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Social media influencers, or digital creators followed by online audiences of thousands and millions, are a hot marketing tool that grows over 50% annually. Recently, the impact of COVID-19 on travel has severely disrupted this developing market, and heavily impacted travel influencers whose livelihood depends on collaboration with tourism, hospitality, and food businesses. This research presents the first “big data” evaluation of the induced transformations among the top Instagram travel influencers. The findings are based on computer image recognition of photographs published by 140 travel Instagrammers ranging from micro- to mega-influencers before and during the COVID-19 pandemic. In addition, the research proposes a methodology of identification of sponsored posts among travel influencers based on the fuzzy matching of post mentions and a global database of 6 million businesses.

A study was conducted to propose a methodological approach of facilitating content analysis of electronic textual data typical in tourism studies in an efficient and transparent way. The textual data are processed using software products CATPAC and WORDER. The advantages and disadvantages of proposed research technique are also discussed.

ABSTRACTTechnological advancements significantly reduce the cost of space travel, thereby making many other activities in space technologically feasible and financially affordable for a larger number of people. This paper considers tourist travel to outer space, that is, beyond the 100-km Karman line altitude accepted by the Fédération Aéronautique Internationale as the border between the Earth atmosphere and outer space. Because of the scarcity of literature on the market for space flights, the study investigates factors that affect enthusiasm for becoming an orbital space tourist among those who are interested in space exploration and, at the same time, are younger and educated. We investigate motivations and risk factors as well as the moderating role of involvement, gender, and age in enthusiasm to become an orbital space tourist and compare the findings with the outcomes of previously conducted marketing research.

Social media platforms such as Reddit, centered on user-generated, anonymous discussions, can facilitate the exchange of information and resources across niche online communities known as "subreddits." Using data mining tools and content analysis methods, our objectives were to identify recurring questions and characterize comment ("response") accuracy from four subreddits focused on hydroponic indoor gardening (r/hydro, r/Hydroponics, r/UrbanFarming, and r/Aerogarden). A total of 1617 original posts (OPs) were classified into one of ten topics and 4891 primary responses were analyzed for accuracy. The three topics with the most OPs (production systems, plant lighting, and root-zone environment), which accounted for 50% of the total OPs, were subcategorized and inductively analyzed. Most posts in the analyzed subreddits related to confusion regarding the design and implementation of appropriate hydroponic production systems. In addition, misinformation about plant lighting is a major part of discussions about growing plants indoors. There are also knowledge gaps regarding nutrient solution management, particularly about fertilizer formulation, pH balance, and on the impact that solution temperature has on plant growth and development. In general, there were no differences among response accuracy for all topics included in our analysis. However, regardless of topic, responses for most OPs had less than 50% accuracy, which demonstrates that misinformation can be disseminated in social media platforms such as Reddit. As suggested by the results of this study, targeted, open access research and outreach efforts offer an opportunity to address knowledge gaps among consumers interested in indoor gardening. [ABSTRACT FROM AUTHOR]

ABSTRACTManagers of protected areas that are popular among tourists need to combine conservation goals with assurances of visitor safety while providing high-quality service. The compromise amongst these goals is reflected in the protected area regulations; however, monitoring of compliance with these regulations is costly and laborious. Social media present a new opportunity to observe visitor behaviour patterns in an unobtrusive and inexpensive manner. We propose a method of using online tourist photographs to monitor unwanted behaviours of the natural area visitors. The specific research object was the Greater Kruger National Park (GKNP) in South Africa, which is one of the largest game parks worldwide. We found that approximately 7% of the photographs depict instances of potentially unwanted behaviour. The results also indicate that the types of unwanted behaviours differ between the federally and privately owned parks, although the overall percentage of unwanted behaviours in the two types of parks is similar.

