Quality Of Decisions Research Articles

From gender policies and practices to organisational performance of sport governing bodies

ABSTRACT Research question Gender policies reflect identity-conscious human resource management structures that aim to reduce unfair treatment based on gender. Drawing on signalling theory and the categorisation-elaboration model, the literature suggests that the relationship between gender policies and organisational performance is moderated by several factors (gender practices, organisational culture, decision-making quality), which have not yet been empirically studied. The purpose of this study is to investigate the presence of gender policies in sport governing bodies (SGBs) and examine their relationship with organisational performance. Research methods An online questionnaire was sent to representatives of German SGBs (n = 202). Structural equation modelling was used to examine the relationships between gender policies, gender practices (i.e. board gender diversity), organisational culture, decision-making quality, and organisational performance. Results and findings Gender policies are rarely present in German SGBs, and can only shape the organisational culture when they are connected to daily routines (in contrast to the simple presence of a written statement). Gender policies and practices are positively associated with decision-making quality and organisational performance. Implications The findings contribute to the literature by shedding light on the theoretical mechanisms (i.e. organisational culture, decision-making quality) through which group diversity affects organisational performance. Sport managers should connect gender policies to the daily work instead of simply including them in good governance guidelines, especially because they also benefit the organisation in terms of better performance. Politicians should consider mandatory regulations to make gender policies more effective.

An Analysis of Women's Roles and Positions in geopolitical organizations

Driven by the global movement for gender equality, women's participation in various fields has gradually increased. This paper describes the evolution of women's roles and status in geopolitical organizations and their current status, and analyzes women's participation in geopolitical organizations, their proportion and positions in leadership, and their influence in policy making. Although the status of women in these organizations has improved, they still face many challenges such as gender stereotypes and inequality. Strategies for promoting the development of women's roles and status in geopolitical organizations include four aspects: formulating and implementing gender equality policies, promoting gender-balanced recruitment and promotion mechanisms, enhancing women's participation in decision-making processes, and establishing women's networks and support systems. These strategies are intended to further promote gender equality in geopolitical organizations and enhance organizational diversity and decision-making quality.

The interplay of humor and creativity: shaping rationality and decision-making quality

AbstractThis study explores the relationship between humor, creativity, rationality, intuitiveness, and decision-making quality, all of which are vital in organizational management and success. Despite the importance attached to decision-making, prior research has not fully addressed how humor and creativity interact with rationality. Drawing from psychology, cognitive science, and management studies, we investigate how humor and creativity jointly influence cognitive reflection and decision quality. Using a sample of 998 participants and employing a mediating-moderation PROCESS model, our findings indicate that rational individuals tend to make higher-quality decisions, with affiliative humor mediating this relationship. Furthermore, we observed that employees’ creativity levels moderated this relationship, especially among rational employees with high creativity, who made higher-quality decisions. These findings highlight the interconnectedness of humor, creativity, and rationality in decision-making processes, offering practical implications for organizational behavior and management practices. Understanding this interplay can provide a nuanced approach to enhancing decision-making processes in management, emphasizing the value of humor in organizational contexts.

Reliability-based ordinal consensus adjustment model for large scale group decision making

In large scale group decision-making (LSGDM), there are the substantial number of decision makers (DMs) with diverse knowledge, backgrounds, and interests related to the decision-making problem, and it is not possible to assure that all DMs are completely reliable. Thus, in order to enhance the quality of decision-making, it is necessary to analyze the reliabilities of DMs in LSGDM. This paper proposes the method to evaluate the reliabilities of DMs, sorts these DMs according to their degree of reliability, and investigates the consensus reaching process based on categories and an ordinal consensus measure. Considering the DMs' trust network, the uncertainty of a DM's evaluation information represented by a fuzzy preference relation (FPR), the deviation between a DM's FPR and those of the other DMs, and additive consistency of FPRs, the reliability of a DM is assessed using four criteria: PageRank centrality, professional competence, collaborative competence, and additive consistency. Following these reliability assessment criteria, ELECTRE-TRI is employed to sort DMs into three ordered categories according to DMs' different levels of reliability under the four assessment criteria. Furthermore, an improved ordinal consensus measure is designed to consider both the importance weights of positions and the deviation of Borda counts of the same alternative in two rankings. As for the consensus reaching process, due to the varied reliabilities of DMs in different categories, we propose a multiple strategies feedback mechanism for DMs in different categories. Finally, a numerical example is provided to illustrate the rationality and validity of the proposed model.

