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With the rapid development of social media, the occurrence and evolution of emergency events are often accompanied by massive users' expressions. The fine-grained analysis on users' expressions can provide accurate and reliable information for event processing. Hence, 2,003,814 expressions on a major malignant emergency event were mined from multiple dimensions in this paper.

This paper conducted finer-grained analysis on users' online expressions in an emergency event. Specifically, the authors firstly selected a major emergency event as the research object and collected the event-related user expressions that lasted nearly two years to describe the dynamic evolution trend of the event. Then, users' expression preferences were identified by detecting anomic expressions, classifying sentiment tendencies and extracting topics in expressions. Finally, the authors measured the explicit and implicit impacts of different expression preferences and obtained relations between the differential expression preferences.

Experimental results showed that users have both short- and long-term attention to emergency events. Their enthusiasm for discussing the event will be quickly dispelled and easily aroused. Meanwhile, most users prefer to make rational and normative expressions of events, and the expression topics are diversified. In addition, compared with anomic negative expressions, anomic expressions in positive sentiments are more common. In conclusion, the integration of multi-dimensional analysis results of users' expression preferences (including discussion heat, preference impacts and preference relations) is an effective means to support emergency event processing.

To the best of the authors' knowledge, it is the first research to conduct in-depth and fine-grained analysis of user expression in emergencies, so as to get in-detail and multi-dimensional characteristics of users' online expressions for supporting event processing.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

This paper aims to present the fabrication of 6061 aluminum alloy (AA6061) using a promising laser additive manufacturing process, called the laser-foil-printing (LFP) process. The process window of AA6061 in LFP was established to optimize process parameters for the fabrication of high strength, dense and crack-free parts even though AA6061 is challenging for laser additive manufacturing processes due to hot-cracking issues.

The multilayers AA6061 parts were fabricated by LFP to characterize for cracks and porosity. Mechanical properties of the LFP-fabricated AA6061 parts were tested using Vicker’s microhardness and tensile testes. The electron backscattered diffraction (EBSD) technique was used to reveal the grain structure and preferred orientation of AA6061 parts.

The crack-free AA6061 parts with a high relative density of 99.8% were successfully fabricated using the optimal process parameters in LFP. The LFP-fabricated parts exhibited exceptional tensile strength and comparable ductility compared to AA6061 samples fabricated by conventional laser powder bed fusion (LPBF) processes. The EBSD result shows the formation of cracks was correlated with the cooling rate of the melt pool as cracks tended to develop within finer grain structures, which were formed in a shorter solidification time and higher cooling rate.

This study presents the pioneering achievement of fabricating crack-free AA6061 parts using LFP without the necessity of preheating the substrate or mixing nanoparticles into the melt pool during the laser melting. The study includes a comprehensive examination of both the mechanical properties and grain structures, with comparisons made to parts produced through the traditional LPBF method.

This study aims to examine the moderating effect of cultural conditions on the relationship between environmental performance and environmental disclosure.

The authors used meta-analysis technique to examine 100 effect sizes from 43 studies published between 1982 and 2023 to integrate the existing results and to detect causes contributing to variability of results across studies.

There is a significant positive relationship between environmental performance and environmental disclosure. Further, the authors found that cultures with long-term orientation positively moderated the relationship, whereas cultures with high uncertainty avoidance and indulgence negatively moderated it.

This study did not account for the problem of endogeneity between environmental performance and environmental disclosure because most of the already published studies included in the authors’ meta-analysis did not address this issue.

This research provides regulators and policymakers insights on the influence of cultural factors on environmental disclosure and performance, critical information to consider when adopting, or revising social and environmental policy and regulations within a country.

To the best of the authors’ knowledge, this is the first meta-analysis study examining different cultural dimensions influencing the relationship between environmental performance and environmental disclosure and contributes new knowledge to the literature on determinants of environmental disclosure.

While previous operations management literature acknowledges the positive influence of Lean and Industry (I4.0) on performance, recent studies examining the synergy between these two factors have produced inconsistent and contradictory results. Therefore, this study aims to provide a comprehensive understanding of the effect of Lean and I4.0 synergy on firm performance.

