Search

The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to advertise and sell products and services is very important. Due to the advancement of artificial intelligence and its development in the field of advertising and sales, professionals now have the tools to completely redefine the current understanding of branding, marketing, advertising and sales. The growing popularity of the Internet and the increased use of mobile devices are generating massive amounts of consumer data that feed artificial intelligence-based systems. This research is a type of mixed research with a qualitative and quantitative approach, which is a survey descriptive study in terms of its purpose, application, and in terms of data collection. The statistical population of the research was managers and experts in the field of digital marketing and IT in the field of advertising and sales, who were selected using the snowball sampling method. In the qualitative part, the tools for collecting information were library and articles review, interviews, and in the quantitative part, questionnaires. In the qualitative part of the data analysis method, using the theme analysis that was compiled with MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test. According to the results of the research, 7 main themes, 22 sub-themes and 44 codes were discovered, which included the consequences of using artificial intelligence and machine learning in advertising and sales

This study reviews the extraordinary effect of simulated intelligence on monetary insight, zeroing in on risk appraisal, execution measurements, network safety, and worldwide coordinated effort. It dives into the turn of events and utilization of man-made reasoning (artificial intelligence) methodologies, for example, AI and information science, and NLP to address worldwide monetary difficulties. Beginning with a verifiable setting of monetary emergencies, the review assesses current monetary knowledge techniques as well as administrative contemplations for simulated intelligence in finance. The developing organization in monetary direction is featured by moral ramifications, human-computer-based intelligence joint effort, and contextual analyses. The study goes on to talk about AI-driven risk assessment, explaining how AI makes it easier to identify risks in real time. Through continuous monitoring, cybersecurity measures, and a comprehensive examination of AI's role in reshaping financial management, the study emphasizes. Finally, the study demonstrates AI's potential to create a financial ecosystem that is more adaptable, efficient, and secure, providing valuable insights for academics, professionals in the industry, and policymakers.

In today's world, the hospitality industry is undergoing significant transformations driven by innovative technologies that open new horizons for enhancing customer service. Hotel management increasingly relies on digital solutions that improve the quality of services, personalize guest interactions, and refine internal business processes. Among the key technologies transforming the hotel sector, artificial intelligence, the Internet of Things, automation, and big data stand out. Their implementation fosters the effective development of hotels and enhances their competitiveness in the global market. In this context, studying contemporary innovations in hotel management, their impact on the customer experience, and analyzing the challenges associated with implementing cutting-edge technologies is a particularly relevant task for the academic community. This article aims to explore how innovative technologies transform the approach to customer service in hotels, specifically through the examples of hotel chains such as Hilton and Marriott International.

Data analytics plays a crucial role in increasing the effectiveness of management decisions by the logistics of intermediaries. The aim of the article is to identify the extent to which the quality of data analytics affects the effectiveness of decision-making in the logistics intermediaries, in particular, the speed of delivery of the studied companies. The study employed regression and correlation analysis to identify key influencing factors in terms of data analytics on the effectiveness of management decisions of the logistics intermediaries. The significance of investment in the qualification of analysts (with a coefficient of -1.6754), analytical tools (with a coefficient of -1.2575), and integration of analytics in decision-making processes (with a coefficient of -3.2511) directly affect the reduction of delivery time. It is emphasized that each analytical project contributes to the reduction of delivery time by 0.48 hours. Correlation analysis confirmed the relationship between the efficiency of logistics and the level of qualification of analysts (-0.283617), investment in analytical tools (-0.257322), the number of analytical projects (-0.343792), the level of integration of analytics (-0.712058). The strongest correlation was observed for the integration of analytics in management decision-making. It is recommended to focus on the development of analytical competencies, increase of investment in tools, intensification of projects, and integration of analytics in strategic management. Further research is planned on the use of artificial intelligence to optimize management decisions in logistics as part of ensuring the company’s sustainable development.

In comparison to numerous other ASEAN nations, Indonesia continues to be categorized as a country with a comparatively high number of fraud instances. To reduce future fraud cases, particularly in Indonesia, studies that keep concentrating on examining different aspects and techniques useful in identifying different kinds of fraud is still required. The current study aims to examine how political skill and big data relate to fraud detection. A survey with a quantitative focus was employed for this study. The instrument for gathering data was questionnaires. The study’s participants comprised 147 auditors employed with the Financial and Development Supervisory Board of the Republic of Indonesia (BPKP RI) and the Audit Board of the Republic of Indonesia (BPK RI). In this study, structural equation modeling (SEM), with support from the SmartPLS application, is the method of statistical testing. Based on the findings, political skill has been demonstrated to improve fraud detection. This study further establishes the beneficial impact of big data on fraud detection.

