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The purpose of this paper is to explore what characterizes cyberbullying when it is performed in digital space and in an increasingly boundary blurred working life context.

Cyberbullying is explored through the lens of Erving Goffman’s theories on everyday life interaction and social media scholars understanding of social life on the internet today. The empirical material for the study is grounded in eight in-depth interviews with individuals who have been subjected to cyberbullying behavior in their professional life. The interview data were analyzed by means of thematic analysis.

Three key themes were identified: spatial interconnectedness, colliding identities and the role of the audience. The empirical data indicate that in order to understand cyberbullying in working life, it is necessary to consider the specific context that emerges with social network sites and blogs. Moreover, this study shows how social network sites tend to blur boundaries between the private and the professional for the targeted individual.

Cyberbullying in working life is a relatively under-researched area. Most existing research on cyberbullying follows the tradition of face-to-face bullying by addressing the phenomenon with quantitative methods. Given the limited potential of this approach to uncover new and unique features, this study makes an important contribution by exploring cyberbullying with a qualitative approach that provides in-depth understanding of the new situations that emerge when bullying is performed online.

Logistical facility location decisions can make a crucial difference in the success or failure of a company. Geographical Information Systems (GIS) have recently become a very popular decision support system to help deal with facility location problems. However, until recently, GIS methodologies have not been fully embraced as a way to deal with new facility location problems in business logistics. This research makes a framework for categorizing empirical facility location problems based on the intensity of the involvement of GIS methodologies in decision making. This framework was built by analyzing facility location models and GIS methodologies. The research results revealed the depth of the embracement of GIS methodologies in logistics for determining new facility location decisions. In the new facility location decisions, spatial data inputs are almost always coupled with the visualization of the problems and solutions. However, the usage of GIS capability solely (i.e. suitability analysis) for problem solving has not been embraced at the same level. In most cases, the suitability analysis is used together with special optimization models for choosing among the multiple alternatives.

The purpose of this paper is to argue for an improved conceptualisation of health service research, using Stengers' (2018) metaphor of “slow science” as a critical yardstick.

The paper is structured in three parts. It first reviews the field of health services research and the approaches that dominate it. It then considers the healthcare research approaches whose principles and methodologies are more aligned with “slow science” before presenting a description of a “slow science” project in which the authors are currently engaged.

Current approaches to health service research struggle to offer adequate resources for resolving frontline complexity, principally because they set more store by knowledge generalisation, disciplinary continuity and integrity and the consolidation of expertise, than by engaging with frontline complexity on its terms, negotiating issues with frontline staff and patients on their terms and framing findings and solutions in ways that key in to the in situ dynamics and complexities that define health service delivery.

There is a need to engage in a paradigm shift that engages health services as co-researchers, prioritising practical change and local involvement over knowledge production. Economics is a research field where the products are of natural appeal to powerful health service managers. A “slow science” approach adopted by the embedded Economist Program with its emphasis on pre-implementation, knowledge mobilisation and parallel site capacity development sets out how research can be flexibly produced to improve health services.

Listening is often considered the cornerstone of the communication process, with feedback being a crucial skill for effective management. The primary objective of this article was to investigate the relationship between managers’ listening skills and feedback skills from their subordinates’ perspectives. Moreover, it explores the mediating effect of message-sending skills and the ability to deal with interference in this relationship.

This article deployed a quantitative, descriptive research design. The authors developed and distributed a self-administered questionnaire via non-probability convenience sampling, resulting in 304 useable responses.

The results of the main direct effect test (model 1) indicate that listening is positively associated with feedback. Model 2 established that message-sending skills did not directly mediate that relationship. On the other hand, the ability to deal with interference was found to mediate the relationship. Finally, model 4 showed the multi-mediating effect of message-sending skills and the ability to deal with interference in the relationship between listening and feedback.

As far as the researchers are aware, this paper is the first of its kind to show the ability to deal with interference as a mediating factor in a statistical model. Moreover, this study is the first to present a continuous intermediary role played by message-sending skills and the ability to deal with interference in the relationship between listening and feedback.

This paper aims to investigate the relationship between academic integrity of online university students and its effects on academic performance and learning quality. The first hypothesis aimed to see if there is statistically significant relationship between academic honesty of students taking online classes and their apparent academic performance. The second hypothesis aimed to see if there is a statistically significant difference in academic integrity among male and female students. The third hypothesis aimed to see if there was a statistically significant relationship between academic honesty of students and their quality of learning.

