Hierarchical Decision Model Research Articles

Industry 4.0 factors affecting SMEs towards sustainable manufacturing

Industry 4.0 (I4.0) is one of the essential topics that has been researched extensively in the research domain. In the same way, there are also several research available to check the connections between I4.0 and sustainable manufacturing. It is because of the increasing concern of stakeholders over environmental challenges that manufacturing units are often blamed. It is also true that a major part of manufacturing is done by SMEs in almost every developed or developing economy of the world including India. Therefore, the present research work took place to identify the factors that are essential for sustainable manufacturing using I4.0 in Indian SMEs. In the present study, a total of six factors were identified from the previous studies, which are technological factors (TF), organizational factors (OF), environmental factors (ENF), societal factors (SF), economic factors (ECF), and external stakeholders' factors (ESF) considering the triple bottom line (TBL) approach of the sustainability model. Each factor consists of a few sub-factors, and a total of thirty-five factors were developed. The best-worst method (BWM) and Hierarchical Decision Model (HDM) were applied to bring the results. The result suggests that OF and TF are ranked number one and two respectively. ECF and ENF ranked at three and four whereas, ESF and SF ranked at five and six respectively. The study helps firm managers revolutionize their organizations’ approach to the sustainability model by looking at the Industry 4.0 factors affecting SMEs toward sustainable manufacturing. When managers and practitioners try to convert their business digitally, this study also enables them to prioritize the many I4.0 factors that are most important to their organization. The model was validated by a regional application in Saudi Arabia.

Technology readiness assessment: Case of clinical decision support systems in healthcare

Clinical Decision Support Systems (CDSS) play a critical role in modern healthcare by supporting healthcare providers in making well-informed decisions, improving patient safety and outcomes, enhancing efficiency, and promoting evidence-based practices. Their integration into clinical workflows can lead to more effective and patient-centered care. CDSS is essential tools for healthcare organizations as well as for healthcare providers to improve clinical care. However, successful implementation of CDSS can be challenging. Therefore, before implementing CDSS, it is crucial to assess the readiness of healthcare organizations to implement these tools. CDSS is essential tools in healthcare for several compelling reasons. For instance, enhanced patient safety, improved diagnostic accuracy, optimized treatment plans, consistency in care, and support for complex decisions. This study's aim is to develop a model that will help healthcare organizations identify the challenges of implementing CDSS, and to assess their readiness for such an implementation in a comprehensive and multi-dimensional manner. Through a literature review, the first step of this research explores the concept of clinical decision support and CDSS, discussing their features, characteristics, and organizational hurdles to implementation. It also provides perspectives on CDSS adoption in the context of information systems and health technology. The review helped to identify research gaps, objectives, and questions. To address these gaps and to attempt to answer the research questions, a Hierarchical Decision Model (HDM) is proposed. The model allows us to assess the readiness of healthcare organizations for CDSS implementation. It presents four perspectives and sixteen criteria for a multi-dimensional assessment. The methodology involves expert panels for the HDM model's refinement, validation, and quantification. Two case studies are then presented to demonstrate the HDM model's application to identify real-world CDSS implementation challenges and to provide insights and recommendations. The research contributions are evaluated against the identified gaps in the literature review, with limitations and future research presented. In conclusion, this research provides valuable insights into CDSS implementation readiness assessment and highlights the need for careful consideration and planning. The proposed HDM model offers a valuable framework for healthcare organizations to evaluate their readiness for CDSS implementation.

Exploring innovation ecosystems to facilitate the adoption of sustainable entrepreneurship: Looking beyond the Western World

ABSTRACT The objective of this research is to evaluate the effectiveness of innovation ecosystem instruments in increasing the adoption of innovation in sustainable entrepreneurship by proposing and developing a comprehensive assessment decision model. The strategy targets used in this research depend on five perspectives that are considered by decision-makers as significant for the adoption process. The decision model linked the five perspectives to ecosystem targets and various innovation ecosystem instruments which are economic, environmental, social, technical, and ethical perspectives. This research employed the Hierarchical Decision Model (HDM) as a methodology to construct a generalized ecosystem assessment model combined with the desirability curves method. The findings indicate that economic feasibility improvement and technical system development perspectives and their associated strategy targets as the most influential and important for fostering the adoption of innovation in sustainable entrepreneurship.

