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Process mining helps organizations improve their business processes in today’s data-rich environment. However, these processes can change over time due to factors like policy shifts or process trends, impacting model performance. This study examines process behavior in event logs and uses machine learning to detect concept drift.

The trace clustering and change mining techniques have been implemented on two processes, namely loan payment and temporary identity creation, to detect drifts. We use the bag-of-activities and edit distance methods, along with K-Mode and agglomerative hierarchical clustering techniques.

This study makes two important findings: trace clustering is a popular choice for detecting drifts, and the bag-of-activities method using K-Mode clustering and hamming distance proved highly effective at spotting drifts in various event logs. It also identifies different types of drifts occurring simultaneously in the processes.

The drifts discovered in different processes provide a real-world example of concept drift in the domains of loans and university administrations. This contributes to improving operational efficiency and overall organizational performance based on these detected drifts and assists in enhancing the process design.

This study is the first to employ a hybrid method of trace clustering and change mining to detect process changes. It is also the first to simultaneously detect sudden and recurring drift in the field of trace clustering in process mining. Furthermore, it stands out for investigating and comparing the performance of multiple clustering methods, in contrast to prior research that used a single technique. Additionally, it is pioneering in applying machine learning methods to detect drift in the domain of loan processes.

The word “digital contact tracing” is often met with different reactions: the reaction that passionately supports it, the reaction that neither supports nor oppose and the one that vehemently opposes it. Those who support the notion of digital contact tracing vouch for its effectiveness and how the complicated process can be made simpler by implementing digital contact tracing, and those who oppose it often criticize the imminent threats it possesses. However, without earning the support of a large population, it would be difficult for any government to implement digital contact tracing to perfection. The purpose of this paper is to analyze, using machine learning, how different continents have different sentiments over digital contact tracing being used as a measure to curb COVID-19.

For the analysis, data were collected from Twitter. Tweets that contain the hashtag and the word “digital contact tracing” were crawled using Python library Tweepy. Tweets across countries of four continents were collected from March 2020 to August 2020. In total, 70,212 tweets were used for this study. Using the machine learning algorithm, the authors detected the sentiment of all the tweets belonging to each continent. Structural topic modeling was used to understand the overall significant issues people voice out by global citizens while sharing their opinions on digital contact tracing.

This study was conducted in two parts. Study one results show that North American and European citizens share more negative sentiments toward “digital contact tracing.” The citizens of the Asian and South American continent mostly share neutral sentiments regarding the digital contact tracing. Overall, only 33% of total tweets were positively related to contact tracing, whereas 52% of the total tweets were neutral. Study two results show that factors such as fear of government using contact tracing to spy on its people, the feeling of being unsafe and contact tracing being used to promote an agenda were the three major issues concerning the overall general public.

Despite numerous studies being conducted about how to implement the contact tracing efficiently, minimal studies were done to explore the possibility and challenges in implementing it. This study fills the gap.

Governments worldwide are taking various measures to prevent the spreading of COVID virus. One such effort is digital contact tracing. However, the aspect of digital contact tracing was met with criticism, as many critics view this as an attempt of the government to control people and a fundamental breach of privacy. Using machine learning techniques, this study aims to deal with understanding the general public’s emotions toward contact tracing and determining whether there is a change in the attitude of the general public toward digital contact tracing in various months of crises. This study also analyzes the significant concerns voiced out by the general public regarding digital contact tracing.

For the analysis, data were collected from Reddit. Reddit posts discussing the digital contact tracing during COVID-19 crises were collected from February 2020 to July 2020. A total of 5,025 original Reddit posts were used for this study. Natural language processing, which is a part of machine learning, was used for this study to understand the sentiments of the general public about contact tracing. Latent Dirichlet allocation was used to understand the significant issues voiced out by the general public while discussing contact tracing.

This study was conducted in two parts. Study 1 results show that the percentage of general public viewing the aspect of contact tracing positively had not changed throughout the time period of Data frame (March 2020 to July 2020). However, compared to the initial month of the crises, the later months saw a considerable increase in negative sentiments and a decrease in neutral sentiments regarding the digital contact tracing. Study 2 finds out the significant issues public voices out in their negative sentiments are a violation of privacy, fear of safety and lack of trust in government.

Although numerous studies were conducted on how to implement contact tracing effectively, to the best of the authors’ knowledge, this is the first study conducted with an objective of understanding the general public’s perception of contact tracing.

Many scholars highlight a need for reflexive methodological accounts to support visual research. Therefore, this paper offers detailed reflection on the methods involved in tracing and analysing 248 commercial images of entrepreneurship. This account supports our published work examining entrepreneurial masculinities and femininities, which conceptualised the gendering of entrepreneurial aesthetics, and proposed the significance of image networks in the reproduction of neoliberal ideals.

Now based on further methodological reflexivity, we offer insights on both the possibilities and challenges of tracing networked images by reviewing four methodological complexities: reflexive engagement with online images; working with and across platforms; tracing as a potentially never-ending process and montage approaches to analysis.

Our account focuses on a specific form of imagery – commercial images – on a certain representation – the gendered entrepreneur – and on a particular complex site of encounter – online. This work mapped a visual repertoire of gendered entrepreneurship online by tracing visual constructions of entrepreneurial masculinity and femininity. In this paper, we open the methodological “black box” of our study and explain our belief that methodological advances can only be built through exposing our working practice.

