Abstract

Keywords

1. Introduction

2. Literature review

3. Research method

4. Results and discussion

5. Conclusions

Impact statement

CRediT authorship contribution statement

Declarations of competing interest

Acknowledgements

References

Vitae

Abstract

Cultural tourism has received increasing attention. Tourists play a crucial role in constructing rewarding intercultural experiences and cultural understanding. The current study explored the concept of tourists’ cultural competence from a cosmopolitan perspective and proposed a framework for understanding the mechanism of tourists’ interactions with cultural experience environment. A total of 33 participants were interviewed and data were analyzed using grounded theory. Cultural openness, understanding, consciousness, and participation were identified as elements of tourists’ cultural competence, which can facilitate positive cultural behavior. This research contributes to the literature of cosmopolitanism and tourists’ cultural behavior. Managerial implications for tourist management and education are also discussed.

1. Introduction

The tourism industry is one of the most rapidly growing industries globally (Mbaiwa & Sakuze, 2009). However, its sustainability is at risk from multiple shocks that threaten resident livelihoods and cultural values (Ponting & O’Brien, 2014; Zhang, Fan, Tse, & King, 2017). Tourism, as a social phenomenon, is a series of processes, activities, and results derived from the interactions between tourists, operators, governments, local communities, and the surrounding environment (Goeldner & Ritchie, 2009). It causes socio-cultural changes to host societies, including changes in value systems, traditional lifestyles, relationships, individual behaviors, and community structures (Ratz, 2002). Therefore, the relationship between tourism factors and local development is a crucial social and cultural concern (Cohen, 1972). Regarding the threat that the Taiwanese aboriginal “Harvest Festival” might be an event only for tourist entertainment, Taiwanese aboriginal singer Chang Chen-Yue had publicly expressed his concerns: “Harvest Festival is an extremely important part of cultural heritage, not just a tourist activity. The government does not emphasize ‘respecting the culture; ’ it only cares about tourism. Sorry that we dance for our ancestors and elders; we do not dance for tourists, so we only welcome tourists with a heart of respect!” (CNA NEWS, 2014). This reflects the commercialization of Harvest Festival. The aboriginal “sacrificial” meaning and “taboo” considerations have been ignored, leaving only the image of singing, dancing, and drinking. Another example is Bali, an Indonesian island that has gained massive popularity as an international destination. This island is also known as the “island of 1000 temples” and religion plays an important role in the local life. In 2016, a tourist wearing bikini performed a downward dog yoga position for pictures in front of a Balinese temple. In 2017, a tourist took a picture of herself posing seductively in front of Bali’s Mount Agung volcano. In 2018, a tourist sat on the Linggih Padmasana shrine at Puhur Lutur Batukaru temple and provoked the criticism on social media. Due to the increasing amount of disrespectful behaviors from the tourists, in 2018, Bali authorities have pledged to make new rules for visiting temples and to stop disrespectful behaviors of tourists. “The temples need to be reserved since they are the spirits of Bali’s cultures and customs”; “We are too open with tourists, so too many come. The quality of tourists is now different from before” said by the deputy governor of Bali (Coconuts Bali, 2018; MailOnline, 2018). These news indicate that interactions among different societies naturally occur, thus increasing the contacts among different cultures (Fennell, 2006; Henderson, 2003). Tourists lacking cultural competence could offend local culture and cause negative cultural impacts.