Evaluating customer orientation in e-commerce: an organization focused technology assessment*

ABSTRACT This research focuses on designing a new maturity model to evaluate and plan an organization's customer-centricity. The Hierarchical Decision Model (HDM) is used as the primary methodology to quantify impacting factors and intensity of influence on the ultimate outcome. To demonstrate the proposed model in the real world, a case study is performed in the e-commerce industry, especially B2C online retailer organizations. This research tackles the scarcity of documentation on creating maturity models that are widely accepted and sustainable, demonstrating the innovative use of the HDM methodology. In summary, the study successfully achieves its overarching objective of crafting a comprehensive model for assessing organizational maturity in the customer-centric approach, encouraging the widespread adoption of the proposed methodology across diverse sectors.This study endeavors to introduce an innovative approach for assessing an organization's customer-centricity, incorporating both product and service deliverables, specifically referred to as Product-Service Systems (PSS). The primary objective of this research is to formulate a novel maturity model, enabling organizations to 1) assess their degree of customer-centricity and 2) receive actionable recommendations aimed at enhancing customer orientation and elevating their customer-centricity maturity level..

Multiple criteria decision modeling in social networks: An application to identify opinion leaders in online public opinion events

For online public opinion events, the misconduct of opinion leaders can easily lead to the spread of negative public opinion, which in turn can adversely affect real society. In order to minimize this adverse impact to the greatest extent, this paper uses social network analysis to explore the attributes of individuals, features of network structure, and social relationships between decision makers (DMs). It constructs the structural system of evaluation criteria and a multiple criteria decision model to comprehensively and reasonably identify opinion leaders. It additionally considers the importance of criteria and the interaction among them. The hierarchical structure is combined with the evaluation level to construct a precise structural system of criteria. To handle the interaction, the Choquet integral is applied as the preference model. Based on the disaggregation principle, a generalized hierarchical level dependent Choquet integral is developed to reproduce additional preference information provided by decision analysts (DAs) and aggregate evaluation values of DMs on multiple interacting criteria. Then, the relevant indices of this integral and the preference information are applied to robust ordinal regression to mine rich preference relations among DMs. Furthermore, the hierarchical level dependent Choquet integral-driven multiple criteria decision model is proposed to rank DMs and identify opinion leaders. Finally, a numerical example and two real datasets validate the effectiveness and advantages of the proposed decision model.

Modeling the enablers of blockchain technology implementation for information management in healthcare supply chains

PurposeBlockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains.Design/methodology/approachThis study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique.FindingsThe results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized.Practical implicationsTo implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study.Originality/valueThis is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.

Entrepreneurial universities: Modelling the link between innovation producers and innovation users shows that team structures in the tech transfer function improves performance

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">To investigate successful technology transfer, the potential path of innovations from the university research bench to the knowledge recipient is modelled. Universities exist in highly regulated environments and the initial path of decision-making is a hierarchical model and where decisions flow upward from manager to manager until a small number of candidate innovations for commercialization remain. These are then routed for further processing to the link connecting to the knowledge recipient, the Technology Transfer Office (TTO). In the TTO, a hierarchical decision-making model can be acceptable in terms of outcomes, but ambidextrous co-operative team structures are much superior in cases where staff have good insight and decision-making abilities. This report represents the first Structured Equation Model investigation of the management architecture of a TTO.</span></p>

Information Security Maturity Model for Healthcare Organizations in the United States

This article provides a maturity model for information security for healthcare organizations in the United States. Healthcare organizations are faced with increasing threats to the security of their information systems. The maturity model identifies specific performance metrics, with relative importance measures, that can be used to enhance information security at healthcare organizations allowing them to focus scarce resources on mitigating the most important information security threat vectors. This generalizable, hierarchical decision model uses both qualitative and quantitative metrics based on objective goals. This model may be used as a baseline by which to measure individual organizational performance, to measure performance against other organizations, or to monitor changes in the information security environment over time.

