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Uncovering customer service experiences with Twitter: the case of airline industry

https://doi.org/10.1108/md-03-2012-0235
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/asi.21067openalex-snapshot
Opinion mining and relationship discovery using CopeOpi opinion analysis system
inferredcites →https://doi.org/10.1002/asi.21462openalex-snapshot
Sentiment in Twitter events
inferredcites →https://doi.org/10.1002/isaf.327openalex-snapshot
THE USE OF SOCIAL MEDIA IN THE SUPPLY CHAIN: SURVEY AND EXTENSIONS
inferredcites →https://doi.org/10.1007/s10257-009-0113-9openalex-snapshot
Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews
inferredcites →https://doi.org/10.1016/j.bushor.2009.09.003openalex-snapshot
Users of the world, unite! The challenges and opportunities of Social Media
inferredcites →https://doi.org/10.1057/palgrave.dddmp.4350098openalex-snapshot
Web 2.0: Conceptual foundations and marketing issues
inferredcites →https://doi.org/10.1080/02672570903566268openalex-snapshot
Why do people read reviews posted on consumer-opinion portals?
inferredcites →https://doi.org/10.1080/14783360903181867openalex-snapshot
Kano model: A dynamic approach for classifying and prioritising requirements of airline travellers with three case studies on international airlines
inferredcites →https://doi.org/10.1108/00251740910984596openalex-snapshot
Exploring virtual worlds: success factors in virtual world marketing
inferredcites →https://doi.org/10.1108/02656710510625211openalex-snapshot
Service quality models: a review
inferredcites →https://doi.org/10.1111/j.1083-6101.2007.00393.xopenalex-snapshot
Social Network Sites: Definition, History, and Scholarship
inferredcites →https://doi.org/10.1111/j.1467-8640.2006.00276.xopenalex-snapshot
THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT
inferredcites →https://doi.org/10.1145/1498765.1498772openalex-snapshot
Our sentiments, exactly
inferredcites →https://doi.org/10.1145/1629175.1629183openalex-snapshot
New search challenges and opportunities
inferredcites →https://doi.org/10.1145/2001269.2001297openalex-snapshot
Don't turn social media into another 'Literary Digest' poll
inferredcites →https://doi.org/10.1145/361219.361220openalex-snapshot
A vector space model for automatic indexing
inferredcites →https://doi.org/10.1145/775152.775226openalex-snapshot
Mining the peanut gallery
inferredcites →https://doi.org/10.1145/945645.945658openalex-snapshot
Sentiment analysis
inferredcites →https://doi.org/10.2307/25148806openalex-snapshot
Enhancing Information Retrieval Through Statistical Natural Language Processing: A Study of Collocation Indexing1
inferredcites →https://doi.org/10.2501/jar-51-1-239-257openalex-snapshot
Online Persuasion: How the Written Word Drives WOM
inferredcites →https://doi.org/10.2753/mis0742-1222280202openalex-snapshot
Measuring Information Diffusion in an Online Community

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3299219openalex-extracted
Tweet Me What You Want, What You Really Really Want - The Informative Value of Tweets for Customer Feedback in Crowdfunding
inferred← citeshttps://doi.org/10.2139/ssrn.4130874openalex-extracted
inferred← citeshttps://doi.org/10.1002/tqem.21576openalex-snapshot
A framework based on Twitter and big data analytics to enhance sustainability performance
inferred← citeshttps://doi.org/10.1007/978-3-030-04257-8_11openalex-snapshot
A Method for Retrieval of Tweets About Hospital Patient Experience
inferred← citeshttps://doi.org/10.1007/978-3-030-20482-2_4openalex-snapshot
A Literature Review on Application Areas of Social Media Analytics
inferred← citeshttps://doi.org/10.1007/978-3-031-37675-7_3openalex-snapshot
Understanding Customer Service for Small and Entrepreneurial Firms
inferred← citeshttps://doi.org/10.1007/978-3-031-49039-2_12openalex-snapshot
