Digital Footprints Research Articles

Workplace performance measurement: digitalization of work observation and analysis

AbstractProcess improvement initiatives require access to frequently updated and good quality data. This is an extremely difficult task in the area of production processes, where the lack of a process digital footprint is a very big challenge. To solve this problem, the authors of this article designed, implemented, and verified the results of a new work measurement method. The Workplace Performance Measurement (WPM) method is focused not only on the measurement of task duration and frequency, but also on searching for potential anomalies and their reasons. The WPM method collects a wide range of workspace parameters, including workers' activities, workers' physiological parameters, and tool usage. An application of Process Mining and Machine Learning solutions has allowed us to not only significantly increase the quality of analysis (compared to analog work sampling methods), but also to implement an automated controlling solution. The genuine value of the WPM is attested to by the achieved results, like increased efficiency of production processes, better visibility of process flow, or delivery of input data to MES solutions. MES systems require good quality, frequently updated information, and this is the role played by the WPM, which can provide this type of data for Master Data as well as for Production Orders. The presented authorial WPM method reduces the gap in available scholarship and practical solutions, enabling the collection of reliable data on the actual flow of business processes without their disruption, relevant for i.a. advanced systems using AI.

Digital Footprint as a Public Participatory Tool: Identifying and Assessing Industrial Heritage Landscape through User-Generated Content on Social Media

International heritage management approaches have developed into a more inclusive process wherein public participation is identified as a pivotal tool. Thus, determining how to assess public interests and include the public’s ideas in heritage protection has become a technical issue, but relevant research still remains limited. This paper aims to test the digital footprints of social media users as a public participatory tool, with the objectives of identifying industrial heritage landscape attributes and assessing associated values. Targeting the Sanxian industrial heritage landscape of Liangdancheng in China as a case study, in this research the data from user-generated content on social media platforms Ctrip, Weibo, and Meituan were collected and processed with ROST CM 6 and NVivo 12, and content analysis (CA) and importance-performance analysis (IPA) were conducted. Results revealed that the industrial heritage landscape of Liangdancheng encompasses various built, cultural, and natural environmental resources, including both tangible and intangible attributes such as architectural constructions, historic artifacts, cultural events, and plants. These attributes were assessed and categorized into four quadrants of importance–performance characteristics, wherein cultural environmental resources show relatively high performance but built environmental resources need further actions to improve their value perception and interpretation among the public. This research demonstrated that the digital footprints of social media users as a participatory tool can work well in terms of data accessibility, value identification, and public representation, advancing the theoretical framework of Chinese industrial heritage management and global practices.

Death and Digital Immortality: Hi-Tech Grief

The study focuses on the issue of commemorative practiсe transformation as a result of digital technology development. The human has been using different means of interaction with the dead in order to receive answers, knowledge, and protection for thousands of years. A modern human needs it as well that leads to the demand for the development of digital technology in commemoration and grief. There is an important aspect related to the commemoration of and respect to the dead that is evidenced by building memorials and preserving their possessions and stories about them. In digitalization, we can see it in the interaction with digital footprints and digital avatars that demonstrate the activity of the human in the virtual space. In spite of increasing demand for interaction with the dead, this contact may bring not only nostalgic feelings or relief but also cause suspiciousness, fear and anxiety, postpone and extend grieving. Detabooing of the subject of death, its more active discussion and availability of information do not always help the human start thinking about their own death leaving the feeling of death anxiety for Another. At the same time, there is a two-way process: information in the virtual space about real deaths, especially detailed one, makes them an unreal narration. A digital avatar of the dead is also awarded with dichotomic features: it is both alive and dead, developing or created with no participation of a certain person (the dead). After death, digital footprints do not belong to them; they become «digital heritage» for the close ones and public that may cause «resurrection» in the virtual space, even if there was no consent to distribute information. As a result, the digital avatar of the dead can be used both to endure grief and manipulate in order to swindle money, involve in destructive communities or lead to suicide.

