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Purpose — We assert that researchers developing new web interaction tools should consider an array of user motives beyond query-based information retrieval. This chapter reports on two probes used to investigate user activities that go beyond search as traditionally conceived.

Design/methodology — This chapter reviews research on user experiences with search engines and general web use. It then describes the design and case study of cards and pebbles, two search engine-based probes developed to help elicit new concepts for web-based experiences.

Findings — Participants reflect on their experiences with the probes and offer ideas regarding how to incrementally shift the traditional search paradigm and conceive of the web in new ways.

Implications/value — This investigation serves as a starting point by offering criteria that should be considered when designing new ‘beyond search’ tools.

This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning.

An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive.

The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon.

This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency.

The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies.

Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis.

This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

Wine has been produced for thousands of years and nowadays we have seen a spread in the wine culture. E-commerce sales of wine have increased considerably and online customer's satisfaction is influenced by quality and price. This paper presents a case study of the company “QuieroVinos, S.L.”, an online wine shop founded in 2015 that sells Spanish wines in two main marketplaces.

With the final target of increasing the company profits it has been designed and developed an application to track the prices of competitors for a set of products. This information will be used to set the product prices in order to offer the products both competitively and profitably in each Marketplace. This application must check, by tacking into account information such as the product cost or the minimum product margin, if it is possible to decrease the price in order to reach the top cheapest position and as a consequence, increase the sales.

The application improved in a notorious way the company's results in terms of sales and shipping costs. It must be said that without the use of the presented application, performing the price comparison process within each one of the marketplaces would have taken a long time. Moreover, as prices change very frequently, the obtained information has a very limited time value, and the competitors prices should be analyzed daily in order to take accurate decisions regarding the company's price policy.

Although the application has been designed for the wine sector and the two named marketplace, it could be exported to other sectors. For that, it should be implemented new modules to collect information regarding the competitor's price of the products selling on each corresponding marketplace.

This paper aims to examine the estate agency sector as a case study of an industry sector in which the internet business model has evolved from experimental dot.com towards the integrated use of the internet to enhance service delivery.

Data are collected by visiting a range of property related sites, including property portals and the sites of individual estate agents. The focus is on UK property sites, but some comparisons are made with US sites. The services offered by property portals are discussed and used to illustrate the potential role of the internet in estate agency.

Portals provide content in the form of information, advice and news, links to other businesses including individual estate agency chains, search facilities, and opportunities for registration which support personalisation of communication with customers. Individual estate agency chains generally have less developed, but adequate, web sites. The sector is described as having evolved through the three stages of experimentation, promotion, customer service, and, for the future, the final stage of optimal integration of the internet into business functions is on the horizon.

Building on an earlier model of the strategic development of e‐business, a four‐stage model of the evolution of internet estate agency is proposed, which includes experimentation, promotion, customer service, and integration. Research and development agendas associated with this final stage, integration, are identified.

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

Explore the use of Gender-Fair Language (GFL) by influencers on Instagram.

The clustering methodology. A digital Bag-of-Words (BoW) Method called GFL Clustering BoW Methodology to identify whether an inclusive marketing (IM) strategy can be used. Thus, this research has a methodological and practical contribution to increasing the number of marketing technology tools.

This study is original as it proposes an inclusive digital marketing strategy and contributes with methods associated with digital transfers in order to improve marketing strategies, tactics and operations for inclusive content with a data integrity approach.

Due to the limitations of the application programming interface (API) of the social network Instagram, a limited number of text data were used, which allowed for retrieving the last 12 publications of each studied profile. In addition, it should be considered that this study only includes the Spanish language and is applied to a sample of influencers from Chile.

The practical contribution of this study will lead to a key finding for the definition of communication strategies in both public and private organizations.

The originality of this work lies in its attractive implications for nonprofit and for-profit organizations, government bodies and private enterprises in the measurement of the success of campaigns with an IM communicational strategy and to incorporate inclusive and non-sexist content for their consumers so as to contribute to society.

