zabadi, Shura and Elsayed (2012) found that the Internet is the most significant reason for people’s dependency on the wireless network, especially young people. However, SMS messages still play a significant role in conveying information to people over the wireless network irrespective of age-related limitations. Nevertheless, continuous advances in technology have largely decreased the demand for SMS advertising. This is because the SMS facility is now being overtaken by comparatively more advanced facilities, such as Multimedia Messaging Service (MMS) or Java-based applications, which mostly use the Internet connection to communicate in the global environment (Kumar, 2013).
According to Salem (2016), the sudden rise in mobile phone use and ongoing technological advances have given rise to new opportunities for businesses to make progress and set higher standards to meet within a specified time-span. Marketers have come up with innovative ways to use the SMS facility to communicate with their target consumers at any time, allowing them to develop a new connection with potential customers and provide them with information about their products and/or services. As a result of increasing demand and its cost effectiveness, the SMS facility has already positioned itself as a ‘powerful means of communication’ (Salem, 2016, pp. 1). Likewise Izquierdo-Yusta, Olarte-Pascual and Reinares-Lara (2015) found that smartphones have an immense capability to deliver advertisements to customers from a marketers’ perspective, due to their high rate of penetration. In addition, smartphones have the distinct feature of portability, which increases marketers’ ability to stay connected with consumers at all times. This largely sets this medium apart from traditional means of advertising.
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2.4. Consumer attitudes towards SMS advertising
The most significant feature of SMS advertising is forced exposure. This refers to the fact that smartphone users are compelled to receive advertisements in the form of messages. Although this is the best way to deliver information about a brand to a target customer, it can also have a negative mental impact, as constantly receiving messages from different corporations can cause frustration. This, in turn, can have a considerable negative effect on individual purchasing behaviour (Chun & Wan, 2009). On the other hand, SMS advertising operates on a real-time approach; hence, it has the potential to reach customers regardless of location and time. Today, it is a medium that is accessible to everybody. Although young people may be able to easily access more advanced media, especially Internet-based ones, this is not always the case with older individuals. Hence, the SMS facility clearly offers a comparatively larger amount of exposure to consumers of all generations (Awad & El-Shihy, 2014; Smutkupt, Krairit, & Esichaikul, 2010).
According to Bhowte (2016), consumer attitudes towards SMS advertising can be understood through three components: cognition, affect and conation. Of these, cognition is considered the most important. Here, cognition refers to the consumers’ mental situation, which affects their acceptance of the SMS advertisement and, in turn, impacts on their purchase decision. If the message is sufficiently informative, credible and entertaining, it will be positively received; otherwise, it may be unsuccessful and lead to irritation in the minds of consumers. In this regard, Almossawi (2014) found that components such as entertainment, irritation, informativeness, personalisation and credibility strongly impact target consumer attitudes towards SMS advertisements, which further affects their intention to consume and purchase the products and/or services. Another key aspect that can hinder SMS advertising is word-of-mouth, which can also positively or negatively impact consumers’ purchasing decisions.
# Consumer Attitudes towards Mobile Advertising: An Updated Vision 23
2.5. Research framework and hypotheses
Two major predictors of the effectiveness of online advertising are entertainment and informativeness (Aaker, Batra, & Myers, 1994; Bauer, Reichardt, Barnes, & Neumann, 2005; Ducoffe, 1996; Wang & Hausman, 2006). Multiple studies have found that these predictors have a positive impact on consumers’ attitudes towards a brand (Mitchell & Olson, 1981; Shimp, 1981). Schlosser, Shavitt and Kanfer (1999) noted that these predictors affect purchasing decisions. However, irritation also influences consumer attitudes towards advertising (Ducoffe, 1996). Brackett and Carr (2001) modified several of the attitudes towards Internet advertising discussed by Ducoffe (1996), MacKenzie and Lutz (1989), and Shavitt, Lowrey and Haefner (1998) and included them in a new model of integrated web advertising. In this model, four major predictors (entertainment, informativeness, irritation and credibility) had a direct effect on attitude towards advertising and its value for consumers.
Drawing on Hypothesis H1 from Tsang et al. (2004) (“the perceived entertainment, informativeness, irritation and credibility of mobile advertisements affect attitude towards mobile advertising”), we advanced the study by dividing the main hypothesis into four sub-hypotheses with a view to better understanding the influence of each variable. Hence, the following sub-hypotheses were proposed:
H1a: The perceived entertainment of mobile advertisements affects attitude towards mobile advertising.
H1b: The perceived informativeness of mobile advertisements affects attitude towards mobile advertising.
H1c: The perceived irritation of mobile advertisements affects attitude towards mobile advertising.
H1d: The perceived credibility of mobile advertisements affects attitude towards mobile advertising.
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Yoon and Kim (2001) showed the similarities between mobile advertising and Internet advertising. However, as both cases require a device, and as those devices are what enable consumers to access any advertisement at any time, Zoller at al. (2001) presented three strategies: permission-based advertising, incentive-based adverting and location-based advertising. Whereas permission- and incentive-based advertising are thought to be the least irritating strategies because consumers must first express their willingness to receive the advertising, either of their own free will or in order to receive a reward, location-based advertising allows advertisers to find the right consumer at the right place. Today, permission-based advertising is used to enhance e-governance (Aramendia-Muneta & Olarte-Pascual, 2019; Singh & Sahu, 2008), and it is a fundamental strategy for communicating with citizens. In fact, Martínez-Ruiz, Izquierdo-Yusta, Olarte-Pascual and Reinares-Lara (2017) found that permission-based advertising enhanced positive emotions in consumer attitude towards mobile advertising. Similarly, Amirkhanpour, Vrontis and Thrassou (2014) have highlighted the connection between permission-based advertising and increased purchase desire. Therefore, the following hypothesis was formulated:
H2: Consumer attitudes are different for permission-based and general mobile advertising.
Davis (1989) and Davis, Bagozzi and Warshaw (1989) developed a model based on five constructs: usefulness, ease of use, attitude, intention and use. Under this model, attitude was considered an important construct directly related to intention and behaviour. These assumptions were later confirmed by Dutta-Bergman (2006) and Shavitt et al. (1998). Favourable attitudes towards mobile advertising are strongly correlated with and positively influence intentions (Lee, Tsai, & Jih, 2006; Lin, Hsu, & Lin, 2017; Xu, 2006). Karjaluoto, Lehto, Leppäniemi and Jayawardhena (2008) further found that women are more influenced by attitudes than men. Finally, Soroa-Koury and Yang (2010) observed that attitude is a predictor of the intention to adopt mobile advertising. Based on the above, the following hypothesis was proposed:
H3: Attitudes towards mobile advertising affect consumers’ intentions to receive mobile advertisements. # Consumer Attitudes towards Mobile Advertising: An Updated Vision 25
Incentive-based advertising is one of the strategies presented in Zoller et al. (2001) as a key means of achieving acceptance of SMS advertising. Similarly, Choi, Hwang and McMillan (2008) showed that Korean companies use incentives to make receiving advertisements on mobile phones more attractive. Likewise, Chowdhury, Parvin, Weitenberner and Becker (2006) identified incentives as a way to help companies increase advertisement success. On the whole, incentive-based advertising offers consumers some financial rewards and increases the desire to receive promotions (Hanley, Becker, & Martinsen, 2006). The following hypothesis was thus proposed:
H4: Providing incentives to receive mobile advertisements can affect consumers’ intentions to receive them.
Using meta-analysis, Kim and Hunter (1993) found that the strongest relationship was that between attitudes and behaviour. In their well-known study, Venkatesh and Davis (2000) provided the rational link between intention and behaviour in the field of technology acceptance. The aforementioned model by Lee et al. (2006) predicted the strong correlation between attitude and both stronger purchase intentions and a tendency for consumers to behave in a positive way. Lee (2009) also confirmed this idea. In light of the connection between intention and behaviour, the following hypothesis was proposed:
H5: Consumers’ intentions to receive mobile advertisements affect how they behave after receiving them.
Figure 2.1 shows the final research framework. The framework is based on: a) Fishbein and Ajzen’s (1975) theory of reasoned action (TRA), which holds that attitude, intention and behaviour are connected and takes into account the psychological aspects of beliefs, attitudes, intentions and behaviour insofar as they affect consumers; b) the permission-based strategy (Barwise & Strong, 2002; Kavassalis et al., 2003); c) Tsang et al.’s (2004, pp. 67) model.
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Figure 2.1. Research Framework
2.6. Empirical study
The survey was conducted in January 2015, replicating the questionnaire used in the original study. In the questionnaire, data were collected according to the above research framework (Figure 2.1). Three major areas were tested: Part A consisted of adapted questions from previous studies (Ducoffe, 1996; Schlosser et al., 1999) on attitudes towards mobile advertising, taking the four constructs: entertainment, informativeness, irritation and credibility into account (Table 2.1). All variables were measured on a 5-point Likert scale, where 1 was strongly disagree and 5 was strongly agree; Part B included questions about attitude (5-point Likert scale, where 1 was strongly disagree and 5 was strongly agree), intention and behaviour with regard to mobile advertisements (Table 2.2); and Part C focused on descriptive data, such as gender, age and occupation, and questions about permission-based advertising (Table 2.3). The research variables in the questions in Table 2.1 were measured on five-point Likert scales, as was one question in Table 2.2. Two consecutive rounds of pre-testing were conducted in two different places to verify proper comprehension of the questionnaire.
# Consumer Attitudes towards Mobile Advertising: An Updated Vision 27
Table 2.1. Questionnaire – Part A
Constructs Questions Entertainment ENT1 I feel that receiving mobile advertisements is enjoyable and entertaining ENT2 I feel that receiving mobile advertisements is pleasant
Informativeness IF1 I feel that mobile advertising is a good source for timely information IF2 Mobile advertisements provide the information I need
Irritation IRT1 I feel that mobile advertising is irritating IRT2 I feel that mobile advertisements are almost everywhere IRT3 Contents in mobile advertisements are often annoying
Credibility CRD1 I use mobile advertising as a reference for purchasing CRD2 I trust mobile advertisements
Table 2.2. Questionnaire – Part B
Constructs Questions Attitude ATT1 Overall, I like mobile advertising
Intention INT1 I am willing to receive mobile advertisements: 1. Less than one message a day 2. Two messages a day 3. Three messages a day 4. Four messages a day 5. Over five messages a day
Behaviour BHV1 What do you do when you receive a mobile advertising message? 1. Ignore it completely 2. Read it occasionally 3. Read it after accumulating too many of them 4. Read it when I get time 5. Read it right away
BHV2 How much do you read of mobile advertising messages you receive? 1. Not at all 2. Read about a quarter of a message 3. Read about half of a message 4. Read about three-quarters of a message 5. Read the whole message
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Table 2.3. Questionnaire – Part C
Theme Questions
Mobile Phone 1. Are you aware that each time you receive an SMS advertising SMS message, it is because you have given your consent? 2. Would you be willing to receive SMS messages, if you received incentives?
SMS use 1. Chat 2. Fast message 3. Personal message 4. University information 5. E-governance 6. Promotions 7. Bank interaction 8. Mobile phone promotions 9. Others
Descriptive 1. Gender 2. Age 3. Level of studies 4. Current occupation
The translated questionnaire was pretested on 36 individuals on 7th and 8th January 2015 and was revised according to their feedback. It was subsequently distributed in person and online in two northern Spanish cities from 12th to 18th January 2015. In order to obtain a sample of 380 individuals that met the necessary requirements in keeping with Tsang et al. (181 males and 199 females; 85% under the age of 30; 76% with at least a college degree; and 60% students), the sample was broken down into four groups:
• Group A: 40 males and 52 females, all students under the age of 30 without a college degree; • Group B: 27 males and 30 females, all non-students over the age of 30 with a college degree; • Group C: 45 males and 50 females, all non-students under the age of 30 with a college degree; • Group D: 69 males and 67 females, all students under the age of 30 with a college degree. # Consumer Attitudes towards Mobile Advertising: An Updated Vision 29
A total of 449 questionnaires were collected, of which 69 were rejected for different reasons: either because they were incomplete or did not fit any of the 4 groups or because the specific sample group was already full.
All respondents were SMS users and received or sent at least one SMS message every two days. On average, they had been using mobile phones for 10 years. 90% received at least one product promotion a week via SMS, and 95% preferred using WhatsApp to SMS messages for sending personal texts. The SMS messages received covered subjects such as e-government, banking, topics related to their university studies, password recovery, registration confirmations, and others.
2.7. Data analysis and findings
2.7.1 Data reliability
Table 2.4 shows the results for the data reliability using Cronbach’s alpha. Hair, Black, Babin, Anderson and Tatham (1998) recommend that values higher than 0.7 be considered acceptable. Hence, the values in Table 2.4 are reliable and suitable for further analysis.
Table 2.4. Data reliability
Constructs Entertainment Informativeness Irritation Credibility Cronbach’s alpha 0.922 0.714 0.734 0.714
2.7.2 Factors affecting attitudes
The average score for overall attitudes was 2.10 on a five-point Likert scale. This is lower than the score of 2.76 (for overall attitudes) reported by Tsang et al. (2004), as well as lower than the score for attitudes towards permission-based advertising (3.27). Although according to EU directives, all advertisements have to be permission-based, the survey included a question asking whether the sampled individuals were aware of this fact. In all, 44.7% of respondents were aware that they had given their consent, whilst 55.3% were not. Table 2.5 shows the average
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for the whole sample versus the averages for the two sub-secondary groups. The average for the ‘aware they had given their consent’ group was higher than that for the ‘unaware they had given their consent’ group. None of the three propositions was higher than the neutral score of 3. Although there was only a slight difference, Hypothesis 2 was accepted. Nevertheless, the negative value was predominant.
Table 2.5. Statistics on consumer attitudes
Consumer Attitudes Number Mean Standard deviation Overall attitude 380 2.10 0.99 Aware they had given their consent 170 2.25 1.00 Unaware they had given their consent 210 1.97 0.98
Table 2.6 shows a correlation analysis indicating the relationship between the constructs. Whilst entertainment, informativeness and credibility were positively correlated to the overall attitude, irritation was negatively correlated to it. A principal component analysis was used to differentiate their individual contributions, instead of the stepwise regression proposed by Tsang et al. (2004), because the contribution to variance is more clearly identified. The results in Table 2.7 show that entertainment is the main factor affecting overall attitude, with a marginal contribution of 61.03% to the variance. Informativeness was the second most important attribute, with a marginal contribution of 18.11%. Irritation ranked third, with a marginal contribution of 15.37%, followed by credibility, with a marginal contribution of 5.48%. Our model differs from that in Tsang et al. because informativeness contributes more than credibility, which ranked second in their model. The only variable they share is entertainment, which was the main variable in the model in both cases.
Table 2.6. Results of the correlation analysis
Entertainment Informativeness Irritation Credibility Informativeness 0.723* Irritation -0.404* -0.396* Credibility 0.480* 0.680* -0.165* Overall attitude 0.959* 0.830* -0.452* 0.594* *The correlation is significant at the 0.01 level (two-tailed)
# Consumer Attitudes towards Mobile Advertising: An Updated Vision 31
Table 2.7. Principal component analysis
Variance % Accumulated ENT1 40.561 40.561 ENT2 20.472 61.033 IF1 10.678 71.711 IF2 7.436 79.147 IRT1 6.174 85.320 IRT2 4.705 90.026 IRT3 4.490 94.516 CRD1 3.931 98.447 CRD2 1.553 100.000
In summary, even though the respondents had given their permission to be sent SMS advertisements, their attitude towards mobile advertising was generally negative. When the respondents were aware that they had given their consent, attitude values were higher than when they were not. Entertainment was the most important attribute affecting consumer attitudes towards mobile advertising. Therefore, and based on these data, unlike in Tsang et al. (2004), support was not found for H1, because, except for the relationships between entertainment and informativeness, on the one hand, and informativeness and credibility, on the other, there were no strong correlations between variables. The correlation between some of the variables was not significant in some cases. Structural equation modelling was used to confirm or reject the four H1 sub-hypotheses.
2.7.3 Relationship between attitudes and intention
The questionnaire also inquired about willingness to receive mobile advertisements. Only 59 respondents agreed, whereas 321 respondents disagreed (see Table 2.8). Overall attitude was significantly correlated to intention (t=6.158, p<0.001). However, when users were offered an incentive or reward, such as a new mobile phone, apps, competitive mobile rates, and others, the figures changed dramatically, with 254 respondents answering affirmatively and 126 negatively. Hence, Hypotheses 3 and 4 were supported. Providing incentives can increase intention (willingness) to receive SMS-based mobile advertisements.
