Investigating the impact of social media images’ value, consumer engagement, and involvement on eWOM of a tourism destination: A transmittal mediation approach

The tourism sector increasingly relies on technology to acquire new clients in a world overflowing with information. So, the main question that needs to be answered is:What digital marketing strategy should be adopted to attract customers and built digital brand name by incorporating websites and social media big data? The authors of this research utilize web analytics and big data to build an innovative methodology in an effort to address this issue. After the data collection, statistical analysis was implemented, followed by a fuzzy cognitive map and an agent-based simulation model in order to illustrate the usage of social media and user experience in multichannel marketing. The findings suggest that, in contrast to the websites of other industries, such as logistics, where customers want to finish their inquiries as quickly as possible and leave the webpage, it is advantageous for tourism websites to keep customers’ attention moreon their website in order to increasevisibility. Additionally, the research further highlights the importance of personalization and user-engagement content to e-WOM, suggesting to tourism businesses to encourage posts made by customers and employees.

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Multichannel Digital Marketing Optimizations through Big Data Analytics in the Tourism and Hospitality Industry

How do external and internal factors drive green innovation practices under the influence of big data analytics capability: Evidence from China

The metaverse has become one of the most popular concepts of recent times. Companies and entrepreneurs are fiercely competing to invest and take part in this virtual world. Millions of people globally are anticipated to spend much of their time in the metaverse, regardless of their age, gender, ethnicity, or culture. There are few comprehensive studies on the positive/negative sentiment and effect of the newly identified, but not well defined, metaverse concept that is already fast evolving the digital landscape. Thereby, this study aimed to better understand the metaverse concept, by, firstly, identifying the positive and negative sentiment characteristics and, secondly, by revealing the associations between the metaverse concept and other related concepts. To do so, this study used Natural Language Processing (NLP) methods, specifically Artificial Intelligence (AI) with computational qualitative analysis. The data comprised metaverse articles from 2021 to 2022 published on The Guardian website, a key global mainstream media outlet. To perform thematic content analysis of the qualitative data, this research used the Leximancer software, and the The Natural Language Toolkit (NLTK) from NLP libraries were used to identify sentiment. Further, an AI-based Monkeylearn API was used to make sectoral classifications of the main topics that emerged in the Leximancer analysis. The key themes which emerged in the Leximancer analysis, included "metaverse", "Facebook", "games" and "platforms". The sentiment analysis revealed that of all articles published in the period of 2021-2022 about the metaverse, 61% (n = 622) were positive, 30% (n = 311) were negative, and 9% (n = 90) were neutral. Positive discourses about the metaverse were found to concern key innovations that the virtual experiences brought to users and companies with the support of the technological infrastructure of blockchain, algorithms, NFTs, led by the gaming world. Negative discourse was found to evidence various problems (misinformation, harmful content, algorithms, data, and equipment) that occur during the use of Facebook and other social media platforms, and that individuals encountered harm in the metaverse or that the metaverse produces new problems. Monkeylearn findings revealed "marketing/advertising/PR" role, "Recreational" business, "Science & Technology" events as the key content topics. This study's contribution is twofold: first, it showcases a novel way to triangulate qualitative data analysis of large unstructured textual data as a method in exploring the metaverse concept; and second, the study reveals the characteristics of the metaverse as a concept, as well as its association with other related concepts. Given that the topic of the metaverse is new, this is the first study, to our knowledge, to do both. 

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An exploratory content and sentiment analysis of the guardian metaverse articles using leximancer and natural language processing

The chapter addresses how small-scale enterprises fundamentally use social media to leave a footmark of their brand and have a direct mode of interaction with customers. The chapter indicates the need for SMEs to develop a social media roadmap to come up with user-friendly ideologies and use of relevant social media platforms to cater to the needs of customers. The chapter also discusses the varied social media platforms that have become an integral part of the marketing function for SMEs and play an impeccable role in successful campaigns with the sole motive of adding value to a brand face. The chapter also provides some managerial guidelines to use social media marketing in the marketing function of SMEs. 

An Unexpected Journey

Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness.

Big data-assisted social media analytics for business model for business decision making system competitive analysis

Business growth is an area that the company is expanding and seeking opportunities to make more gains. The industry decision-making process is a step-by-step process that allows professionals to address challenges by weighing evidence, examining alternatives, and selecting a path. This certain method offers a chance to analyze whether the decision is right at the end of the day. In this paper, an Improved Data Analytics Model (IDAM) has been suggested to develop business and boost industrial decision-making. New industrialization strategies are poor in market acceptance. Data analytics describes a quantitative decision-making approach and allows a company to predict the customer's behavior, boost decision-making in general, and assess the return of marketing activities. By managing these aspects, the company can maintain its market share and grow into new territories. Data analytics offer better operating efficiency and higher sales with increased business growth. The proposed method analyzes data analytics's efficiency from providing greater market value, ensuring growth and investment. The graphical process has demonstrated the financial benefit of observations for market development using data analytics. As a result, IDAM helps industries increase revenues, enhance organizational effectiveness, optimize marketing strategies and endeavor customers.

Honglei Zhang

Papers

Big data-assisted social media analytics for business model for business decision making system competitive analysis

Boosting industrial decision making and business growth using improved data analytics model