Viral marketing: influencer marketing pivots in tourism – a case study of meme influencer instigated travel interest surge

Zhang, Tenghao; Huang, Xinli

doi:10.1080/13683500.2021.1910214

Cited by 45 publications

(23 citation statements)

References 19 publications

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“…Mann-Kendall (MK) [23] is a non-parametric test, which is widely used to detect monotonic trends in a time-series. Its robustness against censored and non-Gaussian distributed data o time series with missing or noisy observations [5] that are frequently encountered in a term occurrence time series makes MK an almost standard trend test method in NLP [1,[6][7][8][9][10][11][12]. A brief description of the MK method is provided below.…”

Section: Mann-kendall Trend Test With Sen's Slope Estimatormentioning

confidence: 99%

“…Due to their reduced sensitivities to outliers [2], the lack of assumptions concerning the data sample distribution [3] or homoscedasticity [4], nonparametric trend tests tend to be favored by researchers over parametric methods. In particular, the Mann-Kendall (MK) test statistic being a robust trend indicator when dealing with censored data, arbitrary non-Gaussian data distributions or time series with missing observations [5] have become almost standard methods for NLP applications [1,[6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

2022

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Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.

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Section: Mann-kendall Trend Test With Sen's Slope Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

2022

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“…It may be because of the influencer"s attractiveness, trustworthiness, or expertise in a certain field (Guerreiro et al, 2019;Jaya et al, 2020). According to the number of followers on different social media platforms, Globally, Brian Kelly has ranked the first position as top travel influencers in 2021, then Kate McCulley as a solo female traveler and Kiersten Rich as a top travel influencer according to Forbes (Zhang & Huang, 2022). Locally, Ali Ghozlan has ranked first rank as an Egyptian travel influencer and has 4 million followers, followed by Passant Nur El-Din, who is working as a tour guide and travel influencer at the same time and has 3.7 million followers, Haggagovic also has 3.2 million followers, and finally, Asmaa Raouf has 1.4 million followers (the culture trip, 2021).…”

Section: Social Media Influencersmentioning

confidence: 99%

Generational Differences in the Travel Decision-Making Process: A Comparative Study Between Generation X, Y and Z as Moderating Variables

Orabi

2022

مجلة کلیة السیاحة والفنادق. جامعة المنصورة

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Travel decision-making is a very complex process. The reason why it is complex is that there are several considerations that travelers must make before travelling. Since travelers cannot see the tourism product before the purchase process, they are highly interested in their travel planning and decision-making. Therefore, they need trusted information sources like travel agencies, social media platforms, and social media influencers to provide them with information and help them to make the optimal decision. Travelers have many tendencies and preferences that differ in the travel decision-making process according to their ages. So, the current study aims to understand the major characteristics of generation X, Generation Y and Generation Z and to identify the main factors that influence the travel decision-making process. To achieve the main objective of this study, a quantitative approach was carried out in order to study generational differences in the travel decision-making process. This research has been based on a survey method through distributing a set of questionnaires

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“…This will allow tourists to obtain a great deal of information about tourist destinations they share and are interested in visiting [14]. Zhang & Huang (2021) [15] also asserted that the presence of SMI can significantly impact people's travel decisions and the number of people who visit certain places.…”

Section: -Introductionmentioning

confidence: 99%

How Instagram Influencers Contribute to Consumer Travel Decision: Insights from SEM and fsQCA

et al. 2022

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In recent years, social media influencers (SMIs) have become independent entities capable of influencing their audiences. Businesses look towards SMIs on Instagram as a marketing communication tool due to their popularity and effectiveness. In tourism, few studies investigate the role of SMI in influencing travel behaviors. The study examines how SMI can influence Instagram followers' travel behaviors. This study determined SMI based on attractiveness, similarity, and expertise. SMI can generate parasocial interactions with followers and build trust by promoting destinations based on these three dimensions. This study tested the research hypothesis on 364 respondents using a dual-approach analysis of structural equation modeling and fuzzy set qualitative comparative analysis. The results of the SEM analysis confirm that consumers were more likely to trust SMI based on their expertise and similarity. Also, this study demonstrated that highly attractive SMI and similarity with followers can lead to parasocial interactions. When consumers trust and feel parasocial interaction with SMI, they are more likely to consider traveling. FsQCA results confirm the presence of two configurations with high travel intention. The causal conditions configuration presented in this study demonstrated the interdimensional relationship between SMIs (attractiveness, similarity, and expertise), trust, and parasocial interaction in terms of travel intention. This study also achieved theoretical contributions and managerial implications on how scholars and tourism managers leverage SMIs to create high travel intentions. Doi: 10.28991/ESJ-2023-07-01-02 Full Text: PDF

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Viral marketing: influencer marketing pivots in tourism – a case study of meme influencer instigated travel interest surge

Cited by 45 publications

References 19 publications

Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

Generational Differences in the Travel Decision-Making Process: A Comparative Study Between Generation X, Y and Z as Moderating Variables

How Instagram Influencers Contribute to Consumer Travel Decision: Insights from SEM and fsQCA

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