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Table 2.8. Intention to receive mobile advertisements
Yes No Total General 59 321 380 With incentives 254 126 380
X2 = 16.654, p<0.05
2.7.4. Relationship between intention and behaviour
The analysis proposed by Tsang et al. (2004) looked at behaviour and intention. Table 2.9 and Table 2.10 show the results regarding the relationship between the extent to which an SMS message would be read and how long it took the recipient to read it once it had been received. Table 2.10 differs from that used in Tsang et al. (2004), as it also includes the answer ‘occasionally’, which was included in Appendix A of Tsang et al. (2004), but not in their table. On the whole, the correlations in both tables (X2 = 23.123, p<0.05; X2 = 14.922, p<0.05) indicate a relationship between intention and subsequent behaviour. The correlation between the extent to which messages were read and intention was statistically significant (t=4.473, p<0.05), as was the correlation between how long it took to read them and intention (t=3.670, p<0.01). Therefore, Hypothesis 5 was supported. However, Tsang et al. (2004) do not mention it.
Table 2.9. Extent of message reading
None About 1/4 About 1/2 About 3/4 Whole Total Yes 5 7 7 11 29 59 No 57 99 52 27 86 321 Total 62 106 59 38 115 380 X2 = 23.123, p<0.05
Table 2.10. Time taken to read the message
Ignore Occasionally When I When I Immediately Total accumulate too have time many Yes 7 10 1 22 19 59 No 66 103 12 88 52 321 Total 73 113 13 110 71 380 X2 = 14.922, p<0.05 # Consumer Attitudes towards Mobile Advertising: An Updated Vision 33
2.7.5. Structural equation modelling
Analyses were conducted using the LISREL 9.2 program developed by Jöreskog and Sörbom (2015). As our model is already permission-based, Table 2.11 shows the fit indices of the confirmatory factor analysis of the relationships between the constructs based on structural equation modelling. The 380 original data sets were again used as the whole sample for the study. The results were within the recommended values, as proposed by Byrne (1998) and Carmines and McIver (1981). The RMSEA was 0.0634 with a 90% confidence interval from 0.0502 to 0.0769. Hu and Bentler (1999) recommended the SRMR index, and the proposed model fits the criteria.
Table 2.11. Fit indices
Fit indices Recommended value Overall structural attitude
R2 N/A 134.07 df (degrees of freedom) N/A 53 R2 / df ≤ 3.00 2.52 Goodness of fit (GFI) ≥ 0.90 0.948 Adjusted goodness of fit (AGFI) ≥ 0.80 0.91 Normalised fit index (NFI) ≥ 0.90 0.964 Comparative fit index (CFI) ≥ 0.90 0.978 Incremental fit index (IFI) ≥ 0.90 0.978 Standardised root mean square ≤ 0.08 0.0676 residual (SRMR) Root mean square error of ≤ 0.08 0.0634 approximation (RMSEA)
As shown in Table 2.12, the resulting models indicate that entertainment significantly affects attitude towards SMS advertisements, whilst credibility has a negative impact on attitude with positive relationships between attitude, intention and behaviour. These results confirm the rejection of Hypothesis 1 as a whole and the support for Hypotheses 3 and 5. Moreover, the subdivision of Hypothesis 1 revealed a positive relationship between entertainment and attitude and a negative one between credibility and attitude.
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Results Supported Supported Supported Supported Not supported Not supported
p 0.392 0.301 0.000* 0.000* 0.000* 0.030**
-
Z 2.164 3.614 0.855 1.035 6.046 4.351 - values
1.688 error 0.0163 0.0187 0.0112 0.0134 0.0506 Standard Standard
0.0290 0.0591 0.0160 0.0116 10.205 0.2200 - Coefficient
Attitude Attitude Attitude Attitude Intention construct Behaviour Dependent Dependent
Attitude Irritation Intention construct Credibility Independent Independent Entertainment Informativeness
Parameter estimates of the general model the general of estimates Parameter
. 12 . 2
Hypothesis H1a H1b H1c H1d H3 H5 Table p<0.001* p<0.05 ** level # Consumer Attitudes towards Mobile Advertising: An Updated Vision 35
2.7.6. Similarities and differences between the two studies
In keeping with the aim of this chapter which was to replicate the study by Tsang et al. (2004), we compared the results. Table 2.13 indicates the main differences and similarities between both studies. Whilst Tsang et al. (2004) found that four variables influenced attitude towards SMS advertising, in the present study, only entertainment was found to affect it positively. This finding is consistent with others reported elsewhere. For instance, the variable irritation has been found to negatively impact consumers (Shaheen, Lodhi, & Abid, 2017), and a connection has been identified between irritation and the usefulness of the message (Martí Parreño, Sanz- Blas, Ruiz-Mafé, & Aldás-Manzano, 2013; Yang, Kim, & Yoo, 2013). SMS advertisements via mobile phones are more irritating than MMS advertisements (Cheng, Blankson, Wang, & Chen, 2009). Credibility is measured by trust in the message and its use as a reference for purchasing; however, in real environments, mobile phone users consult various sources when they need to buy a product (omnichannel shoppers) (Juaneda-Ayensa, Mosquera, & Sierra Murillo, 2016; Verhoef, Kannan, & Inman, 2015). In fact, they are exposed to multiple sources of advertisements, including others on their mobile phones, which can affect the credibility of the message. As consumers receive so much information from so many sources, they may be losing faith in advertising. Thus, the variable credibility had a negative effect on attitudes rather than the positive one reported elsewhere (e.g. Chowdhury et al., 2006; Shaheen et al., 2017). With regard to the third variable, informativeness, Cheng et al. (2009) showed that SMS advertising was less informative than MMS advertising. Since 2004, the perception of SMS advertising has dramatically changed and the perceived entertainment value of such messages, along with the perceived informativeness, credibility and irritation caused by mobile advertising, may not influence attitudes towards it.
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Table 2.13. Differences and similarities between the two studies
Hypotheses Tsang et al. (2004) This study
H1 Supported Not supported H1a Supported Supported H1b Supported Not supported H1c Supported Not supported H1d Supported with positive effect Supported with negative effect H2 Supported Supported H3 Supported Supported H4 Supported Supported H5 Not mentioned Supported
2.8. Discussion and conclusions
Several years after Tsang et al. (2004) published their paper, the reality of mobile advertising has dramatically changed. Today, consumers receive advertisements on their mobile phones through a variety of means, such as apps or via the Internet, when they are using their smartphones to browse, in the same format and offering the same services as on a computer. Consumers are exposed to a wide range and large quantity of mobile advertisements, and this environment can affect their cognitive and affective acceptance of advertising (Olarte-Pascual, Pelegrín- Borondo, & Reinares-Lara, 2016).
Additionally, the TRA model presented in the original paper cannot be extrapolated to the present-day mobile phone situation and should thus be revised. In 2004, entertainment, informativeness, irritation and credibility seemed to be the only factors affecting attitudes towards mobile advertising. However, these factors may need to be updated. In this regard, Assimakopoulos, Papaioannou, Sarmaniotis and Fidanyan (2013) provided a deeper insight into the identification of variables affecting attitudes towards a given technology (in this case, smartphones) using the technology acceptance model. This points to a new avenue of research in relation to our findings on credibility and its negative effect on attitude.
Although the rise of the Internet has opened broader avenues for marketers to reach consumers, SMS advertising is still considered to be one of the best options to # Consumer Attitudes towards Mobile Advertising: An Updated Vision 37
reach a wider range of customers, irrespective of age, location or time (Aramendia- Muneta & Olarte-Pascual, 2019). It can thus be concluded that SMS advertising is still very much alive and that consumers show both positive and negative attitudes towards it. This research has contributed to the understanding of the changing perception of SMS advertising.
# Chapter 3.
Gender Stereotypes in Original Digital
Video Advertising
# Gender Stereotypes in Original Digital Video Advertising 41
3.1. Introduction
Original digital video advertising (ODVA) has the power to alter people’s perceptions as never before and is considered the most effective form of direct advertising to consumers (Advertisers Perceptions, 2018). Consumers increasingly use the Internet as a source of both information and entertainment and are thus consistently exposed to digital video advertising. In fact, eMarketer reports that the US advertising industry will nearly double its investment in digital video advertising spending between 2017 and 2020 (eMarketer, 2017). Given the exponential increase in Internet usage for numerous purposes, especially digital video, and advertisers’ great interest in taking advantage of this channel, it stands to reason that consumers have to become selective in their viewership content.
This opportunity has prompted the design of even more targeted digital video advertisements. Original digital videos (ODVs) are moreover essential to reach audiences that cannot be reached through television and enable greater placement and branding by companies. Specifically, two thirds of advertisers will reallocate funds from television budgets to promote digital video advertising (Advertisers Perceptions, 2018). In this new advertising environment, companies will not create a single video for all their sales channels as they did in the past. Today, companies and marketers recognize ODV campaigns as a primary source of advertising (Advertisers Perceptions, 2018). Consequently, researchers should be looking into this new field.
Digital video advertisement designers have substantial control over how people’s perceptions are shaped through specific content. Perhaps nowhere is this aspect more important than in the shaping of gender expectations, their stereotypical projection, and their enhancement through steady role playing (Collins, 2011). The creation and reinforcement of stereotypes, some more universally typical and prevalent than others, can be highly detrimental to society at large (Coltrane & Adams, 1997). The information projected through digital video advertising also has the ability to alter people’s perceptions, thereby affecting not only their choices but also, ultimately, their behaviour. Consumers choose based on their perceptive understanding and rationalize based on their perceptive exposure. Thus, research on
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gender stereotypes, a well-known concept in the advertising industry that has drawn extensive scholarly attention (e.g. Aramendia-Muneta, Olarte-Pascual, & Hatzithomas, 2019; Bretl & Cantor, 1988; Debevec & Iyer, 1986; Goffman, 1979; Manstead & McCulloch, 1981; McArthur & Resko, 1975), is seeking to determine whether the same aspects of gender stereotyping will exist in ODVs as an independent channel for promotion
This chapter helps to fill this gap on ODVs, reviewing the extant literature on both traditional and digital video advertising, specifically in the context of gender stereotyping and how genders are represented in digital video advertising. The main aim is to study the existence of gender stereotypes in digital video advertising and the different roles played by men and women in ODVs, thereby advancing the knowledge of digital advertising.
3.2. Perceptions and bias generated through digital video advertising
Kay, Matuszek, and Munson (2015) contend that online advertising portrayals of gender stereotypes in occupational contexts have a damaging effect on women’s role in the professional world. This damage is perpetuated through heightened stereotypical perception of the differences between gender portrayals and affects the opportunities available to women, their range of choices, and their compensation. This finding is further substantiated by earlier studies of offline media, especially television (e.g. Jacobs, 1995; Massey, 2007). It is also in line with the Cultivation Theory (Potter, 1993), originally put forward in relation to the then dominant medium of television, ascribing to it a negative impact consisting of professional challenges for women created and reinforced through gender stereotypical advertising. In their qualitative study, Kay et al. (2015) conclude that gender stereotyping in online advertising largely exaggerates stereotypical portrayals. These authors further find that under-representing women helps reinforce people’s perceptions, which have already been shaped by other media, and assure them of the validity of their results. # Gender Stereotypes in Original Digital Video Advertising 43
Both advertisers and researchers have become more aware of the specific effects of gender stereotyping in advertising, as reflected in prior studies, albeit with multiple media. Miller (2014) highlights a promising positive shift at Getty Images and LeanIn.org, which sought to address the negative stereotyping of women in a professional capacity by increasing the depiction of women employees in their stock images. That is a still medium, however, and it tells only half the story compared to video advertising.
Another important consideration in the gender stereotyping debate is the target viewership. McMahan, Hovland, and McMillan (2009) contend that around half of US Web users are women. Hence, almost 52% of the target audience for digital video advertisements consists of women. This statistic should prompt advertisement designers to rethink their content in terms of the creation and projection of stereotypes. The change in viewership is likely to directly impact the perception and interpretation of online video advertisements, once they are viewed. Therefore, marketers need to reassess the situation with regard to gender stereotypical content in their advertisements, especially in Web-based environments. Conversely, McMahan et al. (2009) also note that men use the Internet for both entertainment and information purposes, whilst women use it as a communication tool. However, they further remark that online advertising content should be tailored to the viewership’s gender and that gender stereotyping is a dangerous trend that would thus need to be broken should the different gender-based markets have to be tapped. The target audience for digital video advertising seems to be clearly divided in this context, suggesting that the percentage of men exposed to video advertisements is still higher than the percentage of women.
Some studies that have assessed stereotypical depictions in online video advertisements have found patterns similar to those of traditional advertising. Plakoyiannaki et al. (2008) find that online video advertising uses women in different types of stereotypical roles, portraying them in the role of traditional homemaker, as the siren and seductress, for purely decorative purposes, and in completely neutral roles related to the decorative one. They further report that sexism against women in online videos is deeper than in print media. The bias
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created due to stereotypical representations is quite harmful in a practical sense, as ODVs will presumably follow the same path.
3.3. Online gender stereotypes
The issue of gender stereotypes in advertising has been extensively explored over the years, in multiple media and across different cultures (e.g. Bretl & Cantor, 1988; Debevec & Iyer, 1986; Goffman, 1979; Manstead & McCulloch, 1981; McArthur & Resko, 1975). As marketers have been made suitably aware of gender stereotypical attributes and influences, one might expect to find awareness-driven reform in this area.
Whilst the predominant advertising media in the twentieth century were television, radio, and the printed press, the twenty-first century ushered in a completely new scenario, i.e. the Internet, made even more popular through the introduction of smart phones (Okazaki, 2007). The Internet has changed consumer behaviour, and advertisers have adapted to the new medium, changing their campaigns accordingly. Researchers have also paid special attention to new advertising trends and examined the effect of gender stereotyping in the promotion of products through site recommendations (Garbarino & Strahilevitz, 2004), Web advertising (Wolin & Korgaonkar, 2003), the application of Hofstede’s masculinity index in Web advertising (An & Kim, 2007), the online advertising of global products (Plakoyiannaki et al., 2008), social media (Tortajada, Araüna, & Martínez, 2013), and the interpretation of Web atmospherics in information searches (Tsichla, Hatzithomas, & Boutsouki, 2014), amongst others.
Gender stereotyping has been heavily researched since 1970, across both cultures and countries (Courtney & Lockeretz, 1971). The portrayal of gender in different media, including print, radio, television, and, increasingly, the Internet, has likewise received extensive attention (e.g. Arima, 2003; Kuipers, Van der Laan, & Arfini, 2017; Monk-Turner, Kouts, Parris, & Webb, 2007; Wallis, 2011). Various researchers have tracked the progression of gender stereotyping through the different prevailing media at various points over the past few decades. Women have primarily # Gender Stereotypes in Original Digital Video Advertising 45
been objectified either through the role of dutiful wife, mother, or daughter, in a caring occupation, or through the somewhat dubious role of a symbol of attraction – glorified as a physical beauty, a sex object, or in a similarly decorative role (Kyrousi, Panigyrakis, & Panopoulos, 2016). Although they have also been frequently portrayed as professionals, these portrayals are, again, limited to women- dominated occupations, such as nursing or teaching (Anand, 2013). On the other hand, men are typically depicted as the capable partner, the wiser, more mature, and more authoritative counterpart, regardless of their status or profession (Prieler, Ivanov, & Hagiwara, 2015). In contrast, several studies have found that men and women are portrayed in a more egalitarian way (Hatzithomas, Boutsouki, & Ziamou, 2016; Kotzaivazoglou, Hatzithomas, & Tsichla, 2018), whilst others suggest that there has been a change in roles, such as the new trend featuring men who are concerned about their physical appearance (Barry, 2014).
This chapter will explore the relatively new phenomenon of gender stereotyping within the context of ODVA. Advertising content for purely online purposes is still relatively limited. In contrast, many television advertisements are also used with online media. Therefore, limiting the investigation solely to ODVA can open new avenues for both the advertising industry and research. Zotos and Tsichla (2014) point to postmodern advertising as a promising path to explore. This study will thus focus on gender stereotypes in ODVA, as few studies have been conducted in this particular area, especially in relation to ODVs.
3.4. Hypotheses
As noted, ODV stereotypes should follow the same path as those found in non- exclusively online videos and their counterparts such as television, radio, or magazine advertising. One could expect to find the same core variables and significant sex-role stereotypes between genders. Since the early 1970s, the following main attributes concerning gender stereotypes have been identified: mode of presentation, credibility basis, role, age, argument type, reward type, product type, background, setting, and end comment.
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The mode of presentation is still a frequently used attribute in the measurement of gender stereotypes. Klofstad (2016) concludes that male voices tend to have a higher level of credibility and be more persuasive. In contrast, Martín‐Santana, Muela‐Molina, Reinares‐Lara, and Rodríguez‐Guerra (2015) find no evidence of increased effectiveness due to the use of a male voice in terms of persuasion. Nevertheless, men are predominantly used for voice-over messages, whilst women are most often shown visually and have less of a presence as voice-over narrators (Furnham, Mak, & Tanidjojo, 2000b; Valls-Fernández & Martínez-Vicente, 2007). Based on the above, the following hypothesis is proposed in the new context of ODVA:
H1: Women are more likely to appear in visual situations and men in videos with voice-over.
