Decision Strategies Research Articles

Artificial intelligence enabled product–service innovation: past achievements and future directions

AbstractThis study intends to scrutinize the role of Artificial Intelligence (AI) in Product-Service Innovation (PSI). The literature on AI enabled PSI, other related innovation business models, product-service systems, and servitization has grown significantly since 2018; therefore, there is a need to structure the literature in a systematic manner and add to what has been studied thus far. Product-service innovation is used to represent the relevance of achieving innovation in business models dealing with innovation outcomes including artificial intelligence. This study used bibliographic coupling to analyze 159 articles emerging from the fields of computer sciences, engineering, social sciences, decision sciences, and management. This review depicts structures of the literature comprising five (5) clusters, namely, (1) technology adoption and transformational barriers, which depicts the barriers faced during the adoption of AI-enabled technologies and following transformation; (2) data-driven capabilities and innovation, which highlights the data-based capabilities supported through AI and innovation; (3) digitally enabled business model innovation, which explained how AI-enabled business model innovation occurs; (4) smart design changes and sustainability, which reveals the working of AI in product service environments with different design changes and transformations based on sustainability; and (5) sectorial application, which highlights industry examples. Each cluster is comprehensively analyzed based on its contents, including central themes, models, theories, and methodologies, which help to identify the gaps and support suggestions for future research directions.

A Novel SDN-Based Architecture of Task Offloading in Mobile Ad-Hoc Cloud

As the core function of mobile Ad-hoc cloud, task offloading has always been a research hotspot of mobile cloud computing, and the construction, offloading decision, task division and scheduling strategies of mobile Ad-hoc cloud directly affect the performance of offloading. In this paper, we propose a new mobile Ad-hoc cloud architecture based on SDN solving the performance bottleneck of opportunistic task offloading. The proposed solution constructs terminal clusters in Ad-hoc cloud to provide users with offloading service, and makes offloading decision and task allocation through SDN controller to achieve the effectiveness of task allocation and the rationality of resource scheduling. Simulation results show that compared with traditional scheduling method, our method has the advantages in offloading ratio and execution efficiency, and can be more effectively used in dynamic mobile edge network environment, to improve network performance and user service satisfaction.

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

The scheduling of harbor tugboats is a crucial task in port operations, aiming to optimize resource allocation and reduce operational costs, including fuel consumption of tugboats and the time cost of vessels waiting for operations. Due to the complexity of the port environment, traditional scheduling methods, often based on experience and practice, lack scientific and systematic decision support, making it difficult to cope with real-time changes in vessel dynamics and environmental factors. This often leads to scheduling delays and resource waste. To address this issue, this study proposes a mathematical model based on fuzzy programming, accounting for the uncertainty of the arrival time of target vessels. Additionally, we introduce the NRPER-DDPG algorithm (DDPG Algorithm with Prioritized Experience Replay and Noise Reduction), which combines a prioritized replay mechanism with a decaying noise strategy based on the DDPG algorithm. This approach optimizes the time for tugboats to reach the task location as a continuous action space, aiming to minimize the total system cost and improve scheduling efficiency. To verify the effectiveness of the mathematical model and algorithm, this study conducted experimental validation. Firstly, the optimal algorithm hyperparameter combinations were adjusted through random examples to ensure the stability and reliability of the algorithm. Subsequently, large-scale examples and actual port cases were used to further verify the performance advantages of the algorithm in practical applications. Experimental results demonstrate that the proposed mathematical model and algorithm significantly reduce system costs and improve scheduling efficiency, providing new insights and methods for the sustainable development of port operations.

