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The purpose of this article is to study what platform-related user factors influence the employment potential of a lean platform for self-employed professionals.

The article employs the system data of a Dutch platform firm, which include consumers looking for painters (N = 17,224) and self-employed painters (N = 1,752) who pursue client acquisition by submitting proposals (N = 101,974). This data is analysed using non-parametric tests.

Study of this platform shows that the platform functions as a channel of acquisition for self-employed professionals. This lean platform enables matching of information of supply and demand, thereby facilitating processes of acquisition. The number of competitors, distance to a potential job and non-standard proposals are statistically significant factors that influence whether a consumer is interested in a proposal. Effect sizes are very small.

This platform is a two-way market for information about service jobs, which excludes a price setting mechanism. The findings of this study cannot be generalized to other forms of platforms.

The market for service professionals is very local; therefore, the platform firm may alter the algorithm to accommodate this. Self-employed professionals should approach using the platform in the same way as normal forms of acquisition.

This particular type of two-sided market is an extension of regular forms of acquisition by creating “weak ties” through the platform.

The article uses a unique data set to study the impact and limitations of digitalization of the (labour) market for service professionals.

The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the literature. Many studies have discussed phenomenological benefits of data to service. However, limited research describes managerial issues behind such benefits, although a holistic understanding of the issues is essential in using data to advance service in practice and provides a basis for future research. The purpose of this paper is to address this research gap.

“Using data to advance service” is about change in organizations. Thus, this study uses action research methods of creating real change in organizations together with practitioners, thereby adding to scientific knowledge about practice. The authors participated in five service design projects with industry and government that used different data sets to design new services.

Drawing on lessons learned from the five projects, this study empirically identifies 11 managerial issues that should be considered in data-use for advancing service. In addition, by integrating the issues and relevant literature, this study offers theoretical implications for future research.

“Using data to advance service” is a research topic that emerged originally from practice. Action research or case studies on this topic are valuable in understanding practice and in identifying research priorities by discovering the gap between theory and practice. This study used action research over many years to observe real-world challenges and to make academic research relevant to the challenges. The authors believe that the empirical findings will help improve service practices of data-use and stimulate future research.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

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.