Search results

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

The purpose of this paper is to present the freely available finite element simulation software Pyrit.

In a first step, the design principles and the objective of the software project are defined. Then, the software’s structure is established: The software is organized in packages for which an overview is given. The structure is based on the typical steps of a simulation workflow, i.e., problem definition, problem-solving and post-processing. State-of-the-art software engineering principles are applied to ensure a high code quality at all times. Finally, the modeling and simulation workflow of Pyrit is demonstrated by three examples.

Pyrit is a field simulation software based on the finite element method written in Python to solve coupled systems of partial differential equations. It is designed as a modular software that is easily modifiable and extendable. The framework can, therefore, be adapted to various activities, i.e., research, education and industry collaboration.

The focus of Pyrit are static and quasistatic electromagnetic problems as well as (coupled) heat conduction problems. It allows for both time domain and frequency domain simulations.

In research, problem-specific modifications and direct access to the source code of simulation tools are essential. With Pyrit, the authors present a computationally efficient and platform-independent simulation software for various electromagnetic and thermal field problems.

This paper presents an in-depth investigation on how brands may concur to the co-creation of consumers’ experiences. In particular, the purpose of this paper is to clarify the main types of co-created experiences that consumers may encounter as a result of social media brand communities.

To identify the main types of co-created experiences, a digital investigation has been used as the main method of analysis. The authors draw their digital investigation on the digital methods paradigm.

Four principal types of co-created experiences have been identified and conceptualized, namely, brand’s products’ individual usage experiences, auto-celebrative experiences, brand’s products’ communal usage experiences and collective celebration experiences.

Results stress the importance for brand strategists to involve members of social media brand communities to stimulate co-creation experiences. Specifically, it emerges that the simultaneous interaction among members of the community and the brand may directly affect co-creation experiences.

La presente investigación se propone analizar en profundidad cómo las marcas pueden estar de acuerdo con la co-creación de las experiencias de los consumidores. En particular, el objetivo de la investigación es aclarar cuáles son los principales tipos de experiencias co-creadas que los consumidores pueden experimentar debido a su participación en las comunidades de marcas de redes sociales.

Para hacerlo, en primer lugar, se han identificado los factores que influyen en la co-creación de las experiencias de los miembros de las comunidades de marcas. En particular, el punto de partida de esta investigación está representado por el papel de otros consumidores y de la marca en la co-creación de experiencias. Con el fin de identificar los principales tipos de experiencias co-creadas, se ha utilizado una investigación digital como el principal método de análisis. Dibujamos nuestra investigación digital en el paradigma de Métodos Digitales.

Se identificaron y conceptualizaron cuatro tipos principales de experiencias co-creadas.

Los resultados enfatizan la importancia de que los estrategas de marca involucren a los miembros de las comunidades de marcas de medios sociales para estimular la co-creación de experiencias. Específicamente, surgió cómo la interacción simultánea de otros miembros de la comunidad y la marca puede afectar la co-creación.

Co-creación de valor, Comunidades de marca, Experiencias de los consumidores, Experiencias co-creadas, Investigación digital, Marketing experiencial

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users.

The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models.

The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated.

The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place.

The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions.

This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体（或地点）和用户的感知（或方面）。

我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达（西班牙）的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。

测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。

我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。

该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。

本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT（深度学习模型）训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性（消极性）的原因。

The study aims to promote independent mobility for persons with visual impairment or legal blindness (VIB) by developing a Web-based wayfinding application using geographic information systems (GIS). While the literature mainly focuses on technical devices presenting results for wayfinding, a lack of integration of user needs is identified. The inclusive, participative developed application offers step-by-step directions for pre-trip planning through an accessible user interface.

The paper presents a semi-automated approach to extract a pedestrian routing network data set based on open government data and field survey. User profiles calculate different routes using a weighting scheme for landmarks, orientation hints, infrastructure of crossings and sidewalks. The adoption of ArcGIS Web AppBuilder widgets allows access of the user interface additionally through keyboard-only navigation and therefore screen-reader capability.

GIS offers a powerful tool to design network analysis for persons with VIB. The routing algorithm accesses different user profiles, returning individualized turn-by-turn directions. The complex set of attributes, including shorelines, landmarks and barriers, can be integrated by semi-automated processes.

The paper illustrates the benefit of GIS applications for wayfinding of persons with VIB to raise self-determination and independence.

A ubiquitous pedestrian sidewalk network for a medium-size city comprises a novelty, as research has mainly focused on small areas. The integration of shorelines next to a various number of hints, landmarks and potential barriers through semi-automated processes allows reproducibility and transferability of the model to other cities.

One of the main tasks of a researcher is to properly communicate the results he obtained. The choice of the journal in which to publish the work is therefore very important. However, not all journals have suitable characteristics for a correct dissemination of scientific knowledge. Some publishers turn out to be unreliable and, against a payment, they publish whatever researchers propose. The authors call “predatory journals” these untrustworthy journals. The purpose of this paper is to analyse the incidence of predatory journals in computer science literature and present a tool that was developed for this purpose.

The authors focused their attention on editors, universities and publishers that are involved in this kind of publishing process. The starting point of their research is the list of scholarly open-access publishers and open-access stand-alone journals created by Jeffrey Beall. Specifically, they analysed the presence of predatory journals in the search results obtained from Google Scholar in the engineering and computer science fields. They also studied the change over time of such incidence in the articles published between 2011 and 2015.