Дорогая бесплатность

Digital platforms, as a rule, offer their services to at least one of the parties without a monetary fee, but by charging another resource – information – in the form of personal information and digital footprints. Analysis mechanisms of collected information provide companies with hidden competitive power, which, on the one hand, allows them to increase efficiency, but on the other hand, carries potential threats for many individual users and for economic welfare. The main research question concerns the presence of incentives to maintain or enhance privacy on the Internet, which arise not only from users, but also from business, as well as the role of government regulators in this process. Some of the urgent regulatory decisions to develop a competitive environment in digital markets and ensure transparency of processes within ecosystems, discussed in this article, can provide progress in user control over their personal information. At the same time, the proposed changes will lead to additional information security risks (and other aspects). The question of ensuring the guarantor reliability related to privacy on the Internet remains open.

Digitalization of banks and inclusive finance: New insights from cultural industry's financing constraints

AbstractThe emergence of digital financial technology emerges as a promising solution, allowing banks to leverage unique digital footprints to alleviate information asymmetry and mitigate the light asset company's financing constraints. Through a theoretical analysis, we reveal that the digital transformation of banks significantly increases loan allocations to SMEs within the cultural industry. This development toward inclusive finance is further substantiated by robust empirical evidence, indicating a notable alleviation of financing constraints, and confirming the quantitative and structural transmission pathways influenced by bank digitalization.

In Defence of the Indefensible: Exploring Justification Narratives of Corporate Elites Accused of Corruption

AbstractDrawing on the pragmatic turn in contemporary social theory, we explore how corporate elites accused of corruption in the context of weak institutions engage in their justification works. Empirically, we focus on three high-profile corruption scandals that shook Ghana between 2010 and 2020 and inspired widespread public condemnation. Publicly accessible archival documents, such as court reporting, newspaper stories, press conferences, and the digital footprints of corporate elites implicated in the scandals provide data for our inquiry. Focussing on the juxtaposition of ‘sayings’ and ‘doings’, the findings show justification as performative, and rooted in contextual pragmatism that acknowledges the plurality of logics situated between self-interest and folk-logic. Within this framework, the domestic and civic orders of worth emerge as most prominent, with the justification processes manifesting through victimising, scapegoating, and crusading. Building on these insights, we develop a framework that highlights how the use of justifications serves as a critique of the inadequacies within climates of weak institutional frameworks consequently fostering an atmosphere conducive to framing unethical conducts as morally acceptable.

On the Feasibility of Forgetting in Data Streams

In today's digital age, it is becoming increasingly prevalent to retain digital footprints in the cloud indefinitely. Nonetheless, there is a valid argument that entities should have the authority to decide whether their personal data remains within a specific database or is expunged. Indeed, nations across the globe are increasingly enacting legislation to uphold the "Right To Be Forgotten" for individuals. Investigating computational challenges, including the formalization and implementation of this notion, is crucial due to its relevance in the domains of data privacy and management. This work introduces a new streaming model: the 'Right to be Forgotten Data Streaming Model' (RFDS model). The main feature of this model is that any element in the stream has the right to have its history removed from the stream. Formally, the input is a stream of updates of the form (a, Δ) where Δ ∈ {+, ⊥} and a is an element from a universe U. When the update Δ=+ occurs, the frequency of a, denoted as f a , is incremented to f a +1. When the update Δ=⊥, occurs, f a is set to 0. This feature, which represents the forget request, distinguishes the present model from existing data streaming models. This work systematically investigates computational challenges that arise while incorporating the notion of the right to be forgotten. Our initial considerations reveal that even estimating F 1 (sum of the frequencies of elements) of the stream is a non-trivial problem in this model. Based on the initial investigations, we focus on a modified model which we call α-RFDS where we limit the number of forget operations to be at most α fraction. In this modified model, we focus on estimating F 0 (number of distinct elements) and F 1 . We present algorithms and establish almost-matching lower bounds on the space complexity for these computational tasks.

Indian National Congress: Demagogy, Dynasty, Disunity and Decline

The Congress, the grand old party of India, plunged into a political abyss after consecutive defeats in the national elections of 2014 and 2019. It called for a deep remedial overhaul, but it resorted to ‘Band-Aid political strategies’ for pro-tem gains. It recirculated rootless leaders in various party positions, added digital footprints rather than foot soldiers and launched the ambiguous ‘Bharat Jodo Yatras’ (walkathons) – shepherded by Rahul Gandhi to reclaim its lost political dominance. It becomes contextual to revisit the deinstitutionalization thesis of Congress party and probe criticalities of personalism-dynasticism in top echelons and generational factionalism-leadership conundrums in states in hastening its endemic decline.