Highlights

Abstract

Keywords

1. Introduction

2. Literature review

2.1. Shopping tourism and luxury shopping

2.2. Shopping venues and destinations – attributes and satisfaction

2.3. Chinese outbound tourists’ luxury shopping behavior overseas

3. Methods

3.1. Item generation and instrument design

3.2. Exploratory factor analysis

3.3. Scale validation – confirmatory factor analysis

4. Conclusion, implications, and future studies

Authors contribution

Declaration of competing interest

References

Vitae

Considering rapid economic development, rising household income, and the emergence of middle-class consumers in mainland China, a growing number of Chinese consumers are traveling to other countries and regions for tourism and shopping. Studies have shown that Chinese customers often express intentions to engage in luxury shopping, driven by factors such as brand consciousness, social comparison, and innovative fashion (Zhang & Kim, 2013). Although the trend of Chinese outbound tourism aimed at luxury shopping continues to expand, little is known about Chinese luxury shoppers’ behavior when abroad. This study attempts to investigate Chinese luxury shoppers’ preferences in shopping destinations, relevant attributes of preferred luxury shopping destinations, and potential underlying dimensions among identified attributes. Results revealed an attribute list comprising 3 dimensions (human touch, prestige, and good value) and 17 items. These findings reflect Chinese luxury shoppers’ concerns and preferences about shopping overseas. Implications and future research directions are also discussed.

1. Introduction

Mainland China’s swift economic development, ballooning household income, and emergence of middle-class consumers has led more Chinese to visit various countries and regions for tourism. According to the Ministry of Culture and Tourism, PRC (2019), 149.7 million Chinese tourists traveled overseas in 2018, a 14.7% increase over 2017. Along with the growing popularity of outbound tourism, Chinese tourists’ spending power in overseas destinations is notably disproportionate to their income in many cases (Dai, Jiang, Yang, & Ma, 2017). According to Dai et al. (2017), Chinese tourists spend most of their money on shopping; in some destinations, such spending can constitute as much as 70.4% of tourists’ total trip expenditure. The high proportion of spending allocated to shopping overseas may be due to “daigou” (buying items for third parties), gift-giving, lower taxes, and brands’ pricing policies.

Abstract

Keywords

1. Introduction

2. Background

3. Data and empirical approach

3.1. Tourism

3.2. Worldwide governance indicators

3.3. Other explanatory variables

3.4. Econometric approach

4. Results

4.1. The synthetic index:

4.2. The six dimensions of governance

5. Robustness checks

6. Conclusion

Declaration of Competing Interest

Appendix A. Supplementary data

References

Vitae

Abstract

No effort has been made to connect good governance and the performance of the tourism industry at the country level. We take a first step to provide empirical evidence of this positive effect. Based on a data set of 100 countries between 2002 and 2012, the impact of the Worldwide Governance Indicators (WGI) on the tourism industry is analyzed, controlling also for demographic, economic and environmental factors. Using a dynamic panel data approach, we highlight the role played by good governance in explaining differences in countries’ tourism performances, measured here as inbound tourism expenditures per inhabitant. We also observe that the impact of WGI is even higher among low-openness countries.

1. Introduction

An important strand of research focuses on the role of institutions and economic freedom in explaining observed differences in economic development and performance. Essentially, the economic literature has moved from inputs and technological perspectives to a broader understanding of the prerequisites for growth (Gwartney et al., 1999). Good governance is needed to assure (Dixit, 2009) property right security, contract enforcement, and collective action. As discussed by Khan (2007), the positive impact of good governance arises mainly from two sources. First, it reduces transaction costs, allowing markets to work more efficiently. Second, good governance allows markets to “overcome entrenched market failures in allocating assets, acquiring productivity-enhancing technologies and maintaining political stability in contexts of rapid social transformation”. Recently, using a US state-level/city-level cross-sectional dataset, Detotto and Mccanon (2017) show that good institutions positively affect the development of efficient publicly provided services. Thus, it seems that good governance impacts both market and non-market activities.

Ensuring that tourists behave in a civilized manner is critical to maintaining the competitiveness of tourismdestinations. Hence, the purpose of this study is to understand which factors lead tourists to behave in a civilizedmanner at tourism destinations in order to increase the sustainability of coastal tourism. This study proposes atheoretical framework for civilized tourism behavioral intentions by aggregating two social science theories: thenorm activation model (NAM) and the theory of planned behavior (TPB). The results indicate that attitudes,subjective norms, and personal norms play a significant role in driving civilized behavioral intentions, withpersonal norms mediating subjective norms and intentions. Furthermore, the study suggests two effective pathsthat could likely drive tourists toward positive and civilized behaviors at coastal destinations by utilizing fuzzyset qualitative comparative analysis (fsQCA). Theoretical and practical implications are also discussed.

