Social media adoption and employability performance of Universities

This paper is part of series of papers of ongoing research proposing social media advancement in value creation in higher education institutions with a focus on the employability of students as well as employability performance of the Higher Education Institutions. The main purpose of the study was to explore the state of art practices concerning social media adoption in higher education and employability performance of universities. The study used a traditional literature review approach to conduct the exploratory study, where the literal sources selected were representative of the elements under investigation. The study established that social media is a tool that has been used in Higher Education Institutions to build the employability attributes of students. The social media adoption practices were categorized under three themes, that is, student-instructor level, faculty level, and holistic level practices. It was further identified that social media cannot be utilized solely owing to its constraining features. The study provides an argument for the advancement of social media through integration with more advanced strategic tools. The implications of these findings as well as directions of future studies are also mentioned

A comparative analysis of good enterprise data management practices:insights from literature and artificial intelligence perspectives for business efficiency and effectiveness

Abstract. This thesis presents a comparative analysis of enterprise data management practices based on literature and artificial intelligence (AI) perspectives, focusing on their impact on data quality, business efficiency, and effectiveness. It employs a systematic research methodology comprising of a literature review, an AI-based examination of current practices using ChatGPT, and a comparative analysis of findings. The study highlights the importance of robust data governance, high data quality, data integration, and security, alongside the transformative potential of AI. The limitations revolve around the primarily qualitative nature of the study and potential restrictions in the generalizability of the findings. However, the thesis offers valuable insights and recommendations for enterprises to optimize their data management strategies, underscoring the enhancement potential of AI in traditional practices. The research contributes to scientific discourse in information systems, data science, and business management

Improving data preparation for the application of process mining

Immersed in what is already known as the fourth industrial revolution, automation and data exchange are taking on a particularly relevant role in complex environments, such as industrial manufacturing environments or logistics. This digitisation and transition to the Industry 4.0 paradigm is causing experts to start analysing business processes from other perspectives. Consequently, where management and business intelligence used to dominate, process mining appears as a link, trying to build a bridge between both disciplines to unite and improve them. This new perspective on process analysis helps to improve strategic decision making and competitive capabilities. Process mining brings together data and process perspectives in a single discipline that covers the entire spectrum of process management. Through process mining, and based on observations of their actual operations, organisations can understand the state of their operations, detect deviations, and improve their performance based on what they observe. In this way, process mining is an ally, occupying a large part of current academic and industrial research. However, although this discipline is receiving more and more attention, it presents severe application problems when it is implemented in real environments. The variety of input data in terms of form, content, semantics, and levels of abstraction makes the execution of process mining tasks in industry an iterative, tedious, and manual process, requiring multidisciplinary experts with extensive knowledge of the domain, process management, and data processing. Currently, although there are numerous academic proposals, there are no industrial solutions capable of automating these tasks. For this reason, in this thesis by compendium we address the problem of improving business processes in complex environments thanks to the study of the state-of-the-art and a set of proposals that improve relevant aspects in the life cycle of processes, from the creation of logs, log preparation, process quality assessment, and improvement of business processes. Firstly, for this thesis, a systematic study of the literature was carried out in order to gain an in-depth knowledge of the state-of-the-art in this field, as well as the different challenges faced by this discipline. This in-depth analysis has allowed us to detect a number of challenges that have not been addressed or received insufficient attention, of which three have been selected and presented as the objectives of this thesis. The first challenge is related to the assessment of the quality of input data, known as event logs, since the requeriment of the application of techniques for improving the event log must be based on the level of quality of the initial data, which is why this thesis presents a methodology and a set of metrics that support the expert in selecting which technique to apply to the data according to the quality estimation at each moment, another challenge obtained as a result of our analysis of the literature. Likewise, the use of a set of metrics to evaluate the quality of the resulting process models is also proposed, with the aim of assessing whether improvement in the quality of the input data has a direct impact on the final results. The second challenge identified is the need to improve the input data used in the analysis of business processes. As in any data-driven discipline, the quality of the results strongly depends on the quality of the input data, so the second challenge to be addressed is the improvement of the preparation of event logs. The contribution in this area is the application of natural language processing techniques to relabel activities from textual descriptions of process activities, as well as the application of clustering techniques to help simplify the results, generating more understandable models from a human point of view. Finally, the third challenge detected is related to the process optimisation, so we contribute with an approach for the optimisation of resources associated with business processes, which, through the inclusion of decision-making in the creation of flexible processes, enables significant cost reductions. Furthermore, all the proposals made in this thesis are validated and designed in collaboration with experts from different fields of industry and have been evaluated through real case studies in public and private projects in collaboration with the aeronautical industry and the logistics sector