Log Analyzer and Anomaly Reporting Framework for Proactive System Monitoring.

Abstract: In an era where organizations increasingly rely on intricate software applications, cloud services, and interconnected networks, the significance of analyses cannot be overstated. These tools serve as vigilant custodians of digital footprints, meticulously dissecting the voluminous records encapsulated in log files to extract valuable insights and detect anomalies. As such, log analyses emerge as linchpins in deciphering the meaning behind recorded events, enabling organizations to obtain a more comprehensive understanding of their digital infrastructures and enhance their security posture. This comprehensive exploration aims to untangle the core attributes of log analyses, bringing clarity to their parsing capabilities, the art of information extraction, and the nuanced algorithms that facilitate the conversion of raw logs into actionable insights. Furthermore, against the backdrop of a dynamically evolving cyber threat landscape, the role of log analyses extends beyond conventional diagnostics. These tools have become instrumental in the proactively orchestrated defense against cyber adversaries, empowering organizations to detect and mitigate threats in real-time. Through an in-depth analysis of log analyses and their evolving functionalities, this paper seeks to provide a comprehensive understanding of their Integral role in modern cybersecurity and system management. By elucidating the significance and impact of log analyses, organizations can leverage these tools to fortify their defenses, mitigate risks, and make informed, data-driven decisions in an increasingly complex digital environment

Optimizing Communication Skills: Public Speaking and Positive Social Media Digital Footprint Training For Dhammasavana Tambora Junior High School Students, West Jakarta

The Public Speaking and How to Design a Positive Digital Footprint on Social Media program is a community service initiated by Institut Teknologi dan Bisnis Kalbis communication lecturer as well as Corporate Social Responsibility (CSR) class students. This activity was held at Dhammasavana Tambora Junior High School, West Jakarta, with 30 students from the 8th grade participating. The training emphasized on public speaking skills as well as an understanding of positive digital footprints. The training material was delivered by the Corporate Social Responsibility class students with the aim of introducing and providing training in public speaking skills, focusing on speech preparation and structure. This material is very important for students at an age where they are very close to the digital world, namely social media and the need to apply public speaking skills to support their learning at school. The implementation of the community service resulted in a more engaged environment, with students actively participating in debates and assignments. The relevance of successfully integrating public speaking and social media training, the development of social communication skills, and the importance of digital etiquette awareness is highlighted in the conclusion. This practice is expected to continue in high schools, benefiting students' abilities and understanding.

An Integrated Entropy-Weighted Sum Model Technique for Evaluating the Metrics of Cybersecurity under Uncertainty

Businesses and organizations handle large amounts of data that may be accessed unauthorized by hackers, so we need to protect this data and other resources from malicious users. Here comes the role of cybersecurity. Cybersecurity is a multidisciplinary field touching on the technical sciences as well as the social and behavioral sciences. Cybersecurity is the result of intelligent cyberattacks. Cybersecurity is crucial to protecting sensitive data stored on any device due to the increasing amount of information. So, this study evaluates cybersecurity using neutrosophic multi-criteria decision-making (MCDM) techniques such as entropy to obtain weight and the weighted sum model (WSM), for ranking and obtaining the best digital footprint of modern organization's cybersecurity. The cybersecurity estimation issue illustrates the validity and great performance of the presented method as (1) its capability to deal with uncertainty phenomena (2) its straightforwardness; and (3) its heightened capacity to discern alternatives. The presented case study shows that the best alternative of this study is A3 which refers to (Digital supply chain risk) as the best one of all alternatives.

Examining Online Behaviors of Violent and Non-Violent Right-Wing Extremists During Peak Posting Days

Despite the ongoing need for practitioners to identify violent extremists online before their engagement in violence offline, little is empirically known about their digital footprints generally or differences in their posting behaviors compared to their non-violent counterparts particularly – especially on high-frequency posting days. Content analysis was used to examine postings from a unique sample of violent and non-violent right-wing extremists as well as from a sample of postings within a sub-forum of the largest white supremacy forum during peak and non-peak posting days for comparison purposes. Several noteworthy posting behaviors were identified that may assist in identifying credible threats online.