A Framework for Identifying and Validating Idea Screening Criteria—A Case of Research and Development Projects in the Computing and Automation Industry

This article developed an integrated framework for idea screening in the front end of innovation. A hierarchical decision model was used to develop the integrated framework. The proposed framework is deployed via a questionnaire of 15 experts from 7 well-known high technology organizations across multiple countries, using Delphi method to identify and validate criteria in front end new product idea screening process. The findings from integrating three perspectives (intuition, rationality, and reflections) show that idea screening can be improved in the context of creativity, quality, transparency, and effectiveness in decision making. This integration helped to reveal additional screening criteria to existing studies. The conversion of implicit to explicit knowledge that can be documented is beneficial to continuous improvement in decision making. Combining the three perspectives within the deployed framework contributes to a more in-depth understanding of idea screening that is complementary and represents a robust approach in comparison to existing studies that use either one or two perspectives, particularly since the findings complement the limitations of a human mind; given that much of idea screening occurs in the minds of experts. Future work involves applying the proposed framework in practice, for selecting the best idea to fund in a specific organization.

DEXi-Dairy: an ex post multicriteria tool to assess the sustainability of dairy production systems in various European regions

Growing awareness of global challenges and increasing pressures on the farming sector, including the urgent requirement to rapidly cut greenhouse gases (GHG) emissions, emphasize the need for sustainable production, which is particularly relevant for dairy production systems. Comparing dairy production systems across the three sustainability dimensions is a considerable challenge, notably due to the heterogeneity of production conditions in Europe. To overcome this, we developed an ex post multicriteria assessment tool that adopts a holistic approach across the three sustainability dimensions. This tool is based on the DEXi framework, which associates a hierarchical decision model with an expert perspective and follows a tree shaped structure; thus, we called it the DEXi-Dairy tool. For each dimension of sustainability, qualitative attributes were defined and organized in themes, sub-themes, and indicators. Their choice was guided by three objectives: (i) better describe main challenges faced by European dairy production systems, (ii) point out synergies and trade-offs across sustainability dimensions, and (iii) contribute to the identification of GHG mitigation strategies at the farm level. Qualitative scales for each theme, sub-theme, and indicator were defined together with weighting factors used to aggregate each level of the tree. Based on selected indicators, a list of farm data requirements was developed to populate the sustainability tree. The model was then tested on seven case study farms distributed across Europe. DEXi-Dairy presents a qualitative method that allows for the comparison of different inputs and the evaluation of the three sustainability dimensions in an integrated manner. By assessing synergies and trade-offs across sustainability dimensions, DEXi-Dairy is able to reflect the heterogeneity of dairy production systems. Results indicate that, while trade-offs occasionally exist among respective selected sub-themes, certain farming systems tend to achieve a higher sustainability score than others and hence could serve as benchmarks for further analyses.

Parsing memory and nonmemory contributions to age-related declines in mnemonic discrimination performance: a hierarchical Bayesian diffusion decision modeling approach.

The mnemonic discrimination task (MDT) is a widely used cognitive assessment tool. Performance in this task is believed to indicate an age-related deficit in episodic memory stemming from a decreased ability to pattern-separate among similar experiences. However, cognitive processes other than memory ability might impact task performance. In this study, we investigated whether nonmnemonic decision-making processes contribute to the age-related deficit in the MDT. We applied a hierarchical Bayesian version of the Ratcliff diffusion model to the MDT performance of 26 younger and 31 cognitively normal older adults. It allowed us to decompose decision behavior in the MDT into different underlying cognitive processes, represented by specific model parameters. Model parameters were compared between groups, and differences were evaluated using the Bayes factor. Our results suggest that the age-related decline in MDT performance indicates a predominantly mnemonic deficit rather than differences in nonmnemonic decision-making processes. In addition, this mnemonic deficit might also involve a slowing in processes related to encoding and retrieval strategies, which are relevant for successful memory as well. These findings help to better understand what cognitive processes contribute to the age-related decline in MDT performance and may help to improve the diagnostic value of this popular task.