Compressed Point Cloud Quality Index by Combining Global Appearance and Local Details

In recent years, many standardized algorithms for point cloud compression (PCC) has been developed and achieved remarkable compression ratios. To provide guidance for rate-distortion optimization and codec evaluation, point cloud quality assessment (PCQA) has become a critical problem for PCC. Therefore, in order to achieve a more consistent correlation with human visual perception of a compressed point cloud, we propose a full-reference PCQA algorithm tailored for static point clouds in this article, which can jointly measure geometry and attribute deformations. Specifically, we assume that the quality decision of compressed point clouds is determined by both global appearance (e.g., density, contrast, and complexity) and local details (e.g., gradient and hole). Motivated by the nature of compression distortions and the properties of the human visual system, we derive perceptually effective features for the above two categories, such as content complexity, luminance/geometry gradient, and hole probability. Through systematically incorporating measurements of variations in the local and global characteristics, we derive an effective quality index for the input compressed point clouds. Extensive experiments and analyses conducted on popular PCQA databases show the superiority of the proposed method in evaluating compression distortions. Subsequent investigations validate the efficacy of different components within the model design.

Optimizing underwater connectivity through multi-attribute decision-making for underwater IoT deployments using remote sensing technologies

The underwater Internet of Things (UIoT) and remote sensing are significant for biodiversity preservation, environmental protection, national security, disaster assistance, and technological innovation. Assigning tasks to autonomous underwater vehicles (AUVs) is a fundamental challenge in underwater technology and exploration. Remote sensing and AUVs are vital for pollution detection, disaster prevention, marine observation, and ocean monitoring. This work presents an optimized network connectivity using a multi-attribute decision-making approach for underwater IoT deployment. A feature engineering approach highlights the significant characteristics of underwater things, incorporating remote sensing data, and a multi-objective optimization method is used to select optimal UIoT for effective task allocation in deep-sea environments. A balance between data transmission, energy economy, and operational performance is necessary for efficient task distribution. Effective communication algorithms and protocols are needed to maintain environmental sustainability, protect marine ecosystems, and improve underwater monitoring enhanced by remote sensing technologies. Multi-criteria decision-making (MCDM) is beneficial for addressing various challenges in underwater technology, considering factors such as mission objectives, energy efficiency, environmental conditions, vehicle performance, safety, and much more. The proposed criteria importance through intercriteria correlation (CRITIC) methodology will assess technical competencies like communication, resilience, navigation, and safety in an underwater environment, leveraging remote sensing and aiding decision-makers in selecting appropriate undersea devices and vehicles for enhancing communication and transportation. This method prioritizes characteristics and aligns them with specific objectives, improving decision-making quality in the marine environment.