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

BackgroundPublic health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the “last line of defense” for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies.ObjectiveThis study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves.MethodsA demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients.ResultsData on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic.ConclusionThe first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Analyzing the impact of digitized-education toward the future of education: A comparative study based on students’ evaluation of teaching data

This study investigates the key factors that impact the digitalized-education, and what those mean for the new and emerging educational models of learning. The research is based on state-of-the-art education and assessment models for learning such as Descriptive decision theory, Futures Literacy (FLF), and 8-Affordances framework that theoretically addresses both the need to use multidisciplinary components in identifying the several challenges and opportunities to the use of digital technologies in education (digitized-education) and improvement of the student's learning outcomes. Through a quasi-experimental study design and comparative analysis of the students’ evaluation of teaching (SET) dataset (n = 3178) collected in a higher education setting; the study applies a multivariate analysis of covariance (MANCOVA) and multiple comparisons (post-hoc) tests to determine the impact or association that the evaluation period (between 2019–2021) and type of school or discipline have on the students’ learning performance and evaluation. The results show that students’ satisfaction and experiences with digitized-education have exponentially grown in recent times, and mainly varied across the years, and marginally by discipline or school. In addition, the study empirically sheds light on the importance of the key findings and results by considering their pedagogical, technological, economic infrastructure, and ethical implications for the future of education and sustainable educational practice.

Risky mindset: prior exposure to risk increases utilitarian choices in sacrificial moral scenarios

Evidence of cross-domain spillover into the moral domain has been limited to altruistic and consumption behaviors. Building on the literature on spillover effects and domain-general decision processes, we predicted that choice behavior in the economic domain would affect subsequent choices in sacrificial moral dilemmas. We tested this prediction using hypothetical risky gambles and vignettes for moral dilemmas. We found that prior exposure to risky gambles increased utilitarian responses toward sacrificial moral dilemmas. Mediation analysis suggests that this is due to the spillover of a cost–benefit mindset. This mindset increases the probability of making utilitarian-type choices when faced with moral dilemmas but does not affect moral judgment. These results suggest that moral decisions are susceptible to cross-domain spillover effects. Moral values might get easily traded off in transactional scenarios in which cost–benefit analysis is a dominant decision strategy.

Brand Image: Theoretical Development and Practical Applications

This research presents an in-depth analysis of research on brand image using a bibliometric approach. This research was conducted by detailing the findings from 364 documents published between 2018 and 2024 in the business sector. The analysis uses descriptive analysis of data from Scopus and VosViewer. The research results show that there is a collaboration between authors, which reflects a cross-disciplinary perspective.. Subject analysis shows a primary focus on Business Management (56.1%), while the inclusion of Social Sciences (11.7%), Decision Sciences (6,296%), and Economics (IL196%) reinforce the cross-disciplinary nature of the research. Trends over time show increasing interest in brand image, reflecting ongoing research into the theme of brand image.

Multivariable forecasting approach of high‐speed railway passenger demand based on residual term of Baidu search index and error correction

AbstractAccurate prior information of passenger flow demand on high‐speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high‐speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in‐depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high‐speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real‐world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data‐driven guidance for resource allocation and make scientific decisions in the railway industry.

