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Following the lead of neighboring fields such as strategy and organization studies, entrepreneurship is gradually joining in the adoption of a practice perspective. Entrepreneurship as practice (EaP) is thus a nascent domain of investigation where the methodological debate is still unsettled and very fluid. In this paper, the authors contribute to this debate with a focus on family entrepreneurship.

The authors develop a conceptual paper to discuss what it entails to look at family entrepreneurship through a practice lens and why it is fruitful. Moreover, the authors propose a research strategy novel to the field through which such investigation can be pursued, namely process tracing, and examine its inferential logic.

Process tracing is a strategy of data analysis underpinned by an ontology of causal mechanisms. The authors argue that it complements other practice methods by inferring social mechanisms from empirical evidence and thereby establishing a connection between praxis, practices and practitioners.

Process tracing helps the articulation of an “integrated model” of practice that relates praxis, practices and practitioners to the outcome they jointly produce. By enabling the assessment of impact, process tracing helps providing prima facie evidentiary grounds for policy action and intervention.

Process tracing affinity with the practice perspective has been so far acknowledged only to a limited extent in the social sciences, and it is, in fact, a novel research strategy for the family entrepreneurship field.

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.

Employing a fog computing (FC) network system in the robotic network system is an effective solution to support robotic application issues. The interconnection between robotic devices through an FC network can be referred as the Internet of Robotic Things (IoRT). Although the FC network system can provide number of services closer to IoRT devices, it still faces significant challenges including real-time tracing services and a secure tracing services. Therefore, this paper aims to provide a tracking mobile robot devices in a secure and private manner, with high efficiency performance, is considered essential to ensuring the success of IoRT network applications.

This paper proposes a secure anonymous tracing (SAT) method to support the tracing of IoRT devices through a FC network system based on the Counting Bloom filter (CBF) and elliptic curve cryptography techniques. With the proposed SAT mechanism, a fog node can trace a particular robot device in a secure manner, which means that the fog node can provide a service to a particular robot device without revealing any private data such as the device's identity or location.

Analysis shows that the SAT mechanism is both efficient and resilient against tracing attacks. Simulation results are provided to show that the proposed mechanism is beneficial to support IoRT applications over an FC network system.

This paper represents a SAT method based on CBF and elliptic curve cryptography techniques as an efficient mechanism that is resilient against tracing attacks.

The aims and objectives of this paper were to understand the key influences hindering patients, participation in the contact tracing process for sexually transmissible infection exposure; to study the anatomy of a complex sexual network through the eyes of a committed contact tracer and a group of teenagers; and to identify lessons from the research. Unstructured and group interviews were undertaken with a group of sixth form students and an unstructured interview with a contact tracer. Cue (storyboards) cards and hypothetical sexual networks were used – the outcome demonstrated that generated narrative about sexual network experiences can be analysed using a schema of representation of experience and could be subjected to Labov's structural categories for assignment of spheres of action, to undertake interpretation. Themes identified include: confidentiality, secrecy, friendship, community, the law and social sanctions. We conclude that contact tracing is under the spotlight and that we need to understand the personal experiences of being subjected to a process where little consideration has been given to the social and psychological consequences. Narrative analytic strategies can be applied to gain this much‐needed rich data.

eCash by DigiCash is one of a range of software‐based systems devised for payments to be made over the Internet by the use of electronic tokens or “coins” (known colloquially as “electronic money”). As in the case of other new electronic systems, the functioning of the system gives rise to novel and difficult legal issues, some of which have yet to be resolved. This article will consider the extent to which a user of the system whose value is stolen may use the common law and equitable tracing rules as a means of taking action against the perpetrator of the fraud.

The first case of coronavirus disease 2019 (COVID-19) was documented in China, and the virus was soon to be introduced to its neighboring country – South Korea. South Korea, one of the earliest countries to initiate a national pandemic response to COVID-19 with fairly substantial measures at the individual, societal and governmental level, is an interesting example of a rapid response by the Global South. The current study examines contact tracing mobile applications (hereafter, contact tracing apps) for those who were subject to self-quarantine through the lenses of dataveillance and datafication. This paper analyzes online/digital data from those who were mandatorily self-quarantined by the Korean government largely due to returning from overseas travel.

This study uses an Internet ethnography approach to collect and analyze data. To extract data for this study, self-quarantined Korean individuals' blog entries were collected and verified with a combination of crawling and manual checking. Content analysis was performed with the codes and themes that emerged. In the COVID-19 pandemic era, this method is particularly useful to gain access to those who are affected by the situation. This approach advances the author’s understandings of COVID-19 contact tracing mobile apps and the experiences of self-quarantined people who use them.