The Human RFID Implants Introduce a New Level of Human-Computer Interaction: Twitter Topic Detection Gauges Consumer Opinions
inferred← citeshttps://doi.org/10.1007/978-3-031-50128-9_9openalex-snapshot
Assessing Customer Service for Sustainable Micro, Small, and Medium Entrepreneurial Firms: Lessons from Ghana
inferred← citeshttps://doi.org/10.1007/978-3-031-83896-5_7openalex-snapshot
Efficacy of Cruise Lines’ Crisis Communication Strategies During the Pandemic: Results of Sentiment Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-32689-4_14openalex-snapshot
Needmining: Towards Analytical Support for Service Design
inferred← citeshttps://doi.org/10.1007/978-3-658-13650-5_11openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-13-6142-5_12openalex-snapshot
Conclusion
inferred← citeshttps://doi.org/10.1007/978-981-19-7892-0_12openalex-snapshot
Tweet Classification on the Base of Sentiments Using Deep Learning
inferred← citeshttps://doi.org/10.1007/s12469-021-00289-7openalex-snapshot
Decoding customer experiences in rail transport service: application of hybrid sentiment analysis
inferred← citeshttps://doi.org/10.1007/s12525-019-00351-0openalex-snapshot
Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media
inferred← citeshttps://doi.org/10.1007/s42979-021-00669-7openalex-snapshot
Implicit Aspect-Based Opinion Mining and Analysis of Airline Industry Based on User-Generated Reviews
inferred← citeshttps://doi.org/10.1007/s42979-024-03567-wopenalex-snapshot
Systematic Literature Review on Sentiment Analysis in Airline Industry
inferred← citeshttps://doi.org/10.1016/j.actaastro.2026.06.053openalex-snapshot
Potential space tourists perceived constraints and negotiations
inferred← citeshttps://doi.org/10.1016/j.dajour.2024.100473openalex-snapshot
A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data
inferred← citeshttps://doi.org/10.1016/j.dcm.2021.100572openalex-snapshot
“Thank you for reaching out:” Brand relationship management and the conversational human voice of customer care in online service encounters
inferred← citeshttps://doi.org/10.1016/j.dss.2023.114107openalex-snapshot
Automatically identifying customer needs in user-generated content using token classification
inferred← citeshttps://doi.org/10.1016/j.eswa.2016.11.004openalex-snapshot
Exploring patient perceptions of healthcare service quality through analysis of unstructured feedback
inferred← citeshttps://doi.org/10.1016/j.ijhm.2020.102495openalex-snapshot
Hotel selection driven by online textual reviews: Applying a semantic partitioned sentiment dictionary and evidence theory
inferred← citeshttps://doi.org/10.1016/j.ijhm.2020.102557openalex-snapshot
Optimizing service offerings using asymmetric impact-sentiment-performance analysis
inferred← citeshttps://doi.org/10.1016/j.jairtraman.2018.08.004openalex-snapshot
Using Twitter network to detect market segments in the airline industry
inferred← citeshttps://doi.org/10.1016/j.jairtraman.2019.04.001openalex-snapshot
Voice of airline passenger: A text mining approach to understand customer satisfaction
inferred← citeshttps://doi.org/10.1016/j.jairtraman.2020.101903openalex-snapshot
Analyzing passengers’ emotions following flight delays- a 2011–2019 case study on SKYTRAX comments
inferred← citeshttps://doi.org/10.1016/j.jairtraman.2024.102600openalex-snapshot
Travelers' perception of smart airport facilities: An X (Twitter) sentiment analysis
inferred← citeshttps://doi.org/10.1016/j.jatrs.2024.100016openalex-snapshot
Self-service technology in aviation: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.jbusres.2023.114298openalex-snapshot
Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms
inferred← citeshttps://doi.org/10.1016/j.jclepro.2018.12.003openalex-snapshot
Literature vs. Twitter: Empirical insights on customer needs in e-mobility
inferred← citeshttps://doi.org/10.1016/j.jjimei.2021.100008openalex-snapshot