Exploring Neuro Management: Bridging Science and Leadership – An Overview

Purpose: To develop a comprehensive overview of Neuro Management, by bridging the gap between scientific insights and effective leadership within organizational contexts based on exploring Neuro Management concepts, applications, and their implications for managerial practices. Procedure: The overview begins by delineating the fundamental concepts and applications of Neuro Management, elucidating its relevance in leveraging neuroscience principles for enhancing leadership effectiveness. It then provides an extensive overview of key themes and contributions within the field, identifying foundational works and highlighting significant research directions. A critical analysis of the evolution of Neuro Management concepts is presented, identifying existing research gaps and tracing the evolutionary trajectory of the field. Furthermore, a conceptual framework is developed, integrating neuroscience principles with leadership practices, and delineating a structured approach for applying neuroscience insights in leadership contexts. Analysis: The paper discusses the neural mechanisms underlying various aspects related to Neuro Management, exploring decision-making processes, emotional intelligence, and their neural correlates. Subsequently, it analyzes how Neuro Management concepts bridge the gap between scientific insights and effective leadership, highlighting the translation of neuroscience findings into practical leadership strategies. Moreover, the implications of Neuro Management for organizational practices, decision-making, team dynamics, and productivity are expounded upon, emphasizing its transformative impact on enhancing organizational performance. The study assesses the connection between human productivity and Neuro Management, focusing on how Neuro Management strategies affect organizational effectiveness. The study also critically analyzes leadership and neuro management from a managerial perspective, evaluating their effects on organizational outcomes and their roles in decision-making. Additionally, it explores how different Neuro Management models that are helpful in decision science might be applied to create efficient decision-making processes. Furthermore, the paper adopts an ABCD listing framework, analyzing Neuro Management from Managers' Points of View, evaluating its Advantages, Benefits, Constraints, and Disadvantages. Finally, it suggests postulates for future research and practical applications in Neuro Management, emphasizing areas warranting further exploration and implementation. Originality/Value: This comprehensive overview aims to serve as a foundational resource in the expanding field of Neuro Management, offering insights, analyses, and frameworks for advancing leadership practices informed by neuroscience principles. Type of Paper: Conceptual exploratory research.

An empirical investigation of users' switching intention to public service robots: From the perspective of PPM framework

In recent years, the application of artificial intelligence (AI) technology has become increasingly common in the public sector. Users have been switching their experiences in handling businesses from interactions with human staff to those with robots. Prior studies have focused on investigating the key factors that influence users' adoption of public service robots; however, only a few have considered users' switching behaviors from traditional human services to robotic ones. This study employs a push–pull–mooring (PPM) framework derived from the human migration field to understand the factors that affect users' switching intentions in the context of public service robot applications. The research model was tested with 419 valid responses among users who had experienced both human services and public service robots in Chinese government service halls. The structural equation modeling (SEM) method was applied to quantitatively analyze the data. This study sheds new light on the key determinants of users' switching intentions toward public service robots from the perspectives of push, pull, and mooring effects. The results can help practitioners and managers understand users' intentions for such switches and make scientific decisions to encourage citizens' positive responses to service robots.

Risk control of epidemic in urban cold-chain transportation

The COVID-19 pandemic has severely disrupted the daily running of urban logistics system, thereby increasing the resilience and health requirements in the design of sustainable logistics system. Most scientific contributions have enriched the knowledge of how to deal with viral infection threats from the external environment but ignored the virus transmission from fomites within the logistics system. This study designs an urban cold-chain logistic network with control strategies to reduce the epidemic risk caused by cold-chain fomites while concurrently balancing the total cost and customer satisfaction, which is modeled as a multi-objective vehicle routing problem (MOVRP). The control strategies are modeled by dividing customer points into small groups, and the virus transmission in populations is considered when evaluating epidemic risk. A multi-objective evolutionary algorithm with special initialization (MOEA-SI) is proposed to solve this problem. Simulation results show that all the strategies can efficiently reduce the epidemic risk with reasonable expense of total cost and customer satisfaction. The introduction of special initialization strategy enables the MOEA-SI to have good performance in balancing the three objectives. The above results can enrich the knowledge of how to deal with unexpected epidemics and help the scientific decision-making in sustainable urban management.

Aversion and ambiguity: On the robustness of the macroeconomic uncertainty measure framework

The economic literature has focused on the role of uncertainty in the real economy, employing both measures of risk aversion and ambiguity aversion in structural models. In this connection, concerns about the measures for VIX and EPU, have been raised about whether or not they both measure and identify similar type of uncertainty. Using a structural vector autoregressive (SVAR) approach, we examine the relationship between VIX and EPU, their impact on the real economy and whether, and under which conditions, they can be distinguished between measures of risk aversion and ambiguity aversion. Specifically, we analyse the impact of uncertainty shocks of VIX and EPU on the industrial production, unemployment, and consumer credit in the US. Our main finding is that given their fundamental differences, the two measures are capturing different dimensions of uncertainty, VIX is a measure of risk aversion and EPU is a measure of ambiguity aversion. As such, our results are very important in terms of trading decision strategies to be implemented by investors and portfolio managers as it may help explain the two central behavioral traits affecting economic lifecycle problems, such as production, unemployment and consummation.

