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Space tourism is fairly neglected in academic research and requires further exploration. Public reaction on social media offers great insights to understand the patterns of behaviour but is often ignored as a potential data source. Thus, this study aims to fill the gap by add to the literature on space tourism, social media analytics and behaviour.

The study adopts a qualitative approach and uses Twitter data for drawing conclusions. An exploratory design is used by analysing 10,000 tweets through unsupervised machine learning and two sets of analysis were conducted. First, sentiment analysis is performed using NRC Emotion Lexicon, which classifies the data as per eight basic emotions and polarity as positive and negative. The findings are complemented with a comparison cloud. Second, LDA Topic modelling using Gibbs Method is used to find ten broad topics that are used for discussions in space tourism tweets. Data visualisation technique is used to depict results using R language on RStudio.

A total of 21,784 emotions have tapped using the NRC Emotion Lexicon. Results indicate the dominance of positive sentiments (25%) with it surpassing the negative sentiments by many folds. The top emotions include trust and anticipation. The LDA-based Topic modelling identified seven correlated topic models that have been grouped by the author as space tourism in media, aspirations, ethical issues, criticism, descriptive, symbolism and miscellaneous.

To the best of the author’s knowledge, no study has attempted to study the response of space tourism on social media by tapping discussions in the form of Tweets. Thus, this study adds extensively and acts as a preliminary investigation on the public sentiments of space tourism on social media.

This paper aims to fill the major research gap prevalent in the tourism literature on the new form of tourism branching out from the COVID-19. While there are newspaper reports mentioning about the government’s reaction to vaccine tourism, there is no such study or report that tries to understand what the global masses feel about it; thus, a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter is carried out.

This exploratory study serves as a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter and tries to categorise them into eight basic emotions from Plutchik (1994) “wheel of emotions” as joy, disgust, fear, anger, anticipation, sadness, trust and surprise. The results are presented through data visualisation technique for analysis. The study makes use of R programming languages and the extensive packages offered on RStudio.

A total of 12,258 emotions were captured. It is evident that Vaccine Tourism has got maximum of positive sentiments (28.14%) which is almost double of the negative sentiment (14.05%). It is visible that the highest sentiment is “trust” (12.74%) and is followed by “fear” (8.97%). The least visible sentiment is “surprise” (4.32%). Polarity has been found for maximum tweets as positive (55.52%) which yet again surpasses negative polarity (33.7%), and neutral polarity is the least (10.67%).

It can be said that people bear a positive emotion regarding vaccine tourism such as “trust” and “joy” which also denotes a positive sentiment score for testing polarity. But there are still concerns of high prices of the packages, fear-prevalent people to step out, and the uncertainty of right precautionary measures being taken still puts vaccine tourism under the radar of doubt with a fourth population having negative and neutral sentiments each. This is indicative with “fear” being the second highest emotion to the users. There are mixed emotions for vaccine tourism, but positive dominates the results.

The study attempts to see the global reaction on social media on vaccine tourism trend for giving food for thought to marketers. It can be said that Asians can be the target group.

To the best of the authors’ knowledge, there is no study that addresses the new trend of “Vaccine Tourism” or attempts to understand the emotions and sentiments of people globally.

This study aims to explore how social media affects decision-making among tourists and whether there is a potential effect of age, which is studied through generations. For this purpose, baby boomers, Gen X, Gen Y and Gen Z tourists are studied and real-time implications are offered.

The study adopts a descriptive and exploratory design where the conceptual model of social media-based decision-making is developed through a review of the literature. Quantitative analysis is conducted on primary data from 600 Indian tourists. This is done using a self-administered questionnaire adopted from Gulati (2022) after checking its validity and reliability. The statistical analysis for hypothesis testing is done using PLS-SEM path modelling on pooled data. To study the categorical moderating effect of generations, partial least squares multigroup analysis (PLS-MGA) is performed as a paired comparison on every successive generation.

After testing every successive younger generation with an older generation through PLS-MGA, none of the pairs found any significant differences in path coefficients, as the values obtained were 0.05 < p < 0.95 for all five paths (SM → NR, SM → IS, SM → E, SM → P, SM → PPB). This indicates all the generations behave in a similar manner irrespective of them being older or younger, and age does not moderate social media’s impact on decision-making among Indian tourists.

The study establishes India as a unique geographical market and suggests tourism marketers to treat all generations at par, irrespective of age, as they behave and interact with social media in a similar manner. But, because this study is restricted to a single geographical location, i.e. India, further regions can be explored for global generalisation. Future research can also explore other demographics for combined, moderated analysis. Findings from the study suggest that marketers should ensure that equal attention is given to all generations as they engage with social media in a similar manner. Targeted marketing using artificial intelligence can help in ensuring custom ads. Personalisation according to generations can also facilitate greater purchases.