Multi-criteria decision analysis of succinic acid production using hesitant fuzzy analytical hierarchy process

The selection of a large-scale succinic acid (SA) production process usually considers issues related to technology, economics, and the environment by performing techno-economic analysis (TEA) and life cycle assessment (LCA). This work improves such a decision-making process by additionally applying the hesitant fuzzy analytical hierarchy process (HFAHP) as a multi-criteria decision analysis (MCDA) method considering expert’s opinions in linguistic fuzzy terms. The production route is chosen from four alternatives. The first alternative (AL-1) utilizes both C-6 and C-5 sugar of corn stover for SA and lignin for electricity production. Alternative 2 produced SA, furfural, and electricity from C-6, C-5, and lignin respectively, whilst alternative 3 considered SA production from C-6 sugar and biogas from C-5 sugar for electricity production along with lignin. Alternative 4 produced SA using a fossil-based route. The HFAHP includes a hierarchical decision model consisting of an upper layer with two main criteria and a lower layer with four and three sub-criteria related to the main criteria techno-economics and the environment, respectively. Based on the preferences given by experts for the main criteria, sub-criteria and alternatives, the results indicated a higher weightage of the techno-economic criterion (55.0%) than the environmental criterion (45.0%). Waste (ST-2) and damage to resources (SE-3) were identified as the most influential sub-criteria with an assigned weightage of 32.0% and 34.0%, respectively. Least weightage (12.0%) was assigned to feedstock input (ST-3). An equal weightage (33.0%) was assigned to damages to human health (SE-1) and damages to the ecosystem (SE-2). AL-1 was found to be the best alternative with 35.0% weightage, followed by AL-3 (24.0%), AL-2 (22.0%), and AL-4 (19.0%). The sensitivity analysis demonstrated that AL-1 remains the best alternative and least sensitive in the range of 0.10–1.00 fraction variations of the main criteria weightage.

Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends

Label-free proteomics is a fast-growing methodology to infer abundances in mass spectrometry proteomics. Extensive research has focused on spectral quantification and peptide identification. However, research toward modeling and understanding quantitative proteomics data is scarce. Here we propose a Bayesian hierarchical decision model (Baldur) to test for differences in means between conditions for proteins, peptides, and post-translational modifications. We developed a Bayesian regression model to characterize local mean-variance trends in data, to estimate measurement uncertainty and hyperparameters for the decision model. A key contribution is the development of a new gamma regression model that describes the mean-variance dependency as a mixture of a common and a latent trend—allowing for localized trend estimates. We then evaluate the performance of Baldur, limma-trend, and t test on six benchmark datasets: five total proteomics and one post-translational modification dataset. We find that Baldur drastically improves the decision in noisier post-translational modification data over limma-trend and t test. In addition, we see significant improvements using Baldur over the other methods in the total proteomics datasets. Finally, we analyzed Baldur’s performance when increasing the number of replicates and found that the method always increases precision with sample size, while showing robust control of the false positive rate. We conclude that our model vastly improves over popular data analysis methods (limma-trend and t test) in several spike-in datasets by achieving a high true positive detection rate, while greatly reducing the false-positive rate.

Development of a Maturity Model for Software Quality Assurance Practices

The advancements in the technology landscape and software development in recent years mandate paying attention to Software Quality Assurance (SQA) because it is becoming significantly important and complex. SQA is a set of activities within the software development lifecycle that aims at reducing development and testing costs, improving the quality of the software systems, and increasing customer satisfaction. Thus, the objective of this paper is to build a SQA maturity model, particularly in the telecommunication industry. To achieve this, this research identified perspectives and factors based on a comprehensive literature review and experts’ inputs using Hierarchical Decision Modeling (HDM) as the methodology. The proposed model consists of five perspectives, which are requirements validation, testing, software change management control, technology, and organization and culture with every perspective containing relevant factors. The factors and perspectives are validated and quantified using SQA inputs from subject matter experts. The findings of this study suggest that requirements validation is the most important perspective. Two case studies were conducted to identify the maturity score for each case, demonstrate the practicality of the research model, identify areas of deficiencies, and propose corrective actions. This paper provides an in-depth look at software quality factors and their relative importance, targeting to help SQA practitioners understand and assess their SQA practices.