Veterinary Evidence: moving forward

Unrecognised and unknown information needs can significantly impact on the quality of decision making, advice, and patient care. In the practice of veterinary medicine, additional information may be required in one or more of epidemiology, diagnosis, treatment, prognosis, control (risk reduction), and prevention (risk avoidance), in order to optimise patient care. Veterinary Evidence publishes Knowledge Summaries, which aim to answer a specific and focused clinical query by appraising the best available evidence published in peer-reviewed scientific journals. By presenting the evidence in a structured and easily digestible format, Knowledge Summaries enable veterinary professionals to stay informed and apply the best available evidence to their decision-making. To assist in the identification of unrecognised deficits and the generation of clinical queries Veterinary Evidence has developed a list of common conditions for each species, alongside the different categories of information. The aim is to prompt and support the generation of new clinical queries in a more systematic format. This resource sits alongside our species-based lists of clinical queries that have already been suggested by practicing veterinarians, veterinary nurses, and other paraprofessionals (see veterinaryevidence.org/index.php/ve/clinical-queries). We hope readers and prospective authors will use these resources to generate new clinical queries or as a starting point for writing their own Knowledge Summaries. Most of the papers published in Veterinary Evidence are Knowledge Summaries, but the journal has always accepted and will continue to accept and encourage other types of reviews and original research that provide evidence to inform clinical practice. For example, in this issue we have published a commentary paper by Professor Jonathan Elliott regarding the processes and evidence required for the registration of veterinary medicinal products which provides some important insights and debate into the publication of evidential information (Elliott, 2024). A Knowledge Summary may not always be the best format to synthesise and summarise evidence on a multifaceted topic; scoping reviews, systematic reviews, or narrative reviews maybe more appropriate. Munn et al. (2018) provide a useful account of scoping reviews and how they differ from traditional literature and systematic reviews. In particular, we are keen to receive articles on artificial intelligence applications, sustainability, and quality improvement in clinical practice, as the impact and importance of these topics grows. We are also making some changes to the presentation of Knowledge Summaries in Veterinary Evidence, to help readers more easily understand the details and context of the summarised evidence. In order to support everyone in appraising and understanding quantitative clinical studies, we will be publishing a glossary and guidance on basic statistical terms and tests on the journal website.

Development and validation of a new tool to assess quality of decision-making by older children and parents about research participation

ObjectiveEffective decision-making is crucial for children and young people's trial participation, but specific tools to measure it are lacking. The TRECA (TRials Engagement in Children and Adolescents) Decision-Making Questionnaire (DMQ) was developed to fill this gap and has been evaluated for reliability and validity MethodsWe created the TRECA Decision-Making Questionnaire, based on similar measures for adults, and recruited participants through seven Studies-Within-a-Trial (SWATs). Participants were randomly assigned to receive trial information either as a printed Participant Information Sheet or Multimedia Information, or both, and asked to complete the DMQ. We calculated item completion rates, item-remainder statistics and Cronbach’s Alpha, and conducted factor analysis. Results549 participants (433 parents/guardians, 116 older children) completed a DMQ. It had high completion rates and internal consistency (Alpha = 0.88 for parents/guardians and 0.84 for older children) and moderate to high inter-item correlations. The DMQ had a single factor accounting for 53 % of variance. ConclusionsThe TRECA DMQ is a useful tool for evaluating research participation decisions in older children, as well as parents and guardians. Practice implicationsOur study suggests that the TRECA DMQ can be used to assess the quality of decision-making about trials in parents, guardians and older children.

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Making the people’s voice heard: pathways of E-participative governance in China

The widespread use of information technology has reshaped the public sphere in the digital age, turning various online platforms into novel channels for civic participation. This article delves into the dynamics and political ramifications of China’s digital participatory policymaking from the perspective of state-society interactions. Based on extensive fieldwork spanning over three years in multiple localities across China, we identify and compare three distinct models of e-participative governance: disclosure, consultation, and deliberation. These models, initiated proactively by local governments in response to citizens’ critical demands, vary significantly in key aspects such as the level of institutionalization and the nature and extent of state-citizen interaction. While they have bolstered citizens’ political influence, these models have also enabled governments to solicit public opinions on local issues, enhance the quality of decision-making, and tactfully channeled increased civic activism into non-threatening areas. Our findings underscore the intricacies of China’s e-participative governance, providing valuable insights into understanding the country’s state-society relations in the digital age.

Inequality threat increases laypeople's, but not judges', acceptance of algorithmic decision making in court.