本研究擬探究有影響力的人士在使用即時電報 (Instagram) 時、如何使用性別中立語言。

研究使用了聚類分析法;具體來說, 研究人員採用一個叫 GFL聚類詞袋法的數位詞袋分析法, 去確定研究可否使用信息管理策略。因此, 本研究在行銷科技方面、添加了一個工具, 就此而言, 本研究在學術的研究法和實務方面、均作出貢獻。

本研究建議了一個包括一切的數位行銷策略;研究亦構建了若干與數位傳輸有關的方法, 以能利用數據完整性的理念, 為行銷策略、行銷戰術和市場營銷, 在內容的全面包含度方面取得改善。

因為社交網站即時電報的應用程式介面有其局限, 故使用了少量的文本數據, 這可使每個被探討的傳略的最後12個發佈能被撿回。另外需注意的是、本研究只涵蓋西班牙語, 而且, 研究使用的樣本只是來自智利有影響力的人士。

本研究在實務方面的貢獻是、它為探討在公共機構和私營機構內使用的溝通策略的定義上、帶來重要的啟發和發現。

本研究的原創性在於它給營利和非營利組織、政府機關和私人企業帶來頗具吸引力的啟示。而這些啟示是與測量以包括一切的行銷溝通策略進行的專門活動是否成功有關的。另外, 涵蓋一切和無性別歧視的內容被納入供消費者使用, 以此為社會帶來裨益。

Both Hungary and Germany belong to the old-world wine-producing countries and have long winemaking traditions. This paper aims at exploring and comparing online branding strategies of family SME (small and medium sized enterprises) wineries at Lake Balaton (Hungary) and Lake Constance (Germany), as two wine regions with similar geographic characteristics.

This paper, based on a total sample of 37 family wineries, 15 at Lake Balaton and 22 at Lake Constance, investigates the differences in brand identity on the website, brand image in social media and online communication channels deployed in both wine regions. The study applies a qualitative methodology using MaxQDA software for conducting content analysis of texts in websites and social media. Descriptive statistics and t-test were conducted to compare the usage of different communication channels and determine statistical significance.

At Lake Balaton, the vineyard, the winery and the family, while at Lake Constance, the lake itself and the grape are highlighted regarding family winery brand identity. The customer-based brand image of Hungarian family wineries emphasizes wine, food and service, with the predominant use of Facebook. In the German family wineries, the focus of brand identity is on wine, friendliness and taste and includes more extensive usage of websites.

The paper deploys a novel methodology, both in terms of tools used as well as geographic focus to uncover online branding patterns of family wineries, thereby providing implications for wine and tourism industries at lake regions. It compares the share of selected most-used words in the overall text in websites and in social media, and presents the key findings from this innovative approach.

The establishment of a task force is a common response by businesses seeking to create significant organizational change. Xeptron, a US‐based computer corporation, perceived a need for this approach to improve its reverse logistics management in the Asia‐Pacific region. Reverse logistics means different things to different people. One authority has described it as the “process of moving a product from the point of consumption to another point for the purpose of recapturing the remaining value, or for the eventual proper disposal of the product”.

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

This study aims to analyse and evaluate the methodology followed by the Times Higher Education Impact Rankings (THE-IR), as well as the coverage obtained and the data offered by this ranking, to determine if its methodology reflects the degree of sustainability of universities, and whether their results are accurate enough to be used as a data source for research and strategic decision-making.

A summative content analysis of the THE-IR methodology was conducted, paying special attention to the macro-structure (university score) and micro-structure (sustainable development goals [SDG] score) levels of the research-related metrics. Then, the data published by THE-IR in the 2019, 2020 and 2021 edition was collected via web scraping. After that, all the data was statistically analysed to find out performance rates, SDGs’ success rates and geographic distributions. Finally, a pairwise comparison of the THE-IR against the Times Higher Education World University Rankings (THE-WUR) was conducted to calculate overlap measures.

Severe inconsistencies in the THE-IR methodology have been found, offering a distorted view of sustainability in higher education institutions, allowing different strategic actions to participate in the ranking (interested, strategic, committed and outperformer universities). The observed growing number of universities from developing countries and the absence of world-class universities reflect an opportunity for less-esteemed institutions, which might have a chance to gain reputation based on their efforts towards sustainability, but from a flawed ranking which should be avoided for decision-making.

University managers can be aware of the THE-IR validity when demanding informed decisions. University ranking researchers and practitioners can access a detailed analysis of the THE-IR to determine its properties as a ranking and use raw data from THE-IR in other studies or reports. Policy makers can use the main findings of this work to avoid misinterpretations when developing public policies related to the evaluation of the contribution of universities to the SDGs. Otherwise, these results can help the ranking publisher to improve some of the inconsistencies found in this study.

Given the global audience of the THE-IR, this work contributes to minimising the distorted vision that the THE-IR projects about sustainability in higher education institutions, and alerts governments, higher education bodies and policy makers to take precautions when making decisions based on this ranking.

To the best of the authors’ knowledge, this contribution is the first providing an analysis of the THE-IR’s methodology. The faults in the methodology, the coverage at the country-level and the overlap between THE-IR and THE-WUR have unveiled the existence of specific strategies in the participation of universities, of interest both for experts in university rankings and SDGs.