The study fills a major population and knowledge gap by exploring a topic that has been highly under-researched. Also, the study adopts an inclusive approach by analysing all the generations, both younger and older, to understand the potential effect of age on moderating the impact that social media has on tourist decision-making. Further, real-time suggestions and implications are offered to tourism marketers with special reference to the Indian tourism industry.

Twitter is the most widely used platform with an open network; hence, tourists often resort to Twitter to share their travel experiences, satisfaction/dissatisfaction and other opinions. This study is divided into two sections, first to provide a framework for understanding public sentiments through Twitter for tourism insights, second to provide real-time insights of three Indian heritage sites i.e., the Taj Mahal, Red Fort and Golden Temple by extracting 5,000 tweets each (n = 15,000) using Twitter API. Results are interpreted using NRC emotion lexicon and data visualisation using R.

This study attempts to understand the public sentiment on three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple using a step-by-step approach, hence proposing a framework using Twitter analytics. Extensive use of various packages of R programming from the libraries has been done for various purposes such as extraction, processing and analysing the data from Twitter. A total of 15,000 tweets from January 2015 to January 2021 were collected of the three sites using different key words. An exploratory design and data visualisation technique has been used to interpret results.

After data processing, 12,409 sentiments are extracted. Amongst the three tourists' spots, the greatest number of positive sentiments is for the Taj Mahal and Golden temple with approximately 25% each. While the most negative sentiment can be seen for the Red Fort (17%). Amongst the positive emotions, the maximum joy sentiment (12%) can be seen in the Golden Temple and trust (21%) in the Red Fort. In terms of negative emotions, fear (13%) can be seen in the Red fort. Overall, India's heritage sites have a positive sentiment (20%), which surpasses the negative sentiment (13%). And can be said that the overall polarity is towards positive.

This study provides a framework on how to use Twitter for tourism insights through text mining public sentiments and provides real- time insights from famous Indian heritage sites.

This study aims to explore when and for what purpose is social media used by tourists in their decision-making; and to further interpret the nature of such usage.

An online questionnaire was administered to 530 Indians who qualified as recent tourists and social media users. Partial least square structural equation modeling (PLS-SEM) is used to analyse hypothesised relationships; the results helped us to unveil the tourist social media cycle which is a conceptual model of the results obtained.

PLS-SEM results indicate that social media is predominantly used in the pre-travel stage for evaluation purposes, during travel stage for purchase purposes and post-travel stage for post-purchase behaviour. Also, it is noted that social media use by a tourist is an ongoing process, and thus, a conceptual model in the form of tourist social media cycle is unveiled.

This study reveals the unique behaviour of Indian tourists with respect to using social media for travel decision-making, which establishes a different perspective of understanding and further strategizing social media’s use in tourism for a developing nation. Also, the tourist social media cycle stresses on the importance of social media as a digital repository where continuously the user-generated content is used by self (existing) or “others” (potential tourists).

To the best of the author’s knowledge, this is the first study that addresses the population and knowledge gap of understanding social media–based tourist behaviour providing a perspective of a developing nation, specifically India. Further, it unveils the nature of social media use by tourist in the form of a first ever tourist social media cycle.

While researchers have studied the impact of social media on tourist behaviour Milano et al. (2011) and Hays et al. (2013), very few studies have explored the impact of social media on sustainable tourism, and thus, it can be said to be in its “infancy” (Han et al., 2017). There exists a “knowledge gap” not just with tourists but also with researchers, as the issue on sustainable tourism consumption is “rarely investigated directly in tourism researches” or consumer behaviour studies, and there is often discrepancy in literature on the issue (Cohen et al., 2014; Bray et al., 2011). While the potential for social media in promoting sustainable tourism is known considering the use of it for socialisation and knowledge sharing, empirical researches to make in depth enquiry on the same is “rare” (Ali and Frew, 2010; Sarkar et al., 2014). Also, the differences between markets for sustainable tourism amongst different markets needs to explored further (Weber, 2019). The purpose of this study is to fill this gap by proposing a conceptual framework and empirically testing how social media can be used to generate and promote sustainable tourism demand among through evidences from Indian tourists. RQ1: what are the key drivers to use social media by sustainable tourists? RQ2: can social media promotions help sustainable demand generation through spreading awareness and connecting fellow sustainable tourists?

A total of 350 Indian tourists were reached through Google forms distributed through various channels, amongst which 265 responded with a response rate of 75.7%. The data were coded and checked for any outliers and missing values. To avoid any biases, incomplete forms were not counted and after data cleaning, 250 respondents were found to be appropriate with all the responses. Analysis of demographic and travel profile was done through SPSS 22 to check variances in the groups, frequencies and chi square values were calculated. Since this is an exploratory study, the aim was more on prediction rather than model confirmation, thus the technique used and found to be appropriate was partial least squares structural equation modelling (PLS-SEM).

