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Systems, Volume 11, Issue 5 (May 2023) – 51 articles

Cover Story (view full-size image): The large amount of information handled by organizations has increased their dependence on information technologies, which has made information security management a complex task. In addition, the transition from Industry 4.0 to Industry 5.0 brings further changes, requiring upgraded technologies and a critical rethinking of human resources. Information security frameworks can minimize complexity through documents containing guidelines, standards and requirements to establish procedures, policies and processes for each organization. However, it is necessary to properly select and apply the available frameworks and adapt them to the particularities of each organization. In this article, we review the frameworks ISO/IEC 27001, NIST CSF and MAGERIT to explore their effectiveness and suitability. View this paper
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13 pages, 508 KiB  
Article
The Association between Internet Use and Depression Risk among Chinese Adults, Middle-Aged and Older, with Disabilities
by Xiaodong Zhang, Anqi Li, Niuniu Cui, Bin Guo, Hafiz T. A. Khan and Lei Zhang
Systems 2023, 11(5), 264; https://doi.org/10.3390/systems11050264 - 22 May 2023
Viewed by 2290
Abstract
Background: Globally, nearly 15% of people suffer from various kinds of disabilities, and China has the largest disabled population in the world. The poor mental health status of people with disabilities has become an essential issue in most countries. The main aim of [...] Read more.
Background: Globally, nearly 15% of people suffer from various kinds of disabilities, and China has the largest disabled population in the world. The poor mental health status of people with disabilities has become an essential issue in most countries. The main aim of this study was to explore the potential impact of internet use on depression risk among middle-aged and older adults with different types of disabilities. Methods: The data used in this study were obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS) collected by Peking University. A binary logit model was used to analyze the impact of internet use on the depression risk among adults with disabilities, and the substitute variable method and the propensity score matching method were used to examine the robustness of the results. Results: (1) Internet use was negatively associated with depression risk among disabled people, and the higher the frequency of their internet use, the lower the probability of their depression risk. (2) Different social activities related to the internet had different impacts on the depression risk, and the decline in depression risk was mainly related to watching videos, watching news, and chatting via the internet. (3) Internet use reduced the depression risk of adults with physical disabilities, but had no impact on those with other types of disabilities. Conclusions: Our study suggests that internet use may have a positive spillover effect on decreasing the depression risk of disabled people, but the reduction effect is significantly affected by the social activities related to the internet and the types of disabilities. Full article
(This article belongs to the Section Systems Practice in Social Science)
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22 pages, 2561 KiB  
Article
Assessing the Determinants of Corporate Risk-Taking Using Machine Learning Algorithms
by Caixia Liu, Yu Chen, Sifan Huang, Xuesheng Chen and Feng Liu
Systems 2023, 11(5), 263; https://doi.org/10.3390/systems11050263 - 21 May 2023
Cited by 7 | Viewed by 2943
Abstract
Given that risk-taking is an essential channel for companies to obtain high returns and realize value enhancement, the goal of this study is to holistically explore the determinants of corporate risk-taking using various machine learning algorithms. Based on the data from Chinese listed [...] Read more.
Given that risk-taking is an essential channel for companies to obtain high returns and realize value enhancement, the goal of this study is to holistically explore the determinants of corporate risk-taking using various machine learning algorithms. Based on the data from Chinese listed companies between 2010 and 2019, we document that the adaptive boosting (AdaBoost) model makes better predictions of corporate risk-taking. We further visualize the importance and influence of the firm basic characteristics, firm performance, and chief executive officer (CEO) characteristics and discover that in the AdaBoost model, the firm basic characteristics, and performance factors, such as the firm’s fixed asset investments, size, and return on equity, are important in predicting corporate risk-taking, while CEO characteristics are less important. Finally, the role of variables in corporate risk-taking varies among large and small enterprises. Overall, our findings deepen the comprehension of what drives corporate risk-taking and provide a potential way for real-world firms seeking to adjust their risk-taking level. Full article
(This article belongs to the Section Systems Practice in Social Science)
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25 pages, 2608 KiB  
Article
Dependable and Non-Dependable Multi-Authentication Access Constraints to Regulate Third-Party Libraries and Plug-Ins across Platforms
by Santosh Kumar Henge, Gnaniyan Uma Maheswari, Rajakumar Ramalingam, Sultan S. Alshamrani, Mamoon Rashid and Jayalakshmi Murugan
Systems 2023, 11(5), 262; https://doi.org/10.3390/systems11050262 - 21 May 2023
Cited by 2 | Viewed by 1990
Abstract
This article discusses the importance of cross-platform UX/UI designs and frameworks and their effectiveness in building web applications and websites. Third-party libraries (TPL) and plug-ins are also emphasized, as they can help developers quickly build and compose applications. However, using these libraries can [...] Read more.