Applications of text mining in services management: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.jjimei.2023.100181openalex-snapshot
From the black box to the glass box: Using unsupervised and supervised learning processes to predict user engagement for the airline companies
inferred← citeshttps://doi.org/10.1016/j.jretconser.2019.01.004openalex-snapshot
The role of satisfaction on customer reuse to airline services: An application of Big Data approaches
inferred← citeshttps://doi.org/10.1016/j.procs.2017.11.336openalex-snapshot
Twitter Presence of Jet Airways-Deriving Customer Insights Using Netnography and Wordclouds
inferred← citeshttps://doi.org/10.1016/j.tourman.2020.104127openalex-snapshot
Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics
inferred← citeshttps://doi.org/10.1016/j.trip.2020.100179openalex-snapshot
Impact of Covid-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US Air Transportation System to sleep
inferred← citeshttps://doi.org/10.1080/00913367.2022.2026845openalex-snapshot
Remembering the FCB Grid: Thinking, Feeling, and Involvement in the Age of Social Media
inferred← citeshttps://doi.org/10.1080/0267257x.2022.2105933openalex-snapshot
More than words can say: a multimodal approach to understanding meaning and sentiment in social media
inferred← citeshttps://doi.org/10.1080/10548408.2017.1401508openalex-snapshot
Linking emotion and place on Twitter at Disneyland
inferred← citeshttps://doi.org/10.1080/13683500.2019.1604638openalex-snapshot
Comparisons of service quality perceptions between full service carriers and low cost carriers in airline travel
inferred← citeshttps://doi.org/10.1080/15252019.2020.1840460openalex-snapshot
An Examination of a Campaign Hashtag (#OptOutside) with Google Trends and Twitter
inferred← citeshttps://doi.org/10.1080/1528008x.2022.2094851openalex-snapshot
Voice in Words: A Mixed-Method Approach for Decoding Digital Footprints Using Online Reviews
inferred← citeshttps://doi.org/10.1080/1528008x.2025.2571650openalex-snapshot
Impression Management and Service Quality in Top Airlines’ Replies: Effects on Customer Perceptions
inferred← citeshttps://doi.org/10.1080/15332861.2017.1423533openalex-snapshot
The Future Scope of Netnography and Social Network Analysis in the Field of Marketing
inferred← citeshttps://doi.org/10.1108/978-1-80117-550-020221008openalex-snapshot
Application of Text Mining Approaches in Hospitality and Tourism
inferred← citeshttps://doi.org/10.1108/bij-01-2020-0004openalex-snapshot
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inferred← citeshttps://doi.org/10.1108/bij-11-2022-0702openalex-snapshot
Customer satisfaction and airport efficiency in Brazil: a hybrid NDEA-AHP approach
inferred← citeshttps://doi.org/10.1108/ejm-10-2017-0772openalex-snapshot
Consumer engagement on Twitter: perceptions of the brand matter
inferred← citeshttps://doi.org/10.1108/emjb-01-2024-0017openalex-snapshot
Text analytics and new service development: a hybrid thematic analysis with systematic literature review approach
inferred← citeshttps://doi.org/10.1108/ijaim-02-2014-0010openalex-snapshot
How significant are users’ opinions in social media?
inferred← citeshttps://doi.org/10.1108/ijchm-02-2018-0108openalex-snapshot
Social media, customer engagement and advocacy
inferred← citeshttps://doi.org/10.1108/ijchm-08-2020-0920openalex-snapshot
Revealing industry challenge and business response to Covid-19: a text mining approach
inferred← citeshttps://doi.org/10.1108/ijlm-06-2017-0157openalex-snapshot
Creation of unstructured big data from customer service
inferred← citeshttps://doi.org/10.1108/ijpdlm-01-2015-0007openalex-snapshot
An exploration of logistics-related customer service provision on Twitter
inferred← citeshttps://doi.org/10.1108/ijqss-11-2021-0177openalex-snapshot
Assessing and improving co-creation in services: the customer-centric matrix
inferred← citeshttps://doi.org/10.1108/imds-07-2017-0300openalex-snapshot