Introduction

Coastal tourism has increasingly become a focus for the growth ofChina’s marine economic development under the Maritime PowerStrategy and the Maritime Silk Road National Campaign of 2013. Theadded value of the coastal tourism industry in China reached 1607.8billion Chinese Yuan in 2018, which accounted for 47.8% of the addedvalue of the major marine industries in China (Ministry of NaturalResources of the People’s Republic of China, 2018). It is worth notingthat coastal areas are key destinations for tourists and recreational activitiestypically related to coastal tourism, such as surfing, sunbathing,seaside camping and so on. However, even though coastal areas such asbeaches are vital resources, it is not uncommon to see tourists throwgarbage into the sea, spit on the beach, inscribe or scrawl graffiti ontoseaside walks, and other similar behaviors (Fenghuang, 2013). Thesebehaviors often lead to beach pollution and ruin tourism facilities,especially during tourist seasons (Liu & Liu, 2018).Indeed, coastal tourism is currently facing huge challenges regardinghow to balance environmental problems and tourist activities(Papageorgiou, 2016). Specifically, tourists’ uncivilized behavior is anincreasing concern for China. In fact, the dramatic increase in Chineseoutbound tourists has generated some negative media reports aboutuncivilized behavior (Zhang, Pearce, & Chen, 2019). For example,Youth (2018) pointed out several types of uncivilized tourist behaviorssuch as littering, unhygienic toilet practices, smoking in public areas,fighting for seats, cutting in queues, talking loudly, taking photos inreligious places without approval, etc. Qiu (2016) indicated that theseuncivilized behaviors could not only hurt the sustainable developmentof tourism destinations but also damage the image of Chinese tourists.In fact, it could be argued that tourism is rooted in social and culturalfoundations and also promotes social civilization and progress (Yan,1989).

International tourism has since been recognized as a conduit for poverty alleviation and infrastructure development. Despite the sector’s importance in Africa in terms of its contribution to economic growth, foreign exchange earnings, and employment, very few studies have attempted to uncover its potential. In the case of Tanzania, where the sector’s importance rank high in the country’s development strategy, only two studies exist, and none on the factors that influence international tourism demand. Therefore, this study makes an important contribution to tourism economics literature in Africa by investigating the relevant determinants of international tourism demand using panel data for Tanzania’s top fifteen tourists’ source countries during the 2000–2016 period. Results, based on various panel data estimation techniques, indicate that income of tourists and infrastructure development in Tanzania, are the two main determinants of international tourism demand for Tanzania. These findings hold across model and sample specifications. From a policy perspective, the government of Tanzania and stakeholders should work towards making Tanzania tourism products more competitive by developing/improving infrastructure in the country. Moreover, there should be a policy that encourages developing tourism packages that fit the demands of tourists from relatively high income countries, and also make conscious efforts to market these products in the target countries. Lowering the cost of living and improving the exchange rate are also some of the areas that the government could work on to help grow the tourism industry.

Introduction

Tanzania is endowed with rich and diverse natural resources (particularly; wildlife, forests, mountains and the rift valley) that form the mainstay of the country’s tourism industry. Almost a third of Tanzania’s land area is under government protection; and is reserved for the purpose of either national park, conservation area or game reserve. In total, Tanzania has 16 national and 2 marine parks, 28 game (including marine) reserves, 44 game controlled areas, multiple forest reserves, and one conservation area; which host the world’s renowned biodiversity, wildlife, and unique ecosystems (see Exhibit 1). Thus, it is not surprising that the tourism sector is one of Tanzania’s three growth sectors, and the second largest foreign exchange earner after agriculture. For example, in 2016 alone, it generated US$2.1 billion in revenues (4.7 percent of total GDP), employed approximately 3.9 percent of the country’s total labor force (equivalent to 470,500 jobs); and contributed about 21.4 and 8.7 percent of total export earnings (US $2,446.6 million) and investment (US$ 1.2 billion), respectively (WTTC – Tanzania, 2017). These economic benefits are amplified when linkages with allied sectors such as hospitality, manufacture of arts and crafts, and, transportation and logistics are taken into consideration. For instance, the total contribution of the sector to Tanzania’s GDP and employment in percentage terms in 2016, more than tripled that of direct contribution to roughly 13.5 and 11.6 percent, respectively (WTTC – Tanzania, 2017). Despite the aforementioned attractions and the increasing importance of tourism in the Tanzanian economy, tourism demand (both domestic and international) for Tanzania lags that of other African nations like Egypt, Morocco, Tunisia, and South Africa; and the sector’s total contribution to GDP growth also lags that of Uganda, Botswana, Senegal, Namibia, Kenya, and Republic of Congo (Naudé and Saayman, 2005; WTTC – Tanzania, 2017). Moreover, the Tanzanian tourism products are becoming increasingly noncompetitive in comparison to North African countries, South Africa, Botswana and Kenya.

Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.

Introduction

All over the world, the tourism industry contributes significantly to economic growth (Gunter & Onder, 2015; Song, Li, Witt, & Athanasopoulos, 2011). According to the China National Tourism Administration, in 2016 the tourism income of China reached 4.69 trillion RMB, increasing by 13.6% compared to the previous year, and accounted for 6.3% of China’s GDP. Thus, forecasting tourist volume is becoming increasingly important for predicting future economic development. Tourism demand forecasting may provide basic information for subsequent planning and policy making (Chu, 2008; Witt & Song, 2002). Methods used in tourism modeling and forecasting fall into four groups: time series models, econometrics models, artificial intelligence techniques and qualitative methods (Goh & Law, 2011; Song & Li, 2008). In addition to simple tourist data announced by the State Statistics Bureau, Internet search queries, which reflect the behavior and intentions of tourists, have increasingly been used in tourism forecasting models (Croce, 2017; Goodwin, 2008). However, the search index has created big opportunities in the modeling process of tourism forecasting (Li, Pan, Raw & Huang, 2017). Internet search data has been applied to many aspects, such as hotel registrations (Pan & Yang, 2017; Rivera, 2016), tourist numbers (Bangwayo-Skeete & Skeete, 2015; Yang, Pan, Evans, & Lv, 2015), economic indicators (Choi & Varian, 2012), unemployment rates (Askitas & Zimmermann, 2009), private consumption (Vosen & Schmidt, 2011), and stock returns (Zhu & Bao, 2014). When introducing the Baidu Index or Google Index into forecasting models, keywords and the composition of indexes must be selected carefully. Keywords can be selected according to the correlation coefficient, the tendency chart or the crowd-squared method (Brynjolfsson, Geva, & Reichman, 2016). Additionally, the composition of indexes can be achieved by the HE-TDC method (Peng, Liu, Wang, & Gu, 2017) or the principal component analysis (PCA). Obviously, efforts should be made to avoid problems related to multi-collinearity and over-fitting to the greatest extent possible.

چکیده

مطالعات پیشین ثابت کرده است که داده های برخط از قبیل جستار های موتور جستجو، منبع جدیدی از داده است که می تواند برای پیش بینی تقاضاهای گردشگری مورد استفاده قرار گیرد. در این مطالعه، ما چهارچوب پیش نگریی را پیشنهاد می کنیم که از آموزش ماشین و شاخص های جستجوی اینترنتی به منظور پیش بینی ورود توریست به مقصدهای محبوب و گردشگری در چین استفاده میکند و عملکرد پیش نگریش را با نتایج جستجوی تولید شده با google و baidu به ترتیب مقایسه کرده است. این مطالعه علیت گرانجر Granger و رابطه بهم پیوستگی بین شاخص های جستجوی اینترنتی و ورودی های گردشگری شهر پکن را بازبینی و بررسی میکند. نتایج آزمایشی ما پیشنهاد می کند که در مقایسه با مدلهای مبنایی ، ماشین یادگیری نهایی هسته ای KELM پیشنهاد شده، دنباله های مقادیر توریستی را با شاخص های Baidu و Google ادغام میکند و در نتیجه می توان عملکرد پیش نگری را از هر دو دیدگاه دقت پیش نگری و قدرت آنالیز به طرز چشمگیری اصلاح نمود.