Algorithmic decision making (ADM) takes on increasingly complex tasks in the criminal justice system. Whereas new developments in machine learning could help to improve the quality of judicial decisions, there are legal and ethical concerns that thwart the widespread use of algorithms. Against the backdrop of current efforts to promote the digitization of the German judicial system, this research investigates motivational factors (pragmatic motives, fairness concerns, and self-image-related considerations) that drive or impede the acceptance of ADM in court. We tested two hypotheses: (1) Perceived threat of inequality in legal judgments increases ADM acceptance, and (2) experts (judges) are more skeptical toward technological innovation than novices (general population). We conducted a preregistered experiment with 298 participants from the German general population and 267 judges at regional courts in Bavaria to study how inequality threat (vs. control) relates to ADM acceptance in court, usage intentions, and attitudes. In partial support of the first prediction, inequality threat increased ADM acceptance, effect size d = 0.24, 95% confidence interval (CI) [0.01, 0.47], and usage intentions (d = 0.23, 95% CI [0.00, 0.46]) of laypeople. Unexpectedly, however, this was not the case for experts. Moreover, ADM attitudes remained unaffected by the experimental manipulation in both groups. As predicted, judges held more negative attitudes toward ADM than the general population (d = -0.71, 95% CI [-0.88, -0.54]). Exploratory analysis suggested that generalized attitudes emerged as the strongest predictor of judges' intentions to use ADM in their own court proceedings. These findings elucidate the motivational forces that drive algorithm aversion and acceptance in a criminal justice context and inform the ongoing debate about perceptions of fairness in human-computer interaction. Implications for judicial praxis and the regulation of ADM in the German legal framework are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Features of human capital development and communication

The purpose of this study is to study the sustainability of the personnel management system in the Russian Railways company by analyzing its personnel policy, as well as determining the impact of communication in the team and professional skills on the quality of management decisions. Based on the method of statistical observation of quantitative indicators of the personnel structure of the staff, the following indicators were determined: a change in the percentage of age groups, a change in the percentage of age groups and the level of education of employees. Based on the above indicators, it is possible to track the dynamics of the quality level of personnel management and its impact on the internal personnel processes of the company, and it is also possible to identify the relationship between professional competencies and communications on decisions made by employees. To achieve these goals, a study of generational guidelines, communication in the team, in particular between the immediate supervisor and a specialist, the impact of innovative approaches to training on improving the professional skills of employees, improving the quality of work and relationships in the team was conducted. We will consider various innovative teaching methods such as advanced training courses, retraining, distance learning system and their impact on the professional development of employees. It will also analyze how innovative approaches to learning contribute to the formation of a positive work environment and improve communication in the team. As a result of the research, it will be revealed which specific innovative methods can be most effective in achieving the goals of the organization. The optimal way of developing professional skills and competencies of employees, which can improve the quality of work, is also proposed. The choice of this topic is due to the inherent importance of human capital in the context of achieving the company’s strategic goals. In today’s business world, where competition is becoming more and more acute, the key factor for success is the quality of staff and their professional level. The introduction of innovative approaches in personnel management and building corporate culture is a necessary step to ensure competitiveness and successfully overcome the challenges of the modern market.

Dampak Kualitas Produk dan Harga Terhadap Keputusan Pembelian pada Generasi Milenial: Studi Kasus pada Pelanggan Kartu Xl di Kota Gorontalo

This study aimed to discover the extent to which Product Quality and Price Influence Purchase Decisions among millennials, focusing on XL Card customers in Gorontalo City. The sampling employed in this study was purposive sampling, with a sample size of 100 respondents, which was determined using the Lemeshow formula. Data collection was conducted using questionnaires distributed to the population of XL Card customers in Gorontalo City. The analytical tool used to test this study was multiple linear regression, with data analysis performed using SPSS version 27. The study findings indicated that, partially, Product Quality did not significantly influence Purchase Decisions, with a t-value of 1.023 compared to the critical value of 1.984. Conversely, Price significantly influenced Purchase Decisions, with a t- value of 11.451 exceeding the critical value of 1.984. Furthermore, the simultaneous test results indicated that both Product Quality and Price significantly influenced Purchase Decisions among millennials purchasing XL Cards. The Product Quality and Price variables collectively explained 58.8% of the variance in Purchase Decisions, while the remaining 41.2% can be attributed to other variables not included in this study