Precision medicine (PM) is an approach to healthcare that customizes treatment and prevention strategies based on individual characteristics such as genetics, environment, and lifestyle. By utilizing detailed data from diverse populations, precision medicine aims to provide accurate and effective medical care tailored to each person’s unique profile. This approach relies on the diversity of local populations which is increasingly captured through large-scale data sets, often referred to as Big Data. Due to the underrepresentation of non-European individuals in human genetic studies, many countries with diverse and underrepresented populations must build their own reference database due to a lack of holistic global perspective. This research investigated a population sub-group to better understand the factors shaping their attitudes of PM adoption in an Asian developed context. This research was based on the millennial generation in Singapore (born between 1981 and 1996). The study employed a quantitative online survey, based on the TAM theoretical framework. A total of 377 valid responses were received and the results were analysed using the structural equation modelling (SEM) technique. Results showed that perceived data security, subjective technical knowledge, trust in government, subjective norm and perceived usefulness influenced the attitude of millennials towards precision medicine and provided relevant insights for the successful implementation of PM approach in the future.

The study aims to systematically review the role of big data visualization in the auditing field. Employing a qualitative approach, the research utilized the Systematic Literature Review method to gather pertinent data. The study used 35 published articles extracted from different journals and reputed publishers, which served as the sample. This research provides an overview of the data visualization trend literature in auditing and describes the associated capabilities and practices. The result showed a fluctuating pattern in the publication years, highlighting a relatively recent research area, with the highest volume noted in 2021. The findings indicated that most articles used qualitative methods, such as conceptual or case study research. This suggests that empirical research is needed to add to the body of knowledge of data visualization in auditing. Furthermore, the study identified nine of the main capabilities of data visualization to improve audit quality. The study offers valuable perspectives for both practitioners and researchers, detailing the progress made to date and identifying future avenues of inquiry within the discipline. The study contributes to extending the literature on accounting information systems and auditing and provides a knowledge gap for further analysis.

Esta investigación cualitativa se centra en identificar y caracterizar los errores lógicos frecuentes en el análisis y la visualización de datos dentro de las arquitecturas de Big data, a través de una revisión bibliográfica exhaustiva, en la que se categorizan los errores en falacias, sesgos y paradojas. Este estudio busca servir como guía para profesionales en entornos públicos y privados, así como señalar áreas de investigación relacionadas con la epistemología y la ética en el ámbito del Big data. El texto plantea dos preguntas: ¿Cuáles son los errores lógicos más comunes que se encuentran en el análisis y la visualización de datos dentro de las arquitecturas de Big data? ¿Cómo pueden abordarse estos errores para mejorar tanto la calidad de la toma de decisiones como la ética en este campo? El artículo tiene cuatro objetivos: identificar y caracterizar las falacias, sesgos y paradojas más comunes en el análisis y la visualización de datos en arquitecturas de Big data; proporcionar orientación y conocimiento a profesionales que trabajan en el análisis y la visualización de datos en entornos públicos y privados; destacar la importancia de la epistemología y la ética en el contexto del Big data; y establecer líneas de investigación adicionales relacionadas con la mejora de la calidad de los análisis de datos y la promoción de prácticas éticas.

The article is devoted to the topical issues regarding the implementation of Big Data technologies in public finance management. The application of Big Data has the potential to enhance transparency and accountability in the use of budgetary resources, increase trust in government, improve the efficiency of budget resource utilization, better understand citizens' needs, and engage the public in public finance management. The purpose of the study is to explore theoretical, methodological, and practical aspects, as well as to develop recommendations for the implementation of Big Data processing and analysis technologies to enhance public participation in public financial management. The article examines traditional methods of civil engagement in the budgetary process, identifies their disadvantages, and explores Big Data technology potential based on Computational Linguistics and Machine Learning to strengthen public participation. Developments in sentiment analysis and opinion mining have been adapted to the field of public finance. A generative model for analyzing public sentiment on social networks regarding public finance management has been constructed and tested. The approaches developed for using Big Data technologies can be implemented in the field of public finance to enhance public participation in their management as advisory tools for the realization of representative democracy and require further theoretical elaboration and practical application to improve the analysis of alternative sentiments, prevent manipulation of public opinion, and abuse within the network.