Credibility basis refers to the power to persuade consumers. In this regard, women are generally depicted as non-authoritative users, whilst men are presented as authorities or experts (Aronovsky & Furnham, 2008; Furnham & Paltzer, 2011). Hence, the following hypothesis is formulated in relation to ODVA:
H2: Women are more likely to be depicted as users and men as authorities.
In the second decade of the twenty-first century, men and women’s roles have supposedly dramatically changed. Nevertheless, professional and autonomous roles are more often assigned to men, whilst dependent ones are more often assigned to women (Knoll, Eisend, & Steinhagen, 2011; Zotos & Tsichla, 2014). For instance, digital video advertising still uses women in purely decorative roles (Plakoyiannaki et al., 2008; Tsichla & Zotos, 2016). In contrast, Furnham and Skae (1997) suggest that the role of interviewer/narrator is equally prominent in both genders. Thus, the following hypothesis is proposed in relation to ODVA:
H3: Women are more likely to be portrayed in dependent roles and men in autonomous ones.
The age of an advertisement’s central figures has also been studied as an attribute of gender stereotyping. Men are depicted as more mature figures, in the # Gender Stereotypes in Original Digital Video Advertising 47
36- 50-year-old range, whereas most of the women depicted in commercials are between 20 and 35 years old (Das, 2011; Ganahl et al. 2003). Thus, the following hypothesis is proposed in relation to ODVA:
H4: Women actors tend to be significantly younger than men actors.
Non-argument attributes are significantly more common in women than men, who are given factual arguments (Furnham & Paltzer, 2010; Lim & Furnham, 2016). Opinions, i.e. non-argumentative explanations, are assigned to women. Therefore, the following hypothesis is formulated in relation to ODVA:
H5: Women are more likely to give opinions, whilst men make factual arguments.
Manstead and McCulloch (1981) find a significant difference between men and women in terms of reward type: men are often associated with practical rewards and women with self-enhancing ones. Additionally, women are sometimes depicted as rewards resulting from products supposedly targeted at men (Aronovsky & Furnham, 2008; Prieler, 2016). The following hypothesis is therefore proposed in relation to ODVA:
H6: Women are more likely to be portrayed in videos where the reward is self- enhancement and men in videos where the reward is practical.
Women are mainly featured in relation to certain product categories, such as beauty and personal care products (body products) and, on the whole, products related to aspects of their physical appearance (Bresnahan & Inoue, 2001; Espinar- Ruiz & González-Díaz, 2012; Nassif & Gunter, 2008; Uray & Burnaz, 2003) and household products, appliances, and furnishings (Valls-Fernández & Martínez- Vicente, 2007). In contrast, men are predominantly depicted in connection with cars and automotive accessories and technology (Ganahl et al., 2003; Prieler, 2016). Based on the above analysis, the following hypothesis is formulated in relation to ODVA:
H7: Women are more likely to be used to endorse body-relevant and food products and men to endorse motor-vehicle-related products.
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Men and women are depicted against various backgrounds. The literature is contradictory on this point. Some authors find no significant differences between genders (Furnham & Skae, 1997; Mazzella, Durkin, Cerini, & Buralli, 1992). Others report that men are more likely to be shown with women in the background, whilst women are more likely to be depicted in the company of children or men (Furnham, Babitzkow, & Uguccioni, 2000a; Royo-Vela, Aldas-Manzano, Küster, & Vila, 2008). Still others have shown that men are most often depicted in the company of other men and women in the company of other women (Neto & Pinto, 1998). The following hypothesis is thus formulated in relation to ODVA:
H8: Women are more likely to be shown against female backgrounds and men against male ones.
Most studies have found that women are portrayed in the home or indoors engaging in role-related behaviour, whilst men are shown in settings outside the home, such as occupational ones (Bresnahan & Inoue, 2001; Espinar-Ruiz & González-Díaz, 2012; Milner & Higgs, 2004). On the whole, women are less likely to be depicted in a professional setting than men (Gentry & Harrison, 2010; Verhellen, Dens, & De Pelsmacker, 2016). In this regard, the following hypothesis is formulated in relation to ODVA:
H9: Women are more likely to be shown in domestic settings and men in occupational ones.
Finally, central figures who are men are more likely to make an end comment than central figures who are women (Ali, Ali, Kumar, Hafeez, & Ghufran, 2012; Furnham & Skae, 1997). Therefore, the following hypothesis is proposed in relation to ODVA:
H10: Women are more likely to appear in videos without an end comment and men in videos with one.
# Gender Stereotypes in Original Digital Video Advertising 49
3.5. Methodology
3.5.1. Method
When the first empirical studies on gender stereotypes emerged in the early 1970s, content analysis proved to be an extremely valuable tool for measuring the portrayal of gender stereotypes in advertising (Dominick & Rauch, 1972). This method has continued to be used in a wide range of studies up to the present day (e.g. Furnham & Paltzer, 2010; Grau, Roselli, & Taylor, 2007; Plakoyiannaki & Zotos, 2009; Prieler et al., 2015).
3.5.2. ODVA sample
The research sample was drawn from the Internet Advertising Competition (IAC Award) database. These awards were created by the Web Marketing Association, which promotes Internet marketing and corporate development on the World Wide Web. The category corresponding to ODVs is the format ‘online video’. From 2010 to 2017, 354 videos received awards. Some of these videos are not currently available online, especially the videos from the first years of the competition. In an attempt to remedy this problem, the competition organizers were contacted and asked for access to all the videos for strictly research-related purposes. However, they claimed to have no control over the maintenance of the winning participants’ links. Additionally, some videos receive awards in multiple categories. Such duplicate advertisements were likewise not considered. Therefore, the final sample consisted of 324 videos. See Table 3.1 for technical details of the research.
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Table 3.1. Technical details of the research
Universe ODV gender stereotypes
Sample IAC Award
Format ODV
Reference period 2010-2017
Sample size 324 original digital videos
Average duration 2 minutes and 23 seconds
Method Content analysis
Attributes Mode of presentation, credibility, role, age, argument type, reward type, product type, background, setting and end comment
3.5.3. Coding procedure
Two coders (one woman and one man) received four hours of training on the coding procedures and then coded all the ODVs independently. The one woman-one man coding system has been used elsewhere (e.g. Milner & Higgs, 2004; Uray & Burnaz, 2003). In the training sessions, the categories and study variables were clearly explained to the coders. The coders were also provided with coding guidelines, definitions, and an online table for data input linked to the content analysis of the digital videos. As suggested by Weber (1990), before the study sample was coded, a pilot coding of fifty original video advertisements was conducted in order to reduce differences in the coding and facilitate the reaching of final agreements. This process, consisting of training and prior coding, has been implemented by several researchers and proved to be a valuable method (Plakoyiannaki et al., 2008). ODVs were classified as female or male: if a video highlighted more than one stereotype, it was classified as the dominant one.
Perreault and Leigh’s (1989) reliability index was used by both coders. This index is suitable when two coders are involved. Scores range from 0.0 (no reliability) to 1.0 (perfect reliability). Male gender stereotypes had a reliability score of 0.91, and female ones, 0.93, and the intercoder agreement exceeded 90% for all variables. Both scores are considered very high and are well above the 0.70 score deemed # Gender Stereotypes in Original Digital Video Advertising 51
trustworthy by Rust and Cooil (1994). Each coder worked independently, and any coding discrepancies to appear were later discussed by the two coders until an agreement was reached to obtain the final sample.
3.5.4. Central measures and attributes
This chapter is based on the content analysis categories proposed by McArthur and Resko (1975), a method that has been used in more than 70 studies (Gilly, 1988; Furnham & Paltzer, 2011; Manstead & McCulloch, 1981).
Any adult portrayed in a central role (visually or vocally) is considered the central figure. Of the 324 ODVs, 212 featured a central figure who was a man, and 112, a central figure who was a woman. In all, 18.5% of the ODVs lasted less than 2 minutes and featured a central figure who was a woman vs 40.4% lasting less than 2 minutes and featuring a central figure who was a man. For ODVs lasting between 2 and 4 minutes, these figures were 17.3% (women) and 12.4% (men) respectively, whilst for ODVs lasting over 4 minutes, they were 3.7% (women) and 7.7% (men), respectively.
Building on Gilly (1988) and subsequent studies (e.g. Das, 2011; Milner & Higgs, 2004), the ten measured attributes were as follows:
Mode of presentation. The mode of presentation of the central figure was classified as: voice-over, visual speaking, visual speaking & voice-over, or visual non-speaking.
Credibility. Four main types of credibility were included: user, authority, other, and neither.
Role. The central figure was categorized into one of five roles: dependent, interviewer/narrator, professional, celebrity, and other.
Age. Three categories were used: young (aged 35 and under), middle-aged (ages 36 to 50) and older (over 50).
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Argument type. Four types of arguments were coded: factual/scientific, opinion/non-scientific, other, and none.
Reward type. The central figure was coded as being portrayed against one of the following five types of rewards: social approval, social/self-enhancement, practical, pleasure, and other.
Product type. The videos were classified into the following categories, depending on the type of product the central figures were depicted with: body, home, food, auto, sport, services, financial, technology, property, or other.
Background. The backgrounds for the central figures were classified as: mostly women, mostly men, mixed, mostly children, or none.
Setting. Six types of settings were used: private residence/home, occupational, leisure, fictional, animated, and other.
End comment. This attribute refers to the inclusion of a final brief remark. The following categories were used: present as a voice, present as an image, present as a voice and image, and absent.
3.6. Results
The results for all ten attributes are summarized in Table 3.2 and Table 3.3. An overall significant chi-square was found for the central figures (men or women) (X2 = 204.429, df = 1, p < .001). Therefore, women did not account for half of the central figures (only 34.6%).
Mode of presentation. No statistically significant association was found between the mode of presentation and gender (X2 = 2.705, df = 3, NS). Therefore, H1 was rejected. Both men (52.3%) and women (55.3%) are depicted most frequently in visual speaking roles. Further analysis, in which the effect of music was tested independently, likewise did not reveal any significant association (X2 = 9.116, df = 7, NS). When music was tested, visual speaking was the prevailing combination for both genders. Music plays an important role in ODVs and is present # Gender Stereotypes in Original Digital Video Advertising 53
in 93.7% of ODVs featuring central figures who are women and 86.9% featuring central figures who are men. These results stand in stark contrast to those of previous studies (Furnham et al., 2000b; Manstead & McCulloch, 1981). In the present study, women central figures were portrayed equally to men in terms of the mode of presentation.
Credibility. The overall analysis revealed no significant association between gender and credibility basis (X2 = 6.746, df = 3, NS). A total of 39.3% of the women were portrayed as authorities vs 37.3% of the men, whilst 41.1% of the women were portrayed as product users vs 35.40% of the men. In other words, the percentage of women users was slightly higher than that of men (41.1% vs 35.4%). H2 was thus also rejected, because the credibility attributes have changed in the case of women.
Role. No significant association was found between gender and roles (X2 = 5.072, df = 4, NS). The role of interviewer/narrator was the most common one for both genders (women = 40.2%; men = 43.9%). These results are consistent with those of Furnham and Skae (1997). H3 was thus rejected, as there were no differences between the genders in terms of the role played.
Age. The overall analysis revealed no significant association between gender and age (X2 = 0.733, df = 2, NS). Younger women and younger men were depicted equally (50.0% for both genders). However, the percentage of middle-aged women has increased compared to previous research (Das, 2011; Ganahl et al., 2003), and the percentage of middle-aged men was close to that for women (42.0% vs 44.3%). Therefore, H4 was rejected.
Argument type. No significant association was found between gender and argument type (X2 = 5.090, df = 3, NS). Opinion-based arguments were the prevailing type for both genders (45.5% = women; 47.2% = men). The rest of the measured variables were as likely to occur in men as in women. Hence, H5 was rejected.
Reward type. The overall analysis revealed no significant relationship between gender and reward type (X2 = 2.969, df = 5, NS). Because there were no differences between the genders in terms of reward type, H6 was also rejected. The predominant
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reward type for both genders was a practical reward (33.9% = women; 29.7% = men).
Product type. No numerical gender differences were observed with regard to product type (X2 = 1.969, df = 9, NS). Services were the most common type for both genders (women = 17.9%; men = 19.8%). Body was the second most common product type for both women and men (15.2% vs 13.7%). Thus, H7 was not accepted.
Background. The overall analysis revealed no significant association between gender and background (X2 = 1.818, df = 4, NS). Men and women were equally likely to be shown in mixed backgrounds (33.9% for women vs 37.3% for men). The second most common option for both women and men was to be portrayed with men (20.5% vs 19.3%). Therefore, H8 was not accepted, as there were no differences between the genders.
Setting. No significant association was found between gender and setting (X2 = 1.393, df = 5, NS). An occupational setting was the most likely setting for both genders (women = 30.4%; men = 29.7%). The second most common setting for both genders was a leisure setting (more than 20%). Thus, hypothesis H9 was also rejected, as the setting attributes were quite similar for both women and men.
End comment. The overall analysis revealed no significant association between gender and end comment (X2 = 4.173, df = 3, NS). Further analysis with two variables (present and absent) likewise failed to reveal any significant association between the two variables (X2 = 0.098, df = 1, NS). Therefore, H10 was also rejected.
The findings of this examination of male and female role portrayal indicate that men and women are portrayed in a more egalitarian way in ODVs in terms of traditional gender stereotypes. In general, there was no significant association between gender and any of the ten studied attributes (mode of presentation, credibility, roles, age, argument type, reward type, product type, background, setting, and end comment).
In conclusion, as the chi-square measurements demonstrate, there was no difference between genders; women central figures in ODVs seemed to have the # Gender Stereotypes in Original Digital Video Advertising 55
same attributes as central figures who were men. These results differ from previous findings (e.g. Furnham et al., 2000b; Manstead & McCulloch, 1981; Mazzella et al., 1992; Neto & Pinto, 1998). The only difference found between genders in the present study was with regard to the central figure. Most of the prize-winning videos featured a central figure who was a man, although the attributes of male and female central figures themselves were quite similar.
Table 3.2. Results for mode of presentation, credibility, role, age, argument type, and reward type
Attribute Category Women Men (n=112) (n=212) X2 p Mode of Voice-over 24.1% 31.1% 2.705 ns presentation Visual speaking 48.2% 47.6% Visual speaking & voice-over 7.1% 4.7% Visual non-speaking 20.5% 16.5%
Credibility User 41.1% 35.4% 6.747 ns Authority 39.3% 37.3% Other 0.0% 5.2% Neither 19.6% 22.2%
Role Dependent 16.1% 12.3% 5.072 ns Interviewer/Narrator 40.2% 43.9% Professional 25.0% 28.3% Celebrity 3.6% 6.6% Other 15.2% 9.0%
Age Young (35 or under) 50.0% 50.0% 0.733 ns Middle-aged (36 to 50) 42.0% 44.3% Older (over 50) 8.0% 5.7%
Argument Factual/scientific 31.3% 35.4% 5.090 ns type Opinion/non-scientific 45.5% 47.2% Other 4.5% 7.1% None 18.8% 10.4%
Reward Social approval 20.5% 19.8% 2.969 ns type Social/self-enhancement 20.5% 20.3% Practical 33.9% 29.7% Pleasure 13.4% 20.8% Other 11.6% 9.4% Non-significant (ns)
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Table 3.3. Results for product type, background, setting, and end comment
Attribute Category Women Men (n=112) (n=212) X2 p
Product type Body 15.2% 13.7% 1.969 ns Home 3.6% 3.3% Food 6.3% 8.5% Auto 6.3% 8.0% Sport 1.8% 2.8% Services 17.9% 19.8% Financial 12.5% 10.8% Technology 14.3% 12.7% Property 2.7% 1.9% Other 19.6% 18.4%
Background Mostly women 10.7% 11.8% 1.818 ns Mostly men 20.5% 19.3% Mixed 33.9% 37.3% Mostly children 2.7% 4.7% None 32.1% 26.9%
Setting Private residence/home 16.1% 11.8% 1.393 ns Occupational 30.4% 29.7% Leisure 23.2% 24.5% Fictional 13.4% 15.6% Animated 13.4% 14.6% Other 3.6% 3.8%
End comment Present as voice 1.8% 0.0% 4.173 ns Present as image 64.3% 64.2% Present as voice & image 21.4% 24.5% Absent 12.5% 11.3%
Non-significant (ns)
# Gender Stereotypes in Original Digital Video Advertising 57
3.7. Discussion and conclusions
In 1988, Ferrante, Haynes, and Kingsley pointed to a change in women’s role as depicted by advertisers and marketers. The present findings support that observation, since the analysed prize-winning videos, selected by marketing and advertising professionals, featured women with the same attributes as men.
The lack of significant differences found between men and women in ODVs for attributes related to traditional gender stereotypes point to a need to find new variables better adapted to the independent scenario of ODVs. This is particularly true in light of Eisend’s (2010) writings about the coding scheme and lack of theoretical justification for the categories. The Internet has revolutionized marketing and advertising alike. Therefore, the attributes presented by Goffman (1979) and McArthur and Resko (1975) might be outdated or, at least, ill-suited to ODVs. In fact, this study is one of the first to deal with and present specific data on gender representation in ODVs.