Vector Storage Based Long-term Memory Research on LLM

Abstract Current large language model (LLM) intelligences face the challenges of high inference cost and low decision quality when dealing with complex tasks, and are especially deficient in maintaining context coherence during long tasks. This research presents an innovative vector storage long-term memory mechanism model (VIMBank) to enhance the long-term context retention ability and task execution efficiency of LLM intelligences by storing and retrieving historical interaction data through a vector database. VIMBank utilizes a dynamic memory updating strategy and the Ebbinghaus forgetting curve theory to efficiently manage the memory of intelligences and reinforce critical information, forgetting unimportant data, and optimizing storage and reasoning costs. The experimental results show that VIMBank significantly improves the decision quality and efficiency of LLM intelligences in multi-tasking scenarios and reduces the computational cost. Compared with different agents, the success rate of task decision is increased by 10% to 20%, and the reasoning cost is reduced by about 23%, which provides an important theoretical basis and practical support for the future development of intelligences with long term memory and adaptive learning ability.

On the integration of multiple criteria decision aiding and forecasting: Does it create value in portfolio selection?

A systemic integration of multiple criteria decision aiding (MCDA) and forecasting is presented for enhancing the quality of investment decisions. First, we provide a new methodological approach for combining forward-looking information, as an input to further MCDA analysis and we illustrate that the informational content of the MCDA scores is distinct from that of the original data used as input to the MCDA. Moreover, we provide very compelling evidence that the produced MCDA scores and rankings have incremental information, over and above that being contained in either the original data or the forecasts themselves. Therefore, it can be used successfully in providing methods which will have a high, a priori, chance of resulting in financial performance enhancements; to this end, we have illustrated this finding with a number of different approaches, including asset rotation and portfolio construction. Next, we show that the MCDA ranked variables can be used for both in-sample explaining and out-of-sample forecasting of critical economic variables, such as the real GDP growth or the unemployment rate, so that MCDA might be able to assist in identifying leading indicator properties for economic forecasting. The evaluation of the suggested approach entails extensive empirical testing on the various US economy market sectors, as expressed by the corresponding S&P 500 Exchange Traded Funds (ETFs), for a period of 22 years, documenting specific economic stylized facts, reported for first time in the relevant literature.

Insights into decision‐maker's perceptions of good versus bad decisions in emergency services—A modified Delphi study

AbstractDecision‐making in emergency situations, such as those faced by fire, police, and health service personnel, presents unique challenges due to the high‐stakes and time‐pressured environment. Here we aim to better understand what emergency responders regard as constituting ‘good’ and ‘bad’ decisions in emergency situations. We administered a modified Delphi study, eliciting opinions from decision‐makers across all these sectors towards consensus around the key elements of good and bad decision‐making. Participants were first asked to define what makes a ‘good’ and a ‘bad’ decision, and subsequently to identify the top five most important elements of each. While consensus was not found, important insights were identified that can assist improve the standard of decision making at all levels of emergency response and management. We observed (i) a lack of a common understanding between participants of what a decision is, and how a decision differs from pre‐decision and postdecision components; (ii) responses varied according to whether a free text description or the identification of separate elements was requested; (iii) respondents valued ‘goodness’ across different and at times unrelated components a scaled measure of decision quality as opposed to a binary evaluation of ‘rightness’; and (iv) pre‐ and postdecision elements are considered more important than the decision itself when determining the quality of a decision. To address the issues highlighted by the study we recommend improvements in training, and improvements to organization doctrine related to decision making, risk tolerance, assessment of decision quality and development of intent‐based or principle‐based operational guidance. Ultimately, incident controllers from all services must remain cognizant that they will face considerable scrutiny if they cannot explain how they arrived at the decision they made.