Purpose: The purpose of this paper is to investigate the drivers of visitation intention, including demographic, psychographic (animosity, national attachment and consumer ethnocentrism) and image factors on tourist decision-making in a situation of bilateral conflict between the destination country and the source country, that is, the USA and Russia, respectively. Design/methodology/approach: The data were collected at two different points in time from two different samples but analyzed with the same research question in mind. To increase the robustness and validity of findings, three analytical approaches from the repertoire of statistics and data mining techniques such as hierarchical linear regression, logistic regression and decision tree analysis were applied to two independent samples of tourists with different demographic profiles. This multiple methods research employs a complementary design, as methods used have equal importance and are applied concurrently. Findings: The paper obtained generally consistent results across methods and samples: general animosity, destination image and country image are the most influential factors in the tourists’ decision-making to visit a destination country with which their own country has strained bilateral relations. Practical implications: Destination image is highly important in all analyses and is more favorable than country image. It indicates some degree of separation of the two images in the respondents’ mind, which indicates that marketing a country as a tourist destination on a not-so-friendly tourism market may still be effective. Results also tentatively indicate that for young adults, patriotic feelings matter more in their decision-making, while for more mature tourists, ethnocentric tendencies play a more important role. Originality/value: The paper examines the influence of general and situational animosity, national attachment, and consumer ethnocentrism on visitation intentions in a situation of conflict between the two nations and, thus, expands the empirical evidence accumulated to date on tourist behavior in conflict. The paper acts on the premise that if a connection between any two variables truly exists, it should be able to withstand variations in samples, timing of data collection and measurement, thus, leading to more robust findings.

Not long ago securities were traded by human traders in face-to-face markets. The ecosystem of an open outcry market was well-known, visible to a human eye, and rigidly prescribed. Now trading is increasingly done in anonymous electronic markets where traders do not have designated functions or mandatory roles. In fact, the traders themselves have been replaced by algorithms (machines) operating with little or no human oversight. While the process of electronic trading is not visible to a human eye, machine-learning methods have been developed to recognize persistent patterns in the data. In this study, we develop a dynamic machine-learning method that designates traders in an anonymous electronic market into five persistent categories: high frequency traders, market makers, opportunistic traders, fundamental traders, and small traders. Our method extends a plaid clustering technique with a smoothing framework that filters out transient patterns. The method is fast, robust, and suitable for a discovering trading ecosystems in a large number of electronic markets.

We propose and study a stylization of high frequency trading(HFT). Our interest is an order book which consists of orders from slow liquidity traders and orders from high-frequency traders. We would like to frame a model which is amenable to the (seemingly natural) mathematical toolkit of separation of scalesand which can be used to address some of the larger issues involved in HFT. The main issue to which we address our model is volatility. An important question is how volatility is affected by HFT. In our stylized model, we show how HFT increases volatility, and can quantify this effect as a function of the parameters in our model and the separation of scales.

Abstract: Content analysis of mass media publications has become a major scientific method used to analyze public discourse on climate change. We propose a computer-assisted content analysis method to extract prevalent themes and analyze discourse changes over an extended period in an objective and quantifiable manner. The method includes the following: (1) sample selection; (2) preparation of the text segments for computer processing; (3) identifying themes in the texts using exploratory factor analysis; (4) combining identified themes into higher order themes using confirmatory factor analysis; (5) using factor scores to interpret themes obtained from public discourse; and (6) tracking the main themes of public discourse through time. We apply the proposed methodology to the analysis of the articles published in the New York Times on climate change during the period from 1995 to 2010. We found a gradual decline in the volume of material within the “Science” topic and an expansion of themes classified under the “Politics” topic.

This experimental study addresses the effect of a destination promotional video on tourists' perceptions, attitudes, and intent to visit that destination depending on the viewing mode of the video: traditional versus semi-immersive. Two approaches were used to examine this effect: self-reported cognitive evaluations obtained via questionnaires and psychophysiological measures such as galvanic skin response (GSR) and heart rate. The context of this study was the country of Bahrain. Fifty-eight people participated in the experiment and were randomly assigned to either traditional or semi-immersive group. The study found that the overall effect of the promotional video was quite large; however, the viewing mode was not a factor in the effect of the video. The study also found that psychophysiological measures and self-reported measures were not in complete agreement with each other. Both affective and cognitive appraisals were responsible for the posttest change in participants' perceptions and overall attitude toward the destination.