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Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.

Firstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.

The results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.

The research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.

The paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

This study aims to investigate the influence of resource slack on the decline of Brazilian companies, with a particular focus on the moderating role of environmental dynamism. The authors specifically examine three types of resource slack: available, potential and recoverable. These represent surplus resources that exceed what is necessary for the organization’s basic operations. The role of environmental dynamism, characterized by rapid changes in customer preferences, technologies and competitive dynamics, is considered as a moderating factor in this relationship.

The authors used data from Brazilian companies spanning from 1997 to 2008. The research sample was composed using the matching-pairs method, which included a group of publicly traded companies that experienced decline (43 companies) and a group that did not (40 companies) within the specified timeframe.

Findings of this study indicate that the presence of available slack, being more liquid resources, decreases the likelihood of organizational decline. Furthermore, the moderation effect of potential resource slack can mitigate decline in companies operating in dynamic industries.

This research provides valuable insights into the impact of slack resources on potential organizational turnarounds. Given the relative scarcity of resources in these companies compared to those in developed countries, whether they be financial, human or technological, the study highlights the unique influence of slack in a less explored institutional environment. This research underscores the importance of examining the decline of Brazilian companies from a broader perspective, emphasizing that decisions regarding resource use can have significant implications on a company’s trajectory, either amplifying or mitigating its decline.

¿Cuál es el impacto del slack de recursos en el declive de grandes empresas brasileñas? Para responder a esta pregunta, hemos probado hipótesis por separado para tres tipos de salck de recursos: disponible, potencial y recuperable. Estos excedentes consisten en recursos en exceso más allá de lo necesario para mantener la organización funcionando.

Desarrollamos un estudio empírico cuantitativo y longitudinal con datos de empresas brasileñas de 1997 a 2008. Adoptamos el método de pares emparejados, componiendo la muestra de investigación con un grupo de empresas cotizadas en bolsa que declinaron (43 empresas) y otro que no declinó (40 empresas) en el período de tiempo.

Encontramos que la disponibilidad de recursos más líquidos reduce la posibilidad de declive. El efecto de moderación en el slack de recursos potenciales para empresas en industrias dinámicas puede mitigar el declive.

Esta investigación contribuye a una mejor comprensión del efecto del excedente en posibles recuperaciones. Extender los estudios de recursos excedentes al contexto de empresas brasileñas mostró la influencia que el excedente ejerce en un ambiente institucional relativamente menos explorado. Ya sea financiero, humano o tecnológico, la escasez de recursos es más pronunciada que en empresas de países desarrollados. Esta investigación llama la atención sobre el hecho de que la declinación de empresas brasileñas se analiza desde una perspectiva más amplia. Las decisiones sobre cómo la empresa usa sus recursos pueden afectar positiva o negativamente la declinación de las empresas, reforzando la importancia de discutir esta relación.

A Qual é o impacto da folga de recursos no declínio de grandes empresas brasileiras? Para responder a essa pergunta, testamos hipóteses separadamente para três tipos de folga de recursos: disponível, potencial e recuperável. Essas folgas consistem em recursos além do necessário para manter a organização funcionando.

Desenvolvemos um estudo empírico quantitativo e longitudinal com dados de empresas brasileiras de 1997 a 2008. Adotamos o método de pares combinados, compondo a amostra de pesquisa com um grupo de empresas de capital aberto que declinaram (43 empresas) e outro que não declinou (40 empresas) no período.

Descobrimos que a disponibilidade de recursos mais líquidos reduz a possibilidade de declínio. O efeito moderador na folga de recursos potenciais para empresas em indústrias dinâmicas pode mitigar o declínio.

Esta pesquisa contribui para uma melhor compreensão do efeito da folga sobre possíveis recuperações. A extensão dos estudos de folgas de recursos para o contexto de empresas brasileiras mostrou a influência que a folga exerce em um ambiente institucional relativamente menos explorado. Seja financeiro, humano ou tecnológico, a escassez de recursos é mais pronunciada do que em empresas de países desenvolvidos. Esta pesquisa chama a atenção para o fato de que o declínio de empresas brasileiras é analisado sob uma perspectiva mais ampla. Decisões sobre como a empresa usa seus recursos podem afetar positiva ou negativamente o declínio das empresas, reforçando a importância de discutir essa relação.

Employee recognition systems are ubiquitous in organizations (WorldatWork, 2019) and have positive effects on work outcomes (e.g. Stajkovic and Luthans, 2001). However, psychologically meaningful recognition relies on the recognition giver being motivated to observe and recognize coworkers. Crises such as the COVID-19 pandemic may impact recognition giving in varying ways, yet little research considers this possibility.

This longitudinal field study examined the impact of the COVID-19 crisis on recognition and acknowledgment giving among frontline and nonfrontline healthcare workers at daily and aggregated levels. We tested the relationships between publicly available daily indicators of COVID-19 and objectively measured daily recognition and acknowledgment giving within a web-based platform.

We found that the amount of daily recognition giving was no different during the crisis compared to the year before, but fewer employees gave recognition, and significantly more recognition was given on days when COVID-19 indicators were relatively high. In contrast, the amount of acknowledgment giving was significantly lower in frontline staff and significantly higher in nonfrontline staff during the pandemic than before, but on a daily-level, acknowledgment was unrelated to COVID-19 indicators.

Our results suggest that organizational crises may at once inhibit and stimulate employee recognition and acknowledgment.

Our research is the first to empirically demonstrate that situational factors associated with a crisis can impact recognition giving behavior, and they do so in ways consistent with ostensibly contradictory theories.

Grounded in dynamic capabilities theory, this study aims to examine how dynamic capabilities and a transactive memory system (TMS) can build the resilience of service organizations and improve their financial performance. Limited studies examine the link between a TMS and organizational resilience.

The authors test a theoretical model on a sample of 350 UK service firms that were impacted by the COVID-19 pandemic and analyze the data using partial least square structural equation modeling.

Results highlight the positive effects of a TMS and dynamic capabilities on organizational resilience. Only a TMS and organizational resilience have direct positive effects on financial performance.

To the best of the authors’ knowledge, this is the first study to ascertain the influence of a TMS on organizational resilience in service firms following adversity.

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

The present study aims to investigate the psychometric properties of the picture story exercise (PSE), a tool for measuring implicit motivations in Iran.

The research method is descriptive correlation and was conducted in two studies. In the first study 24 psychology master’s students were selected by convenience sampling method and tested them to check retest and inter-coder reliability. The second study investigated the correlation between self-reports and the PSE test on a sample of managers. Its statistical population comprised all the managers of a refinery company, 50 people were selected by convenience sampling method. To check the concurrent validity of the PSE test, the participants of the second study, completing the PSE, also completed the needs questionnaire of Steers and Porter (1979).

The findings showed that the PSE test in the Iranian sample had high retest reliability (0.62 on mean) and inter-coder reliability (0.87 on mean), and, following previous research, it does not show a significant relationship with self-reported motives.

PSE can be used in future research as a tool that has demonstrated its reliability and validity in the Iranian sample.

Measurement of implicit motives is a practical factor for predicting people’s behavior, the necessity of using tools that can accurately evaluate implicit motives is strongly felt. Taking into account the fact that so far, in Iranian samples, implicit motivations have not been measured, therefore, the current research is trying to answer this question, whether one of the most important and prominent tools that were made for this purpose and used in various studies can also be used in Iranian samples.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.

In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.

Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.

The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.