Shaping innovation pathways: Metaverse application configurations in high-technology small- and medium-sized enterprises

The emergence of Industry 4.0, characterized by rapid technological change and fierce competition, challenges technology firms to make strategic innovation decisions. Central to this is the metaverse, a hybrid virtual space combining virtual reality, augmented reality, and the internet. Recognizing that the implications of metaverse applications extend beyond individual organizations, this research examines its adoption configurations. Using the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, we analyze survey data from 116 high-technology small and medium-sized enterprises in China using fuzzy-set qualitative comparative analysis (fsQCA). Our research reveals that no isolated factor within the TAM-TOE framework solely affects innovation decision-making. Instead, we identify three configurations that enhance decision-making quality and three that increase its speed, leading to improved innovation performance. In this way, this research advances the understanding of technology adoption configurations in the innovation processes of young tech firms.

Pricing and green quality decisions in two-stage green supply chain for substitutable green products: A game-theoretic approach

Growing businesses are concerned with doing well both environmentally and economically. Pointing out this view, this paper explores the game theoretical approach (Stackelberg and Bertrand) for a two-echelon green supply chain where the duopolistic manufacturers produce two substitutable green products and sell their products through a common retailer. The demands for both green products are functions of the selling prices and green levels (GLs). The effects of power structures on optimal price and green level decisions and associated equilibrium decisions are examined in three scenarios. Firstly, trilateral competition manufacturer-led Stackelberg (MS); secondly, retailer-led Stackelberg (RS); and thirdly, vertical collaboration, and compares the optimal decisions analytically. Our investigations show that, in addition to increasing the product’s greening level, vertical collaboration creates a win–win situation for collaboration members, whereas the manufacturer outside the collaboration experiences a decline in profits. Additionally, we find that the overall profit from vertical collaboration is greater than the sum of the individual profits corresponding to two participants in the trilateral competition models (MS and RS). Further, a selection criterion is developed for retailer to select the most suitable manufacturer for vertical collaboration. Finally, a numerical example and a sensitivity analysis are performed to determine the impact of parameters.

지속가능경영보고서 발간이 기업지배구조 및 재무성과 개선에 미치는 영향

[Purpose] As corporate social responsibility gains importance, sustainability reports have become essential for demonstrating a company’s commitment to sustainable management across environmental, social, and economic dimensions. [Purpose] This study examines the relationship between the publication of sustainability reports and corporate self－improvement, focusing on listed companies in South Korea from 2018 to 2022. [Findings] The analysis reveals that sustainability reports positively influence corporate governance and have a limited yet favorable impact on financial performance, as measured by return on assets (ROA). [Implications] These findings indicate that publishing sustainability reports can lead to voluntary enhancements in corporate governance, improving management efficiency and decision－making quality, which ultimately boosts profitability. Thus, issuing sustainability reports is proposed as a valuable strategy for future－oriented corporate development.

The Influence of Product Quality, Promotion, Price, Trust and Purchase Decision on Consumer Loyalty at PT. X

Increasingly intense competition between product provider institutions recently is not only caused by globalization. But more because customers are getting smarter, price conscious, more demanding, less forgiving, and approached by many products. Consumer loyalty is an important factor in maintaining existence in market competition, increasing sales and as a step in purchasing decisions and to achieve goals. This study uses quantitative methods. The aim is to determine customer loyalty and purchase decisions as intervening variables. The research sample is all consumers at PT X with a total sample of 150 respondents. The data analysis method used in this study is SPSS and also Smart PLS version 3. The results of this study indicate product quality, promotion, price, significant trust in purchasing decisions. as well as a significant purchase decision on customer loyalty.