This article discusses the importance of cross-platform UX/UI designs and frameworks and their effectiveness in building web applications and websites. Third-party libraries (TPL) and plug-ins are also emphasized, as they can help developers quickly build and compose applications. However, using these libraries can also pose security risks, as a vulnerability in any library can compromise an entire server and customer data. The paper proposes using multi-authentication with specific parameters to analyze third-party applications and libraries used in cross-platform development. Based on multi-authentication, the proposed model will make setting up web desensitization methods and access control parameters easier. The study also uses various end-user and client-based decision-making indicators, supporting factors, and data metrics to help make accurate decisions about avoiding and blocking unwanted libraries and plug-ins. The research is based on experimentation with five web environments using specific parameters, affecting factors, and supporting data matrices. Full article
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19 pages, 4750 KiB  
Article
Comparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions
by Alper Sen and Kutalmis Gumus
Systems 2023, 11(5), 261; https://doi.org/10.3390/systems11050261 - 19 May 2023
Cited by 2 | Viewed by 1980
Abstract
Digital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimating the height values of the grid cells. [...] Read more.
Digital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimating the height values of the grid cells. The use of machine learning methods, such as artificial neural networks for spatial interpolation, contributes to spatial interpolation with more accuracy. In this study, the performances of FBNN interpolation based on different parameters such as the number of hidden layers and neurons, epoch number, processing time, and training functions (gradient optimization algorithms) were compared, and the differences were evaluated statistically using an analysis of variance (ANOVA) test. This research offers significant insights into the optimization of neural network gradients, with a particular focus on spatial interpolation. The accuracy of the Levenberg–Marquardt training function was the best, whereas the most significantly different training functions, gradient descent backpropagation and gradient descent with momentum and adaptive learning rule backpropagation, were the worst. Thus, this study contributes to the investigation of parameter selection of ANN for spatial interpolation in DEM height estimation for different terrain types and point distributions. Full article
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18 pages, 3992 KiB  
Article
Monitoring and Early Warning of SMEs’ Shutdown Risk under the Impact of Global Pandemic Shock
by Xiaoliang Xie, Xiaomin Jin, Guo Wei and Ching-Ter Chang
Systems 2023, 11(5), 260; https://doi.org/10.3390/systems11050260 - 19 May 2023
Cited by 62 | Viewed by 2914
Abstract
The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This [...] Read more.
The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs’ shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs’ shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster’s rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model. Full article
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16 pages, 2465 KiB  
Article
A Hybrid Metaheuristic Solution Method to Traveling Salesman Problem with Drone
by Noyan Sebla Gunay-Sezer, Emre Cakmak and Serol Bulkan
Systems 2023, 11(5), 259; https://doi.org/10.3390/systems11050259 - 19 May 2023
Cited by 10 | Viewed by 3057
Abstract
The challenging idea of using drones in last-mile delivery systems of logistics addresses a new routing problem referred to as the traveling salesman problem with drone (TSP-D). TSP-D aims to construct a route to deliver parcels to a set of customers by either [...] Read more.