The digital revolution has led to the proliferation of big data, which poses distinct obstacles to data analysis due to its large quantity, diverse nature, and high speed. These challenges are a direct consequence of the digital revolution. Hence, our article examines the notable impact of big data technology on investors' decision-making. This is seen in the choices made by investors to divest from their investments or reallocate their funds to other assets in response to perceived dangers in the Iraq market and by considering the indirect influence of the timing of financial reporting on gathering the necessary data. A survey was created based on the topics discussed in the studies. Our analysis focuses on the period from January to March 2024. A total of 157 respondents participated in this research, comprising academics from universities, financial experts, and investors in companies listed on the Iraq Stock Exchange. This study used the statistical program SPSS and the course analysis method. The study discovered a statistically significant impact of big data, including its attributes, such as quantity, diversity, and speed, on the timing of financial reporting. The effect of big data on investors' decision-making is statistically significant. The time of issuing information has a significant impact on investors' judgments. Furthermore, investing decisions in Iraqi companies listed on the Iraqi Stock Exchange are marginally impacted by the preparation of financial reports inside the big data framework.

The enormous personal data breach of recent years is pushing regulators to tighten control over big data. The paper aims to analyse changes in big data regulation in Russia, and its possible effects on the competitive environment and economic performance of digital markets. New institutional economics theory, as well as some elements of law economics and industrial organization theory constitute the theoretical framework of the study. The research methods include comparison of structural alternatives and economic and statistical analysis. The empirical evidence covers 2021 data by Russia’s Federal State Statistics Service (Rosstat). The results of the study demonstrate that strengthening regulation of personal data (1) will widely affect all businesses involved in big data processing in all sectors of the economy, (2) will reduce innovation activity and investment attractiveness of digital markets in the short term, (3) will have a negative impact on small and medium-sized businesses. The statistical analysis indicates that there is room for applying an industry-wide approach, primarily ‘framework’ regulation, to big data markets. The research findings can be used by the regulator when developing or altering big data legal standards to prevent adverse economic implications.

The research aims to build a model based on sales data for all automotive products in Indonesia using data mining with a k-means approach. This study uses automotive product sales data from January 2017 to September 2022. The lowest Davis-Bouldin index shows that three clusters (k=3) have the best performance. Based on the clustering results, 92% of the items are in cluster 0, 1% in cluster 1, and 7% in cluster 2. In addition, the clustering results show that cluster 1 is a car product with high sales volume. Cluster 2 is a car product with medium sales volume. Furthermore, cluster 0 is a car product with low sales volume. Business people or related parties can use data visualization and extraction from clustering results to learn the latest insights and information in determining business strategies, policies, and decisions to improve business competitiveness.

The study analyzes the impact of e-commerce on the purchase of natural health products in Ecuador, seeking to understand how to overcome traditional access barriers and improve the shopping experience. Ecommerce offers convenience by allowing purchases from home, covering a variety of nutritional supplements and providing detailed product information. This promotes health and wellness while fostering an online community. Safety, reliability and local customization are critical to ensure a positive experience. In a context where e-commerce is increasingly relevant, this study offers valuable information to improve access and utilization of natural health products through online platforms in Ecuador. [ABSTRACT FROM AUTHOR]