Improving sentiment scoring mechanism: a case study on airline services
inferred← citeshttps://doi.org/10.1108/itp-08-2016-0178openalex-snapshot
Selfie appropriation by young British South Asian adults
inferred← citeshttps://doi.org/10.1108/jabs-09-2024-0559openalex-snapshot
Optimizing airline services: leveraging data-driven strategies for enhanced customer satisfaction and engagement
inferred← citeshttps://doi.org/10.1108/jamr-08-2019-0164openalex-snapshot
Analyzing the startup ecosystem of India: a Twitter analytics perspective
inferred← citeshttps://doi.org/10.1108/jhtt-08-2016-0042openalex-snapshot
Analyzing Twitter to explore perceptions of Asian restaurants
inferred← citeshttps://doi.org/10.1108/jhtt-09-2017-0090openalex-snapshot
Benefits and pitfalls of using tweets to assess destination sentiment
inferred← citeshttps://doi.org/10.1108/josm-10-2021-0407openalex-snapshot
Affordances advancing user-created communication (UCC) in service: interactivity, visibility and anonymity
inferred← citeshttps://doi.org/10.1108/jrim-03-2020-0045openalex-snapshot
Reading between the lines: untwining online user-generated content using sentiment analysis
inferred← citeshttps://doi.org/10.1108/jsm-08-2017-0294openalex-snapshot
Netnography as a tool for understanding customers: implications for service research and practice
inferred← citeshttps://doi.org/10.1108/md-05-2022-0673openalex-snapshot
Do they see eye to eye? Managing customer experience in phygital high-tech retail
inferred← citeshttps://doi.org/10.1108/md-06-2016-0398openalex-snapshot
Exploring the determinants of and predicting the helpfulness of online user reviews using decision trees
inferred← citeshttps://doi.org/10.1108/md-06-2017-0561openalex-snapshot
The interactive impact of online word-of-mouth and review helpfulness on box office revenue
inferred← citeshttps://doi.org/10.1108/md-11-2015-0526openalex-snapshot
Factors enhancing the posting of negative behavior in social media and its impact on venting negative emotions
inferred← citeshttps://doi.org/10.1108/s1746-979120200000016011openalex-snapshot
Opportunities, Tools, and New Insights: Evidence on Emotions in Service from Analyses of Digital Traces Data
inferred← citeshttps://doi.org/10.1108/s2398-391420170000002011openalex-snapshot
Social Media: The Dialogue Myth? How Organizations Use Social Media for Stakeholder Dialogue
inferred← citeshttps://doi.org/10.1108/sd-09-2014-0127openalex-snapshot
Reading between the tweets
inferred← citeshttps://doi.org/10.1108/sjme-06-2018-0032openalex-snapshot
Facebook’s power: factors influencing followers’ visit intentions
inferred← citeshttps://doi.org/10.1109/access.2017.2780046openalex-snapshot
Identifying Product Opportunities Using Social Media Mining: Application of Topic Modeling and Chance Discovery Theory
inferred← citeshttps://doi.org/10.1109/access.2019.2947593openalex-snapshot
Enabling Deeper Linguistic-Based Text Analytics—Construct Development for the Criticality of Negative Service Experience
inferred← citeshttps://doi.org/10.1109/aina.2016.148openalex-snapshot
Sentiment Analysis and Classification for Software as a Service Reviews
inferred← citeshttps://doi.org/10.1109/bigdatacongress.2018.00017openalex-snapshot
Analysing Customer Engagement of Turkish Airlines Using Big Social Data
inferred← citeshttps://doi.org/10.1109/elecom54934.2022.9965246openalex-snapshot
Artifact for Strategic Decision-Making by Telecommunication Firms
inferred← citeshttps://doi.org/10.1109/icetcs61022.2024.10544041openalex-snapshot
Airline Customer Reviews Analysis and Booking Completion Prediction using Data Visualization and Machine Learning for British Airways
inferred← citeshttps://doi.org/10.1109/icoac48765.2019.246832openalex-snapshot
An examination of customer reviews for service process innovations: A study with respect to E-travel services
inferred← citeshttps://doi.org/10.1109/tem.2026.3664701openalex-snapshot