The findings are consistent with those of other recent research. For instance, Kay et al. (2015) found evidence of stereotypical representations, but at a declining rate. Their conclusion supports the view that people’s conscious desire to be represented in a truly social manner, as opposed to in hypothetically stereotypical ones, provides evidence of changing perceptions, requirements, and desires. This needs to be incorporated as soon as possible in actual practice with regard to visual online marketing content.
Studies looking into work roles (Matthes, Prieler, & Adams, 2016) and advertisements aired during the Super Bowl (Hatzithomas et al., 2016) have found some evidence that the differences between women and men have been lessening. Similar structural features can be found in Grau and Zotos (2016) and Rubie-Davies, Liu, and Lee (2013), who find that women are equally depicted in more egalitarian societal roles.
Another interesting study (Åkestam, Rosengren, & Dahlen, 2017) examined femvertising as female empowerment advertising. The findings of that study highlight that reducing female stereotypes enhances brand attitude. ODVs seem to offer a clear example of femvertising and of how companies are changing their
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advertisements to spotlight a more equal culture. It is necessary to further explore the influence the carefully constructed stereotypes are likely to have on people’s perceptions of social gender roles and how these perceptions are constructing the social fibre of our relationship environments. It is hoped that such an understanding will make marketers and users more aware of the dangers of stereotypical associations in society.
There seems to be a trend towards creating videos in a more neutral environment. This neutral approach could breathe new life into the research, allowing researchers to create new variables and measurements. The present findings indicate that the number of animated ODVs is increasing each day. Given the lack of research on that topic, trying to identify the gender stereotypes in animated videos could also be an area worth looking into.
# Chapter 4. Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram
# Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 63
4.1. Introduction
Instagram has more than 1 billion monthly active users and more than 500 million daily active users, with 400 million Instagrammers sharing stories every day (Instagram, 2018). This colossal user network for sharing pictures and connecting people is an extraordinary source of information (Aramendia-Muneta, 2017) and offers new avenues to researchers, especially with regard to the tourism industry. The international tourism industry registered some 1.326 billion tourist arrivals at destinations around the world in 2017, up 7.0% from the previous year, and has a growth forecast of 3.8% per year until 2020 (UNWTO, 2018). Unsurprisingly, this industry has a deep impact on the global economy, where social networks play a major role in customer engagement with destinations.
While there are several studies in the tourism industry about the impact of social networks such as Facebook and virtual collaborative communities such as TripAdvisor, few studies have examined Instagram (Hanan & Putit, 2014). In fact, Instagram is a key factor in destination choice among millennials, and destinations are chosen by how “Instagrammable” they are (Arnold, 2018). The present study thus focuses on Beautiful Destinations, the leader on Instagram and an example of a destination management organization (DMO). With more than 9,000 posts and 12 million followers in 180 countries, Beautiful Destinations has become the world’s largest travel influencer on Instagram, and other DMOs partner with it. Photographs on the @beautifuldestinations Instagram account spread a desire to visit places among users.
When influencers in Instagram really believe that a service or product could benefit a consumer, their pictures improve the credibility of the destination image and increase visit intention. Such influencers, on the whole, are considered as leaders by Instagrammers (Aramendia-Muneta, 2017). When their messages in the picture are more accurate and comprehensive, followers react more strongly to this information (Bao & Chang, 2014; Godes & Mayzlin, 2009). Influencers have the role of leadership and that is why, tourism DMOs should find the leader to spread their information so as to profit from that (return on investment in e-Marketing) or
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generate a multiplier or ripple effect. Overall, an authoritative image entices future visits or fosters a behavioural outcome.
Visual images are a powerful tool for tourist destination organizations in all forms of tourism promotion (Jenkins, 2003). The mental image of a destination is a set of characteristics perceived by tourists that defines their behaviour (Beerli & Martín, 2004; Yüksel & Akgül, 2007). Moreover, when tourists have limited information about a particular destination, this image can be essential in influencing their choices (Beerli & Martín, 2004; Yüksel & Akgül, 2007). In this regard, photographs posted to Instagram are a valuable source of information for DMOs.
The present study seeks to contribute to the emerging body of literature on Instagram, but from a tourism perspective. Because Instagram is one of the largest social platforms, marketers are keen to engage its audience and monetize it (DeMers, 2017). DMOs need to control tourism destination images, which are a stimulus for Instagrammers, in order to influence them through likes and comments (response). In this correlation, DMOs could then receive a response from potential tourists. The stimulus-organism-response (S-O-R) framework (Mehrabian & Russell, 1974) is thus adapted to Instagram and tourism.
Specifically, the aim of this study is to identify the key destination image attributes (stimulus) to ensure successful image content and engagement by users in the form of likes and comments (response), in order to understand their behaviour towards an image. Such an understanding would help DMOs create more alluring photographs to attract more potential customers and would have marketing implications for DMOs to improve their impact on potential customers.
The remainder of this chapter is organized as follows. The next section discusses the theoretical background on Instagram in the tourism industry, the importance of images for DMOs, and the relationship between content analysis and destination images. It then discusses the attributes that have been used in the literature and were used in the present study to formulate the research questions, the main goal of which is to identify the key elements linking image with the number of likes and comments received. The subsequent sections describe the research # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 65
methodology, data collection process, and results. The final section consists of the discussion and conclusions.
4.2. Literature review
4.2.1. Instagram as a major source of information about tourism destinations
Instagram and the tourism industry have attracted the attention of researchers over the last decade. The areas of study have ranged from cities to museums or protected areas. Weilenmann, Hillman, and Jungselius (2013) find that Instagram showcases the value of museums, as part of a destination organization, by allowing visitors to share their experiences and create new ways of narrating their feelings. Consequently, museums can learn from what visitors actually see to recategorize and reconfigure the museum environment. In a study about nature-based experiences in protected areas, Hausmann et al. (2018) find that Instagram can be used to monitor biodiversity and human activities in such places.
In their study of images from Tokyo and New York City, Hochman and Schwartz (2012) use cultural analytics visualization techniques to identify different visual rhythms for each city. New York and Tokyo are portrayed in diverse cultural ways and, thus, have distinctive beats. A replication of the study using the same technique in Tel Aviv revealed a city with diverse social, cultural, and political aspects depending on people’s activity (Hochman & Manovich, 2013). Both studies show the relationship between Instagram pictures and the daily life of a city through multiple spatial and temporal scales. Single cities have also been examined through a different line of research. In the case of Macau, Yu and Sun (2019) consider the role of UNESCO Creative City of Gastronomy status as a brand that influences Instagrammers and enhances Macau’s image with regard to food.
In addition to DMO facilities and the study of specific cities or places, researchers have used several other methods. Fatanti and Suyadnya (2015) describe modern tourism promotion, finding that destinations such as Indonesia, Bali, and Malang should use Instagram as a communication tool to obtain user-generated content in the form of pictures to encourage tourists to visit specific destinations.
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Paül i Agustí (2018) applies a mixed-method approach to determine that tourist destinations should differentiate their various forms of media and try to avoid overlapping images. Researchers using a range of methods have emphasized the medium of Instagram as a remarkable tool for DMOs.
Instagram is not only a platform for uploading images, but also a medium for interaction among Instagrammers. Camprubí, Guia, and Comas (2013) contend that images shared on Instagram convey emotions, thoughts, realities, and feelings that cannot be truly described in words. All of these features are part of the visual communication about a destination and are included on social-media platforms. Images with many likes represent interesting places for the whole community and increase the interest of other users (Mukhina, Rakitin, & Visheratin, 2017). Sharing images and comments on Instagram is a physical-emotional form of bonding with the destination and impacts the link between the destination and electronic word-of- mouth (Baksi, 2016). Consequently, Instagrammers can create their own tribe and community around a tourist destination.
In this interconnected process, everyday digital photography from Instagrammers provides the audience with a more personal and authentic image of a place (Thelander & Cassinger, 2017). Nevertheless, DMOs should control, at least, the impressions that they themselves wish to show the public to engage actual and potential visitors. In this regard, Nixon, Popova, and Önder (2017) examine the process of selecting appropriate images and hashtags to promote a destination and improve its image among consumers as part of a marketing strategy. This points to the need to know which of the DMO’s images have a real impact on potential tourists.
4.2.2. The power of images in the tourism industry
The tourism industry has realized that classical advertising no longer reaches potential tourists because online innovations affect buying and selling behaviour (Aramendia-Muneta, 2012). MacKay and Couldwell (2004) highlight the power of photographs to create and communicate images of a destination, noting that they remain vital to success in the tourism industry. Accordingly, the industry is # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 67
increasingly turning to social-media platforms and, in particular, to Instagram to market holiday destinations. It thus recognizes that the content and likes and comments received by a picture posted to Instagram by an influencer could be the best approach to attracting potential tourists to the destination.
Although no empirical studies have demonstrated how views, likes, content, and comments about a picture posted on Instagram can be converted into real tourist visits to a destination, there is evidence suggesting that tourism information presented in the form of a picture influences potential tourists’ choice of a destination (Bell & Davison, 2013; Choi, Lehto, & Morrison, 2007). In this regard, Decrop (1999) acknowledges that visual practices are an important part of tourism experiences and influence tourists’ decision-making. Photography creates an image of the purchased service and offers travellers a specific sight to visit with a sense of authenticity (Pan, Lee, & Tsai, 2014). Gallarza, Gil Saura, and Calderón García (2002) note that the intangible nature of tourism and travel services makes it hard for travellers to imagine a destination. These authors emphasize that photographs are a powerful means of generating booking inquiries and travel engagement. However, images offered through Instagram allow travellers to acquire visual knowledge of the place they wish to visit and the services being offered.
According to Stepchenkova and Zhan (2013), photographs capture reality and provide an opportunity for travellers to share their experiences with others. In keeping with this notion, Groves and Timothy (2001) suggest that the integration of mobile technology with social media makes it easier and more enjoyable for tourists to share photographs, leading to an increase in the potential audience. On the whole, images are an effective tool for: promoting, advertising, and distributing goods and ideas; marketing; and providing fast, accurate, and precise information about destinations to travellers (Dredge & Jenkins, 2003; Garrod, 2009). Yüksel and Akgül (2007) find evidence of a relationship between postcards and positive emotions, which affect a destination. In this regard, these authors emphasize the power of images as a key factor influencing travellers’ destination choices. Additionally, online photography in the tourism context makes it easier for destinations to cultivate a good image in tourists’ minds and serves as a symbol of user experience and reality to tourists.
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The existence of multiple images for a given tourism destination is not always negative. On the one hand, the literature accepts the existence of multi-images as a common and easily occurring trend, and Pike (2005) observes that DMOs are responsible for coordinating the tourism industry, in addition to enhancing destination image. On the other hand, all tourism agents, particularly in the private- sector (hotels, restaurants, leisure facilities, and others), logically project their destination’s tourism image from the point of view of their product. In this context, the DMO must act to properly manage and control image fragmentation so as to avoid the ensuing negative effects.
4.2.3. Destination images and content analysis
Visual images of tourist destinations are an under-used but powerful qualitative research instrument (Haywood, 1990). Content analysis is the most well-known and widely used way of taking advantage of this tool among tourism researchers. It is the most frequently used method in tourism research of visual images (Kümpel, Karnowski, & Keyling, 2015). It can also be used to assess and identify the motivations driving tourism industry players to post travel photographs as interactive media (Skalski, Neuendorf, & Cajigas, 2017). In general, content analysis provides an empirical basis to compare and contrast features within a large data set (Albers & James, 1988).
Several postcard studies have used content analysis to obtain their results. In a qualitative content analysis of portrayals of Berlin, Milman (2012) finds a lack of depictions of contemporary Berlin. In their research on scenic postcards as items for the spatial analysis of the Savoy region, Foltête and Litot (2015) find that postcards have a dual location, namely, in the landscape or at the site and at the point of sale.
Researchers’ contributions have also been highly diverse in terms of their methodology. In a study of visitor-employed photography combining content analysis and quantitative statistical techniques, Garrod (2009) proposes that photography and tourism are intrinsically linked through multiple forms of media, such as postcards, television commercials, and brochures. # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 69
Content analysis is used for several purposes. Camprubí (2015) compares the online image fragmentation of two capital cities (Paris and New York). Stepchenkova and Zhan (2013) construct maps representing projected and perceived images of Peru. Similar structural features can be found in the pictorial analysis of Portugal on Instagram by Kuhzady and Ghasemi (2019). In a study of Australian newspaper travel sections (non-news journalism), Hanusch (2011) uses visual content to document travel stories. Finally, tourists have a specific role in the hermeneutic circle of the representation of a destination, with the power to create a specific circle of tourism consumption (Masip Hernández, Camprubí, & Coromina, 2018).
4.3. Formulation of the research questions
4.3.1. S-O-R model for Instagram and DMOs
Laroche (2010) assesses that the stimulus-organism-response (S-O-R) pattern is the most suitable model to clarify online consumer behaviour, even better than the technology acceptance model of Davis (1989). The main reason is that the Internet is a medium well-known by researchers and they need new adapted models which can provide better solutions to the interaction between users and businesses in an online environment.
The original model of S-O-R was presented to the research world by Mehrabian and Russell (1974). These researchers describe the correlation between stimulus, organism, and response with subjective data. These subjective data affect the psychology of consumers and in addition, they generate emotional and cognitive reaction (Kim et al., 2019).
On the basis of Mehrabian and Russel (1974), in Figure 4.1, the revised model represents stimuli as attributes of images and the response from Instagrammers is measured in the form of likes and comments. Thus, DMOs are able to understand how to improve responses in order to engage users and develop better strategies to attract an increased number of tourists.
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Figure 4.1. S-O-R model for Instagram and DMOs
4.3.2. Tourism destination image attributes (stimulus)
A review of the literature points to eight main tourism destination image attributes: the main theme, centricity, time of day, colours, people, water, animals, and repetition. The following paragraphs explain each of these attributes and its characteristics. A summary of the attributes is provided in Table 4.1.
The pictorial images from the sample were categorized into eight subcategories based on the results of a preliminary data analysis of the visual information (Choi et al., 2007; Dadgostar & Isotalo, 1996; Stabler, 1995; Timothy & Groves, 2001). Moreover, Lai and To (2015) find that destination images on social media feature two main subjects: cultural heritage, on the one hand, and hotels and entertainment facilities, on the other. The eight subcategories were: historic buildings and heritage; parks and gardens (places to take a break from city life); tourism facilities and infrastructure (transportation, facilities, hotels, and others); panoramic view of cities or villages or views of natural scenery (e.g., mountains, lakes, national parks, beaches, the sea); special events (e.g., festivals); restaurants and dining facilities; entertainment and leisure; other photographic subjects (e.g., roads).
Another variable was a “centric” theme or centricity. Tussyadiah and Fesenmaier (2009) propose four different “centric” categories: activity-centric, self- centric, site-centric, and other centric. When a picture features a place without showing any activity or people, offers a tourist perspective, and affords viewers a view of a place of interest, it is categorized as site-centric. The category other centric refers to special or foreign events or activities such as habits, lifestyles, or simply the subway. Activity-centric refers to photographs featuring the different kinds of activities tourists can do at a destination, including images featuring people playing, # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 71
biking, partying, and others. Finally, the self-centric category, rather than being generic, offers the audience an expression of the photographer’s self-image.
The time of day, especially, sunset, reveals emotions related to the sky and colours, which ultimately have a positive impact on the viewer (Fiallos, Jimenes, Fiallos, & Figueroa, 2018). In fact, sunsets are a favourite subject among users over the age of 50 (Han et al., 2018) and are a frequent setting, as the light at dusk is good for taking pictures (Boy & Uitermark, 2015). Hunter (2016) describes four main times of day for depicting an image: in daylight, at sunset, at night, and at night with fireworks. However, for the purposes of this study, only three times were used: daylight, sunset, and night.
Singh (2006) defines colour as an important source of information for marketing purposes, because more than 60 percent of customer assessments are based on colours. Darker colours have been found to enhance the functionality of social media (Kietzmann, Hermkens, McCarthy, & Silvestre, 2011). Conversely, Bakhshi and Gilbert (2015) highlight the value of red, purple, and pink in promoting dissemination on social media, while finding that green, blue, black, and yellow have the opposite effect. For colour-based features, the colour space most closely related to human vision is used (Au-Yong-Oliveira & Pinto Ferreira, 2014; Ferwerda, Schedl, & Tkalcic, 2015) in order to understand the influence of colour. Thus, colour-centric features can affect the audience’s response. To this end, twelve colours were used: blue, black, brown, cream, grey, green, orange, red, rose, violet, white, and yellow.