Through our detailed reflective account, we aim to open discussions to aid development and use of complex visual methods online.

This chapter focuses on the actors who engage in policymaking to offer alternative understandings of informality and the context in which this occurs. Using ethnographic vignettes from Canada and Australia as illustrations, theoretical and methodological goals are pursued through adopting the anthropological concept of ‘traces’ to show how informality both mediates and transcends across non-fixed physical, temporal and conceptual boundaries. With an underlying premise that normative understandings of informality are shaped by the policymaking ‘black box’ metaphor and a lack of access to policymaking spaces and actors, this chapter argues against the association of informality with illegitimate and invisible policy processes. Instead, experience of the policy process gained through professional and ethnographic engagement, or an ‘insider’ perspective, shifts the researcher’s gaze beyond physical barriers or separations to show that ‘traces’ formed through in|formal encounters create opportunities for relationality through which policy is conceived, deliberated and, in part, created.

During 2020, governments around the world introduced contact-tracing apps to fight the COVID-19 pandemic. In order for contact-tracing apps to be efficient tools in combatting pandemics, a significant proportion of the population has to install it. However, in many countries, the success of apps introduced during the COVID-19 pandemic has been limited due to lack of public support. This paper aims to better understand why consumers seem unwilling to install and use a contact-tracing app.

In this study, the authors test a number of determinants hypothesized to influence acceptance of contact-tracing apps based on the theory of privacy calculus (Dinev and Hart, 2006). Both perceived privacy concerns, as well as perceived hedonic, utilitarian and pro-social benefits are included. The hypotheses are tested through SEM analysis on a representative sample of 1,007 Swedish citizens.

The results indicate significant privacy concerns with using contact-tracing apps. However, this is to some extent offset by perceived hedonic and pro-social positive consequences of using the app. This study further shows that a general positive attitude towards innovation increases acceptance of the app.

The study contributes to research on consumer privacy, both in general in its application of the calculus model but also specifically in the context of contact-tracing apps. Moreover, as the results highlight which aspects that are important for consumers to accept and install an app of this kind, they also represent an important contribution to policymakers in countries around the world.

The Australian and New Zealand governments have released smartphone-based apps to complement contact tracing in the event that they face a resurgence of COVID-19 infections. The apps form part of both countries’ policies to support a return to social and economic engagement following extended lockdowns. This paper aims to investigate the extent to which the two approaches are fit for purpose and compare their functional characteristics.

Using process mapping and analysis, this paper evaluates the potential of the two apps to improve the performance of existing contact tracing systems across a range of efficiency and effectiveness criteria with an emphasis on the framework proposed by Verrall (2020).

The Bluetooth-based Australian app appears likely to assist that country’s contact tracing system to perform more efficiently and effectively in the event of a resurgence of the virus and should increase confidence in re-engagement. The New Zealand QR code-based app, however, is not well-aligned with these objectives. Its interaction with a range of other regulations and obligations, combined with the inconvenience it imposes on its users, is likely to militate against its use. Bluetooth-based apps based on interactions between individuals likely better support these population-based objectives than QR code-based location-specific apps.

This paper provides an original and extensive analysis of the functionality and effectiveness of Australia and New Zealand’s official contact-tracing apps.

Relational dynamics between patients and staff in forensic settings can be complicated and demanding for both sides. Reflective practice groups (RPGs) bring clinicians together to reflect on these dynamics. To date, evaluation of RPGs has lacked quantitative focus and a suitable quantitative tool. Therefore, a self-report tool was designed. This paper aims to pilot The Relational Aspects of CarE (TRACE) scale with clinicians in a high-secure hospital and investigate its psychometric properties.

A multi-professional sample of 80 clinicians were recruited, completing TRACE and attitudes to personality disorder questionnaire (APDQ). Exploratory factor analysis (EFA) determined factor structure and internal consistency of TRACE. A subset was selected to measure test–retest reliability. TRACE was cross-validated against the APDQ.

EFA found five factors underlying the 20 TRACE items: “awareness of common responses,” “discussing and normalising feelings;” “utilising feelings,” “wish to care” and “awareness of complicated affects.” This factor structure is complex, but items clustered logically to key areas originally used to generate items. Internal consistency (α = 0.66, 95% confidence interval (CI) = 0.55–0.76) demonstrated borderline acceptability. TRACE demonstrated good test–retest reliability (intra-class correlation = 0.94, 95% CI = 0.78–0.98) and face validity. TRACE indicated a slight negative correlation with APDQ. A larger data set is needed to substantiate these preliminary findings.

Early indications suggested TRACE was valid and reliable, suitable to measure the effectiveness of reflective practice.

The TRACE was a distinctive measure that filled a methodological gap in the literature.

‘Tracing’ is the means by which a person who has been wrongly deprived of a thing attempts to identify and follow that thing, or its substitute in value, into the hands of other persons to whom the thing or the value of the thing has passed. Once the value of the thing has been identified in the hands of the potential defendant, depending on the facts, the plaintiff is then in a position to bring a common law action for wrongful interference with his assets and/or make a claim in restitution in respect of those assets.

The law relating to tracing is complicated, littered with inconsistencies and possibly now verging on a state of disarray. The complications are ever increasing as the topic becomes inevitably intertwined with the law relating to constructive trusts and restitution. This article concentrates on a specific aspect of the law of tracing, namely, the extent to which one can trace money paid into an overdrawn bank account.