مقدمه

در سراسر جهان صنعت گردشگری به رشد اقتصادی کمک می کند، براساس آمار سازمان ملی گردشگری چین، درآمد حاصل از گردشگری کشور چین در سال 2016 به 4.69 ترلیون RBM رسید. که رشدی 13.6 درصدی در مقایسه با سال قبل را نشان می داد و به طور کلی 6.3 درصد از تولید ناخالص داخلی چین را به خود اختصاص داد. بنابراین اهمیت پیش بینی حجم گردشگری برای پیش بینی توسعه اقتصادی آینده روز به روز در حال افزایش است. پیش بینی تقاضای گردشگری می تواند اطلاعات اولیه ای برای برنامه ریزی و سیاست گذاری های بعدی فراهم کند. روش های مورد استفاده در مدل سازی و پیش بینی گردشگری به چهار گروه تقسیم می شوند: مدل های سری زمانی، مدل های اقتصادسنجی، تکنیک های هوش مصنوعی و متد های کیفی. علاوه بر داده های آماری اولیه ای که توسط سازمان ها و وزرات امور خارجه کشورها منتشر می شود، داده های حاصل از جست و جوهای اینترنتی، که رفتار و گرایش های مختلف توریست ها و گردشگران را نشان می دهند، به میزان چشمگیری مدت هاست در مدل های پیش بینی و آنالیز رفتار و گرایشات گردشگران مورد استفاده قرار می گیرد. با این حال، شاخص جستجو فرصت های زیادی را در فرآیند مدل سازی پیش بینی گردشگری ایجاد کرده است. داده های مربوط به جستجوهای اینترنتی در جوانب بسیاری مورد استفاده قرار گرفته است. از جمله آن می توان: رزرو هتل، شماره های توریستی، شاخص های اقتصادی، نرخ بیکاری، مصرف شخصی و خصوصی و بازده سهام را نام برد. هنگام معرفی شاخص های آماری دولت ها و یا شاخص های جست و جوی گوگل، کلمات کلیدی و ترکیب شاخص ها باید به دقت انجام شود. کلمات کلیدی می توانند براساس ضرایب همبستگی، نمودارهای تمایل و یا متدهای مربع جمعیت انتخاب شوند. علاوه بر آن برای ترکیب شاخص ها می توان از متدهای HE-TDC و یا آنالیز مولفه های اصلی(PCA) استفاده کرد. بدیهی است که تا جای ممکن باید از بروز مشكلات مربوط به همبستگی چندتایی و هم پوشانی بیش از حد جلوگیری کرد.

1- Introduction

2- Literature review

3- Data and variables

4- Methodology

5- Results

6- Discussion

7- Conclusions

References

Abstract

This article analyses the determinants of attendees’ tourism spending at professional basketball matches during the 2012/2013 season. For this purpose, it applies a linear quantile regression and considers the effect of specific sports event variables which have rarely been assessed in this type of study. Empirical results confirm that the determinants of expenditure have a different influence depending on the spending level. Individual spending is principally influenced by the origin of the attendees as well as by several other sports factors such as the time the match takes place, the admission price, or the sporting level of the rival team. The study establishes two levels of spending to identify the different behaviors that correspond to each of the factors under study. The findings could provide a useful input into tourism strategies related to the hosting of sport events.