Feature Classification Methods for Measuring User Engagement in Social Media Campaigns
inferred← citeshttps://doi.org/10.1111/ijcs.12644openalex-snapshot
Voluntary simplicity: An exploration through text analysis
inferred← citeshttps://doi.org/10.1111/jbl.12120openalex-snapshot
Accessing Online Data: Web‐Crawling and Information‐Scraping Techniques to Automate the Assembly of Research Data
inferred← citeshttps://doi.org/10.1145/3184558.3191631openalex-snapshot
An Evaluation of Performance and Competition in Customer Services on Twitter
inferred← citeshttps://doi.org/10.1145/3397125.3397147openalex-snapshot
Hotel Reviews Analysis based on Sentiment Classification
inferred← citeshttps://doi.org/10.1145/3469028openalex-snapshot
Data Analytics for Air Travel Data: A Survey and New Perspectives
inferred← citeshttps://doi.org/10.1155/2017/3197585openalex-snapshot
Key Enablers of Effective Implementation of TQM in Royal Jet Airways
inferred← citeshttps://doi.org/10.1175/wcas-d-21-0039.1openalex-snapshot
Tourists’ perceptions of climate: Application of machine learning to climate and weather data from Chinese social media
inferred← citeshttps://doi.org/10.1177/0022242919873903openalex-snapshot
The Role of Marketer-Generated Content in Customer Engagement Marketing
inferred← citeshttps://doi.org/10.1177/0047287517747753openalex-snapshot
Sentiment Analysis in Tourism: Capitalizing on Big Data
inferred← citeshttps://doi.org/10.1177/03611981231172948openalex-snapshot
Machine Learning Approach to Analyze the Sentiment of Airline Passengers’ Tweets
inferred← citeshttps://doi.org/10.1177/09722629251355308openalex-snapshot
Social Media Platforms and Micro, Small and Medium Enterprises: Bibliometric Analysis and Systematic Review
inferred← citeshttps://doi.org/10.1177/1062860616688080openalex-snapshot
Social Media as a Tool to Reduce Sexual Misconduct in Medical Facilities
inferred← citeshttps://doi.org/10.1177/14673584221085470openalex-snapshot
Topic and sentiment analysis of crisis communications about the COVID-19 pandemic in Twitter’s tourism hashtags
inferred← citeshttps://doi.org/10.1177/1470785318771451openalex-snapshot
Clustering halal food consumers: A Twitter sentiment analysis
inferred← citeshttps://doi.org/10.1177/21582440211047572openalex-snapshot
Linguistic Studies on Social Media: A Bibliometric Analysis
inferred← citeshttps://doi.org/10.1504/ijwbc.2017.10005189openalex-snapshot
Security information sharing via Twitter: 'Heartbleed' as a case study
inferred← citeshttps://doi.org/10.15207/jkcs.2020.11.3.201openalex-snapshot
The Convergence Effect of Gender, Age, Motivation, Sensitivity and Information Acceptance of Aviation Related Social Media Users
inferred← citeshttps://doi.org/10.18053/jctres.03.2017s3.002openalex-snapshot
Social media as a tool to monitor adherence to HIV antiretroviral therapy
inferred← citeshttps://doi.org/10.18653/v1/2022.nlpcss-1.12openalex-snapshot
Linguistic Elements of Engaging Customer Service Discourse on Social Media
inferred← citeshttps://doi.org/10.19168/jyasar.928843openalex-snapshot
Türkçe Metinlerde Duygu Analizi
inferred← citeshttps://doi.org/10.21076/vizyoner.1553607openalex-snapshot
Evaluation of Customer Satisfaction Dimensions in Full Service and Low-Cost Airlines Via Dynamic Topic Modeling Approach
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3475984/v1openalex-snapshot
A Systematic Review on Airlines Industries based on Sentiment Analysis and Topic Modeling
inferred← citeshttps://doi.org/10.22598/mt/2019.31.1.23openalex-snapshot
What Determines the Loyalty of Airline Passengers? Findings of a Quantitative Data-Mining Study
inferred← citeshttps://doi.org/10.24251/hicss.2018.258openalex-snapshot
Automatically Quantifying Customer Need Tweets: Towards a Supervised Machine Learning Approach
inferred← citeshttps://doi.org/10.24251/hicss.2022.165openalex-snapshot
Use of Electronic Word of Mouth as Quality Metrics: A Comparison of Airline Reviews on Twitter and Skytrax