This study utilised a meta-analysis approach, examining 23 empirical studies exploring multiple effects of the Lean and I4.0 synergy on firm performance. Multiple subgroup analyses were conducted to assess the contradictory outcomes and identify in what conditions such synergy may achieve performance.

The results affirm the prevailing positivist perspective among most scholars regarding the positive influence of the Lean and I4.0 synergy on firm performance. However, the overall effect size derived from the studies indicates a weak relationship, suggesting that this synergy alone is not the sole determinant factor of firm performance. In addition, the subgroup analyses reveal the presence of contingent conditions that may affect the performance outcomes when integrating Lean and I4.0, as most effects exhibit a weak relationship.

This study represents the first meta-analysis investigating the relationship between the Lean and I4.0 synergy on firm performance. By shedding light on the contradictory effects often depicted in the operations management literature, this study provides a critical reflection for researchers who tend to adopt an overly optimistic view of such synergy.

We explore the relationship between urbanicity and police officers’ perceptions of changes in their reactive and proactive work during the COVID-19 pandemic.

Using the 2021 survey of 1,262 Croatian police offices (436 police officers from a large urban community, 471 police officers from small towns and 155 from rural communities), we examine the perceived changes in their reactive activities (e.g. responses to the calls for service, arrests for minor crimes) and proactive activities (e.g. community policing activities, directed patrols) during the peak month of the pandemic compared to before the pandemic.

The majority of police officers in the study, regardless of the size of the community where they lived, reported no changes before and during the pandemic in reactive and proactive activities. Police officers from urban communities and small towns were more likely to note an increase in domestic violence calls for service. Police officers from urban communities were also more likely than the respondents from small towns and rural communities to report an increase in the responses to the disturbances of public order. Finally, police officers from small communities were most likely to observe a change in the frequency of traffic stops during the pandemic.

This study is the first one to explore the differences in perceptions of COVID-19-related changes in reactive and proactive police activities in a centralized police system.

Adoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.

A systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.

Thirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.

This study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.

To the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

This paper aims to explore how the maieutic role of management accounting (MA) can be sustained in the context of MA digitalization.

The paper begins with practitioners’ descriptions of the context that makes the MA support of non-routine decisions maieutic. To understand how the maieutic characteristics can be sustained in future MA digitalization, the authors then analyze the discourses these practitioners have about artificial intelligence (AI) in providing MA support.

As a basis, the authors’ data show various maieutic characteristics within the use of MA answers in decision-making as well as within the MA process of generating such answers. The paper then identifies three MA digitalization discourses, namely, “computation,” “judgment” and human-AI “interaction” discourse, each with their unique agendas on how AI should be used.

The paper is based on the premises that AI and digitalization are often discussed without sufficient understanding about the context being digitalized. The authors’ data suggest that MA support in non-routine decision-making is fundamentally maieutic, and AI – as it currently stands – is not expected to change this by providing perfect answers. The authors provide novel insights about maieutic MA support and the current discourses on using AI in MA support, and how digitalization does not necessarily compromise maieutic MA support but instead has the potential to sustain or even enhance it.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

The purpose of this study was to gather insights from sport marketing professionals and identify key opportunities, challenges and knowledge that sport marketing educators and researchers could utilize in developing curriculum and research agendas.

A qualitative approach was used, and data were collected through in-depth interviews with 15 sport marketing professionals. Participants were asked questions related to the knowledge, skills and experiences that they believe are important for students to have to be successful in the industry, as well as the types of research that would be most useful in their day-to-day work.

Industry professionals noted collaboration, transformation in digital marketing, data and analytics and experiential marketing as key trends facing the industry today. The findings suggest that sport marketing curriculum should focus on soft skill development such as communication, relationship building and empathy alongside hard skill development such as data analysis and storytelling. As well, findings show research areas where scholars can aid practitioners with a focus on consumer insights, technology, measuring ROI and experiential marketing.

With these findings, educators and scholars can better prepare students for successful careers in industry and contribute to the ongoing advancement of the scholarly field. This study serves as a starting point for further research in this area, and it is hoped that it will spark continued collaboration between academia and industry.