Introduction

The analysis of the individual spending behavior of visitors to specific destinations is increasingly becoming a topic of interest, according to Brida, Disegna, and Osti (2013), Craggs and Schofield (2009), Dolnicar et al. (2008), Hung, Shang, and Wang (2012) and Nicolau and Más (2005). Tourism demand is mainly analyzed using a macro approach, in which the unit of analysis is a set of aggregated data. These kinds of studies use economic indicators such as the influence of the tourism sector on GDP, GVA or employment atthe national or regional level. Individual spending behavior and the sociodemographic and economic factors that affect spending patterns (i.e., the micro approach) have been studied to a lesser extent (Brida et al., 2013; Fredman, 2008; Laesser & Crouch, 2006). Reviews of the previous literature on tourism demand including the works of Lim (1997, 2006) and Crouch (1994) reflect that there is a lack of studies employing micro-economic analysis. Lim (2006) explains that only 8 out ofthe 124 articles studied employed this type of analysis. Wang and Davidson (2010) indicate that the first studies concerning demand at a micro level were carried out by Mak, Moncur, and Yonamine (1977). Subsequently, this type of work aroused the interest of researchers once again in the 1990s. As of then, interest increased rapidly, especially at the turn of the century. In the case of sports tourism, the same situation is observed. Studies have neglected the specific aspects of products and services and have focused on economic impact and the use of aggregated data. It therefore now seems reasonable to also focus on understanding the spending behavior of sports tourists who come as spectators (Brida, Schubert, Osti, & Barquet 2011; Cannon & Ford, 2002; Yusof, Shah, & Geok, 2012) and participants (Dixon, Backman, Backman, & Norman, 2012; Downward, Lumsdon, & Weston, 2009; Gibson, 2005).

Food is considered to be one of the significant aspects that influence visitors’ satisfaction with a destination, as it allows for a more fulfilling sensory experience by utilizing all of a tourist’s senses (Hall, 2003). Expenditure on food is a major item in the tourist budget accounting for one-third of tourism expenditures and a primary source of earning tourism revenue (Meler & Cerovic, 2003). Local cuisines provide tourists with genuine cultural experiences of the host destination and serve as a core manifestation of its intangible heritage (Okumus, Okumus, & McKercher, 2007), thus providing marketable images of the destination (Henderson, 2009; Quan & Wang, 2004). Historically, services and facilities frequented by Muslim tourists are different from those of conventional tourists with the availability of Halal cuisine being the most pertinent and sought-after choice for them (Dinar standard, 2012). This has led to an increased interest in Halal tourism, an emerging trend in the tourism and hospitality domain, which primarily focuses on the development and provision of different tourism products and services to fulfill the distinctive requirements of Muslim tourists in conformance with their religious teachings (Battour & Ismail, 2016; El-Gohary, 2016). The global Muslim population is overall large and growing. It was estimated to be 1.8 billion in 2015, making up approximately 24.1% of the world’s population, a proportion that is projected to increase to 31.1% with 3 billion inhabitants in the year 2060 (Pew Research Center, 2017). The Muslim travel market is emerging as the fastest growing segment in the global travel industry and generates impressive statistics. In the year 2015, international Muslim travelers were estimated to be 117 million, a figure that is projected to ascend to 168 million by the year 2020, with tourism receipts in excess of USD 200 billion (Global Muslim Travel Index, 2016). To put this into perspective, in the year 2013, the largest source destinations for outbound tourism in the world, i.e., China and the USA, generated tourism receipts of USD 131.3 million and USD 121.3 million, respectively, while the revenues spawned by Muslim travelers amounted to USD 140 million alone, representing 11.6% of global market share (Mayock, 2015). The Muslim travel market is relatively young, and it is becoming increasingly affluent (Reuters & Standard, 2013). The commercial aspects of the Muslim travel market, coupled with Muslim tourists’ adherence to the complete code of conduct levied by their religion, demand appreciation of the distinctive characteristics and preferences of Muslim tourists by tourism bureaus and destination marketing organizations (Henderson, 2010). Overall, there is notably limited research on Muslim tourism (Kim, Im, & King, 2015). The existing research on Muslim tourists is predominantly atheoretical and descriptive in nature (Oktadiana, Pierce & Chon, 2016), articulating the knowledge related to Islamic perspectives on tourism (El-Gohary, 2016) and deliberating the features of Muslim tourist behavior that comply with the Islamic teachings (Ryan, 2016).