Variances of social media were used in the demographic groups and found that there was no significant difference found as per age and education. Even variation of travel habits was found to be not significant with social media use. It can be seen that social media promotion activities indeed create awareness amongst people about sustainability; creating awareness about sustainability indeed leads to connecting green tourists together. On the other hand, social media promotional activities may not necessarily connect potential green tourists together; awareness positively impacts the generation of sustainable demand is also supported. It can be seen that merely connecting potential green tourists does not necessarily create demand. Social media promotion activities are indeed instrumental in creating a sustainable demand amongst tourist.

This study takes into account the informational and environmental knowledge aspect of sustainable behaviour adoption by green tourists (Swarbrooke and Horner, 2007; Miller, 2003; Chan et al., 2014). This study draws implications based on the behaviour of Indian tourists and thus, the generalisation to other countries may not be as accurate as Indians differ culturally from the world at large. Even though the study involves different age groups, the mean age is of younger Indian tourists, which is also often considered as the most active users of social media and thus likely to be impacted more. Also, it is believed that these younger green consumers who are already inclined towards sustainability tend to seek more information on ethical issues (Finney, 2014). The sample size of the study is very small (n = 250) and was only checked for variation amongst gender and profession; other aspects such as income and employment (Swarbrooke and Horner, 2007; Miller, 2003) have been kept out of the study and thus not included as a part of demographics. More demographic characteristics can be taken into account to study if they play a mediating role in generating sustainable demand as a part of the analysis of this conceptual framework proposed.

Internet allows users to obtain information about products and brands to its users through various social media such as blogs, forums, wikis content sharing, etc. (Hennig-Thurau and Walsh, 2003; Reilly and Weirup, 2010). Strategies can thus be formulated to target sustainable tourists with sustainable destinations and amenities, so as to attract those tourists who appreciate and commit to the cause of sustainability (López-Sánchez and Pulido-Fernández, 2016). Using social media as a promotion tool can help in educating customers (Xiang and Gretzel, 2010) on the various sustainable tourism services they intend to offer, as the results supports the use of social media promotion for generating sustainable demand amongst the tourists. Also, the sustainable tourism providers can make use of promotional activities focussing on building online pro environmental tourist-based communities using the user generated contents which can positively lead to adoption of collective social behaviour and sustainable practices (Han et al., 2017). Specifically, the use of short interactive messages can be used for social media promotions, so as to increase “organisation prominence” amongst the green target groups online (Strähle and Gräff, 2017). While it was noted that different markets react differently to the idea of sustainability, and thus, different markets need to be explored deeper (Weber, 2019); this study attempts to provide a perspective of Indian tourists which not just adds to the literature on Indian tourist behaviour but also provides a viewpoint of the Asian tourists. It also enriches the existing literature on the use of AIDA model for services and tourism specifically which as a theoretical base is unexplored.

The study adds to the rising interest in understanding the behavioural changes in tourists (Hall, 2016) and provides insights on “the versatility of the topic of relationship between sustainable tourism and marketing” (Font and McCabe, 2017). While ethical consumption is essential to preserve resources, the study of this area of consumer behaviour is “under examined”, as majority studies focus on products, very few studies elucidate on encouraging it through social marketing (Newholm and Shaw, 2007). Thus, this study attempts to fill the gap in the literature by proposing a framework for generating sustainable demand amongst tourists so that they are inclined socially and sustainably both through the use of social media. It is often noted that tourists are unwilling to change their travel behaviour as a result of lack of awareness or due to hesitance of sacrificing (Juvan and Dolnicar, 2014; Miller et al., 2010). This study provides empirical evidence that supports the awareness of sustainability as a means of generating sustainable demand amongst tourists. Knowledge regarding tourism demand helps in the further development of tourism products and its marketing initiatives (Weber, 2019). Thus, it allows tourism players to understand that Indian tourists are open to sustainable tourist practices if enough information is provided to them.

The purpose of this paper is to examine industry herding among the institutional investors and to find whether their herding behaviour is intentional or unintentional.

The study uses Lakonishok et al. (1992) model to examine the presence of industry herding behaviour among institutional investors. To determine whether the herding observed is intentional or unintentional, herding measure is regressed with volatility, volume, beta and return. The period of the study is from 1 April 2005-31 March 2015.

The findings of the study showed that though institutional investors have herding tendency towards most of the industries, in the overall period industry herding was not significant. The herding found in some industrial sectors was linked to economic performance of those sectors in India during the period of study and hence the herding was unintentional in nature.

This is the first attempt to study industry herding among institutional investors and their intent in Indian market ever since the country opened its market to foreign investors in a big way. Present study is limited to the use of only bulk/block data instead of the entire trading data for the period.

This study is the first attempt to investigate industry herding behaviour of institutional investors in the market using their bulk and block trading data. The herding observed in well performing industries has been shown to be unintentional and hence rational. The results indicate that the entry of big institutional investors, including foreign institutions into the Indian market has not destabilised the market by irrational herding.