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Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

The study explores the decision-making logics driving the usage of social media among internationalising small and medium-sized enterprises (SMEs), and how it supports entrepreneurial opportunity recognition. The author draws on qualitative data from four internationalising firms all of which had adopted social media. The findings show that causal decision-making logic is the main element driving the initial usage of social media (i.e. pre-adoption). Concerning ongoing usage, the author found evidence of both causal and effectual logic; the choice being dependent on the firm’s resources. The findings also show that both drivers of social media users have a paramount role in entrepreneurial opportunity recognition.

This research aims to explore the potential of big data technology for sustainable management and investigate its impact on tourism. Its goal is to obtain meaningful results related to sustainable tourism to understand better how big data technology plays a role in decision-making by looking at it through the lens of various studies.

A systematic review, which is a qualitative method, was used in this study. The analysis was conducted using secondary data from the Web of Science Core Collections databases.

Big data technology has many economic benefits for businesses, but it also has managerial benefits such as forecasting, decision-making and tracking human and machine behaviour. Furthermore, big data technology offers sustainability benefits such as resource efficiency, preventive quality systems, carbon reduction and environmentally friendly production.

Big data's capabilities enable businesses to make more informed business decisions, improve overall business performance and contribute to achieving various SDGs. Big data, which aids in developing smart and sustainable tourism in the tourism sector, assists tourism managers in making economically, socially and environmentally sound decisions.

Climate change is a global issue with far-reaching environmental, social and economic consequences. As more people become aware of these consequences, pressure is mounting on governments and businesses to implement ambitious and required climate mitigation and adaptation plans to reduce and finally stop making the climate crisis worse. One of these strategies is just transition, which is defined as the call for climate transformation that prioritises the social and environmental needs of workers and vulnerable groups, especially in the context of transitioning away from fossil fuels, while leaving no one behind. This chapter first provides an overview of just transition through a review of the literature and bibliometric analysis. Then, it discusses just transition in policymaking, comprising reactive, proactive and transformational just transition approaches. This is followed by a discussion on barriers to just transition. Finally, the chapter offers a practical example of transformational just transition approach by reporting some preliminary findings from a case study in the coal mining town of Collie on Wilman Boodja, Western Australia.

The pandemic has affected people across the world. Businesses have been impacted and in a way have affected the employment scenario worldwide. The skill development initiative is playing a major role in bringing back the affected to the mainstream. Skill development efforts have always been a true solution to income generation especially in context to developing the underprivileged segment of the nations. Numerous countries are enabling skill development through the corporate social responsibility (CSR) practices. India is one such country which has undertaken lot of effort in enabling Skill Development by encouraging impact-based CSR practices. It would be interesting to understand the impact of pandemic on work and how the skillsets are expected to change with the change in the working environment. The chapter mainly focusses in identifying the workplace changes with special reference to Education, Information technology, Retail, Mining and Power Generation sectors in the Indian context. It has also attempted to share the existing skill gap in fulfiling the organisation's expectations and the strategies addressing the skill gap. The chapter also shares best practices in skill development and focusses mainly on understanding the impact of such efforts. The chapter uses case study as a methodology and includes qualitative data analysis of impact interviews. The chapter uniquely attempts to share the policy, programmes, outcomes and major road blocks especially in context to skill development in the Indian scenario and how CSR drive has been instrumental in addressing the skill gap in India.

Contemporary organisations are data-driven with sophisticated and strong Information Technology (IT) supporting the Business Intelligence (BI) systems. Due to the Industrial Revolution 4.0, businesses are subjected to volatility, uncertainty, complexity, and ambiguity (VUCA). The accuracy and agility of decision making (DM) play a key role in the success of contemporary organisations. Traditional methods of DM, i.e. based on tacit knowledge, are no longer relevant in the constantly altering business scenarios. Innovations in the IT domain have accomplished systems to gather and process business data at an exponential speed. Context-driven analytics along with computation capability and performance-driven visualisation have become an implicit need for businesses. BI systems offer the capabilities of data-driven DM simultaneously allowing organisations to predict the future business scenarios. Qualitative research is conducted in this chapter. In the research, interviews, questionnaires, and secondary data from previous research are used as data source. Case studies are discussed to clarify the business use cases of BI systems and their impact on managerial DM. Theoretical foundations are stated basis a thorough literature review of the available body of knowledge. The current environment demands data-driven DM in an organisation at all levels, i.e. strategic, tactical, and operational. Heterogeneous data sources add unlimited value to the decision support systems (DSSs). The BI systems have become an integral part of the technology landscape and an essential element in managerial DM. Contemporary businesses have deployed BI systems in all the functions.