The challenging idea of using drones in last-mile delivery systems of logistics addresses a new routing problem referred to as the traveling salesman problem with drone (TSP-D). TSP-D aims to construct a route to deliver parcels to a set of customers by either a truck or a drone, thereby minimizing operational costs. Since TSP-D is considered NP-hard, using metaheuristics is one of the most promising solutions. This paper presents a hybrid metaheuristic solution method of TSP-D based on two state-of-the-art algorithms: the genetic algorithm and ant colony optimization algorithm. Heuristics in TSP-D literature are based on two consequent decisions: truck routing and drone assignment. Unlike those in the existing literature, the proposed metaheuristic constructs both truck and drone routes simultaneously. Additionally, to the best of our knowledge, we introduce for the first time a solution method on the basis of an ant colony optimization approach to TSP-D. Additionally, we propose a binary pheromone framework for both drone and truck, diverging from the traditional pheromone structure. Computational experiments indicate that the proposed hybrid metaheuristic algorithm is able to generate optimal routes for provided instances of TSP-D benchmarking. In addition, the algorithm improves the best-known solutions of some instances found by rival heuristics. Full article
(This article belongs to the Section Systems Engineering)
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20 pages, 1445 KiB  
Article
Managing the Lean–Agile Paradox in Complex Environments
by Andrea Furlan, Roberto Grandinetti and Alberto F. De Toni
Systems 2023, 11(5), 258; https://doi.org/10.3390/systems11050258 - 19 May 2023
Cited by 3 | Viewed by 3310
Abstract
The decision to incrementally improve existing processes and products or introduce breakthrough innovations depends on the context a company is facing. In situations where problems are known, it is better to incrementally improve, while in complex situations where problems are not known, a [...] Read more.
The decision to incrementally improve existing processes and products or introduce breakthrough innovations depends on the context a company is facing. In situations where problems are known, it is better to incrementally improve, while in complex situations where problems are not known, a probe-sense-respond approach based on experimentation and the exploration of new solutions is preferable. Lean management adapts well to the first type of context, while agile management fits the second type of context. However, organizations must increasingly consider both approaches and become ambidextrous by introducing incremental improvements and breakthrough innovations simultaneously. This requires embracing the paradox between exploiting and exploring, adopting a new leadership mindset, and dual strategic, organizational, and behavioral models. This paper proposed a framework to implement lean and agile approaches simultaneously using the paradox theory to justify and manage this co-existence. This framework is threefold. First, managers need to differentiate between lean and agile, finding ways of keeping the two approaches separated. Second, lean and agile should be integrated so that synergies between the two approaches can be generated. Finally, managers need to achieve a dynamic equilibrium over time between lean and agile. Contributions to the theory and practice of this approach were discussed. Full article
(This article belongs to the Special Issue Managing Complexity: A Practitioner's Guide)
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19 pages, 721 KiB  
Article
The Neglected Focus on Managerial Aspect of Transfer Pricing Policy in Multidivisional Companies—Case of Serbia
by Jelena Demko-Rihter, Vojislav Sekerez, Dejan Spasić and Nevena Conić
Systems 2023, 11(5), 257; https://doi.org/10.3390/systems11050257 - 18 May 2023
Cited by 1 | Viewed by 2163
Abstract
Prices applied to internal transactions between the business segments or divisions of a company in transactions between related entities within a group (transfer pricing) can have a significant impact on a company’s competitive advantage. Transfer pricing policy influences the profits of operating segments, [...] Read more.
Prices applied to internal transactions between the business segments or divisions of a company in transactions between related entities within a group (transfer pricing) can have a significant impact on a company’s competitive advantage. Transfer pricing policy influences the profits of operating segments, resource allocation and the need for segment reporting. The two main approaches to transfer pricing are the tax and managerial approaches. The aim of this research was to test whether multidivisional companies operating in Serbia give more importance to the tax or the managerial aspect of transfer pricing policy. Another research aim was to determine whether segment reporting is more developed in companies in Serbia that have the legal obligation to prepare consolidated financial statements. Both research hypotheses were confirmed using the questionnaire method on a final sample of 52 large and medium-sized companies (out of 1912 large and medium-sized companies operating in Serbia). First, our findings show that tax compliance is more dominant in transfer pricing than the managerial perspective in the Serbian companies analyzed. Second, we found that mandatory consolidated financial reporting and related segment reporting can influence the managerial approach to transfer pricing in Serbian multidivisional companies and groups. Other factors (production orientation of companies, developed responsibility accounting and managers’ bonuses, for example) also encourage this approach. Full article
11 pages, 1650 KiB  
Article
AatMatch: Adaptive Adversarial Training in Semi-Supervised Learning Based on Data-Driven Decision-Making Models
by Kuan Li, Qianzhi Lian, Can Gao and Fuyong Zhang
Systems 2023, 11(5), 256; https://doi.org/10.3390/systems11050256 - 18 May 2023
Cited by 1 | Viewed by 1563
Abstract
Data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. The quality of unlabeled data in real-world scenarios presents challenges for semi-supervised learning. Effectively leveraging unlabeled data for learning is [...] Read more.