Hierarchical decision model for in vitro bilirubin content prediction from absorption spectrum of whole blood.

Bilirubin forms by the breakdown of heme proteins in the liver, but a newborn's sluggish liver can lead to elevated serum bilirubin levels that cross the blood-brain barrier and result in kernicterus. Earlier studies have used the 400 to 500nm optical wavelength range to characterize the bilirubin content. There is not a universally established correlation among other wavelengths and the amount of bilirubin in clinical whole blood samples. We demonstrated that the amount of bilirubin could be quantified with accuracy in a label-free, self-referenced manner using only a few wavelengths, viz. 468, 492, 500, 560, 605, 645, 660, and 675nm, wherein band-averaged absorption measurements are used. We addressed the above problem by conducting a preliminary study containing 50 neonates through an absorption spectrum measurement of whole blood in 3 to samples from the neonates. We constructed a hierarchical decision method that first grossly divides the 30 neonates of the training set into and bilirubin level cohorts. A subsequent boundary condition further divides the group into two and bilirubin level cohorts. A finer measure later predicted the bilirubin content of each of these groups as low (), medium (10 to ), and high (). Using this hierarchical decision model statistical approach, we quantified the amount of bilirubin in the 20 testing set samples with 82% accuracy. We formulated a biostatistical model in which we automated the spectrometric determination of total bilirubin in the whole blood for patients of neonatal hyperbilirubinemia.

Electric vehicle charging pile capacity planning based on normal distribution Monte Carlo sampling model

Abstract In recent years, environmental issues have gradually become one of the most concerning issues in the world. New energy vehicles have attracted wide attention by their good environmental and social benefits such as zero exhaust emissions and low noise pollution. This paper proposes a regional charging demand forecasting method for electric vehicles (EVs) based on hierarchical charging decision model to solve the problem of charging pile capacity planning, which affects the charging decision behavior of EVs users. The Monte Carlo sampling model is adopted to obtain the time distribution of electric vehicle arriving at each functional area, the distribution of battery state of charge when arriving, and the distribution of average daily mileage. The negative exponential distribution model is used to obtain the average mileage distribution. The fuzzy system theory is proposed to calculate the charging probability, and the fuzzy rule base is adjusted according to the actual data to ensure that the fuzzy rule base is real and effective. Finally, the charging demand distribution curve of electric vehicles in each functional area is obtained by sampling simulation. Through experimental analysis, optimizing the capacity configuration of charging facilities in the station can well describe how electric vehicle users make charging decisions in the actual situation. The research on electric vehicle charging stations can slow down the waiting time of electric vehicle charging, and then affect the process of replacing fuel vehicles with electric vehicles to achieve the development goal of popularizing electric vehicles. It is of great significance for the promotion of new energy vehicles to rationally plan the location and capacity of electric vehicle charging stations.

Evaluating R&D Projects in Regulated Utilities: The Case of Power Transmission Utilities

Research and development (R&D) project selection is essential for many organizations; however, it is a complex decision since it is affected by many factors. These factors vary among organizations because of their different objectives and conditions and limited budgets for the investments. Therefore, the main motivation of this article is to create a method to help improve the ex-ante selection of R&D projects in regulated organizations. More importantly, the case application is the electric transmission utilities sector, which plays one of the most critical roles in the entire electric power system. The main objective of this article is to develop a model to select R&D projects based on a holistic approach aligned to strategies, utility objectives, and market conditions in the electric transmission sector. At the same time, it identifies, categorizes, and quantifies the factors associated with R&D projects in the power sector. The analysis is framed into a multicriteria model, hierarchical decision model—HDM proposed by (Kocaoglu, 1983), which considers all the aspects associated with R&D projects. The model and the application are potentially applicable to regulated organizations around the world. Moreover, the flexibility of the model allows it to be adopted by electric transmission utilities with similar characteristics to utilities in the United States. A complete analysis of criteria and subcriteria has been done. There are gaps in the literature that have been identified and that support the idea of using a multicriteria analysis to evaluate R&D projects. The methodology is described, and the application of the model is provided.