Photographs can feature people prominently in order to be more successful and achieve a certain level of impact. According to Ferwerda et al. (2015), images that include people (e.g., selfies) elicit a greater response in terms of the number of likes and comments than those showing mostly things. Bakhshi, Shamma and Gilbert (2014) and Souza et al. (2015) report similar findings. Moreover, including images of the photographer him or herself, whether just parts, such as a hand or foot, or the whole body, generates self-testimony and increases his or her credibility as the main actor personally documenting and sharing lived experiences (Nunes, 2017). Likewise, selfies attract more attention from Instagrammers, thereby boosting engagement (Souza et al., 2015).
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Environmental variables such as water can play a main role in pictures to emphasize nature and give the photograph an added outdoor-recreation value for viewers (Arriaza, Cañas-Ortega, Cañas-Madueño, & Ruiz-Aviles, 2004; Martín- López, García-Llorente, Peri, Lencinas, & Martínez Pastur, 2015). When audiences notice the presence of water, the perceived visual quality increases (Arriaza et al., 2004). Furthermore, the reflection of light on water amplifies the existence of colours (Singh, 2006) and may thus be more engaging for viewers. The presence of water could thus be an alluring factor for users.
The presence of animals may also be related to the number of likes and increases in followers (Jang, Han, & Lee, 2015). Although there is very little literature on this aspect, it has been included for the purposes of this study as the presence of animals in an image could have a higher impact on Instagrammers.
Not only does each stakeholder in the tourism industry have its own image of a given destination, so does each tourist. Therefore, different images may be projected at the same time through induced information sources, such as social media or brochures and other promotional material (Camprubí, 2015). Such differences (or dysfunctions) are often used to present the reality of a destination, selectively highlighting certain aspects, while ignoring others that might provide a more global image (Camprubí, 2015; Govers & Go, 2004). In these cases, the dysfunction occurs when tourism images are mutually inconsistent, because they ignore different aspects of the destination reality (Camprubí, 2015). Image dysfunction will thus be negative when a multi-image results from inconsistent images that do not depict the reality of the destination, such as repetitive images, due to information overload or information similarity or ambiguity confusion (Mitchell, Walsh, & Yamin, 2005). The variable repetitive country was thus also considered.
# Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 73
Table 4.1. Attribute categories
Attributes Author(s)
Main theme Choi et al. (2007); Dadgostar & Isotalo (1996); Stabler (1995); Timothy & Groves (2011)
Centricity Tussyadiah & Fesenmaier (2009)
Time of day Fiallos et al. (2018); Hunter (2016)
Bakhshi & Gilbert (2015); Kietzmann et al. (2011); Singh Colours (2006)
Bakhshi et al. (2014); Ferwerda et al. (2015); Souza et al. People (2015)
Water Arriaza et al. (2004); Martín-López et al. (2015)
Animals Jang et al. (2015)
Repetitive (dysfunction) Camprubí (2015)
4.3.3. Research questions
The purpose of this study is to identify the key attributes (stimulus) to boost success among Instagrammers (response) and help DMOs improve their Instagrammer engagement and attract more potential tourists. Therefore, the research questions focus on the interconnection between likes, comments, and attributes as follows:
RQ1. What are the key destination image attributes (stimulus) influencing the number of likes (response)?
RQ2. What are the key destination image attributes (stimulus) influencing the number of comments (response)?
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4.4. Methodology
4.4.1. Research design
According to Hunter (2008) and Choi et al. (2007), among others, a qualitative content analysis approach is suitable for research on visual images. In this specific field, Albers and James (1988) describe the method of dividing the image into single parts, quantifying the results into frequencies, and applying distributions. In the present study, this research approach made it possible to identify common themes in the available data with a view to shedding light on how DMOs use Instagram. The use of a qualitative approach supports the presentation of a typology, in addition to diverse forms of expression by users. Typology analysis, in turn, has made it easier to reflect the impact of new technologies such as Instagram for influencing tourists’ interests. A qualitative approach has also been used to facilitate the assessment of consumer preferences for the tourism-influencing tools offered by Beautiful Destinations (Flick, 2009).
The present study consists of a qualitative content analysis to investigate the impact of Instagram on the tourism industry in the case of Beautiful Destinations. The main aim of using this method is to identify the links between image content and the numbers of follower likes and comments about the pictures shared through the Beautiful Destinations Instagram account. This method is used not only to study the features of the image content through a systematic classification process, but also to draw inferences about the responses of the communicators, i.e., the followers, in order to identify themes or patterns (Hsieh & Shannon, 2005; Zhang & Wildemuth, 2009). The use of content analysis also offers an insight into the use of communication indicators, in this case, Beautiful Destinations, to enhance the tourism industry performance.
4.4.2. Sampling and coding process
First, a copy of each image from the @beautifuldestinations Instagram account was downloaded, and a preliminary sample with the number of comments and likes, country, and place was created. This primary data collection process yielded a final # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 75
sample of 1,094 images (with a total of 131,116,800 likes and 2,859,448 comments). Two independent coders (one analyst and one judge) were then used to ensure reliability as described in Küster (2006). Both were required to have experience in Instagram and tourism, as well as experience living abroad as part of a multicultural reality to ensure a broader perspective in their approach to the content analysis. Additionally, a convenience sample of 50 images was used to provide specific training on the accurate identification of the attributes. A third trained coder was responsible for validating the resulting classifications and settling disagreements to reach a final consensus. Intercoder reliability is a method used by researchers to establish consistency within a coder’s own coding process (Wimmer & Dominick, 2011). At 93.5%, the intercoder reliability for the present data was greater than 0.9 and thus acceptable according to Neuendorf (2002).
4.4.3. Statistical analysis
The data were analysed using ordinary least squares (OLS) models due to the nature of the variables in order to assess the relationship between the photographs’ attributes and the number of likes (Model 1) or comments (Model 2). The quantitative dependent variables were the number of likes and comments. The independent variables were the photograph attributes, specifically: main theme, centricity, time of day, colours, people, water, animals, and, as a disruptive attribute, repetition. Out of these eight attributes, main theme, centricity, time of day, and colours were categorical variables, for which a nominal scale was used that differed for each attribute (eight, four, three, and twelve categories, respectively). In contrast, people, water, animals, and repetition were dichotomous variables, for which a nominal scale of two was used, indicating only the attribute’s presence or absence. The OLS models were estimated using SATA 14 software to measure which image attributes might influence the number of likes and comments received. Table 4.2 summarizes the technical details of the research.
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Table 4.2. Technical details of the research
Objective Instagram images Case @beautifuldestinations Reference period 2015 Sample size 1,094 images Likes 131,116,800 likes Comments 2,859,448 comments Methodology Content analysis Statistical analysis Ordinary least squares
4.4.4. Sample description
The sample of 1,094 images received a total of 131,161,800 likes, with a mean of 119,850.8 (standard deviation (SD) of 15,229.65), and a total of 2,859,448 comments, with a mean of 2,613.755 (SD 1,432.44). Of the total number of images, 44.1% received an above-average number of likes, while 39.9% of the pictures received an above-average number of comments. The number of likes received ranged from 65,400 to 210,000, while the number of comments ranged from 376 to 9,286.
Table 4.3 shows the descriptive data for each attribute. With regard to the attribute main theme, at nearly 60%, panoramic views were the predominant perspective, showing tourism destinations as a whole. They were followed by historic buildings and heritage (15.72%) and tourism facilities and infrastructure (6.58%). As for the second attribute, centricity, the characteristic site-centric, featuring a view of the destination as a place of interest, was identified in 40.49% of the cases, while the other three characteristics were all identified in around 20% of the cases (activity-centric: 22.76%; self-centric, 19.01%; other centric: 17.43%). In terms of the time of day, in daylight (60.97%) was predominant, followed by at sunset (24.86%), and at night (14.17%). The most frequently registered colour was green (20.57%), followed by a second group, found in around 10% of the cases, consisting of blue, cream, grey, and orange, and a third group of less common colours (black, brown, red, rose, violet, white, and yellow) identified just under 10% of the time. People (present in 41.32% of the images) and water (present in 69.29%) were key elements in the pictures, while animals (present in 4.11%) were not. # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 77
Finally, the variable repetitive country, implying that a consecutive image of a country is shown as a disruptive attribute, was identified in around 36% of cases.
Table 4.3. Main descriptive data of dependent and independent variables
Attribute Category %
Main theme Historic 15.73% Entertainment 3.56% Panoramic view 59.14% Festival 2.74% Hotel 6.58% Park 3.20% Restaurant 1.65% Other 7.40%
Centricity Activity-centric 22.76% Self-centric 19.01% Site-centric 40.49% Other centric 17.74%
Time of day Daylight 60.97% Night 14.17% Sunset 24.86%
Colours Black 4.02% Blue 10.69% Brown 4.48% Cream 10.42% Grey 11.15% Green 20.57% Orange 13.16% Red 4.84% Rose 2.74% Violet 4.48% White 5.22% Yellow 8.23%
People Present 41.32%
Water Present 69.29%
Animals Present 4.11%
Repetitive (dysfunction) Present 36.65%
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4.5. Results
Table 4.4 provides the estimated results assessing the relationship between the photo attributes and the number of likes and comments received by means of multivariate analysis, using OLS. For the dichotomous variables, OLS treats the absence of the feature as a dummy variable. Conversely, when the categorical variables had more than just a yes or no option, as with the attributes main theme, centricity, time of day, and colours, a dummy variable had to be ascribed. This variable was called the reference group. In these cases, the reference groups were: festivals for main theme, self-centric for centricity, night for time of day, and red for colours. Each specific characteristic was compared to the reference group attribute. A positive regression coefficient meant that success, as measured by likes or comments, was higher for the specific characteristic than for the reference group dummy variable, while a negative regression coefficient meant the opposite. If the regression coefficient was statistically significant, the success, as measured by likes or comments, of the reference group attribute was also statistically significant.
With 1,094 observations, the comments model (Model 2) had a higher level of model fit than the likes model (17.96% vs 4.88%). The statistical solution shows the attributes with the greatest impact in each column. On the whole, more image attributes seem to have an effect on comments than on likes (4 vs 6), while in both models attributes were important factors to consider in explaining success in terms of likes and comments. In fact, the constant (-cons) was significantly different from zero at p<0.001.
In the likes model (Model 1), attributes such as main theme, time of day, water and animals did not have a statistically significant effect on the number of likes. In contrast, photographs featuring people (coefficient 3,557.665, p<0.01) received more likes than those that did not, while repetitive pictures (coefficient - 1,926.837, p<0.05) received fewer likes than those that did not have a disruptive effect. As for the centricity attribute, site-centric photographs (coefficient 4,340.232, p<0.01) and other-centric photographs (coefficient 5,182.604, p<0.001) attracted more likes than self-centric photographs, whereas the effect of activity-centric photographs did not differ from that of self-centric photographs. The colour rose had a positive effect # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 79
(coefficient 6,502.314, p<0.10), and cream, a negative one (coefficient - 4,460.684, p<0.10). Rose attracted more likes, and cream fewer likes, than the red baseline.
With regard to the number of comments (Model 2), more attributes seem to have an effect on Instagrammers. Specifically, photographs featuring water and animals received more comments than those that did not (coefficient 89.876, p<0.001; coefficient 357.699, p<0.001, respectively), while the attributes people and repetitive did not seem to impact the number of comments. With regard to the centricity attribute, activity-centric (coefficient 161.428, p<0.05), other-centric (coefficient 332.848, p<0.05) and site-centric (coefficient 290.664, p<0.10) photographs received more comments than self-centric photographs. Time of day also had an effect on the number of comments. Photographs showing images at sunset (coefficient - 306.261, p<0.05) received fewer comments than photographs taken at night. The attribute colour also impacted the number of comments. Compared with photographs in which the main colour is red, those in which the predominant colour was cream (coefficient 282.460, p<0.01), green (coefficient 530.339, p<0.05), orange (coefficient 206.484, p<0.001), rose (coefficient 794.446, p<0.05), or yellow (coefficient 602.306, p<0.05) received more likes, whereas the effect of the rest of the analysed colours did not differ from that of red. Finally, the attribute main theme also affected the number of comments. Compared to the main theme of festivals, photographs in which the main theme was entertainment (coefficient - 1,019.205, p<0.01), a panoramic view (coefficient - 1,120.642, p<0.01), or restaurants (coefficient - 545.613, p<0.001) received fewer comments. In contrast, those primarily featuring a hotel (coefficient 337.092, p<0.001) received more comments than those depicting a festival.
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Table 4.4. Relationship between attributes and likes and comments
Category Model 1: Likes Model 2: Comments Coefficient SD Coefficient SD Historic -2,140.008 ns 3,157.095 -1,162.369 ns 275.772 Entertainment -1,126.133 ns 3,798.324 -1,019.205 *** 331.783 Panoramic view -2,383.540 ns 3,017.962 -1,120.642 *** 263.619
Festivalª Hotel -1,536.138 ns 3,496.847 337.092 **** 305.449 Park -3,261.310 ns 3,886.089 -1,074.843 ns 339.450 Restaurants 1,117.506 ns 4,653.827 -545.613 **** 406.511 Other -864.753 ns 3,397.469 -1,139.409 ns 296.769
Activity-centric 1,988.519 ns 1,629.243 161.428 ** 142.314 Self-centricª Site-centric 4,340.232 *** 1,374.430 290.664 * 120.056 Other centric 5,182.604 **** 11,585.032 332.848 ** 138.452
Daylight -233.573 ns 1,576.944 -125.769 ns 137.746 Nightª Sunset -1,530.443 ns 1,643.556 -306.261 ** 143.565
Blue -669.872 ns 2,585.968 766.150 ns 225.884 Black -3,887.116 ns 3,241.251 445.999 ns 283.123 Brown -1,052.431 ns 3,005.320 -37.803 ns 262.515 Cream -4,460.684 * 2,553.587 282.460 *** 223.056 Gray -1,308.595 ns 2,516.674 269.944 ns 219.831 Green -2,986.816 ns 2,371.403 530.339 ** 207.144 Orange -952.703 ns 2,481.733 206.486 **** 216.779
Redª Rose 6,502.314 * 3,474.003 794.446 ** 303.454 Violet -2,179.451 ns 3,040.471 318.416 ns 265.585 White -3,491.518 ns 2,927.779 409.205 ns 255.741 Yellow -1,682.291 ns 2,695.252 602.306 ** 235.430
People 3,557.665 *** 1,155.294 627.743 ns 100.915
Water -1,352.489 ns 1,114.890 89.876 **** 97.386
Animals 2,567.985 ns 2,392.428 357.699 **** 208.978
Repetitive -1,926.837 ** 969.608 -210.202 ns 84.695
_cons 121,195.600 **** 3,790.586 2,878.189 **** 331.107 N 1,094 1,094 R2 (%) 4.88% 17.96% *p < .10; **p < .05; ***p < .01; ****p < .001; ns: non-significant; a Dummy variables
# Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 81
4.6. Discussion and conclusions
With the increasing use of the Internet and growing corporate interest in accessing various social-media sites such as Beautiful Destinations, there is immense scope to enhance tourist industry performance by attracting tourists and catering to their individual needs (Wessels, 2014). In the current technology-driven era, the impact of using an Instagram account, e.g., Beautiful Destinations in the case of the tourism industry, is clear and will likely become even more prominent with continuous promotion on the Internet (Wally & Koshy, 2014). Moreover, photographs are a powerful medium for promoting a tourism destination (Hunter, 2008), and the key attributes of those photographs can be a means of engaging more tourists.
A contribution of this research is related to the first research question and the key destination image attributes influencing the number of likes on Instagram. Although the present study focuses on the attributes of photographs posted to Instagram proposed in the literature (e.g., Choi et al., 2007; Timothy & Groves, 2001), the results show that there are indeed differences among the types of attributes. Some attributes have a greater impact on the number of likes than others. The presence of people in a picture, an argument previously supported by Ferwerda et al. (2015), Bakhshi et al. (2014), and Souza et al. (2015), encourages more Instagrammers to hit the like button, while repetitive use of the same country is found to be disruptive and has the opposite effect due to the resulting multi-image, which can cause various types of confusion in the Instagrammer (Mitchell et al., 2005); overload confusion (too much information about the same country), similarity confusion (too much similar information about the same country), or ambiguity confusion (too much ambiguity about the same country). This, in turn, can lead to a state of doubt regarding which image the user likes most. The centricity attribute proposed by Tussyadiah and Fesenmaier (2009) shows that a photograph featuring a view of a place of interest or a special or foreign event or activity receives more likes than an image showing a personal vision of the photographer him or herself. Finally, the colour rose, which was considered a deterrent to promoting dissemination on social media by Bakhshi and Gilbert (2015), positively impacted the number of likes in the present study, while the colour cream had a negative impact.