Minería de calidad de datos : aplicación de técnicas de minería de datos para la evaluación de la calidad de los datos

El aseguramiento de la calidad de los datos con los cuales se trabaja es crucial para tomar decisiones acertadas, efectivas y a tiempo. Lograr una buena calidad de datos no solo implica trabajar con datos que no contengan errores, sino que también incluye características tales como la completitud (tener la mayor cantidad posible de datos), la actualidad (que los datos sean lo más actuales posibles), la usabilidad (que los datos sean adecuados y comprensibles), y la disponibilidad (que se pueda acceder a ellos cuando se los necesita), entre muchas otras. La minería de datos, por otra parte, permite descubrir información oculta en los datos, utilizando un paradigma inverso al usual: mientras normalmente se comienza planteando una hipótesis para luego tratar de confirmarla, la minería de datos propone identificar en forma automatizada patrones que pueden resultar interesantes y que posiblemente no hayan sido imaginados por los analistas. Si bien ambas áreas son altamente relevantes en el mundo académico e industrial de la actualidad, donde la informática brinda un soporte tecnológico apropiado, la literatura existente y algunas experiencias muestran que existe muy poca o nula integración entre la calidad de datos y la minería de datos. En general, los trabajos pertenecientes a un área suelen ser ajenos a los existentes en la otra. En este trabajo se realiza un estudio en profundidad de las dos áreas introducidas para luego hacer un análisis de los mecanismos que permitirían vincularlas, y finalmente implementar técnicas que permitan abordar el análisis de la calidad de conjuntos de datos aprovechando las capacidades inherentes de la minería de datos. El trabajo presenta dos propuestas nuevas para la aplicación de técnicas de minería de datos para la evaluación de la calidad de datos, que fueron presentadas en dos eventos internacionales especializados. Una de ellas se orienta a la determinación de si un conjunto de datos es suficientemente actualizado, y la otra se orienta al análisis de datos faltantes. Además, se presenta también una tercera propuesta, aún en etapa de formulación, para evaluar qué tan usable es un conjunto de datos en base a sus características. Palabras clave: calidad de datos, minería de datos, minería de calidad de datos. El trabajo presenta dos propuestas nuevas para la aplicación de técnicas de minería de datos parala evaluación de la calidad de datos, que fueron presentadas en dos eventos internacionales especializados. Una de ellas se orienta a la determinación de si un conjunto de datos es suficientemente actualizado, y la otra se orienta al análisis de datos faltantes. Además, se presenta también una tercera propuesta, aún en etapa de formulación, para evaluar qué tan usable es un conjunto de datos en base a sus características

Financial reporting with XBRL and its impact on the accounting profession

Since 2010, XBRL (eXtensible Business Reporting Language) has been widely adopted throughout the world. In 2013, both the HMRC (Inland Revenue) and Companies House in the UK accepted XBRL in the iXBRL (inline XBRL) format. Investors have had to face various issues related to XBRL-reported financial information, such as accuracy and interpretability, as well as potential risks with respect to this new format of financial reporting. The purpose of this study is to explore the impact of XBRL on the quality of financial reports and the accounting profession. For this study, a quality index evaluation model was built to examine the quality of financial reports. Over a thousand XBRL and non-XBRL formatted financial reports from three typical XBRL-adopting regions were then evaluated. This study finds that some of the contextual and accessibility qualities of financial reports have been greatly improved after using the XBRL format. However, the issue of accuracy has become more visible in current XBRL filings, due to the smaller and less comprehensive quantity of data stored in such filing systems. Using quality index scoring system, the trained professionals participating in this study confirm that XBRL-formatted financial reports demonstrate a greatly improved searching efficiency. Moreover, these reports generally display a quality superior to non-XBRL formatted financial reports under the designed quality index. More importantly, the quality of XBRL-formatted financial reports uploaded in the same database has been improving year by year. XBRL has not directly affected the accounting profession, being that most companies have outsourced the preparation of XBRL reports. However, it should additionally be noted that the questionnaires and interviews conducted with accountants in XBRL-adopting companies also reveal that these professionals feel increasing pressure both to prepare and to utilise XBRL-formatted financial information internally