Due to the nature of their services, hotels use a significant amount of energy, water, and non-reusable products that may cause potential environmental damage (Han and Yoon, 2015). The practice of corporate social responsibility (CSR) for these hospitality providers is important and has received increasing attention among scholars (e.g., Bolton and Mattila, 2015; Kim et al., 2013; Kucukusta et al., 2013; Lee, Seo et al., 2013; Lee, Kim et al., 2014; Martínez and Rodríguez del Bosque, 2013; Nyahunzvi, 2013; Park and Levy, 2014). Much of this research has focused on the relationship between CSR activities and financial outcomes (e.g., Inoue and Lee, 2011; Kang et al., 2012) or the impact of CSR on customer responses and behaviors toward a company (e.g., Grappi et al., 2013; Kim et al., 2013; Liu et al., 2014; Pérez and Rodriguez del Bosque, 2015; Romani et al., 2013; Xu, 2014; Zhang, 2014). The primary focus of these studies has been on firm development, with very little attention to how CSR activities might elicit reciprocal socially responsible responses from customers. Applying the Stimulus-Organism-Response model to a hospitality lodging context, the current study proposes that perceived CSR (Stimuli) will trigger customers’ internal states (Organism). In this study, we examined the internal states of positive and negative consumption emotions (i.e., emotional responses prompted by consumption experiences) and customer-company identification (i.e., a customer’s perceived connection with an organization). The resulting approach or avoidance consequence investigated is green consumer behavior (Response).

مقاله سال 2016

Dawkins (1976) defines a meme as a unit of cultural transmission that includes a piece of information stored in memory. A meme represents perceptions related to an image, brand, and all other real or fictional entities (Henderson, Iacobucci, & Calder, 1998; Krishnan, 1996). The Theory of Spreading Activation, as a foundational theoretical model of Memetics, postulates that memes link to each other in a person’s memory through semantic similarity lines that demonstrate the association between pairs of memes (Collins & Loftus, 1975; Quillian, 1967). A meme map is a generic network comprising of memes and the similarity lines between these memes. Foundational articles in the marketing field contribute to the evolution of Memetics knowledge and literature. For example, Krishnan (1996) examines the characteristics of brand associations following Keller’s (1993) consumer-based brand equity conceptualization. In marketing, the term “brand association” refers to a meme map. Similarly, Keller (1993, p. 3) defines an image as “perceptions about a brand as reflected by the brand associations held in consumer memory.” Even though image and brand of a destination are two different constructs, the brand’s existence depends on the destination’s image formation (Cai, 2002). Image formation is the construction of a destination’s mental representation influenced by the information cues transferred by image information agents and personal factors (Alhemoud & Armstrong, 1996; Gunn, 1972). Foundational travel decision-making models explicitly state that travelers’ perceived images of destinations play a signifi- cant role in their travel decisions, such as making the choice to return to a destination (e.g., Baloglu & McCleary, 1999; Um & Crompton, 1990; Woodside & Lysonski, 1989). At a theoretical level, parallel literature on the subject of destination image and Memetics point to a unifying postulation that destination images and meme maps are highly related mental constructs. Further, meme maps are antecedents of images whereas images are the byproducts of meme maps. Thus, the generation of travelers’ meme maps regarding a destination can thoroughly reflect their destination images for a specific destination. A review of the relevant marketing literature suggests highly developed and multi-dimensional meme maps represent well-known brands including favorite tourism destinations (e.g., Low & Lamb, 2000).

Antalya, as the host city of EXPO2016, is the fourth most visited tourism destination in the world with around 300 sunny days in a year (EXPO, 2016). Antalya, known to the visitors as heaven on earth and the Turkish Riviera, is a mature and successful tourism destination. German and Russian travelers represent the top two source markets for Antalya (Turkish Ministry of Culture & Tourism, 2016), despite recent political events (e.g., violent tension between Turkey and Russia, and political instability) temporarily curtailing tourism activity.