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As regards the second research question, another notable contribution is the finding concerning the number of attributes influencing the number of comments on Instagram. On the one hand, leaving a comment requires more effort, commitment, and time from the Instagrammer; therefore, the number of comments is always lower than the number of likes. In fact, the number of likes and comments can be used to distinguish whether engagement on Instagram with the audience is one-way and related to self-presentation (likes) or two-way involving feedback from Instagrammers (comments) (Russmann & Svensson, 2016). On the other hand, the attributes with the greatest influence on the number of comments provide more detail, as if images required a higher degree of complexity with regard to their content. Thus, the inclusion of environmental factors such as water or the presence of animals ensures an increase in the number of comments, which is consistent with previous research regarding the presence of water (Arriaza et al., 2004) and animals (Jang et al., 2015). However, the present study focused on Instagram. Furthermore, colours such as cream, green, orange, rose, and yellow were more likely to increase success in terms of comments than red. With regard to this variable, the results were slightly different from those reported by Bakhshi and Gilbert (2015), who assert that green and yellow have a negative impact, and Kietzmann et al. (2011), who find that dark colours enhance the functionality of social media. Tussyadiah and Fensemaier’s (2009) centricity attribute had a strong impact on comments. All the characteristics in this attribute – the view of a place, special or foreign events, and various types of tourist activities – were more likely to elicit comments than the expression of self-image by the photographer. However, the results for the time of day attribute show that photographs taken at sunset elicited fewer comments than photographs taken at night. Finally, with regard to the main theme, hotels and tourist infrastructure were more likely to receive comments than festivals, and entertainment, panoramic views of places, and restaurants were less likely to receive comments.
Depending on the content of the image, all the previous conclusions are worth implementing to ensure successful photographs for DMOs in terms of likes and comments, such as predominance of the colour rose or the use of site-centric and other-centric images. To increase likes, photographs should depict people and avoid using predominantly cream colours, whereas to increase comments, featuring water # Key Image Attributes to Elicit Likes and Comments for Tourism Destinations on Instagram 83
and/or animals is basic to engaging users, as is focusing on a hotel. Colours such as cream, green, orange, and yellow and activity-centric photographs can also be useful on Instagram.
# Chapter 5. “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations
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5.1. Introduction
Instagram reached one billion monthly active users in June 2018, which makes it unique in the social media world (Instagram & TechCrunch, 2018). Sharing images and connecting people are the main goals of this social medium; thereby becoming an extraordinary source of information for researchers, opening new avenues, above all, in the tourism industry. The tourism industry reached US$ 1,340 billion in turnover in 2017, an increase of 5% from the previous year (UNWTO, 2018). In fact, as the UNWTO (2018) says, tourism matters, because it has a deep impact on job creation, economic growth, country development and promotes cultural preservation, environmental protection and peace and security.
Hanan and Putit (2014) emphasized that there were few studies about Instagram in comparison to Facebook or virtual collaborative communities. “Instagram is changing tourism marketing”, stated Convince & Convert Consulting in a recent report (2019, pp. 3). Furthermore, they confirmed that travel and tourism industry companies need to be on Instagram, as 48% of Instagrammers use the app to find new travel destinations and new places to discover. Instagram is a boon for tourism destinations in reaching and engaging potential travellers. Its influence underpins consumers’ decision-making and foments consumers’ desires to see images. Images are fundamental to destinations’ success as channels of communication (MacKay & Couldwell, 2004) and have the power to be effective tools for promoting tourism destinations (Garrod, 2009).
Images posted on Instagram generate four times more user engagement than other user content on other social media (Buryan, 2018). On the whole, images are considered as the most prevalent form of self-presentation techniques on social media (Engelmann & Grossklags, 2019). Destination marketing organizations on Instagram, such as Beautiful Destination, are influencers for tourism country destinations; images have higher value than other media content, such as videos. Therefore, the present study focuses on @beautifuldestinations, the world’s largest tourism community, with 12.7 million followers and more than 9,000 images on Instagram, whose goal is to inspire people to open their minds and to impact on Instagrammers in the field of tourist destination depictions.
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Attributes in an image can be classified as products which provide benefits to marketers (Engelmann & Grossklags, 2019). Moreover, Mukhina et al. (2017) suggested that there is a need to study the interaction between users, in terms of likes and comments, because these show users’ concerns. In fact, the number of likes and comments is considered to be a two-way information exchange and represents Instagrammers’ feedback (Russmann & Svensson, 2017). Pictures depicting multiple interactions evoke interest in viewers, thus, the place where a photo was taken could attract the attention of potential tourists.
The present study helps to fill in the gap in research into Instagram and tourism as regards likes and comments. As suggested by Tsiotsou and Ratten (2010), most tourism research has relied on field surveys and neglected qualitative methods. To provide a qualitative perspective the present study applies the Stimulus-Organism- Response (S-O-R) conceptual model (Mehrabian & Russell, 1974), adapted to tourism and Instagram, where attributes of pictures are the stimuli and Instagrammers’ reactions are measured by their response in terms of likes and comments. The main aim is to study which countries have higher impact on Instagrammers (cluster analysis) and the connection of picture content with a country’s success, thereby increasing knowledge of Instagram and tourism country destinations and to help marketers and advertisers improve country image and engage with Instagrammers. The research question of this chapter is: What are the key destination image attributes (stimulus) by cross-country cluster that influence the number of likes and comments (response)?
5.2. Instagram and Tourism Destinations
While there is a wide range of studies into Instagram that examine: individuals and their self-presentation (Sheldon & Bryant, 2016); the motivation for, and gratification in, using the platform (Reichart Smith & Sanderson, 2015; Ting, Ming, De Run, & Choo, 2015); affection and sociability (Phua, Jin, & Kim, 2017); its positive effect on loneliness, happiness and satisfaction (Ahadzadeh, Sharif, & Ong, 2017) among other aspects, there are fewer focusing solely on Instagram and the tourism sector. However, this link has recently garnered significant levels of # “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations 89
attention among academics. Four main areas have been examined: the general impact of Instagram on tourism; countries; cities from different perspectives; and specific places within cities.
The literature has predicted a positive relationship between Instagram and tourism destinations. Hanan and Putit (2014) emphasised the extraordinary value of a picture compared to words for destination brands and Instagram brings added value by attracting and engaging a potential clientele. The power of Instagram is ascribed to the wider audience that it provides for sharing travel experiences (Gibson, 2019). Not only tourism destinations as brands, but local residents also help to configure an icon of exoticism that imbues the tourist’s experience with authenticity (Smith, 2018).
Using Portugal as the scenario, Kuhzady and Ghasemi (2019) showed that natural attractions are the most projected destination image and that food and drink themes received the highest Instagrammer engagement. An interesting study carried out in Ecuador tested the effects of picture content themes associated with a particular hashtag, and identified three main topics: natural tourist attractions, cities and people (Fiallos et al., 2018). The study of Paül i Agustí (2019) established that Uruguayan rural areas lack a tourist image, whereas urban spaces received the most attention. It is also worth mentioning one of the first attempts at focusing on destination marketing organizations in Jordan and Costa Rica, using focus groups and surveys. Nixon et al. (2017) reported the value of photographs to country image, noting that users normally have a prior mental impression of the destination, which might influence the picture’s appreciation a posteriori. The same idea is debated by Masip Hernández et al. (2018), who described this process as a hermeneutic reproduction circle.
Much of the Instagram literature is focused on cities. Zulzilah, Prihantoro and Masitoh (2019) studied Bandung, Indonesia. Through analysing comments and online questionnaires, their research showed that tourism images on Instagram improved the rankings of the hotel sector, accommodation, restaurants, spas, travel agencies and tourist attractions in Bandung. Furthermore, in the same country, Fatanti and Suyadnya (2015), focusing on Bali and Malang, based their study on photographs, likes, comments, and a hashtag using photo elicitation interviews and
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found that Instagram helped to improve destination brand image. Emphasizing the power of Instagram for sharing images and comments, Baksi (2016) featured the case of Santiniketan, India, and evaluated the link between destination and electronic-word-of-mouth (eWOM); this author found that eWOM added an emotional dimension to the destination. Yu and Sun (2019) examined Macau, which UNESCO has designated as a Creative City of Gastronomy, and showed that Instagrammers improved Macau’s image in the area of cuisine/food.
This gastronomy research approach was followed by Falcão Durão, Dos Santos, Avelino, and Borba da Mota Silveira (2017) in Recife, Brazil. Using a mixed- methodology of netnography, non-participant observation and semi-structured interviews, they showed that restaurants’ dishes and promotional menu information received the highest number of likes. Another significant addition to the study of Instagram and tourism in South America was provided by Paül i Agustí (2018), who characterized the city of Montevideo, Uruguay through three dimensions (official tourist brochures, travel guides and Instagram), and argued that there was a partial overlapping of images posted on Instagram and those published in official media. In Mexico City, Bernkopf and Nixon (2019) contrasted different test groups and suggested that user-generated-content on Instagram is more effective for improving destination image than photographs reposted by the destination marketing organization.
Mukhina et al. (2017) examined the power of likes given to destinations both by tourists and locals in terms of interest evoked in other users by analysing St Petersburg, Russia, and Thelander and Cassinger (2017) examined the Swedish city of Landskrona; their results showed that pictures of everyday life situations gave the location more authenticity and provided the audience with more personal information. The city of Venice, Italy was studied by Rossi, Boscaro and Torsello (2018); they classified the photographs into six categories: lagoon, townscape, art, folklore, food, and others; the most popular photographs were linked to international and external events, such as folklore and art festivals. Amsterdam, in the Netherlands, was studied by Boy and Uitermark (2015), who showed that the destination image was positively affected by Instagram not only due to the # “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations 91
photographs of the most iconic attractions but also by photographs of other small, picturesque locations.
While previous researchers have focused on cities as a whole, Qian and Heath (2019) investigated, through content analysis, the understanding of portals of two Chinese recreational venues, Sanlitun Village, Beijing and Xintiandi, Shanghai; they identified three types of portals: “the doorway”, “the showroom” and “the place”. This may help urban designers to improve destination images. Again looking at a specific area, Weilenmann et al. (2013) studied New York City’s museum experiences through social photo sharing on Instagram and interviews. Their results explained the role of Instagram in visitors’ engagement with museum exhibits. Conversely, Budge and Burness (2018) looked into the Australian Museum of Contemporary Art, and their findings demonstrated decreased public engagement. Another factor that might be related to Instagram in tourism is the notable increase in environmental concerns and, particularly, nature-based experiences in protected areas; Hausmann et al. (2018) demonstrated that Instagram can be used to monitor biodiversity and human activities to preserve protected areas. Finally, a culture and gastronomy festival in the city of Tiradentes, Brazil was analysed by De Oliveira Santos, De Oliveira Cabral, Gosling and Magalhães Christino (2017); their findings illustrated that the event helped to enhance the city’s image, not only by focusing on the festival’s characteristics, but also the tourist experience in the historic city, its cityscape and its scenery.
5.3. Methodology
5.3.1. S-O-R model for Instagram and country image
Laroche (2010) argued that the Stimulus-Organism-Response (S-O-R) paradigm is more useful for explaining online consumer behaviour and providing productive solutions than the technology acceptance model (Davis, 1989) as the Internet is a universal medium and research in this area is far from being in its earliest stages. In fact, the social media environment differs from traditional environments by fostering
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new forms of interaction between the providers of information and users (Aramendia-Muneta, 2012; Gatautis et al., 2016; Munar, 2012).
The S-O-R paradigm refers to the sequential correlation among stimulus, organism and response: the S-O-R model is based on environmental psychology, which is a challenging field of research because of its use of subjective data (Mehrabian & Russell, 1974). This present study evaluates which attributes of tourism photographs (stimulus) provoke the highest emotional and/or cognitive reaction (organism), which leads to likes and comments (response). The S-O-R model has been adapted in the present study to Instagram and tourism, where the attributes of countries’ pictures are the stimulus and the likes and comments are the Instagrammers’ response (Figure 5.1); it was also adapted to the online environment by Gatautis et al. (2016) in gamification and Kim et al. (2019) in virtual reality tourism.
Figure 5.1. S-O-R model for Instagram and country image
5.3.2. Qualitative content analysis and Attributes of tourism image destination (stimulus)
Qualitative content analysis is the most suitable research approach for visual images (Choi et al., 2007; Hunter, 2008). Nonetheless, there are few visual analysis studies (Bakhshi, Shamma, Kennedy, & Gilbert, 2015). In the tourism photograph field, Albers and James (1988) advised dividing the image into sections and quantifying the results. This division facilitates the identification of common themes by attribute (stimulus) to each cluster and helps find the connections between attributes and success in terms of likes and comments. The literature review revealed seven main tourism destination image attributes: main theme, centricity, time of day (each one # “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations 93
having several different subcategories), people, water and, finally, dysfunctional attributes, such as repetitive pictures and photomontage.
Regarding the main theme variable, the pictorial images of a country are categorized into eleven subcategories based on the results of previous analyses of visual information in tourism destinations (Choi et al., 2007; Dadgostar & Isotalo, 1996; Stabler, 1995; Timothy & Groves, 2001). These categories are: historic buildings and heritage; parks and gardens (places for relaxation in the city); tourism facilities and infrastructure (transportation, facilities, hotels, and others); panoramic city views; panoramic village views; panoramic views of natural inland scenery (e.g. mountains, lakes, national parks); panoramic views of natural coastal scenery (beaches, sea); special events (e.g. festivals); restaurant/dining facilities; entertainment and leisure; and other photographs (e.g. roads). Choi et al. (2007) and Lai and To (2015) suggested that the two most popular main themes in tourism destinations are historic buildings and tourism facilities and infrastructure.
Tussyadiah and Fesenmaier (2009) divided the centricity attribute into four different categories: activity-centric, self-centric, site-centric and other centric. When the picture shows viewers touristic activities, such as children playing or people cycling, it is categorized as activity-centric. The personal preferences of the self-image of the photographer, which by definition will not be generic, are termed self-centric. Site-centric pictures feature places of interest in the destination without focus on any activity or people. The other-centric category targets aspects of a place perceived as special or foreign, such as habits, lifestyles, entertainment and transportation systems.
Hunter (2016) specified in his research four main times of day depicted in pictures: daylight, sunset, night, and at night with fireworks. Researchers tend to remark on the great value of pictures of sunsets, which have a positive impact on the viewer and create more engagement (Fiallos et al., 2018; Scott & Vargas, 2007).
Photographs depicting people had a higher impact on the number of likes and comments than images which portrayed mostly a general view (Ferwerda et al., 2015). As mentioned in the literature review, Instagram researchers have focused on the subject of people (Sheldon & Bryant, 2016; Ting et al., 2015), as it is a source
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where a large number of pictures on the subject can be found. Thus, Instagrammers might be influenced by the presence of people in tourist pictures.
When viewers see water in a picture, they tend to see it as having higher quality (Arriaza et al., 2004) and, hence, tend to increase their number of likes and comments. In general, water when used in outdoor leisure activities adds value, as emphasized by Fredman, Wall-Reinius and Grundén, 2012.
In contrast to the main theme, centricity, time of the day, people and water attributes, dysfunctional attributes might have a negative impact on likes and comments. Image dysfunction resulted when there was a lack of coherence among photographs of destinations (Camprubí, 2015). This dysfunctionality will be negative when multi-images are exhibited or images do not display the reality of the destination (Camprubí, 2015), as viewers can be confused by information overload, by repeated similar content and by ambiguity (Mitchell et al., 2005). These dysfunctionalities might be caused by the repetition of various images from the same destination or fake images which do not display tourism reality, such as photomontages.
5.3.3. The sample
Beautifuldestinations’ Instagram account is the source of the sample; first, images were downloaded and a sample was created with data of the number of likes and comments and country of origin. The final sample was formed by 1,094 images with a total amount of 131,116,800 likes (average 119,850.8) and 2,859,448 comments (average 2,613.76). Two independent codifiers, one analyst and one judge, as suggested by Küster (2006), to ensure reliability, were trained with a convenience sample of fifty images in order to accurately identify the determinant attributes and to resolve controversies until a final consensus was reached. The intercoder data reliability was 93.5%, which is higher than the 0.9 value recommended by Neuendorf (2002). Intercoder reliability is used by researchers to determine consistency within the coding process (Wimmer & Dominick, 2011). Table 5.1 summarises the technical details of the research. # “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations 95
Table 5.1. Technical details of the research
Research What are the key destination image attributes (stimulus) by cross-country question cluster that influence the number of likes and comments (response)? Case study Instagram, @beautifuldestinations Sample size 1,094 pictures taken in the year 2015 Countries 69 Method Content analysis of image attributes Main theme, centricity, time of day, people, water, dysfunction (repetition Attributes and photomontage) Statistical Cluster analysis
5.4. Results
This section is structured as follows: first, basic characteristics of the sample are described by continent and country in relation with three variables: amount of observations, the mean of likes and comments and a perceptual map with the countries that have a deeper impact on the sample. Then, the cluster analysis results are presented with the relationship between the seven attributes and the whole sample of 1,094.
5.4.1. Images, Likes and Comments by continent
Table 5.2 shows the total number of observations per continent and its mean and standard deviation of likes and comments. From the global sample, Europe is the continent more representative with over 44%, followed by America with 29.8%, then Asia 18.5%. Oceania and Africa represent less than 5%. In fact, Asia and Europe have the highest mean in likes and comments, with more than 120,000 in likes and more than 2,600 in comments.