Interactive Allocation of Water Pollutant Initial Emission Rights in a Basin under Total Amount Control: A Leader-Follower Hierarchical Decision Model.

The initial emission rights allocation is the key measure to achieve the goal of total amount control and deepen the emission trading system. Although many studies have focused on the modeling of initial emission rights allocation, such as using game theory and multi-objective optimization methods, few studies have observed the hierarchical relationship of mutual interference and restriction between watershed management agency and local governments in each subarea during allocation. This relationship directly affects the rationality of the results of regional emission rights allocation. In this study, a leader-follower hierarchical decision model (LFHDM) for allocating initial emission rights in a basin is developed. Based on the bilevel programming approach, the model simulates the interactive decision-making process between the watershed management agency of the upper-level model (LFHDM-U) and the local government of the lower-level model (LFHDM-L) in the allocation under total amount control. A case study of China's Yellow River Basin is conducted to demonstrate the feasibility and practicality of the model. Findings reveal that, compared with the single-level model, the developed LFHDM has higher satisfaction with the allocation scheme. Under different scenarios, the overall satisfaction of the configuration schemes of COD and NH3-N in each province and autonomous region remains above 0.9. In addition, the allocation volumes of COD and NH3-N in each province of the Yellow River Basin in planning year increase with the enhancement of allowable assimilative capacity of water bodies, but the interval gap of satisfaction with allocation schemes gradually narrows. It shows that when the allowable assimilation capacity of a water body is low, the decision-making of the allocation scheme needs to be more cautious. Moreover, for the Yellow River Basin, apart from Qinghai and Sichuan, the task of reducing water pollutants in other provinces in the next few years is very arduous. The average reduction of total COD and NH3-N in the basin is about 48% and 46%, respectively.

Human Factors Analysis and Classification System - AHP Drone Model Assessment

Drone systems have become increasingly applied to various commercial, technical, agricultural, and military sectors. With the emerging significant effects on the frequency of drone accidents in many situations and areas, it has become increasingly necessary to form unmanned aircraft systems and usage limitations. The first step to do that is by highlighting the sources of accidents in the current process. The Human Factors Analysis and Classification System (HFACS) is the main technique for accident risk assessments in drone systems which clusters the accident key factors causes into 4 basic domains. In this study, 4 categories of drone experts participated in the study. The research utilized a new approach that integrates an analytical hierarchical decision-making model with the HFACS for drone accident causation investigation. The results of the study showed discrepancies among the different groups of drone operators and identified organizational factors and unsafe actions as being key issues in the evaluation.

Generalized multigranulation sequential three-way decision models for hierarchical classification

Hierarchical classification is an important research hotspot in machine learning due to the widespread existence of data with hierarchical class structures. The existing sequential three-way decision models mainly constructed the hierarchical condition information granules via concept hierarchy tree to discuss the three probabilistic regions for flat classification. However, in real-world applications, one may face not only the tree-structured data with hierarchical condition attributes but also more often the multi-level data with hierarchical decision attribute (hierarchical class labels). How to obtain acceptable decisions under different levels of granularity is the most important issue within the multi-level and multi-view data. To this end, we construct a generalized hierarchical decision table and propose a generalized hierarchical multigranulation sequential three-way decision model by combining multi-granularity and sequential three-way decisions. Specifically, we first design a generalized hierarchical decision table using concept hierarchy trees of all conditional attributes and decision attribute, and explore some basic properties. Then we decompose and aggregate condition and decision granules under different levels of granularity, propose the optimistic and pessimistic generalized hierarchical multigranulation three-way decision models to update the three probabilistic regions for flat and hierarchical classification, and discuss the relationships between these two models. Finally, the experimental results demonstrate that the proposed models are more suitable for different applications. These models will provide a novel insight and enrich the development of multigranulation three-way decisions from the perspective of multi-level and multi-view.