inferred← citeshttps://doi.org/10.24251/hicss.2023.264openalex-snapshot
What Do Customers Say About My Products? Benchmarking Machine Learning Models for Need Identification
inferred← citeshttps://doi.org/10.2478/emj-2024-0003openalex-snapshot
Supervised multilabel classification techniques for categorising customer requirements during the conceptual phase in the new product development
inferred← citeshttps://doi.org/10.26668/businessreview/2024.v9i3.4546openalex-snapshot
THE IMPORTANCE OF USER-GENERATED CONTENT IN TOURISM EVENTS FOR CREATING ENGAGEMENT
inferred← citeshttps://doi.org/10.29036/jots.v11i21.163openalex-snapshot
Comparative Content Analysis of Hotel Reviews by Mass Tourism Destination
inferred← citeshttps://doi.org/10.30518/jav.1464379openalex-snapshot
Do We Scapegoat The Pandemic? Investigating The Changes in Satisfaction Drivers of Air Passengers With DWLS-SEM
inferred← citeshttps://doi.org/10.30519/ahtr.784232openalex-snapshot
How Delightful is Indian Wellness Tourism? A Netnographic Study
inferred← citeshttps://doi.org/10.30783/nevsosbilen.1485298openalex-snapshot
Havayolu işletmelerinde müşterilerin yaşadıkları kritik olayların incelenmesi
inferred← citeshttps://doi.org/10.31449/inf.v43i3.2615openalex-snapshot
Twitter-based Opinion Mining for Flight Service utilizing Machine Learning
inferred← citeshttps://doi.org/10.31620/jccc.06.20/07openalex-snapshot
TWITTER AS A CUSTOMER SERVICE MANAGEMENT PLATFORM: A STUDY ON INDIAN BANKS
inferred← citeshttps://doi.org/10.32038/sem.2019.01.01openalex-snapshot
The Importance of Engagement on Social Media Platforms: A Case Study
inferred← citeshttps://doi.org/10.33422/3rd.imeconf.2020.09.202openalex-snapshot
Analysis of Airline Sentiment Data: Does the social media image reflect real performance?
inferred← citeshttps://doi.org/10.3389/fpsyg.2022.983987openalex-snapshot
Is negative e-WOM more powerful? Multimodal data analysis on air passengers’ perception of COVID-19 safety measures
inferred← citeshttps://doi.org/10.3390/electronics15143086openalex-snapshot
Social Media Sentiment and Service Quality Evidence in Public Transport and Passenger Mobility: A Review
inferred← citeshttps://doi.org/10.3390/fi15040150openalex-snapshot
Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data
inferred← citeshttps://doi.org/10.3390/fi15100346openalex-snapshot
A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023
inferred← citeshttps://doi.org/10.3390/su11185070openalex-snapshot
Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions
inferred← citeshttps://doi.org/10.35562/balisages.499openalex-snapshot
Le pouvoir de Facebook
inferred← citeshttps://doi.org/10.3917/dunod.delac.2021.01.0027openalex-snapshot
Chapitre 2. Concevoir et mener une enquête qualitative
inferred← citeshttps://doi.org/10.4018/978-1-4666-8408-9.ch001openalex-snapshot
Marketing with Twitter
inferred← citeshttps://doi.org/10.4018/978-1-5225-2255-3.ch351openalex-snapshot
The Effect of Social Media Networking in the Travel Industry
inferred← citeshttps://doi.org/10.4018/978-1-5225-5187-4.ch036openalex-snapshot
Marketing With Twitter
inferred← citeshttps://doi.org/10.4018/978-1-5225-5637-4.ch015openalex-snapshot
Marketing With Twitter
inferred← citeshttps://doi.org/10.4018/978-1-5225-7766-9.ch040openalex-snapshot
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inferred← citeshttps://doi.org/10.4018/978-1-6684-4380-4.ch006openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.2003.05917openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.2101.06146openalex-snapshot
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inferred← citeshttps://doi.org/10.70675/94410bcaz53d7z4309z8923za8daafd57b11openalex-snapshot
Passengers : customers, actors and sensors of the air transportation system
inferred← citeshttps://doi.org/10.9716/kits.2016.15.1.197openalex-snapshot
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