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The purpose of this paper is to examine the predictive power of anger and its associated appraisal dimensions of consumer responses to two different public relations incidents.

A natural quasi-experiment was conducted within a month after the public relations incidents. Participants randomly viewed one of the two videos relating to the incidents. Path analysis was used to examine the direct and indirect effects of anger, acceptability appraisal, motivational incongruence appraisal, relevance appraisal and other accountability appraisal on consumers’ intention to harm the brand and future purchase intention.

Appraisals of acceptability, motivational incongruence and relevance, but not other accountability, have both direct and indirect effects on anger and its motivational tendency. Acceptability appraisal directly increases consumers’ intention to harm, whereas relevance appraisal directly increases their intention to harm and reduces future purchase intention. The degree to which these appraisal structure and anger occur account for the level of negative consumer responses toward the two public relations incidents.

The current findings empirically replicate the diverse consumer responses toward two public relations incidents and use anger and its appraisal structure to account for the negative responses. This provides researchers and practitioners a framework to explain and manage consumers’ reaction toward different public relations incidents.

The current findings not only support the motivational role of anger and its accompanying appraisals in public relations incidents, but also demonstrate their predictive power in the given contexts.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

The COVID-19 pandemic created a broad array of challenges for hospitals. These challenges included restrictions on admissions and procedures, patient surges, rising costs of labor and supplies, and a disparate impact on already disadvantaged populations. Many of these intersecting challenges put pressure on hospitals' finances. There was concern that financial pressure would be particularly acute for hospitals serving vulnerable populations, including safety-net (SN) hospitals and critical access hospitals (CAHs). Using data from hospitals in Washington State, we examined changes in operating margins for SN hospitals, CAHs, and other acute care hospitals in 2020 and 2021. We found that the operating margins for all three categories of hospitals fell from 2019 to 2020, with SNs and CAHs sustaining the largest declines. During 2021, operating margins improved for all three hospital categories but SN operating margins still remained negative. Both changes in revenue and changes in expenses contributed to observed changes in operating margins. Our study is one of the first to describe how the financial effects of COVID-19 differed for SNs, CAHs, and other acute care hospitals over the first two years of the pandemic. Our results highlight the continuing financial vulnerability of SNs and demonstrate how the factors that contribute to profitability can shift over time.

The purpose of this study is to offer the bibliometric analysis of the Journal of Knowledge Management (JKM) to understand how the literature has developed over time.

This study used bibliometric approaches to analyze a sample of 669 studies from 1997 to 2021. This study focused on performance analysis and scientific mapping of articles using the R package.

The results indicate that the number of publications during the period has significantly increased which shows a growing interest of researchers in the JKM. This study highlights new emerging themes such as change management, change readiness, product innovation and digital libraries which uncover exciting avenues for new research opportunities. USA and UK were the most productive countries in terms of the number of citations followed by few European countries including Spain, Finland, Germany and Sweden. However, it is worth noting that India was the most productive country in the emerging economies.

This study will act as a guide for researchers of various fields to evaluate the development of scientific publications in a particular theme over time, especially for those who are in the field of knowledge management (KM).

This study aims to accomplish the systematic bibliometric analysis of the JKM for more than two decades, providing useful insights into the key developments in the field of KM. This study is more rigorous and comprehensive in terms of the analytical techniques used.

As the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).

The paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.

The digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.

This research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.

In recent decades, interest in digital transformation (DX) within the architecture, engineering, and construction (AEC) industry has significantly increased. Despite the existence of several literature reviews on DX research, there remains a notable lack of systematic quantitative and visual investigations into the structure and evolution of this field. This study aims to address this gap by uncovering the current state, key topics, keywords, and emerging areas in DX research specific to the AEC sector.

Employing a holistic review approach, this study undertook a thorough and systematic analysis of the literature concerning DX in the AEC industry. Utilizing a bibliometric analysis, 3,656 papers were retrieved from the Web of Science spanning the years 1990–2023. A scientometric analysis was then applied to these publications to discern patterns in publication years, geographical distribution, journals, authors, citations, and keywords.

The findings identify China, the USA, and England as the leading contributors in the field of DX in AEC sector. Prominent keywords include “building information modeling”, “design”, “system”, “framework”, “adoption”, “model”, “safety”, “internet of things”, and “innovation”. Emerging areas of interest are “deep learning”, “embodied energy”, and “machine learning”. A cluster analysis of keywords reveals key research themes such as “deep learning”, “smart buildings”, “virtual reality”, “augmented reality”, “smart contracts”, “sustainable development”, “building information modeling”, “big data”, and “3D printing”.

This study is among the earliest to provide a comprehensive scientometric mapping of the DX field. The findings presented here have significant implications for both industry practitioners and the scientific community, offering a thorough overview of the current state, prominent keywords, topics, and emerging areas within DX in the AEC industry. Additionally, this research serves as an invaluable reference and guideline for scholars interested in this subject.