The analysis shows that the phenomenon of predatory journals somehow decreased in 2015, probably due to a greater awareness of the risks related to the reputation of the authors.

We focused on computer science field, using a specific sample of queries. We developed a software to automatically make queries to the search engine, and to detect predatory journals, using Beall’s list.

The purpose of this article is to explore how participants with different motivations (educational or leisure), familiarity with the medium (newbies and active Twitter users), and participating instructions respond to a highly structured digital social reading (DSR) activity in terms of intensity of engagement and social interaction.

A case study involving students and teachers of 211 Italian high school classes and 242 other Twitter users, who generated a total of 18,962 tweets commenting on a literary text, was conducted. The authors performed both a quantitative analysis focusing on the number of tweets/retweets generated by participants and a network analysis exploiting the study of interactions between them. The authors also classified the tweets with respect to their originality, by using both automated text reuse detection approaches and manual categorization, to identify quotations, paraphrases and other forms of reader response.

The decoupling (both in space and time) of text read (in class) and comments (on Twitter) likely led users to mainly share text excerpts rather than original personal reactions to the story. There was almost no interaction outside the classroom, neither with other students nor with generic Twitter users, characterizing this project as a shared experience of “audiencing” a media event. The intensity of social interactions is more related to the breadth of the audience reached by the user-generated content and to a strong retweeting activity. In general, better familiarity with digital (social) media is related to an increase in the level of social interaction.

The authors analyzed one of the largest educational social reading projects ever realized, contributing to the still scarce empirical research about DSR. The authors employed state-of-the-art automated text reuse detection to classify reader response.

Cryptomarkets on the dark web have emerged as a hub for the sale of illicit drugs. They have made it easier for the customers to get access to illicit drugs online while ensuring their anonymity. The easy availability of potentially harmful drugs has resulted in a significant impact on public health. Consequently, law enforcement agencies put a lot of effort and resources into shutting down online markets on the dark web. A lot of research work has also been conducted to understand the working of customers and vendors involved in the cryptomarkets that may help the law enforcement agencies. In this research, we present a ranking methodology to identify and rank top markets dealing in harmful illicit drugs. Using named entity recognition, a harm score of a drug market is calculated to indicate the degree of threat followed by the ranking of drug markets. The top-ranked markets are the ones selling the most harmful drugs. The rankings thus obtained can be helpful to law enforcement agencies by locating specific markets selling harmful illicit drugs and their further monitoring.

Because of the expansion of the internet and Web 2.0 phenomenon, new challenges are emerging in the disclosure practises adopted by organisations in the public-sector. This study aims to examine local governments’ (LGOs) use of social media (SM) in disclosing environmental actions/plans/information as a new way to improve accountability to citizens to obtain organisational legitimacy and the related sentiment of citizens’ judgements.

This paper analyses the content of 39 Italian LGOs’ public pages on Facebook. After the distinction between five classes of environmental issues (air, water, energy, waste and territory), an initial study is performed to detect possible sub-topics applying latent Dirichlet allocation. Having a list of posts related to specific environmental themes, the researchers computed the sentiment of citizens’ comments. To measure sentiment, two different approaches were implemented: one based on a lexicon dictionary and the other based on convolutional neural networks.

Facebook is used by LGOs to disclose environmental issues, focussing on their main interest in obtaining organisational legitimacy, and the analysis shows an increasing impact of Web 2.0 in the direct interaction of LGOs with citizens. On the other hand, there is a clear divergence of interest on environmental topics between LGOs and citizens in a dialogic accountability framework.

Sentiment analysis (SA) could be used by politicians, but also by managers/entrepreneurs in the business sector, to analyse stakeholders’ judgements of their communications/actions and plans on corporate social responsibility. This tool gives a result on time (i.e. not months or years after, as for the reporting system). It is cheaper than a survey and allows a first “photograph” of stakeholders’ sentiment. It can also be a useful tool for supporting, developing and improving environmental reporting.

To the best of the authors’ knowledge, this paper is one of the first to apply SA to environmental disclosure via SM in the public sphere. The study links modern techniques in natural language processing and machine learning with the important aspects of environmental communication between LGOs and citizens.

The paper has a twofold purpose: (1) to test the application of means-end theory to providers of hospitality goods and services, and (2) to explore this question in the context of winery tasting rooms when they had a unique opportunity to restructure their hospitality experience due to government restrictions in response to COVID.

A qualitative approach was adopted, and a convenience sample was used to conduct semi-structured laddering interviews. Forty interview transcripts were coded as means-end ladders, which were analyzed using a custom computer program to develop the implication matrix and the hierarchical value map.

This paper demonstrates that means-end is a useful approach to investigate the values and behaviors of the producer, specifically hospitality hosts. It finds that the principal goal of tasting rooms is to generate sales, and offering a compelling guest experience is the characteristic that contributes the most to achieving that goal. The staff and the atmosphere created for the guests are the two factors with the greatest influence on the guest experience.

This is the first paper to use means-end theory to study the hospitality host, or the producer of goods and services in general, and the first to study winery hospitality primarily through the lens of means-end theory. The study also helps fill a gap in research on tasting room sales focused on the winery’s goals.