Twitter's capacity to forecast tourism demand: the case of way of Saint James

PurposeRadical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.Design/methodology/approachThis study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.FindingsThe results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.Originality/valueThis study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

Making Indonesia Sustainable: Shaping the Law to Reduce Digital Carbon Footprint

Indonesia needs to reduce the carbon footprint from the The Paris Agreement result, a legally binding international treaty on climate change, which entered into force on 4 November 2016. One of the outputs from the Paris Agreement is that the country needs to submit their national climate action plans, known as nationally determined contributions (NDCs). To achieve a sustainable future, Indonesia has Enhanced National Determined Contribution (ENDC) and committed to reducing its carbon emissions by 41 percent by 2030. This commitment is included in the Indonesia Emas 2045 (Golden Indonesia 2045) Program. However, the goals are still focusing on the physical carbon footprint. In the Indonesian Net Zero Emission Goals which needs to be achieved in 2060 the five steps mentioned are increasing the utilization of new renewable energy, reduction of fossil energy, the use of electric vehicles in the transportation sector, increasing electricity use in households and industry, and finally the utilization of Carbon Capture and Storage (CCS). Indonesia also needs to focus on reducing the digital carbon footprint as it currently increases with the usage of digital technologies. The research questions discussed in this paper are how Indonesia currently regulates digital carbon footprint in Indonesia, and what can Indonesia do to reduce digital carbon footprint. This research aims to analyze the current condition and current regulation on digital carbon footprint in Indonesia, and how Indonesian government can learn from other countries to reduce the digital carbon footprint in Indonesia. Methodologies used in this research are normative approach and comparative approach. The output of this research is a regulation to reduce digital carbon footprint in Indonesia, and to regulate the business actors to create ESG reports on a company's sustainability.

Socialization 2.5: Social Education in the era of Aggregator Platform

The article is devoted to the analysis of an urgent problem – the emergence and development of new phenomena in the field of Russian social education – children’s public organizations integrated into state macro-information systems. The “Movement of the First” is the most striking example of this kind, in which its digital and “living” human components are closely interconnected. The metaphor “Socialization 2.5” in the title of the article reflects the position of the authors that socialization under the conditions of the developing Internet environment in Russian conditions has already outgrown the distinctive features of “Web 2.0”, but has taken a different path than the proposed version of “Web 3.0”. The article argues the position according to which children’s public organizations (movements) engaged in social education are independent socio-technogenic factors. Organizations (movements) of this kind are a tool for removing, levelling the social and cultural characteristics of differentiated social groups and solving a number of other social, educational and cultural tasks. It is shown that the real conceptual model of the subject area of business processes of the “Movement of the First” (romanized: Dvizheniye pervykh – RDDM activities) is described in the language of economics, sociology, computer science, the language of business trainings adapted to a youth audience, but does not use the language of traditional Russian academic pedagogy. The article identifies a number of new problems of social education, which are a consequence of its development in conditions of integration with digital technologies: the inclusion of man-made components in the process of identifying a child with the movement, in acts of initiation (occupation of a certain status, including membership in the movement), in communication, the absolute absence of anonymity. Possible threats to the development of the children’s organization “Movement of the First” are noted as: a bias towards “virtualization” of the proposed activities, up to the replacement of activities in the physical world with its “digital footprint”, the risk of using exclusively formal indicators of movement development, the problem of motivating the activity of subjects, the problem of balance between the requests of movement participants and the “resource intensity” of the proposed “activities”. In the perspective, it can be assumed that the digital component of the movement will develop intensively, expanding the degree of automation, the use of artificial intelligence systems, which may lead to further deformation and adaptation of the system of social interactions within the movement to the possibilities of the digital environment. All named problems are a challenge in relation to the theory of social education and require serious research based on an appropriate methodological basis.

