Search results

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

This study aims to analyze the factors that drive purchases via Instagram and contribute to the growth of Instagram Commerce and examine the moderating role of gender, age and experience in Instagram use in the proposed relationship between six variables derived from commitment–trust theory, the technology acceptance model and consumer decision-making theory.

A survey was completed by respondents after watching a video about Instagram Commerce. A total of 404 valid responses were collected. The research model was analyzed using partial least squares structural equation modeling.

This study makes numerous contributions to Instagram Commerce and holds significant implications for professionals in the social commerce field. Among other results, we found that trust, attitude, perceived usefulness and alternative evaluation significantly affected consumers’ purchase intentions. However, this study found no relationship between trust or ease of use and purchase intention. Finally, it demonstrates the moderating role of gender, age and experience on some of these relationships.

This research centers on an analysis of consumer purchase behavior on Instagram Commerce, taking a highly innovative approach. The particular originality of this study lies in the proposed model of adoption of social commerce via Instagram, based on a critical framework. This study also provides an original analysis of the moderating effect of the classification variables: gender, age and experience in Instagram use.

Factores que impulsan la intención de compra en Istagram Commerce

Resumen

Los objetivos de la presente investigación son (i) analizar los factores que impulsan las compras a través de Instagram y contribuyen al crecimiento del comercio en Instagram y (ii) examinar el papel moderador del género, la edad y la experiencia en el uso de Instagram sobre la relación propuesta a partir de seis variables derivadas de la Teoría del Compromiso-Confianza, el modelo TAM y la Teoría de la Toma de Decisiones del Consumidor.

Los encuestados completaron una encuesta después de ver un vídeo sobre Instagram Commerce. Se recogieron un total de 404 respuestas válidas. El modelo de investigación se analizó mediante un modelo de ecuaciones estructurales de mínimos cuadrados parciales.

El presente estudio hace numerosas contribuciones al Instagram Commerce y tiene importantes implicaciones para los profesionales del campo del comercio social. Entre otros resultados, encontramos que la confianza, la actitud, la utilidad percibida y la evaluación alternativa afectan significativamente a las intenciones de compra de los consumidores. Sin embargo, este estudio no encontró ninguna relación entre la confianza o la facilidad de uso y la intención de compra. Por último, se demuestra el papel moderador del género, la edad y la experiencia en algunas de estas relaciones.

Esta investigación se centra en un análisis del comportamiento de compra de los consumidores en Instagram Commerce, adoptando un enfoque muy innovador. La originalidad particular del trabajo radica en la propuesta de un modelo de adopción del comercio social a través de Instagram, basado en un marco crítico. El estudio también proporciona un análisis original del efecto moderador de las variables de clasificación: género, edad y experiencia en el uso de Instagram.

本研究的目的是(i)分析推动通过Instagram购买并促进Instagram商务发展的因素; (ii)研究性别、年龄和使用Instagram的经验对源自承诺-信任理论、TAM模型和消费者决策理论的六个变量之间的拟议关系的调节作用。

受访者在观看了有关Instagram商业的视频后完成了一项调查。共收集到404份有效的回复。研究模型采用偏最小二乘法结构方程模型进行分析。

本研究对Instagram商务做出了许多贡献, 并对社交商务领域的专业人士具有重要意义。在其他结果中, 我们发现信任、态度、感知有用性和替代性评价对消费者的购买意向有显著影响。然而, 本研究发现信任或易用性与购买意向之间没有关系。最后, 它证明了性别、年龄和经验对其中一些关系的调节作用。

这项研究以分析消费者在Instagram商务上的购买行为为中心, 采取了一种高度创新的方法。这项研究的独特之处在于提出了一个基于批判性框架的通过Instagram进行社交商务的模型。该研究还对分类变量的调节作用进行了原创性分析：性别、年龄和使用Instagram的经验。

Although Public Research Institutions (PRIs) are large technology producers, they lack automated information tools that follow technical and scientific criteria for assessing and valuing patents. The assessment and valuation processes are stages of technology transfer (TT) that make it possible to obtain productive arrangements and guide the efforts of those involved in the development, maintenance and negotiation. This study aims to analyze the hybrid model of assessment and valuation of technologies by Soares (2018), applying the ‘Valorativo' software. In addition to patent value and indicator scores, the methods allow an understanding of the technology portfolio and its management.

This research is quali-quantitative, following an approach of applied nature and descriptive objectives. The research has bibliographical, documental and case study features based on the software development methodologies described in the study and the theoretical framework.

The Valorativo software assisted in the analysis of ten patents on PRIs. With the data collection and patent analysis, PAT1 scored highest among engineering patents, PAT3 scored highest among pharmaceutical patents and PAT10 scored highest among biotechnology patents. Five of the assessed patents resulted in a surplus of net present value (NPV), final net present value (NPVF) and royalties; revenue expectations outpaced investments.

The authors based the developed software on Soares’s (2018) methodology, with additional calculations and graphs. The Web software and the spreadsheet with Visual Basic for Application (VBA) were developed to deal with the patents assessment and valuation, helping in the analysis of their Legal Value, Technological Value and Market Conditions in the assessment process, and the Discounted Cash Flow and NPV in the valuation process.

The software helps with patent analysis and can generate indicators for traders, technology holders and researchers. Thus, it was necessary to understand and develop a theoretical-applied framework to outline and replicate the methodology clearly and easily.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

This paper aims to map literature about innovation capabilities (IC) taking into consideration industrial clusters to propose a conceptual framework that synthetizes the main factors and subfactors responsible for ICs; in addition, the paper also proposes a research agenda.

A systematic literature review (SLR) was performed; academic papers were analyzed qualitatively and quantitatively.

The authors provide a descriptive analysis followed by a thematic synthesis, in which we present 05 enablers and 20 critical factors (CF) of IC in clusters. The proposed framework emphasizes what needs to be done or improved to increase IC in cluster-based companies. Based on this systematic review and the framework proposed, the authors identified opportunities for future research.

The enablers and CF identified through SLR were not validated empirically. Therefore, future studies on the current topic are required to validate the framework by investigating which factors are more relevant to cluster-based companies that intend to improve their innovative performance.

The present findings have important implications for the identification of the factors and subfactors that may contribute to the development of IC, which may help managers and decision-makers in recognizing which factors are the most responsible for business innovation.

The paper identifies enablers related to the development of IC in industrial cluster and presents a research agenda. The framework represents a guideline for companies to achieve better innovation performance.

The growing complexity of consumer engagement (CE) due to the impact of Internet of things (IoT) has been attracting significant attention from both academics and industry practitioners especially in recent times. Hence, understanding this phenomenon remains very crucial to the body of knowledge. This study conducted a systematic review on IoT and CE with the aim of proposing future research opportunities using the TCCM model.

Extant literature studies were systematically examined by sourcing high ranking ABS journals from EBSCO, ScienceDirect and Emerald. A total of 58 articles were included in the final analysis of this research.

The analysis established the need to conduct more research on CE due to the impact of new technological implementation in retail. The results further suggest the need for extensive research across African countries and emerging markets to enable broader empirical generalizations of research outcomes. Using the TCCM framework, the authors indicated directions for future empirical research.

This study exposes the current trends in CE and IoT. The results and analysis are both compelling and verifiable, hence, establishing a firm base of reference for future research in related fields.