PhishingRTDS: A real-time detection system for phishing attacks using a Deep Learning model

In recent years, phishing attacks have become more intelligent and more challenging to detect using typical phishing methods. Moreover, attackers have leveraged some web development techniques to increase the website's legitimacy in victims' eyes, such as using JFrame to design a window that looks like a browser inside the webpage. In this paper, we design a system that detects three types of phishing attacks: Tiny Uniform Resource Locators (TinyURLs), Browsers in the Browser (BiTB), and regular phishing attacks. In this system, we aim to protect victims from mistakenly downloading malicious software into their systems. We split our system into three parts: Deep Learning model (DL), browser extension, and docker container. First, we design a DL model using bidirectional long short-term memory (BiLSTM) and an attention mechanism to classify the URL as phishing or benign. Our model shows 99% in its precision, recall, and F1 score. Second, we design a browser extension to extract the original URL from the suspect webpage and then send it to the docker container. Then, the docker container opens the webpage and extracts all URLs from its HyperText Markup Language (HTML) and JavaScript. Then, each URL passes to a DL model for classification, resulting in a list of labels for each webpage. Therefore, we use three decision strategies: Single Phishing Strategy (SPhS), Mean Sum Strategy (MSS), and Weighted Average Strategy (WeAS) to decide whether the webpage is phishing or benign. Our findings indicate that the best results among the three strategies were WeAS.

Supply Chain Economics: A Fresh Lens for Holistic Analysis

Global pandemics, military conflicts, and natural disasters are crescively disrupting increasingly complex supply chains. But neither the social science of economics or decision science of supply chain management were sufficient to anticipate, much less mitigate the depth, breadth, or duration of the disruptions and damage wrought by COVID-19 or Russia’s invasion of Ukraine. Economics is the study of production, distribution, and consumption of goods and services, and creation and transfer of wealth. Supply chain management is the management of a network of organizations, internal and external, by which an organization pursues its own goals and objectives. Process focus is integral to supply chain management, as operations management is the management of an organization’s processes to pursue its goals and objectives. Adherence to processes is necessary for organizations to consistently reach their goals and objectives. When organizations execute consistently, they can leverage that quality for lower costs, greater volume, higher prices, or additional stakeholder goodwill. By combining these concepts, supply chain economics can be framed as the systematic study of the production, distribution, and consumption of goods, services, and capital by networks of organizations, internal and external, with which any given organization pursues its goals and objectives.

Roles of Value in the Evaluation and Modeling of Decision Strategies for Pipe Maintenance in Water Distribution Networks

Roles of Value in the Evaluation and Modeling of Decision Strategies for Pipe Maintenance in Water Distribution Networks

Measurement of Supply Chain Resilience and Safety Level of China Lighting Industry Chain

The supply chain resilience and safety level of lighting industry chain are one of the key fields of domestic concern at present. Based on a literature review, the ideas and methods of measuring the supply chain resilience and safety level of lighting industry chain were put forward. Then, an evaluation index system of industry chain resilience and safety was established, which included five dimensions: the risk management level, integrity, control force, stability, and competitiveness of industry chain. Considering the resilience of China’s industry chain and the main safety difficulties faced, the evaluation system was used to measure and evaluate the supply chain resilience and safety level of China’s lighting industry chain on the basis of the specific connotations and core elements of industry chain safety. The evaluation results show that the total score of the supply chain resilience and safety level of China’s lighting industry chain is 0.92, indicating the relatively high supply chain resilience and safety level of China’s lighting industry chain. Suggestions are put forward to strengthen the management of technological innovation ability, low-carbon ability, and domestic self-sufficiency rate of input products, so as to boost the high-quality, sustainable, and rapid development of China’s lighting industry. The research results can provide a scientific decision basis for the supply chain resilience and safety level of China’s lighting industry chain.

