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Disasters and armed conflict often co-occur, but does that imply that disasters trigger or fuel conflict? In the small but growing body of literature attempting to answer this question, divergent findings indicate the complex and contextual nature of a potential answer to this question. The purpose of this study is to contribute a robust cross-country analysis of the co-occurrence of disaster and conflict, with a particular focus on the potential role played by disaster.

Grounded in a theoretical model of disaster–conflict co-occurrence, this study merges data from 163 countries between 1990 and 2017 on armed conflict, disasters and relevant control variables (low human development, weak democratic institutions, natural resource dependence and large population size/density).

The main results of this study show that, despite a sharp increase in the co-occurrence of disasters and armed conflict over time, disasters do not appear to have a direct statistically significant relation with the occurrence of armed conflict. This result contributes to the understanding of disasters and conflicts as indirectly related via co-creation mechanisms and other factors.

This study is a novel contribution, as it provides a fresh analysis with updated data and includes different control variables that allow for a significant contribution to the field.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Mixed methods research is useful to enhance theoretical and practical research contributions. However, single methods have predominated much logistics and supply chain management (LSCM) research. This paper presents a review of mixed methods research across ten years in LSCM to determine their usage, identify benefits and inhibitors, and provide suggestions for LSCM researchers to realise the benefits from using mixed methods.

This paper adopts a mixed methods approach through a quantitative analysis of methods used in six leading LSCM journals, an e-mail survey of mixed methods article authors during the review period, and four published case studies that used mixed methods.

Only 144 (ten percent) of all empirical articles were published using mixed methods during the review period. A range of benefits and inhibitors regarding mixed methods adoption were found. Suggestions for LSCM authors include research training in mixed methods use and developing a project-specific research design due to the specificity and complexity associated with mixed methods research.

LSCM is at a critical juncture, shaped by new contexts, themes and challenges, and would benefit from different research approaches and methods. This paper contributes to the LSCM domain through analysing the current state, benefits and inhibitors of mixed methods research in LSCM journals to provide a renewed call to action and guidelines for mixed methods LSCM research, and suggesting research design adaptation to enable agile and resilient research when investigating rapidly changing and complex phenomena.

This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

Previous research has studied interactive value formation (IVF) using resource- or practice-based approaches but has neglected the role of emotions. This article aims to show how emotions are correlated in problematic social media interactions and explore their role in IVF.

By combining a text mining algorithm, nonparametric Spearman's rho and thematic qualitative analysis in an explanatory sequential mixed-method design, the authors (1) categorize customers' comments as positive, neutral or negative; (2) pinpoint peaks of negative comments; (3) classify problematic interactions as detrimental, contradictory or conflictual; (4) identify customers' main positive (joy, trust and surprise) and negative emotions (anger, dissatisfaction, disgust, fear and sadness) and (5) correlate these emotions.

Despite several problematic social interactions, the same pattern of emotions appears but with different intensities. Additionally, value co-creation, value no-creation and value co-destruction co-occur in a context of problematic social interactions (peak of negative comments).

This study provides new insights into the effect of customers' emotions during IVF by studying the links between positive and negative emotions and their effects on different sorts of problematic social interactions.

The objective of this paper is to propose a methodology to estimate the benefits and costs of stakeholder engagement (SE). Indeed, in the transport sector, it is consolidated that a good decision-making process foresees the involvement of the main stakeholders, but what are the benefits and costs of the SE? How to quantify these impacts and explicitly take them into account in a cost-benefit analysis? In this paper, an attempt to answer these questions is provided.

In this paper, a methodology is proposed to estimate the benefits and costs of SE. Moreover, the proposed methodology is applied to a case study with an attempt to identify direct and indirect cost and benefit drivers within the context.

A range of examples of the monetary costs and benefits of SE is provided through the case study of the high-speed rail corridor connecting Bari and Naples in Italy.

Limits in quantifying all the aspects of engagement.

To be adopted by public administrations when deciding whether carrying out a project.

Social inclusion is a must in any decision-making process concerning big projects affecting the community.

The original value of this paper is to provide a contribution to the current literature on the quantitative representation of the impacts of SE. Indeed, a methodology to quantify and monetize the costs and benefits of SE is proposed.

This research addresses the professional logics of street-level managers (SLMs) and bureaucrats (SLBs) working in the Brazilian National Social Security Agency (INSS) through their perceptions of distributive justice and discretion. Since SLMs have the authority to influence SLBs' actions, we investigate whether these two groups hold similar viewpoints.

We integrate the administrative data and survey responses (n = 678) with earlier thematic content analysis (n = 350) in three stages: mean-testing, regression analyses and complementary qualitative analysis, integrated through a mixed-methods matrix.

Whilst no significant differences emerge in distributive justice ideas between groups, SLMs demand wider benefit-granting discretion, praising professionalism whilst adopting managerial posture and jargon.

