What motivates posting online travel reviews? Integrating gratifications with technological acceptance factors

Bakshi, Shuchita; Dogra, Nikita; Gupta, Anil

doi:10.20867/thm.25.2.5

Cited by 23 publications

(18 citation statements)

References 92 publications

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“…Further, the effort expectancy and social influence emerged as insignificant for behavioural intentions. Although, similar results were established in prior research (Bakshi et al, 2019;Jacob & Pattusamy, 2020;Schapsis et al, 2021).…”

Section: Discussionsupporting

confidence: 91%

“…It is a dependent variable. The extant literature signifies behavioural intentions as a predicted variable for mobile internet (Alwahaishi & Snášel, 2013), online shopping (Celik, 2016), mobile-based IT solutions for TB treatment monitoring (Seethamraju et al, 2018), online travel reviews (Bakshi et al, 2019), Airbnb app (Nathan et al, 2020) . Wang et al, (2011) describe aesthetic appeal as something which someone regards as creative and fascinating.…”

Section: Behavioural Intentionsmentioning

confidence: 99%

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Analysing Sustainability Reporting Content for Creating Value through Engaging Stakeholders: A Qualitative Approach

Arora¹,

Rathore²

2022

JCCC

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Digital signage is a network of exclusive screens installed in retail stores to display diverse content. Fashion retail stores recognize digital signage as a promising technology in the retail environment. This study investigates how aesthetic appeal and designed information are in the extended Unified Theory of Acceptance and Use of Technology (UTAUT 2) model. Brick-and-mortar retailers have embraced digital signages to converse better and change the way individuals get cues in fashion apparel stores. This research analyses what factors influence an individual's decision to use digital signages in fashion apparel retail stores. The partial least squares method evaluated the research model and validated the hypotheses of the data comprised of 418 self-administered individual responses. The results reveal that designed information is the key predictor of behavioural intentions stimulated by digital signages. The results confirm that aesthetic appeal, designed information, habit, and performance expectancy of behavioural intentions are significant predictors. In contrast, two constructs, effort expectancy and social influence, are non-significant for fashion apparel retail stores

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Section: Discussionsupporting

confidence: 91%

Section: Behavioural Intentionsmentioning

confidence: 99%

Analysing Sustainability Reporting Content for Creating Value through Engaging Stakeholders: A Qualitative Approach

Arora¹,

Rathore²

2022

JCCC

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“…Prior studies prove the predictive power of PEOU on intention of adopting travel-related social media. For instance, Bakshi et al (2019) prove that tourists' effort expectancy of posting online travel review significantly affect the intention to post. Bilgihan et al (2016) discover that PEOU is the prominent driver for the intention of sharing tourism knowledge online.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Besides, Amaro and Duarte (2015) proved ATT to be the most relevant motivation for the intention of online travel shopping. It is found that if tourists perceived the behavior of posting online reviews to be hedonic, then they are willing to post (Bakshi et al, 2019). Therefore, a hypothesis is posited:…”

Section: Attitude (Att)mentioning

confidence: 99%

Influencing factors on the intention of sharing heritage tourism experience in social media

Lin¹,

Rasoolimanesh²

2023

JHTT

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Purpose Combining technology acceptance model and the theory of planned behavior, this study aims to analyze influencing factors on intention of sharing heritage tourism experience in social media from technological, psychological and experience perspectives. The moderating effects of age and gender are also tested. Design/methodology/approach This study applies a quantitative method using online questionnaires. A total number of 323 questionnaires are collected. The data are analyzed using partial least squares-structural equation modeling to assess measurement and structural models and multi-group analysis to compare differences among age and gender groups. Findings The results of this study indicate that subjective norm, perceived behavioral control and tourism experience significantly and positively influence the intention of sharing heritage tourism experience in social media, while the effects of perceived usefulness, perceived ease of use and attitude are non-significant. Results reveal the differences in antecedents of sharing heritage tourism experience in social media between males and females and between younger and older tourists. Originality/value Developing a model integrating technology acceptance model and theory of planned behavior, this paper analyzes the intention of sharing heritage tourism experience in social media through technological, psychological and tourism experience perspectives. Besides, heterogeneous comparisons between younger and older tourists and between males and females offer significant academic and practical insights.

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“…It is important to identify the reasons why someone might want to share their experiences on different social networks since this affects the quality of the content published about tourism in a certain place. Various authors (Assaker, 2020; Bakshi et al , 2019) found that altruism, reciprocity, effort expectancy, habit and hedonic motivations have a positive impact, whereas economic rewards have a negative impact on the intentions of travelers when it comes to posting online reviews.…”

Section: Related Workmentioning

confidence: 99%

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

Viñán-Ludeña

Campos

2021

JHTI

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PurposeThe main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.Design/methodology/approachIn order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).FindingsThe authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.Research limitations/implicationsExtremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.Originality/valueThis study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.

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What motivates posting online travel reviews? Integrating gratifications with technological acceptance factors

Cited by 23 publications

References 92 publications

Analysing Sustainability Reporting Content for Creating Value through Engaging Stakeholders: A Qualitative Approach

Analysing Sustainability Reporting Content for Creating Value through Engaging Stakeholders: A Qualitative Approach

Influencing factors on the intention of sharing heritage tourism experience in social media

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

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