The current study’s purpose is twofold: (1) to identify the overall Destination Image Meme Maps (DIMMs) of Antalya, Turkey, and the tourists’ favorite tourism destination, and (2) to identify and evaluate the similarities and differences in the structures of the previously generated two categories of DIMMs. Both the Spreading Activation and the Symbolic Interaction Theories (Blumer, 1969; Quillian, 1967) provide the basis for the study. Although Memetics has been around for over two decades, researchers still consider Memetics a new paradigm in consumer research. To date, methodological challenges involving the conceptualization and operationalization of constructs limit Memetic’s use. The present study is likely the first in tourism and advances the knowledge and application of Memetics. This Memetics study’s methodology expands the contents of the toolbox available to tourism researchers by shedding a better light on decision-making processes and consumer behavior. This new research paradigm’s results will influence new marketing communication and positioning strategies that can activate the most influential and common brand memes stored within individuals’ minds as network of meme-maps.

مقاله سال 2016

In response to the rapidly increasing tourism demand and to attract backpacking tourists, many airline companies have built a new strategy using low-cost airlines to increase their market share (Francis et al., 2004) and improve the service quality to fit tourist’s expectations (Nagar, 2013; Wittman, 2014). The critical attributes for low-cost airlines to create a market competitive advantage are not only dependent on operating effectively but also on market reputation and marketing strategy (Akamavi et al., 2015). By developing tourists’ loyalty, low-cost airlines can significantly improve service reliability, service quality, and brand reputation to enhance trust and satisfaction (Akamavi et al., 2015; O’Connell and Williams, 2005). To manage these complexities and the competitive global environment, airlines have developed and perfected dynamic capacities to adjust their strategies in response to dynamic environments and changing demand in new generations (Bilotkach et al., 2015; Morandi et al., 2015). Thus, there is a growing need to examine the extent to which service dimensions (e.g., service quality) are actually associated with an understanding of tourists’ behaviors and feelings, and why these dimensions might also increase in that regard (Hutchinson et al., 2009).

Recently, some tourism and hospitality scholars have argued that service quality can positively or negatively impact a tourist’s consequential consumption behavior (Ye et al., 2014). For example, Ye et al. (2014) found that individual perceptions of service or products provide a good opportunity for researchers to investigate the relationship between the value evaluation of a product or service, recommendations and satisfaction. It would seem that airlines’ service value and corporate image being consistent with passengers’ needs and expectations may influence their behavioral intentions, such as their willingness to recommend to others or repurchase (Park et al., 2004). To deliver better service to passengers, airlines need to understand the context of passengers’ service demands and expectations (Aksoy et al., 2003). This is highly challenging for low-cost airlines because service quality is significantly connected with tourists’ behavioral intentions, such as satisfaction and feedback (Saha and Theingi. 2009). However, few scholars have investigated whether and how service quality can influence tourists’ expectations from a low-cost airline and the consequential behaviors, such as wordof-mouth recommendation, corporate reputation and purchase * Corresponding author. intention, of individual tourists.

The majority of past research has been focused on creating a conceptual model for service quality (Parasuraman et al., 1985) or has focused on identifying critical attributes for SERVQUAL dimensions (Carman, 1990; Parasuraman et al., 1991). However, researchers have indicated that service quality may be the key influence of perception, feeling, and the formation of consumers’ purchase intentions (e.g., Taylor and Baker, 1994). These perceptions and feelings, in turn, influence the evaluation of service providers’ reputations and the customer’s word-of-mouth recommendations about the service providers. As such, corporate reputation or WOM may constitute a key mediating mechanism between service quality and consumer behavior. This study develops and empirically examines an integrated model that uniquely depicts low-cost airline tourists’ expectation as a central mechanism through which service quality is related to important marketing strategy. This model looks at word-of-mouth recommendations and corporate reputation as various outcomes of service quality which simultaneously promote tourists’ purchase intentions. In addition, this study identifies a potentially important attribute that reflects customers’ affective characteristic, empathy, and explores its role in the service process and experience. Defined as a customer’s “specific expectations/requests regarding service needs with the performance after use of that service” (Dedeoglu and Demirer, 2015: 132), empathy is predicted to increase purchase intention by helping a customer to detect, experience, feel, understand, and regulate pleasant interactions stemming from service quality.