This is a quantitative study; data was collected via student emails from 155 active online university students.

There was a positive linear relationship for the first hypothesis, the relationship is relatively weak as the value of Pearson correlation was (0.172). For the second hypothesis, the results showed that there was no significant difference between males and females. The results for the third hypothesis showed that there is a statistically significant relationship between academic integrity of students taking online classes and academic learning quality. This relationship is relatively strong.

The sample size may have been a limitation for generalizing the results.

As a practical implication, authors recommend that education administrators focus on training their faculty members to stress and instill strong ethical values, such as academic integrity and honesty, in their students all throughout their academic journey.

As for social implication, the embracing of ethical values in students, graduates may continue to embrace such values in the workplace which may lead to more reputable and profitable work environment where the society at large benefits.

This research is among the pioneers that attempted to study the connection of academic integrity and learning quality from the students’ perspective.

The purpose of this paper is to study the relationship between organizational adaptability, institutional leadership and business process reengineering performance using the tested complexity theory in a developing economy setting.

This study is correlation and cross-sectional and adopts institutional-level data collected via questionnaires from reengineered microfinance institutions in Uganda. Cluster analysis as data mining technique was used to classify cases based on respondents’ opinions into homogeneous clusters. Nvivo was used to understand the perceptions of business process reengineering performance based on qualitative data. The authors used structural equation modeling to derive the predictive model of business process reengineering performance in a developing world setting.

The authors find that organizational adaptability and institutional leadership are key predictors of business process reengineering performance. Results reveal a predictive model of 61 per cent based on structural equation modeling for the study variables. Cluster analysis as data mining approach explored complex patterns of reengineered business processes.

The use of cluster analysis is susceptible to problems associated with sampling error and absence of fit indices. However, the likelihood of these problems is reduced by the interaction with the data, practical implications and use of smart partial least square to generate structural equations based on derived measurement models of each study variable.

Policymakers of Bank of Uganda, Ministry of Finance and Economic Planning, should develop sound policies in relation to knowledge management, institutional leadership and adaptive mechanisms to enhance business process reengineering performance to take advantage of new knowledge opportunities for the improvement of their businesses.

Given the results from structural equations generated, managers need to consider institutional leadership and organizational adaptability as key drivers of business process reengineering performance in microfinance institutions. The results confirm the significant role of institutional leadership, organizational adaptability in determining business process reengineering performance outcomes.

Unlike most of the business process reengineering literature, this study contributes to literature by domesticating and testing complexity theory to explain business process reengineering performance in developing economies.

The purpose of this study is to examine the South African banks’ customer due diligence (CDD) practices in the fintech era to mitigate money laundering (ML) risks and ensure financial stability. Financial technologies have brought substantial transformations to the financial services sector. However, such technologies have exposed the sector to emerging risks that threaten the integrity and stability of the financial system globally. Before any bank–customer relationship is established, proper customer background checks must be conducted. These background checks enable financial institutions to validate information provided and ensure customers are properly risk profiled. Failure to risk profile customers could result in financial institutions being used as conduits for ML. Undoubtedly, CDD procedures are pivotal to overall anti-money laundering efforts and curbing financing terrorism in a regulatory framework.

A qualitative research approach was adopted to address the research questions of the study. Given the confidentiality associated with the financial services sector, data triangulation was used in blending mainly secondary and primary data sources. Secondary data sources used in the study were published reports available in the public domain that were corroborated with subject matter experts’ interviews.

Based on the findings of this study, it is concluded that in South Africa, technological solutions have been incorporated into CDD functions, which is now risk-based (enhanced due diligence). Also, legally, South Africa has incorporated the biometrics, integration with Department of Home Affairs and Companies and Intellectual Property Commission databases, customer consent to third-party sources with the Financial Intelligence Centre Act and the Protection of Personal Information Act.

The shift towards digital banking in South Africa results in increased data and dynamic risk profiling. This study advocates a policy shift requiring a risk-based approach to mitigating emerging ML risks (in particular digital laundering), especially in the wake of South Africa’s recent greylisting by the Financial Action Task Force.