The study adds to the theoretical discussions concerning SLM’s influence on SLB’s decision-making, suggesting that other factors outweigh it. The finding concerning the managers’ demand for wider discretion asks for further in-depth approaches.

Findings supply valuable insights for policymakers and managers steering administrative reforms, by questioning whether some roles SLMs play are limited to symbolic levels. Further, SLBs’ heterogenous formations might be more relevant to policy divergence than managerial influence and perhaps an underutilised source of innovation.

By approaching street-level management professional logics within a Global South welfare state through a mixed-methods approach, this study offers a holistic understanding of complex dynamics, providing novel insights for public sector management.

This study evaluated the enhancement of information sharing practices with third party logistics service providers (3PLs) in a supply chain solutions company that provides transport and warehousing software in Johannesburg, South Africa.

A mixed methods case study was conducted to investigate the problem. Both strands of quantitative and qualitative data were given equal priority. Three rounds of primary data were sequentially collected, analysed and triangulated. An online questionnaire was distributed to a sampling frame of seventeen companies which were randomly selected from the population. Three company executives were purposively sampled to participate in a focus group interview. Data from an open-ended questionnaire were used to explain and validate the findings from clients and executives who participated in the two preceding rounds of data collection.

It was found that information sharing improves the collaboration of channel members, increases competitive advantage and ultimately leads to better customer service. The improvement of relationships and continuous technological upgrades are recommended for improving visibility of information and effectiveness in the management of supply chains.

This study is limited by the characteristics of the case study methodology. Case study research suffers from restricted generalisability, problems with cross-checking and the risk of bias and subjectivity. This implies that the results of this case study may not be generalised to the overall population. The selection of a mixed methods design was intended to curb the limitations that are inherent to this study.

This study was limited to a few respondents and participants, which raises concerns about both the statistical power and the generalisability of the results. However, the results provide useful insights into some of the information sharing practices in the industry.

The value of the study contributes to the supply chain's dependence on 3PLs for value creation and the reliance on technology to share information amongst channel members. This study highlights a need for organisations to build collaborative relationships with 3PLs and continuously update technological infrastructure in order to meet supply chain network goals.

This uses quantitative and qualitative methods in assessing performance and process outcomes in a team lecture hybrid (TLH) instructional design applied in a public affairs course.

Within a non-experimental prospective design, individual and team outcomes were assessed as follows: survey of student perceptions of learning outcomes; comparison of individual and group scores on in-class case analyses using paired t-tests; external reviewers' observations of traditional lecture versus TLH activities; and analysis of students' reflections on team dynamics using a team process reflection tool adapted from four team development stages.

The following student learning outcomes results were observed: increased use of critical thinking; higher student interaction with other students and the instructor; higher student engagement in initiating or contributing to content or other learning activities; higher student enthusiasm; increased use of problem-solving skills; improved performance evidenced by quality of individual versus group products; evidence suggesting improvements in student learning outcomes when active learners and an active instructor interact in a learning environment.

Instructor practice tips were provided in the following areas: use of assessment methods; student engagement as an active instructor; motivational tips for classes with students from a variety of disciplines; and individual team member accountability.

This paper contributes to the scholarship of teaching and learning (SOTL) by addressing limitations in both traditional and collaborative learning models and expanding holistic evaluations in SOTL.

Over the past decade, researchers have witnessed an exponential growth in the number of publications on IR. This paper aims to understand the state of the art of the research field and to highlight the areas where further academic research is needed, guiding developments in theory, research, policy and practices.

The authors apply the dynamic literature review method called “Systematic Literature Network Analysis”, which combines systematic literature review and bibliographic network analysis. Furthermore, to overcome some of the limitations connected to the methodology, the authors integrate the literature with a manual content analysis of papers.

IR adoption and practices and their determinants represent the most analyzed aspects of literature. Over time, attention has been paid to more specific issues, such as the relationship between IR and other disclosure mechanisms, IR quality and its assurance, the critical analysis of the IR framework and principles and difficulties in IR adoption. Although the literature on IR can be considered to be in its mature stage, many aspects are still under-researched, so there is plenty of space for future research.

The authors propose the following main issues as subjects to be investigated in future studies: IR is not simply an evolution of sustainability reporting, but an innovative communication tool; the debate on who the recipients of value are (shareholders or stakeholders) and on what the definition of value adopted by IR is still remains an open issue; more attention should be given to the role of IR as a managerial tool, which could support strategy formation and communication, and influence internal processes of performance measurement and evaluation; what the future of IR will be in light of recent EU Corporate Sustainability Reporting Directive and new ISSB's standards is still an open question. From a methodological perspective, little is known about structured approaches in accounting studies. The authors confirm how methodologies, such as that of this paper, may be exploited as a tool to support dynamic analysis for setting the agendas for future studies in the accounting field.