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This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

The history of online trading began in the 1960s with the emergence of electronic communication networks, which allowed the electronic execution of trades outside traditional exchanges. The internet revolution led to the development of online brokerage platforms such as E*Trade and Schwab, enabling non-institutional investors to participate in the digital trading revolution. These platforms have evolved to serve the retail investor market, eventually adapting to mobile-first and commission-free models, significantly lowering the barriers to entry for financial markets. Platforms like Robinhood and other fintech firms have rapidly gained market share by offering services and products previously unavailable, such as commission-free trades, mobile trading, and novel products such as fractional shares and cryptocurrency investing. This chapter provides an overview of the history of online trading. It also introduces several new developments in fintech and the online trading industry and discusses various controversies and future implications of new technologies.

Built upon customer engagement marketing theory and uses and gratification theory, this study examines the link between individual social media marketing (SMM) performance indicators and restaurant sales performance at the firm level. Moreover, the study investigates the moderating effect of advertising expenditure on this proposed relationship.

Random effect regression models were developed in Stata to examine the associations between SMM performance indicators, advertising expenditure, and restaurant firm revenue. Twelve years of SMM data from brands' Facebook pages were collected with a web scraper built in Python. Natural language processing was used to analyze the sentiment of user-generated content (UGC).

The results suggest that restaurant annual sales revenue increases as the volume of brand posts, “like”s, “share”s and positive comments on restaurants' Facebook pages increase. However, the total number of comments and the number of negative comments show non-significant associations with revenue. Firm advertising expenditure negatively moderates the relationships between sales revenue and the number of “like”s, “share”s, total comments and positive comments.

Restaurants benefit from making frequent posts on SNSs. Promotions that motivate online users to “like”, share, and comment on brand posts should be implemented. Firms with limited advertising budgets are encouraged to actively create buzz on SNSs due to evidenced stronger effects of UGC on sales performance than large advertisers.

This research bridges the gap by studying the effects of individual SMM performance indicators on restaurant financial outcomes. The findings support the effectiveness of SMM; and, for the first time, demonstrate that SMM could generate a more profound impact for firms with low advertising budgets.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

This review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?

There are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.

Collecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.

The study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.

The outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.

Internship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.

While surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.

This study aims to understand the perceived emotions of human–artificial intelligence (AI) interactions in the private sector. Moreover, this research discusses the transferability of these lessons to the public sector.

This research analysed the comments posted between June 2022 and June 2023 in the global open Reddit online community. A data mining approach was conducted, including a sentiment analysis technique and a qualitative approach.

The results show a prevalence of positive emotions. In addition, a pertinent percentage of negative emotions were found, such as hate, anger and frustration, due to human–AI interactions.

The insights from human–AI interactions in the private sector can be transferred to the governmental sector to leverage organisational performance, governmental decision-making, public service delivery and the creation of economic and social value.

Beyond the positive impacts of AI in government strategies, implementing AI can elicit negative emotions in users and potentially negatively impact the brand of private and government organisations. To the best of the authors’ knowledge, this is the first research bridging the gap by identifying the predominant negative emotions after a human–AI interaction.

This study aims to identify the key hotel characteristics and facilities that significantly influence customer satisfaction based on Google review scores. By applying decision tree analysis, the research seeks to determine which aspects, such as service quality, hotel facilities and location, play pivotal roles in shaping customer experiences. The objective is to provide professional with practical recommendations to improve service quality and cultivate enduring customer loyalty.

The research used a data set collected from Hotels.com, featuring various characteristics of 802 hotels in Izmir Province. Decision tree analysis was conducted using Classification and Regression Tree algorithm to explore the relationship between hotel characteristics and facilities with customer satisfaction.

The analysis revealed that the number of rooms is the primary factor influencing hotel ratings, with proximity to the airport and hotel classification also being significant. Additional factors such as public transportation distance and laundry services were important, while facilities such as ATMs, beach access and spas showed no significant impact on customer satisfaction. These findings emphasize the importance of core facilities and accessibility.

This study contributes to the literature by offering a novel approach, using decision tree analysis to assess hotel customer satisfaction with structured data. It provides practical implications for hotel managers, enabling them to make data-driven improvements to achieve customer satisfaction. The integration rules created by the decision tree model into hotel management systems can enhance operational efficiency and competitive advantage in the hospitality industry.