A Knowledge Graph Service Composition Model for E-Government

In an era of Artificial Intelligence (AI), governments around the world are advancing their digital footprints, and using AI to achieving their objectives of effective and efficient public service delivery and citizens engagement. Knowledge Graph (KG), a constituent AI technology, has proven to be the means through which a government can use to manage the complexity of an e-Government instance, and realize its objective. One area in e-Government that KG is used to address, is the challenge of automated composition of web services, where web services are created and maintained by one government department, but is used by another, in an integrated and interoperable manner. Service composition is the combination of two or more web services to create a composite service in order to accomplish a task. In this paper, we present a KG-based approach for automatically composing web services in an e-Government context. Our aim is to advance the e-Government objective of effective and efficient public service delivery and citizens engagement, given a complex e-Government. We focus on creating a Service Dependency Graph (SDG), which captures the semantic relationships between the services, and we further describe the services with QoS characteristics, and then we create a weighted graph based on these two descriptions. We then adapted the Uniform Cost Search algorithm to find the shortest path between a start node and a goal node. We implement our algorithm, and carried-out experiments to obtain execution times, and we compared our results with benchmark results of comparable experimental setup. The difference in execution times of the service composition task is dependent on factors such as the design of the composition model, the complexity of the composition graph, and the hardware and software environments. Our research approach affords us the means to analyze and design our model; and validate and evaluate our work. Our model and the development approach help to achieve the e-Government goals of efficient and effective service delivery and citizens engagement.

Building a knowledge graph to enrich ChatGPT responses in manufacturing service discovery

Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers’ weblinks, manufacturing services, certifications, and location entity types.

Transforming Customer Digital Footprints into Decision Enablers in Hospitality

The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making.

A Machine Learning-Based PE Header Analysis for Malware Detection

The malware is file or piece of code which is delivered over network that infects or conducts any behavior as attacker desired. So, it is one of the most serious threats to modern world specially who are in touch of computerand information technology. The older signature-based detection is not convenient all the time. This was not the perfect approach as it was detection which uses unique signature or digital footprint from software running on secured system. This method is used in antivirus programs. These programs scan any software program and try to identify the signatures. These signatures are then compared to signature of known malwares. But signatures may not be known to us every time. This method has some lots of limitations. It is unable to the new patterns or indicators of new threats that are not already known. As a result, security professionals often this method in conjunction with tools that provide context into their network behavior. The PE is actually file layout that is present in .exe, .dll file formats and other machine level code and their PE headers contains information that can help us distinguish between malicious malware files and legitimate files. This method is helpful to find hidden patterns and to establish new techniques to recognize the files. The virus share suffixedfiles are also the files which performs malicious activities and malware in nature. Even this type of files can be trained and recognized with the help of PE headers-based method to recognize the nature of file. Its possible to identify the malware by looking at some key features from headers such as checksum, section name, initialized data Size, DLL characteristics and major image version.Looking at the PE header is much faster than scanning the whole information in the PE. Thus, the prediction of files are possible even with faster rate. In this paper, we will understand the different attributes available in PE headers to carefully analyses the trends and to distinguish the given executable files as malicious or legitimate on basis of PE headers using advance machine learning algorithms.

Exploring the destination network in the context of tourism mobility: a multi-scale analytical framework

ABSTRACT Tourist mobility has attracted considerable attention from tourism academics and industry practitioners. This study explores the destination network, which is constructed based on tourists’ digital footprint data collected from online travel diaries, by proposing a multi-scale analytical framework. In particular, to analyze the tourism destination network comprehensively, the proposed framework integrates traditional quantitative analysis (gravity center model, statistics of flow directions) and three scales of network analysis (macroscopic, mesoscopic and microscopic network metrics), to reveal the structural patterns of the underlying pattern characteristics; furthermore, by using the multiple regression quadratic assignment procedure (MRQAP), the framework also explores the formation mechanism of the destination network by measuring the correlation and causality between the destination network and the corresponding transportation network, combining other potential driving factors. Based on illustrative case study data, which contains 18, 939 travel diaries and 51, 603, 044 transportation records from Chengdu-Chongqing economic circle in China, the empirical results show that the destination network is dynamically changed during four-year period, and the transportation network has great potential in explaining the formation of destination network. The dynamic characteristics of destination network and the exploration of formation mechanism offer the opportunities for improving destination planning and management.