The paper shows Korean citizens' understandings and views of using the COVID-19 self-tracing application in South Korea through examining their experiences. The research argues that the application functions as a datafication tool that collects the self-quarantined people's information and performs dataveillance on the self-quarantined people. This research further offers insights for various agreements/disagreements at different actors (i.e. the self-quarantined, their families, contact tracers/government officials) in the process of contact tracing for COVID-19.

This study also provides insights into the implications of information and technology as they affect datafication and dataveillance conducted on the public. This study investigates an ongoing debate of COVID-19's contact tracing method concerning privacy and builds upon an emerging body of literature on datafication, dataveillance, social control and digital sociology.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0377

There has been a long-standing concern and controversy in the consumer behavior and related literature about the accuracy of consumers’ self-reports of their mental processes. While some researchers have found such reports to be susceptible to a number of inaccuracies, others have found them to be reasonably accurate. This study contributes to the debate by comparing consumers’ self-reports of their information acquisition processes in decision making with process-tracing measures of the same processes, and doing so within the context of research design recommendations suggested by Ericsson and Simon (1980, 1984, 1993) for collecting valid retrospective self-reports. Data for the study were collected in a decision-making experiment in which a custom-made computer software was used to administer two decision tasks and to unobtrusively measure subjects’ information acquisition patterns (process-tracing measures). Self-report measures were collected by means of a questionnaire administered after subjects completed both tasks. Descriptive discriminant analysis (with self-report measures as dependent and scores on process-tracing measures as independent variables) shows that subjects were able to accurately report their information acquisition patterns. For both decision tasks, the majority of subjects who were classified by the discriminant functions as using a particular information acquisition strategy actually reported using such a strategy. Implications of the findings are outlined and discussed.

T-spline is the latest powerful modeling tool in the field of computer-aided design. It has all the merits of non-uniform rational B-spline (NURBS) whilst resolving some flaws in it. This work applies T-spline surfaces to additive manufacturing (AM). Most current AM products are based on Stereolithograph models. It is a kind of discrete polyhedron model with huge amounts of data and some inherent defects. T-spline offers a better choice for the design and manufacture of complex models.

In this paper, a direct slicing algorithm of T-spline surfaces for AM is proposed. Initially, a T-spline surface is designed in commercial software and saved as a T-spline mesh file. Then, a numerical method is used to directly calculate all the slicing points on the surface. To achieve higher manufacturing efficiency, an adaptive slicing algorithm is applied according to the geometrical properties of the T-spline surface.

Experimental results indicate that this algorithm is effective and reliable. The quality of AM can be enhanced at both the designing and slicing stages.

The T-spline and direct slicing algorithm discussed here will be a powerful supplement to current technologies in AM.

The purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease.

The authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time.

Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper.

The work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper.

Fighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis.

To the best of the authors’ knowledge, the lowest common ancestor technique in m-ary data aggregation tree in the fog computing-enhanced IoT to contact trace the individuals who have infected or were infected during the transmission of COVID-19 is first of its kind proposed. Creating a graph or an m-ary tree based on the interactions/connections between the people in a particular community like location, friends and time, the authors can attempt to traverse it to find out who infected any two persons or a group of persons or was infected by exploiting the technique of finding out the lowest common ancestor in a m-ary tree.

The purpose of this paper is to provide an analysis of processes in operation during the implementation of a reform programme in Kazakhstan culminating in the widespread adoption of Lesson Study (LS).

The study is positioned within a critical realist theoretical perspective, drawing on Archer’s social theory to focus on the social world of the school while changes to classroom practice are being made. This is a case study using process tracing methods to analyse how school actions and interactions are used during the change process resulting in widespread implementation of LS.

Three key mechanisms for implementing the structural changes are identified; increasing teacher’s pedagogical knowledge, collaborative working structures and active collective inquiry. The capacity to change practice is underpinned by reflection on classroom interactions and in having the necessary skills and available time to analyse the effect on pupils’ learning. Engaging in reflexive deliberation is dependent on having access to new knowledge, together with the opportunity to collaborate in supportive groups.

This study provides an insight into what changes were made and why these support the spread of LS in Kazakhstan, drawing on Archer’s social theory and using theory building process tracing methods to delve deeper into the empirical fingerprints left during the intervention. LS is an important structural factor which is still supporting change in Kazakhstani classrooms.