A Comprehensive Exploration of Decision Science: The Concept of Set and the Foundation of Modern Mathematics

A Comprehensive Exploration of Decision Science: The Concept of Set and the Foundation of Modern Mathematics

Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies

ObjectivesTo develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS).MethodsThis retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012–10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics.ResultsA total of 1604 patients aged 67 (interquartile range, 61–73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80–0.97) and 0.84 (0.70–0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68–0.74)/0.73 (0.70–0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10–20%) to 43% (30–44%; p < 0.001) with similar sensitivity (93% (89–96%)/97% (94–99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104–142)/165 (146–185); p < 0.001).ConclusionUsing sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers.Clinical relevance statementTransition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers.Key Points• Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer.• PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only.• TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

Research on the mechanism and policies of polycentric agglomeration for promoting the upgrading of China's position in the global production value chain

Recently, with the increasing trend of anti-globalization, China's manufacturing industry has encountered instability in the supply of upstream products in international trade, a challenge that has given rise to an urgent demand for ways to improve China's position in the global production chain. In response to this challenge, the 14th Five-Year Plan puts forward the strategic goal of building multi-level, multi-node, networked city clusters and trade powerhouses, which provides new development opportunities for China to strengthen its position in the global production chain through the model of polycentric agglomeration. In order to gain a deeper understanding of the impact of polycentric agglomeration on the position of enterprises in the global production chain, this project will use the instrumental variable estimation method to conduct an empirical study. This method can help us accurately identify the causal relationship between polycentric agglomeration and global production chain position and assess the differential impact of different agglomeration patterns on firms' production chain position. Through this study, we expect to reveal how polycentric agglomeration can enhance firms' global production chain positions through mechanisms such as optimizing resource allocation, promoting technological innovation, and strengthening industrial chain synergies. In addition, we plan to explore the impact of polycentric agglomeration on the evolution of firms' roles in global value chains and on international trade competitiveness. The objective of this study is to provide targeted recommendations for policymakers and enterprises to help them achieve high-quality development of China's manufacturing industry by optimizing the structure of polycentric agglomeration in the context of global production chain restructuring. Through this comprehensive study, we expect to provide scientific decision support and action guidelines for China's sustainable upgrading and competitiveness enhancement in global production chains.

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Transformer technologies, like generative pre-trained transformers (GPTs) and bidirectional encoder representations from transformers (BERT) are increasingly utilized for understanding diverse social media content. Despite their popularity, there is a notable absence of a systematic literature review on their application in disaster analytics. This study investigates the utilization of transformer-based technology in analyzing social media data for disaster and emergency crisis events. Leveraging a systematic review methodology, 114 related works were collated from popular databases like Web of Science and Scopus. After deduplication and following the exclusion criteria, 53 scholarly articles were analyzed, revealing insights into the geographical distribution of research efforts, trends in publication output over time, publication venues, primary research domains, and prevalently used technology. The results show a significant increase in publications since 2020, with a predominant focus on computer science, followed by engineering and decision sciences. The results emphasize that within the realm of social-media-based disaster analytics, BERT was utilized in 29 papers, BERT-based methods were employed in 28 papers, and GPT-based approaches were featured in 4 papers, indicating their predominant usage in the field. Additionally, this study presents a novel classification scheme consisting of 10 distinct categories that thoroughly categorize all existing scholarly works on disaster monitoring. However, the study acknowledges limitations related to sycophantic behavior and hallucinations in GPT-based systems and raises ethical considerations and privacy concerns associated with the use of social media data. To address these issues, it proposes strategies for enhancing model robustness, refining data validation techniques, and integrating human oversight mechanisms.