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Table 5.2. Sample characteristics by continent
Continent Likes Comments N N % M SD M SD
America 326 29.8% 117,361.3 14,868.90 2,279.06 1,343.29 Asia 202 18.5% 121,182.7 13,745.85 3,121.78 1,404.17 Africa 32 2.9% 118,312.5 11,568.69 2,264.28 935.08 Europe 485 44.3% 121,418.8 16,228.98 2,669.82 1,422.66 Oceania 49 4.5% 116,408.2 12,771.91 2,421.57 1,811.36 Total 1,094 100.0% 119,850.8 15,229.65 2,613.76 1,432.45
Number of images (N), mean (M), standard deviation (SD)
5.4.2. Images, Likes and Comments by country
The number of images (observations) between countries differs (Table 5.3). Firstly, 54% of the sample is made up by Canada, France, Greece, Italy, Turkey, the United Kingdom and the USA, with Italy and the USA having the highest percentages. Out of this 54%, Greece surpasses the others in number of likes and comments. In the remaining 46%, the Netherlands has the highest average number of likes (136,260), whereas Spain has the highest number of comments (4,279.4). Countries such as French Polynesia (126,381) and Spain (130,000) have higher means in likes. In terms of comments, Thailand (4,171.64) and French Polynesia (4,039.14) have higher means in comments. On the other hand, Australia, New Zealand and Norway (114,680; 114,473.7; 112,148.1, respectively, vs the overall mean of 119,850.8) in number of likes, and Switzerland, the United Kingdom and Australia (1,946.4; 1,822.38; 1,758.12, respectively, vs the overall mean of 2,613.76) in number of comments, present results significantly below average.
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Table 5.3. Sample characteristics by country
Country Images Likes Comments N N % M SD M SD
United States of America 197 18.01% 115,953.3 15,444.62 2,034.09 1,279.07 Italy 109 9.96% 121,781.7 13,200.04 2,713.99 1,039.81 France 80 7.31% 121,753.8 15,424.67 2,515.21 1,256.72 Canada 61 5.58% 118,704.9 14,584.63 1,991.98 870.71 Turkey 57 5.21% 123,193.0 15,498.20 2,774.12 1,141.66 Greece 47 4.30% 123,997.9 14,899.23 3,927.15 1,982.12 United Kingdom 40 3.66% 118,625.0 16,066.67 1,822.38 701.41 Philippines 30 2.74% 121,600.0 1,1842.65 3,260.50 1,184.90 Switzerland 30 2.74% 120,366.7 10,962.27 1,946.40 1,121.22 Norway 27 2.47% 112,148.1 9,163.91 2,123.30 736.70 Germany 26 2.38% 122,000.0 19,875.61 2,166.31 1,091.49 Australia 25 2.29% 114,680.0 12,847.57 1,758.12 917.20 Austria 25 2.29% 118,600.0 12,426.45 2,296.04 1,010.45 Thailand 25 2.29% 122,800.0 12,096.83 4,171.64 1,296.23 Netherlands 23 2.10% 136,260.9 25,330.93 3,125.96 1,497.05 Indonesia 22 2.01% 118,500.0 10,486.95 3,495.77 1,721.39 French Polynesia 21 1.92% 126,381.0 14,510.26 4,039.14 1,894.46 New Zealand 19 1.74% 114,473.7 8,694.62 2,755.32 1,700.92 Brazil 17 1.55% 118,352.9 14,734.91 2,811.65 1,332.33 Maldives 16 1.46% 116,456.3 11,231.98 4,026.56 1,315.95 Spain 15 1.37% 130,000.0 20,918.21 4,279.40 2,374.41 United Arab Emirates 15 1.37% 117,733.3 8,232.92 2,410.60 1,060.37 Japan 14 1.28% 119,692.9 13,145.25 2,609.57 800.47 Croatia 12 1.10% 119,500.0 17,106.62 2,692.25 1,309.70 India 11 1.01% 121,909.1 13,881.32 2,148.27 1,122.65 Belgium 10 0.91% 122,000.0 16,753.11 3,723.30 1,798.45 Other - 43 countries 120 10.97% 118,588.3 15,794.28 2,673.53 1,348.56
Number of images (N), mean (M), standard deviation (SD)
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5.4.3. Perceptual Maps
To determine the influence of nationality and to emphasize the strategy of each DMO, a perceptual map approach is used on the sample. This technique provides a holistic approach. Examining the map (Figure 5.2), the relationship between number of likes and number of comments seems to cluster into four quadrants created by the intersection of the total average of likes and comments. The first quadrant points at the Netherlands as the country far ahead of the rest, together with Belgium, French Polynesia, Greece, Italy, the Philippines, Thailand and Turkey, whose mean in comments and likes are higher than the overall mean, although Italy and Turkey are near to the cross point of the mean of the quadrant. France, Germany, India and Switzerland are part of the second quadrant, whose characteristic is the impact in the number of likes. The third quadrant includes countries whose number of comments is higher than the mean, and that is where Brazil, Croatia, Indonesia, Maldives and New Zeeland and the rest of the countries belong. Finally, the fourth quadrant, including Australia, Austria, Canada, Norway, Switzerland and the USA refers to countries where the number of likes and comments are below the mean.
As can be seen, some countries have more success than others; consequently, it can be presumed that the number of likes and comments could be variables affecting the cluster analysis. It is remarkable that countries with similar Hofstede’s cultural values (1980), such as the Netherlands and Norway, have opposite results in likes and comments, which might suggest that there are differences in the way their country destination images are presented. # “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations 99
Perceptual Map Perceptual
. 2 . 5 Figure
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5.4.4. Cluster analysis
The sample identifies 69 countries; three main variables were extracted from Instagram - average number of likes, average number of comments and the quantity of pictures per country. First, before clustering, the data must be standardised by rescaling each variable so that they have a standard deviation of 1 and a mean of 0 (Romesburg, 1985). Thereafter, the TwoStep procedure is applied to determine the optimal number of clusters by comparing the three values across the possible cluster solutions (Malhotra, Nunan, & Birks, 2017); the results suggest that two main clusters would be the optimal solution. As recommended by Malhotra et al. (2017), hierarchical and non-hierarchical methods were used in tandem. An initial clustering solution was obtained by using Ward’s method and the square Euclidian distance. Everitt, Landau and Leese (2001) argued that Ward’s method performs better than other procedures. The aim of the hierarchical cluster analysis is to select candidate number of clusters and to obtain centroids of clusters (Everitt et al., 2001; Punj & Stewart, 2006). Finally, a k-means cluster analysis was applied for two clusters of the previous results whose validations were confirmed by variance and discriminant analysis. The test of significance of function is based on Wilks’ Lambda (Wilks’ Lambda = .430, Chi-square = 55.265, df = 3, and p-value = .000), which demonstrated that there are differences between the groups and, thus, the null hypothesis is rejected. The canonical correlation between groups is .833, supporting the validity of the analysis. In the discriminant analysis, Wilks’ Lambda and univariate ANOVA (tests of equality of group means) were used to assess the significance between the means of the three independent variables (N, Comments and Likes) for the two groups. N (Wilks’ Lambda = .988, F = .804, df1 = 1, df2 = 67, p = .373) suggests that the number of pictures does not influence the formation of the clusters, however Comments (Wilks’ Lambda = .472, F = 75.033, df1 = 1, df2 = 67, p = .000) and Likes (Wilks’ Lambda = .645, F = 36.934, df1 = 1, df2 = 67, p = .000) are the key variables that differentiate between the two groups.
The first group, “the best”, is formed by 22 countries with a high number of likes and comments (Belgium, Czech Republic, Fiji, French Polynesia, Greece, Iceland, Indonesia, Malaysia, Maldives, Morocco, Myanmar, Nepal, the Netherlands, Panama, Peru, the Philippines, Seychelles, South Korea, Spain, Sri # “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations 101
Lanka, Tanzania and Thailand). The second group, “the least”, is formed by 47 countries with fewer likes and comments (Argentina, Australia, Austria, Bahamas, Bhutan, Bosnia, Brazil, Canada, Chile, China, Croatia, Cyprus, Denmark, Ecuador, Finland, France, Germany, Hong Kong, Hungary, India, Ireland, Italy, Japan, Jordan, Kenya, Malta, Mexico, Minor Antilles, Montenegro, New Zealand, Norway, Portugal, Russia, Samoa, Singapore, Slovenia, South Africa, Sweden, Switzerland, Tonga, Turkey, United Arab Emirates, United Kingdom, United States of America, Vietnam, Zambia and Zimbabwe). The groups were assigned names reflecting their success in terms of likes and comments.
5.4.5. Cluster characteristics
The results for all seven attributes (main theme, centricity, time of day, people, water and the two disruptive variables - photomontage and repetitive) are summarized in Table 5.4.
Main theme. A statistically significant relationship was found between the main theme and level of success (X2 = 148.336 df = 10, p<.000). “The best” cluster is characterised by having a main theme of tourism facilities (22%) and panoramic coastal views (26%), and “the least” by having main themes of an inland panoramic view (23.7%) and historic buildings (18.8%).
Centricity. The overall analysis showed a statistically significant relationship between level of success and the centric-theme (X2 =46.083, df = 3, p<.000). A total of 34.1% of “the best” group were activity-centric vs 19.5% of “the least” group. Other-centric characteristics are more prevalent in the group with the highest number of likes and comments than in “the least” group (23.6% vs 16%). Whilst the site-centric and self-centric groups achieved the highest percentages (42.5% and 22.1%, respectively) in “the least” group, only the site-centric category has a similar percentage to “the best” group (33.7% and 34.1%, respectively).
Time of day. A significant association was found between time of day and both clusters (X2 =28.081 df = 3, p<.000). Pictures taken during daylight hours are the
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most popular in “the best” cluster (75.2%), while “the least” cluster scores 56.8% for sunset and night scenes.
People. Numerical cluster differences were observed with regard to the presence of people (X2 = 13.232, df = 1 p<.000). There are more people in the pictures in “the best” group than in “the least” group (52% vs 39%).
Water. The overall analysis showed significant relationships between “the best” group in likes and comments and water (X2 = 21.525, df = 1, p<.000). Water, as an environmental variable has a higher percentage than in “the least” group (81.3% vs 65.8%).
Photomontage (disruptive). A significant relationship was found between success in terms of likes and comments and photomontages (X2 = 14.357, df = 1 p<.000). The results indicate that fewer likes and comments are associated with (“the least”) countries with a higher number of pictures, whose visual effect might be perceived as a photomontage.
Repetitive country (disruptive). The overall analysis showed a significant relationship between the two clusters and repetitive pictures of the same country (X2 = 583.754, df = 1, p<.000). The percentage of repetition in “the least” cluster is more than double that of “the best” group (41.6% versus 15%).
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Table 5.4. Characterization of country cluster
Attribute Category Group 1 Group 2 The best The least (n=246) (n=848) X2 p
Main theme Historic Buildings 6.9% 18.8% 148.336 .000 Parks and Gardens 0.0% 4.1% Tourism Facilities 22.0% 7.9% Panoramic-cities 15.0% 14.9% Panoramic-village 8.9% 10.1% Panoramic-inland 7.7% 23.7% Panoramic-coastal 26.0% 7.2% Special events 1.6% 3.1% Restaurant 2.4% 1.4% Entertainment 5.7% 4.2% Other 3.7% 4.6%
Centricity Site-centric 33.7% 42.5% 46.083 .000 Self-centric 8.5% 22.1% Activity-centric 34.1% 19.5% Other centric 23.6% 16.0%
Time of day Daylight 75.2% 56.8% 28.081 .000 Sunset 15.0% 27.7% Night 9.8% 14.7% Night fireworks 0.0% 0.7%
People Presence 52.0% 39.0% 13.232 .000 Absence 48.0% 61.0%
Water Presence 81.3% 65.8% 21.525 .000 Absence 18.7% 34.2%
Photomontage Presence 3.7% 11.9% 14.357 .000 Absence 96.3% 88.1%
Repetitive Presence 15.0% 41.6% 58.754 .000 Absence 85.0% 58.4%
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5.5. Discussion and conclusions
The analysis of Instagram and tourism country destination represents a new avenue for marketing scholars and tourism business managers due to the need to adapt image strategies to consumers’ new interests (Budge & Burness, 2018; Mukhina et al., 2017; Yu & Sun, 2019). A major contribution of this study is the exploration of how the relationships among attributes (stimulus) and success in likes and comments (response) differ between two clusters based on the S-O-R model.
The results for main themes show that tourism facilities, for example, hotels and entertainment facilities, and panoramic views of natural coastal scenery, such as beaches and the sea, increase the engagement of Instagrammers in terms of likes and comments. This conclusion is in keeping with the study of Zulzilah et al. (2019) about Indonesia, which belongs in the successful group. Panoramic views of natural inland scenery, such as lakes and mountains, had a deep impact on “the least” group. In this line, McCready (2019) supported the negative impact of inconsistency on destination image, above all in countries where tourism is nature-based.
Activity-centric and other-centric describe situations where tourists interact with the destination through activities or by taking part in its normal life, which is connected to the value of the presence of people in a picture. Moreover, images of tourists or local residents taking part in their normal lives or activities in the destination demonstrated authenticity and increased viewer engagement (Smith, 2018). Self-centricity is prevalent in “the least” cluster. This characteristic presents the self-image of the photographer and might have a negative impact on the number of likes and comments because it might not present the reality of the destination. Site-centric is prevalent in “the least” group and achieved the second highest percentage in “the best”. However, general perspectives of a destination might lose the feelings of connectedness and happiness, which is transmitted through images (Pittman, & Reich, 2016).
Daytime is the most popular time for “the best” group. Although several researchers have emphasized the effect of sunset on viewers (Fiallos et al., 2018; Scott & Vargas, 2007), these pictures can show, through their natural colours, as opposed to shaded colours, more of the authenticity of the destination. In addition, # “The best” and “The least”: Cross-Country Cluster Analysis of Instagram and Tourism Destinations 105
if photographs of the same country follow, this can have a negative effect on Instagrammers. Above all, daytime pictures depict destinations with non-shaded colours; shaded colours, normally seen at sunset and at night, can cause confusion in the consumer (Singh, 2006).
Tourism influencers on Instagram should endeavour to provide many pictures to their communities to secure higher engagement and repeat visits in terms of comments and likes, with the aim of increasing Instagrammers’ trust in the tourism destination. Providing appropriate picture content might attract highly positive Instagrammer feedback, such as likes and comments, which can affect level of interest in the country and motivate Instagrammers to visit. In that way, monitoring likes and comments should provide benefits to the country’s image and should be part of the country’s strategy.
The presence of people and water in pictures had a powerful positive impact, which is consistent with previous research (Arriaza et al., 2004; Ferwerda et al., 2015). Instagram as a social media was originally based on images of people, thus Instagrammers might see themselves as the people inside the images and want to be in that destination. In this line, Day, Skidmore, and Koller (2002) suggested that tourists are motivated to provide likes and comments by photographs with activities involving people. Second, water as an environmental factor in its several forms of sea, rain, rivers, for example, creates more engagement if it involves an activity that the viewer can take part in (Fredman et al., 2012).
The dysfunctional variables, repetitive and photomontage, have negative impact (Camprubí, 2005). Repetition in the sense of overloading Instagrammers with pictures of the same country might cause confusion by providing too much similar information (Mitchell et al., 2005) and photomontages might create ambiguity about the reality of the destination by showing the viewer an impossible image. Narrative in visual images provides a framework and consistent information (Carah & Shaul, 2016), while the use of repetitive images and photomontage might negatively affect the narrative. Overall, the results of this study are analogous to McCready’s advice (2019) to country destination organizations about image and social media.
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McCready (2019) recommended the Fijian image strategy and also gave New Zealand as an example of a destination that lacked a cohesive narrative, which it should address to improve its image destination and gain social media engagement. In fact, the present study’s qualitative and quantitative data correlates with McCready (2019); it is found that Fiji is the first country, among the 69 of the sample with the highest average number of comments and likes, and New Zealand is in “the least” cluster. Therefore, in light of the results, the present study might help destination management organizations and marketers to improve destination image.
A good example of how managing destination image is useful is seen in the Philippines; @beautifuldestinations teamed up with the Philippines as a competitive tourist destination (Government of the Philippines, 2016). In fact, in the cluster analysis, the Philippines is positioned as a successful destination in terms of likes and comments; this suggests that images projected by destination management organizations on Instagram, such as beautiful locations, can enhance the destination’s image and help attract potential tourists; Instagram, markedly, seems to provide opportunities for improvement.
# Chapter 6. Conclusion
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6.1. Contributions and conclusions
Advertising is a complex phenomenon, which due to new technologies and consumer behaviour changes, is both constantly being updated and challenging researchers, advertisers, marketers and businesses. To attend to these issues, this doctoral thesis approaches the study of advertising by means of delving more deeply into advertising formats and media under present-day conditions during the communication process. First, it explores mobile advertisement and consumers’ attitudes, secondly, original digital video and advertisers’ encoding processes of gender stereotypes, and thirdly, Instagram and users’ feedback on tourist destination images. For this purpose and from a deep analysis of the relevant literature, four studies have been conducted through the application of their relevant methodologies. The results obtained have theoretical and practical implications and open up new research avenues.
