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By examining the link between the two types of FDI and intrastate conflict from 1990 to 2015 in 138 countries, this paper intends to test the peace-through-FDI thesis.

To empirically test the hypotheses, this study examines county-year observations from 1990 to 2015 for 138 countries. An instrumental variable method is utilized to this end.

This paper shows that, while greenfield FDI generates pacifying effects on intrastate conflict, M&A investment is likely to promote the onset of intrastate conflict.

Despite the extensive literature on FDI and the onset of intrastate conflict, many have approached FDI as a singular phenomenon, and have not broken it down into its constituent parts of greenfield and brownfield investment types. Theorizing that this practice had oversimplified and blurred the relationship of FDI on intrastate conflict onset, the authors pursued the collection of novel data in order to more completely distinguish between the two types of FDI. With this novel approach dividing FDI into its component parts, the authors break open the black box of FDI to empirically find out the extent of its diverse influence on the onset of intrastate conflict.

Contemporary supply chain relationships inherently rely on delegation of work between organizations and, thus, are subject to agency problems for which a wide range of governance mechanisms exist. This review of agency theory (AT), across four distinct fields, explains the connection between governance mechanisms and supply chain relationship types.

The study uses a systematic literature review (SLR) of articles using AT in a supply chain context from the operations and supply chain management, general management, marketing, and economics fields.

The authors categorize the governance mechanisms identified to create a typology of agency relationships in supply chains.

The developed typology provides parsimonious theory on different forms of supply chain agency relationships and takes a step towards a “supply chain-oriented agency theory” explaining and predicting relationship types and governance in supply chains. Furthermore, a future research agenda calls for more accurate measuring of agency costs, to examine residual gains alongside residual losses, to take a dual-sided perspective of agency relations and to adopt AT to examine more complex supply networks.

The review provides a menu of governance mechanisms and describes situations under which these mechanisms could be deployed to guide managers when developing their supply chain relationships.

The first review to combine and elaborate views from four major disciplines using AT as a lens to supply chain relationships. Expanding the traditional set of governance mechanisms provides academics and practitioners with a bigger “menu” of options to consider.

The purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations.

This study reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer.

The findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed.

This study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design.

Empirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems.

Unlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.

Though the circular economy (CE) is a current buzzword, this still lacks a precise definition. In the absence of a clear notion of what that term includes, actions taken by the government and companies may not be well informed. In particular, those actions need to consider what people mean when people talk about the CE, either to refocus people's decisions or to undertake a more effective communications strategy.

Since people voice people's opinions mainly through social media nowadays, special attention has to be paid to discussions on those media. In this paper, the authors focus on Twitter as a popular social platform to deliver one's thoughts quickly and fast. The authors' research aim is to get the perceptions of people about the CE. After collecting more than 100,000 tweets over 16 weeks, the authors analyse those tweets to understand the public discussion about the CE. The authors conduct a frequency analysis of the most recurring words, including the words' association with other words in the same context and categorise them into a set of topics.

The authors show that the discussion focuses on the usage of resources and materials that heavily endanger sustainability, i.e. carbon and plastic and the harmful habit of wasting. On the other hand, the two most common good practices associated with the CE and sustainability emerge as recycling and reuse (the latter being mentioned far less). Also, the business side of the CE appears to be relevant.

The outcome of this analysis can drive suitable communication strategies by which companies and governments interested in the development of the CE can understand what is actually discussed on social media and call for the attention.

This paper addresses the lack of a standard definition the authors highlighted in the Introduction. The results confirm that people understand CE by looking both at CE's constituent activities and CE's expected consequences, namely the reduction of waste, the transition to a green economy free of plastic and other pollutants and the improvement of the world climate.

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 twofold: first, to describe Lean maturity in primary care using a questionnaire based on Liker’s description of Lean, complemented with observations; and second, to determine the extent to which Lean maturity is associated with quality of care measured as staff-rated satisfaction with care and adherence to national guidelines (NG). High Lean maturity indicates adoption of all Lean principles throughout the organization and by all staff.

Data were collected using a survey based on Liker’s four principles, divided into 16 items (n=298 staff in 45 units). Complementary observations (n=28 staff) were carried out at four units.

Lean maturity varied both between and within units. The highest Lean maturity was found for “adhering to routines” and the lowest for “having a change agent at the unit.” Lean maturity was positively associated with satisfaction with care and with adherence to NG to improve healthcare quality.

Quality of primary care may benefit from increasing Lean maturity. When implementing Lean, managers could benefit from measuring and adopting Lean maturity repeatedly, addressing all Liker’s principles and using the results as guidance for further development.

This is one of the first studies to evaluate Lean maturity in primary care, addressing all Liker’s principles from the perspective of quality of care. The results suggest that repeated actions based on evaluations of Lean maturity may help to improve quality of care.

While there are well-established personal benefits to being a mentor, such as increased life satisfaction and job performance (Ramaswami & Dreher, 2007), how mentors grow and develop requires exploration. We meet this need by presenting six key themes from two recent research studies related to the experiences that mentors perceived as contributing to their development. The growth of two leadership theories in particular were explored: generativity and Psychological Capital. Six themes emerged: (a) curricular training, (b) exposure to leadership outcomes, (c) being mentored by peers, (d) experiences with mentee, (e) reflection, and (f ) observing a ripple effect. These themes offer insights on how curricular and co-curricular experiences might maximize leadership development of students and ground leadership interventions, such as mentoring, in theory and research.