The quantity of data collected has expanded tremendously as the world becomes more dependent on technology and digitization, offering organizations and sectors chances for innovation and development. Concerns regarding the effects of such fast expansion on the environment have been expressed as a result of this increase, however. Prioritizing environmental sustainability is now more crucial than ever in this situation. This research develops a theoretical model to assess the relative advantages Big Data have on R&D via "substitution effects" and "complementary effects," and further leads to directed technological change and its influence on environmental quality. We examine various effects on environmental quality while taking into account and without taking into account the extensive data application to understand the function of Big Data better. Big Data and environmental sustainability may be used to provide major commercial advantages, such as cost savings, higher productivity, and enhanced reputation. Big Data in environmental monitoring may be used to better detect and solve sustainability challenges including resource depletion, waste management, and air and water pollution. Big Data analytics may facilitate better forecasting and decision-making, enabling businesses and industries to develop more sustainable practices and strategies. Big Data management needs a dedication to sustainability principles and practices, as well as a thorough awareness of the environmental impact of the company or sector. In the era of Big Data, cooperation between enterprises, industries, governments, and other stakeholders is crucial for advancing environmental sustainability. The paper also addresses some of the difficulties in using Big Data for environmental sustainability, including issues with data privacy, quality, and security. In conclusion, the fusion of Big Data with environmental sustainability presents both enormous potential and formidable difficulties for enterprises and sectors. Addressing these issues and fostering stakeholder engagement are crucial if Big Data is to achieve its full promise for environmental sustainability. [ABSTRACT FROM AUTHOR]

Credit rationing hindered the development of MSEs. Big data credit technology creates a great opportunity to address this issue. Then, how does big data credit technology affect and to what extent can it alleviate the credit rationing of MSEs? Based on the bounded rationality economic man hypothesis, the evolutionary game model of banks and MSEs under the traditional mode and big data credit technology are constructed, respectively, in this paper, and the evolutionary trajectory of bank-enterprise credit strategies under the two modes are comparatively analyzed. The results show that it is hard to alleviate the credit rationing of MSEs under the traditional mode. However, under big data credit technology, when the overall credit level of MSEs is high, the credit rationing of MSEs will be effectively alleviated. When the overall credit level of MSEs is too low, it is difficult to determine whether big data credit technology can alleviate the credit rationing of MSEs. In order to verify the feasibility of big data credit technology in alleviating the credit rationing of MSEs, a simulation experiment is conducted to compare the differences in the credit rationing of MSEs with different credit levels under the two credit modes. We found that the credit rationing of MSEs is always lower under big data credit technology than under the traditional mode. Therefore, big data credit technology can effectively alleviate the credit rationing of MSEs under any circumstances. The research provides theoretical support for banks to apply big data credit technology to achieve a win-win situation for both parties.

Abstract This research aims at producing a thorough and accurate analysis on how big data as a service and business intelligence capabilities provide a series of opportunities for small and medium enterprises to succeed in E-commerce. The statistical data were drawn from samples of accountants within small and medium enterprises, spanning two periods, the period of COVID-19 pandemic and the period of new normal. The structural equation modeling was applied in this study. The value of fine-grained insights in this work could guide policy-makers and practitioners to sense and seize how to implement E-commerce in an efficient and effective manner.

The contribution examines how the accounting profession is affected by specific technologies, which are artificial intelligence, big data, blockchain and cloud computing, and thus contributes to the discussion about the use of technology in the accounting profession with its content. According to the literature review, these technologies can dramatically automate and digitize the work of accountants. For that reason, it is important for accountants to understand these technologies, as these technologies are constantly evolving and more companies, will incorporate them into their business processes. The aim of the overview article is to theoretically define artificial intelligence, big data, blockchain and cloud computing, based on an overview of selected research, to analyze available articles on this topic and to propose appropriate recommendations. Although digital tools can replace a whole range of accounting activities, it is important for accountants not to consider them as a threat, but rather as an aid in their work. For this purpose, it is advisable for accountants to acquire new skills and characteristics, or deepen existing ones in such a way as to be competitive in the digital world. Emphasis needs to be placed on the education of employees in companies, but also on changing the content of teaching in educational institutions, namely in the field of digital technologies and skills in the field of information and communication technologies, which already greatly influence contemporary accounting. At the same time, it is necessary to make such legislative changes that will reflect the current problem of the entire industry 4.0.

In this paper, we aim to showcase the general context, characteristics of NoSQL, data management using NoSQL database and an exemplification in MongoDB. Moreover, organization and management of large volumes of data (Big Data), the trend in the field of advanced database systems and business intelligence will be depicted in this research. The large volumes of data in various fields of activity require adequate management in order to gain a competitive advantage or to achieve the quality of decisions. Various fields generate large volumes of data, such as: consumerism and home devices, smart infrastructure, security and surveillance, healthcare system, transportation, retail and industrial sectors, others.

Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors are expected to influence customer evaluations. Therefore, the present study aims to: (1) analyze customer reviews of hands-on HMD VR devices, provided with new user experience (UX), using text mining, and artificial neural network techniques; (2) comprehensively examine variables that affect user evaluations of VR devices; and (3) suggest major implications for the future development of VR devices. The research procedure consisted of four steps. First, customer reviews on HMD VR devices were collected from Amazon.com. Second, candidate variables were selected based on a literature review, and sentiment scores were extracted. Third, variables were determined through topic modeling, in-depth interviews, and a review of previous studies. Fourth, an artificial neural network analysis was performed by setting customer evaluation as a dependent variable, and the influence of each variable was checked through feature importance. The results indicate that feature importance can be derived from variables, and actionable implications can be identified, unlike in general sentiment analysis.

Background/ Objectives: Big Data is a tremendously large data set that has been analyzed, managed, managed and validated through a regular info management application. Banks are of all financial services industries, that handle overwhelming quantities of transaction info managed, scrutinized, in addition to employed for the gain of banks and definitely the customers. Thus, this specific analysis paper examined how BDA is handled in Malay/Indian/Indonesian manufacturing banks. The elements which have a larger impact on banks in handling large data were analyzed, in addition to how analytics generates worth for the business. Method/Statistical Analysis: Secondary data was collected from several sources, such as articles, journals and websites. The elements including great data management, consumer segmentation, risk management, fraud detection, in addition to business value of banking industries are studied. A conceptual framework was produced to spotlight the components that have a much better impact on big data management in the banking business. Findings: From the analysis, it's examined that big data analytics has pushed a noticeable change in the business worth of banks, as well as the components that influence the organization. Application/Improvements: Banks must revamp their software architecture for dealing with key information, and stick to the brand new technologies which boost the internet business worth of the company.

This research has been done with the aim of developing Iran's banking scenarios with an emphasis on big data. The current research is practical in terms of orientation and exploratory in terms of the goal. It is also mixed in terms of its philosophical, pragmatic and methodological foundations. To carry out the research in the first stage, 20 key drivers of the research were extracted through literature review and interviews with banking and technology experts. After screening with the fuzzy Delphi method, 8 factors were removed and the rest were evaluated with the Marcus decision making technique. The findings of the research show that the two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. According to the findings of the research, considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.

AbstractAs the concept of Big Data takes hold in the corporate world, modern businesses are making concerted efforts to manage data silos in advance of centralized data management. The multi-cloud structure of the data fabric provides a realistic approach to managing various forms of data. This study explores how data fabric, which is a useful way of organizing data, affects decision-making and risk assessment through Structural Equation Modeling (SEM) using IBM AMOS software. The study collected data from 200 respondents, representing a 67% response rate, out of 300 management experts in Amman-Jordan who conditionally agreed to participate in the research. The study finds a positive relationship between data fabric and decision-making, data fabric, and risk management. The findings of this study suggest that strengthening the relationships between data fabric, decision-making, and risk management can play a critical role in fostering successful decision-making for crucial elements and risk management in crises, ultimately contributing to the overall success of the business.

AbstractThis study examines the interplay of air connectivity, sports events, infrastructures, and fiscal support during the period 2017 and 2022 in a designated area called Special Economic Zone in Mandalika, Lombok Island, West Nusa Tenggara to boost tourism development in Indonesia by utilizing big data cognitive analytics. We examine the tourism development impacted by the MotoGP event in 2022 and air connectivity. Further, this paper discusses the network connectivity of flights at Zainuddin Abdul Madjid International Airport during the COVID-19 Pandemic and the new normal. We found that the combination of an international airport, globally recognized sports events, and government support has directly and positively improved the tourism industry’s performance in the country and especially within Lombok Island. We suggest policy recommendations to support economic activities in Mandalika’s Special Economic Zone and its hinterland to maintain business sustainability and utilize the existing infrastructures at the optimum level. Lessons learned from the Indonesian experience could help other developing countries that are devising policies and strategies to develop the tourism industry by employing proper instruments such as infrastructure, events, and fiscal policies.

The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions.