As a general conclusion, it can be asserted that although new technologies emerge on the scene, it is still necessary to keep previous research and literature in mind and adapt them, if necessary, to the new requirements and trends in advertising and to up-to-date methodologies. In addition, this doctoral thesis emphasises the use of qualitative methodologies and the valuable information that it provides to researchers and marketers, above all, when cognitive aspects are implied. Another main contribution is that the doctoral thesis does not only focus on a consumer point of view in the process of communication of an advertisement, but it also takes into account advertisers and marketers when they are in the process of creating an advertisement. In such a way, this doctoral thesis regards all the main actors in the advertising process from senders to receivers.
The doctoral thesis makes several theoretical contributions to the literature. First, it intensifies the need to replicate previous studies as Eisend et al. (2016) recommended. In fact, the studies presented about mobile advertising and gender stereotypes, based on the same methodologies as previous presumptions, have obtained different results. Secondly, another theoretical contribution is that non- statistical significance does not mean non-theoretical implications as it sometimes supposes, but it also contributes to the literature review (Amrhein, Greenland, &
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McShane, 2019; Gilly, 1999). Thirdly, the process of encoding to increase the destination image and the engagement with Instagrammers is a major contribution.
Based on the three main research axes, the theoretical contributions and conclusions of this doctoral thesis are presented below:
a) The first research axis focuses on consumers’ attitudes towards mobile advertisement and provides updated insights into the field of theory of reasoned action (TRA) proposed by Fischbein and Aijzen (1975) with the three main variables implied (attitudes, intention and behaviour) which was then adapted to mobile phones by Tsang et al. (2004). After the replication of the model proposed by Tsang et al. (2004), the whole model cannot be extrapolated to the present-day context. The main theoretical contribution that there is a relation between the three main variables (attitudes, intention and behaviour) and the original TRA model is confirmed. However, attitudes toward mobile advertisement are no longer based on entertainment, informativeness, irritation and credibility. They seem to not be the only factors affecting attitudes towards mobile advertising. Apart from the theoretical contributions, the replication of the TRA model adapted to mobile phones allows for the contribution of the following theoretical conclusions:
• It seems that with smartphones and the Internet, users received advertisements from several sources. Therefore, SMSs do not provoke irritation as they previously did because the content of SMSs is more useful, and mobile phone users are more receptive to receive SMSs than before.
• Permission-based mobile marketing directly affects the attitude towards SMSs positively. Nevertheless, mobile phone users are still not aware of this requirement, even if they signed it.
• Fostering incentives to allow the reception of advertisements has a positive effect on the intention and, of course, on the behaviour. Consumers offered incentives are more willing to react positively to the information in the advertisements and are more willing to buy the product and service. # Conclusion 113
b) The second research axis analyses the new format Original Digital Video (ODV) and the encoding process of gender stereotypes. The findings of this examination of male and female role portrayals indicate that men and women are portrayed in a more egalitarian way in ODVs in terms of traditional gender stereotypes. The theoretical problem is very relevant in the present field of advertising and well known in the research literature since the 1970s (Bretl & Cantor, 1988; Debevec & Iyer, 1986; Goffman, 1979; Manstead & McCulloch, 1981; McArthur & Resko, 1975, among others). As a general theoretical contribution, there was no significant association between gender and any of the main attributes that measured gender stereotypes (mode of presentation, credibility, roles, age, argument type, reward type, product type, background, setting, and end comment) in ODV format. Through the use of traditional attributes as a traditional model but applied to a new format, the following theoretical conclusions have been extracted:
• There was no difference between genders; women central figures in ODVs seemed to have the same attributes as central figures who were men. These results differ from previous findings (e.g. Furnham et al., 2000b; Mazzella et al., 1992). In fact, media have the power to change how we think and process information (McLuhan, 1967), not only by means of information about a brand or product or service that an advertisement contains but also by the intrinsic information (encoding process) that might have an effect on society’s values such as the acceptance of a promising energy source with education values or just the representation of gender inequality.
• Even though there is no difference between genders in terms of gender stereotypes, there are still more men who are represented as the main actor than women.
• As a new format, ODV could modify gender stereotypes in a way previous formats have failed to do.
The present study is the first attempt to explore this new format (ODV), and it is also one of the first studies to use a sample created by experienced advertising
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professionals with an in-depth understanding of the current state of the art in advertising design.
c) The third research axis considers the new channel of communication in social networks, Instagram and users’ feedback in terms of likes and comments for tourist destination images. For this purpose, the Stimulus-Organism-Response (S-O-R) model of Mehrabian and Russel (1974) is adapted to Instagram and tourism destinations. The S-O-R paradigm is the sequential correlation between stimulus, organism and response: the S-O-R model is based on environmental psychology, which is a challenging field of research because of its use of subjective data. Laroche (2010) argued that the S-O-R paradigm is more useful for explaining online consumer behaviour and providing productive solutions than the technology acceptance model (Davis, 1989). In fact, the social media environment differs from traditional environments by fostering new forms of interaction between the providers of information and users. Based on the S-O-R model, the studies in chapters four and five make several theoretical contributions to the literature on Instagram in relation to the tourism industry and are one of the first attempts to unify the attributes of a destination image and its influence on Instagram user responses as measured by likes and comments. First, the use of the S-O-R model is helpful and provides a productive solution for how Instagrammers behave with regard to certain photo attributes of tourist destinations. Second, DMOs could leverage this research to improve the final response by tailoring their projected destination image to attract more potential tourists. Thirdly, through qualitative and quantitative data, the results obtained are in line with marketers’ advice to improve country engagement in social networks, on the whole by improving the encoding process by tourist destination in order to enhance engagement. Moreover, the following theoretical conclusions have been obtained:
• Instagrammers behave in a different way towards likes and comments. Comments have more engagement with attributes of the pictures. The differences between photograph likes and comments suggest that these two areas should be managed separately in order to attend to the DMO’s ultimate purpose. # Conclusion 115
• The presence of people, animals and water in a photograph has a positive impact on Instagrammers, while the high frequency repetition of a country image and a lack of authenticity regarding the destination in a photomontage have the opposite effect. The negative impact comes from the overloading, similarity and ambiguity with respect to image information that a DMO provides.
• Pictures showing tourism facilities as the main theme and those that focus on activities and special or foreign events, positively affect the success of a country and DMO.
6.2. Managerial implications
A doctoral thesis has the ultimate goal of not only contributing theoretical conclusions, but also of helping in the development of businesses and the improvement of social welfare. In addition to the theoretical implications highlighted in the previous section, in what follows, practical managerial implications are presented. These managerial implications are of particular interest to advertisers who invest in communication, advertising agencies who counsel them, media that disseminate the messages and the connected audiences that receive or forward these messages. The managerial implications are going to be addressed according to the threefold perspectives:
a) With regard to mobile advertising, consumers’ perception of SMS advertising has changed. Not only can governments take advantage of this change but also brands, which can use SMS messages to attract consumers and encourage them to make purchases. These findings clearly highlight interesting opportunities for companies to engage consumers with mobile advertising in today’s digital world. This is an interesting opportunity for small businesses which through this medium and with the consent of customers can build and strengthen relationships with them.
As the above discussion shows, continuous technological advances have provided individuals with access to simpler and faster means of communication. Businesses and marketers take full advantage of these advances to reach a larger
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group of customers in order to introduce their products and/or services and to influence their purchasing behaviour. In line with this, the development of easy-to- use smart mobile phones is recommended to create, maintain and strengthen links with customers and other stakeholders. In addition, as there still exist certain segments that are not accustomed to intensively utilise other applications on the mobile phones and are therefore difficult to reach, SMS is still a great support for different types of messages adapted to these segments.
A second recommendation addressed to advertisers in this medium is to perform actions that increase the usefulness of receiving advertisements via mobiles, such as, for example, attaching a discount for the recipient, attaching in the advertisement a suitable application (for their own or a third party's account) or making tangible or intangible gifts (for example, additional memory in a virtual device) that can be exchanged with the password obtained in the SMS, among other possible actions.
The third recommendation is related to positive emotions that the managers of advertising communications seek to develop through the mobile phone. Generating positive attitudes towards this channel and preventing negative emotions from occurring (especially the sense of being deceived through fake news and other forms of communication) should be one of the main objectives for mobile phone businesses. In fact, certain advertisements developed for mobile phones have been successful in getting the audience to appreciate the good art and decide to share it with other people such as family or friends. It is worth mentioning that mobile phone users value highly the importance of credibility, and advertisers thus should take care to fulfil their promises. In accordance with the current campaign of self-control and smartphones, it is necessary to work on truthfulness, legalities, honesty and loyalty in advertising.
b) In the field of content in a new format such as ODV and the research line of gender stereotypes in original digital advertising, the study emphasises the prominence of encoding an advertisement without gender stereotypes. There is thus still inequality in the advertisements: women are less likely to play the central figure, which may undermine their role as company leaders or the main image of a brand. Marketers, advertisers and enterprises should consider changing central figures to # Conclusion 117
create more ODVs depicting women in a main role. Such a transformation would not only help achieve gender equality but would also enhance women’s roles as leaders.
Marketers and advertisers might appear to have a more neutral point of view according to the sample, which could suggest that there is a cognitive difference between what marketers and advertisers approve of and what consumers actually receive from the advertising. It is thus worth considering whether there is an underlying pressure from brands to create stereotyped advertising because when marketing and advertising experts share their judgement criteria with the online advertising community, they tend to reward ODVs that apparently lack stereotypes. When advertising agencies win awards, it intensifies their clients’ trust (Davies & Prince, 2005), which could lead to an increase in advertising with no or fewer stereotypes and greater resistance to pressure from brands to depict stereotypical behaviour.
Moreover, if an advertisement contains gender stereotypes, it shows society how to behave and act in a particular manner. Above all, advertisers who target children and youth and have the possibility of shaping and influencing them at this earlier stage need to be aware of this position of power or influence. In fact, advertising, with its new formats, can contribute to social change and present new realities that reflect current diversity and are consistent with the Sustainable Development Goal (SDG) about gender equality, thereby contributing to a fairer and more equal society.
c) The studies of Instagram and tourism have several practical implications for the marketing practice of DMOs in terms of creating a better destination image and promoting tourist visits. The disparities between photograph likes and comments suggest that these two areas should be managed separately in order to serve the DMO’s ultimate purpose and avoid the negative impact of disruptive photographs. Several destinations in the sample repeated the same historical building or panoramic view of a place three or more times in a row from different perspectives. This results in information overload for the user. Likewise, many presented different places from the same country or city constantly, leading to ambiguity and similarity confusion among Instagrammers.
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Tourism influencers on Instagram should endeavour to provide many pictures to their communities to secure higher engagement and repeat visits in terms of comments and likes, with the aim of increasing Instagrammers’ trust in the tourism destination. Delivering the appropriate content of a picture to attract highly positive Instagrammers’ feedback, such as likes and comments, might affect the level of interest in the country and motivate Instagrammers to visit. In that way, monitoring likes and comments should provide benefits to the country’s image and should be part of the country’s strategy. In fact, a better destination image could have an impact on an international level, with the resulting possibility of receiving an award as recognition.
In addition to the tourist destinations, Instagram is currently a medium that small businesses in the tourism industry can also use in relation to, for example, restaurants or hotels. Both studies in chapters four and five can provide a framework for them when posting their images on Instagram. General recommendation are: to be consistent with the image they would like to provide and share with viewers by optimising the profile, to define the audience and what the audience could do for them by monitoring the performance, to share updated content for the business on a regular basis, to create a thrilling narrative of the place near to the reality in order to avoid a disappointed tourist experience, to interact with relevant Instagrammers, to use the appropriate hashtags, and to answer all the comments and mentions.
6.3. Limitations and future research lines
A general limitation is the use of three samples: one being of Spanish users and limited to the original sample in terms of age, gender and education; the second one being original digital videos that have won awards from professional marketers at the Internet Advertising Competition; and the last one being images from the Beautiful Destinations Instagram account, limited to one year and to one account. However, the variety of the samples contributes to the enrichment of the whole doctoral thesis as it sees the advertising industry from three different perspectives. # Conclusion 119
In general, a line for future research is to follow new technologies in advertising as every year new innovations are incorporated which transform the medium that supports advertisements. Moreover, the three perspectives’ axes provide several future research lines.
Whilst SMS advertisements must be sent with the users’ consent under EU directives, other advertisements delivered to mobile phones via the Internet are subject to the same regulations as computer-based advertising. This opens a new avenue of research into consumers’ awareness of their need to provide explicit consent to be sent SMS advertisements. Free mobile phone apps follow a similar model, whereby consumers agree to receive advertisements. Although several studies have examined consumer engagement with brand apps and the positive persuasive impact on consumers (Bellman et al., 2011; Kim et al., 2013), researchers should take into account that mobile phone users download thousands of apps each day that are not directly connected to brands and are mostly free. These apps expose consumers to multiple advertisements, but they might not be aware of the permission-based implication. As consumers receive advertisements from various sources, such as apps, or through the Internet, when browsing on their mobile phones, SMS advertising is considered an integral part of the e-governance model (Singh & Sahu, 2008). Therefore, future research might also explore the changing cognitive values of SMS messages. Lastly, future research might also aim to identify new variables that today affect SMS advertising. This points to a new avenue of research in relation to our findings on credibility and its negative effect on attitude.
As per the research line about gender stereotypes in original digital advertising, it would be worth studying whether the new advertising format also creates unrealistic expectations for men (Moss-Racusin & Good, 2015), in which case, men and women might feel obliged to take on unpredictable roles even if they do not wish to. It would also be interesting to study whether the information in advertisements featuring changing roles could impact the real attitudes of men and women. There seems to be a trend towards creating videos in a more neutral environment. This neutral approach could breathe new life into the research, allowing researchers to create new variables and measurements. The present findings indicate that the number of animated ODVs is increasing each day. Given
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the lack of research on that topic, trying to identify the gender stereotypes in animated videos could also be an area worth looking into. Apart from the research focused on women, Barry (2014) found that men are being depicted in non- stereotypical roles not previously assigned to men. The changing role portrayal for men is thus another avenue of research to be explored. As a final point, ODVA research should continue to be pursued, using a more representative sample, increasing the number of videos, and taking into account what consumers actually receive or are forced to watch. However, brands will be required to identify which format they are using to help researchers take a specific format or platform into account. Within this line of research, it would be quite helpful to determine whether specific features of ODV, such as the duration or where the video is placed, could affect the perception of gender stereotypes. It would likewise be interesting to explore whether an opt-in or opt-out model, in line with a permission-based concept of advertising, would encourage consumers to stop or continue watching a video and whether the videos served under either option are more likely to be related to gender stereotypes.
Finally, as per the studies of Instagram and tourism, De Bruyn and Lilien (2008) demonstrate that certain characteristics of online reviews written by other users can decrease or increase tourist visits and develop consumers’ expectations regarding a tourism destination. As a new line of research, emoticons left in comments have been found to be an important source of information for connecting the emotions triggered by a specific destination and could be examined in greater depth in the future. Additionally, similar research could be undertaken using a mixed approach, wherein the qualitative as well as the quantitative results are explored and analysed to derive suitable study outcomes and draw conclusions. In that case, future research could focus on the influence of the individual characteristics of the Instagrammer as a user. As a future research, how the Instagrammer’ profile (age, gender and demographic) with respect to the creation of likes and comments and how the country-specific cultural values of the Instagrammer could affect interaction with a specific country could be explored. Further analysis in specific country cases or cities could provide tips, above all for these countries for which engagement is low in terms of likes and comments. To conclude, applying the S-O-R model to other social media or to Instagram and another industry could also be an avenue of # Conclusion 121
research worth pursuing. Another line of research might be to create a meaningful narrative for the tourism market by deploying images with appropriate information about the destination countries.
To conclude this doctoral thesis, it should be noted that this research work has been able to empirically contrast the change that is taking place in advertising. Marketing is simultaneously a system of thought and a system of actions, whose ultimate goal and concurrent essential purpose is to help build a set of joint frameworks for its individual evolution in the fields of social and organisational interests. In fact, communication is key in this process. The basis of the epistemological development of any field of knowledge and, in particular, of marketing lies in the theoretical-empirical research in which it is intended to continue. It is not possible to consider this discipline from a single point of view and accept the contradictions that occur between studies, which are surely only partial aspects of a common fund that escapes the methodological approaches used. By seeing and accepting the various results, researchers may, perhaps, obtain a more enriching vision, forgetting the apparent logic of unidirectional constructions. Finally, this doctoral thesis responds to a research task that raises a critical position not previously explored and which is of interest to all, including researchers of business activities concerned with improving the management and/or effectiveness of organisations, and media managers who can use it as an instrument for introspective reflection in order to help them build a company capable of fulfilling advertising’s social responsibilities.
# References
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