The fusion of cloud computing with big data analytics has emerged as a catalyst for transformative insights and operational efficiency in a data-driven environment. The study investigates the function of cloud computing in the context of big data analytics, outlining its importance, potential, and constraints. It demonstrates the development of cloud computing platforms and the services they provide for storing, processing, and analyzing data. In addition, it explores the broad spectrum of big data analytics tools, platforms, and methodologies, highlighting the scalability and adaptability they accomplish in cloud contexts. This study explores the distinct benefits and difficulties that arise as firms progressively move their analytical workloads to the cloud, examining crucial factors including security, compliance, and costeffectiveness. The article examines the transformative effects of cloud-based big data analytics on several industries, from healthcare to finance, revealing creative solutions and predictive capabilities. It does this by drawing conclusions from real-world case studies. [ABSTRACT FROM AUTHOR]

Big data is at the heart of the insurance industry through the uses it provides, where the year 2022 is considered the beginning of the “digital revolution” when humans were able to store more digital information in technological tools than ever before. Research results have shown the impact relationship between big data and various industries, including the insurance industry. Big data has improved all aspects of the insurance process, from pricing and underwriting to claims management and customer service to ultimately more effective risk management. Based on practical and theoretical practices in this framework, the question arises whether big data has brought about development in the insurance industry. Therefore, the purpose of this study was to gain a better understanding of the impact of big data on all aspects of the insurance industry. The research findings showed that the quantity and quality of data collected and used by insurance companies directly impact the services produced and developed. Big data enables insurers to identify patterns, trends and behaviors, allowing them to develop customized products and services. Also, by collecting and utilizing quality big data, insurance companies can provide more efficient and effective services, improving customer satisfaction and increasing profitability. Although big data is a lucrative opportunity for the insurance industry, it is also a threat as companies that need the means to access big data, technologies and skills will see their competitiveness drop significantly in the future. On the other hand, intermediary platforms, particularly GAFTA (Google, Apple, Facebook, Twitter, Amazon) that control the entire data value chain, can seek a large percentage of profits by providing the value chain to insurers, or the purchase of these platforms for vulnerable insurance companies, allowing them to dominate the insurance market.

Social media is an essential tool and channel for tourism industry research. It has created significant value for tourism consumption, management, and trends. However, rare scientific achievements uncover sustainable tourism on Twitter, a famous social media worldwide. Therefore, this study aims to conceptualize sustainable tourism from the social media perspective. Based on 2,201 tweets, the study uses Nvivo12.0 to encode content thematically to explore the connotations and concepts of sustainable tourism. The study identified three crucial dimensions: tourism for sustainable development, sustainable tourism management, and sustainable tourism types. Tourism for sustainable development aims to promote the positive effects of sustainable practices in the tourism industry on various areas of life, economy, society, and environment. Moreover, its content serves for the better development in the future. Sustainable tourism management focuses on industries, leaders, experts, programs, and sectors. In addition, the study categorized sustainable tourism by entity, purpose, destination, and mode to determine the type of sustainable tourism. The paper helps to inspire stakeholders in the tourism industry to understand and pay attention to the content in the post-Covid-19 era and also provides recommendations for the tourism market, the tourism sector, and tourism consumers.

Systematic Literature Review is a term that refers to a particular study or research methodology designed to collect and evaluate research on a particular topic. SLR research is conducted for a variety of purposes, including identifying, reviewing, evaluating, and interpreting all available research on a topic of interest with a specific research question of interest. Cloud is a computing method based on internet technology. The object of this research is to find out how to implement Cloud Computing in the business world. The research method used is in the form of Research Questions (RQ), Search Process, Inclusion and Exclusion Criteria, Quality Assessment, Data Collection, Data Analysis, and Deviation from Protocol (Direct Reports). From journal sources that have carried out the inclusion and exclusion process and research (QA). It is known that there are many applications of Cloud Computing in the business world, including: 1. Data Storage and Collaboration, 2. Large-scale computing (Big Data), 3. Business Applications, 4. Provision of Development Environments, etc. Meanwhile, the benefits can be Scalability and Elasticity, Cost Efficiency, Global Accessibility, Data Security, etc. From the literature review research that has been done, it can be concluded that cloud computing is a computing paradigm or model that involves providing computing resources, such as infrastructure, platforms, or software, via an internet network. From the journals that have been reviewed, it is explained that there are many applications of cloud computing in the business world such as storing and sharing data using Google Drive, using Big Data using Google BigQuery, business applications such as Microsoft Office 365 for office productivity, providing an environment development using Google App Engine, infrastructure virtualization, and Internet of Things (IoT). [ABSTRACT FROM AUTHOR]