Data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. The quality of unlabeled data in real-world scenarios presents challenges for semi-supervised learning. Effectively leveraging unlabeled data for learning is challenging due to the need for labeled information, while the scarcity of labeled data requires efficient and flexible data augmentation methods. To address these challenges, this paper proposes the AatMatch algorithm, which uses a momentum model, coarse learning, and adversarial training to generate adversarial examples for different classes. The algorithm sets the threshold for generating pseudo-labels and reinforces the results with adversarial perturbations based on evaluation results. In addition, a more refined learning strategy for unlabeled data is adjusted by setting adaptive weights based on the confidence of each unlabeled data point, thereby mitigating the adverse effects of low-confidence unlabeled data on the model. Experimental evaluations on several datasets, including CIFAR-10, CIFAR-100, and SVHN, demonstrate the effectiveness of the proposed AatMatch algorithm in semi-supervised learning. Specifically, the algorithm achieves the lowest error rates for multiple scenarios on these datasets. Full article
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14 pages, 1603 KiB  
Article
Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream Data
by Zhanming Wen, Weizhen Lin and Hongwei Liu
Systems 2023, 11(5), 255; https://doi.org/10.3390/systems11050255 - 18 May 2023
Cited by 4 | Viewed by 2914
Abstract
Since online shopping has become an important way for consumers to make purchases, consumers have signed up to e-commerce platforms to shop online. However, retailers are beginning to realise the critical role of predicting anonymous consumer purchase intent to improve purchase conversion rates [...] Read more.
Since online shopping has become an important way for consumers to make purchases, consumers have signed up to e-commerce platforms to shop online. However, retailers are beginning to realise the critical role of predicting anonymous consumer purchase intent to improve purchase conversion rates and store profitability. Therefore, this study aims to investigate the prediction of anonymous consumer purchase intent. This research presents a machine learning model (MBT-POP) for predicting customer purchase behaviour based on multi-behavioural trendiness (MBT) and product popularity (POP) using 33,339,730 clicks generated from 445,336 sessions of real e-commerce customers. The results show that the MBT-POP model can effectively predict the purchase behaviour of anonymous customers (F1 = 0.9031), and it achieves the best prediction result with a sliding window of 2 days. Compared to existing studies, the MBT-POP model not only improves the model performance, but also compresses the number of days required for accurate prediction. The present research has argued that product trendiness and popularity can significantly improve the predictive performance of the customer purchase behaviour model and can play an important role in predicting the purchase behaviour of anonymous customers. Full article
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24 pages, 1755 KiB  
Article
Using Social Network Analysis to Identify the Critical Factors Influencing Residents’ Green Consumption Behavior
by Changlu Zhang, Liqian Tang, Jian Zhang and Zongshui Wang
Systems 2023, 11(5), 254; https://doi.org/10.3390/systems11050254 - 17 May 2023
Cited by 6 | Viewed by 2718
Abstract
Green consumption is an important tool to accelerate the circular economy and promote sustainable development. The identification of critical influencing factors for green consumption is the key to promoting green consumption behavior (GCB). Firstly, based on the joint framework of theory of planned [...] Read more.
Green consumption is an important tool to accelerate the circular economy and promote sustainable development. The identification of critical influencing factors for green consumption is the key to promoting green consumption behavior (GCB). Firstly, based on the joint framework of theory of planned behavior (TPB) and the attitude–behavior–context (ABC) theory, we summarized 32 influencing factors from six dimensions: consumer attitude, cognitive factors, sense of responsibility, economic factors, government regulation, and green product supply. Secondly, the Delphi method was used to modify and optimize the initial influencing factor index. Thirdly, we constructed a social network analysis (SNA) model of influencing factors to determine the causal relationships between each influencing factor. All factors were divided into driving factors and result factors via the calculation of degree centrality, and the critical influencing factors and influencing paths of residents’ GCB were ultimately determined. Finally, based on the empirical research results, corresponding countermeasures and suggestions were put forward. The results show that the top five critical influencing factors include green purchase intention, willingness to pay, risk perception, green product certification, publicity and education, green product price, and green attribute information. Among them, green product certification, publicity and education, and green product price are critical driving factors in GCB. Full article
(This article belongs to the Special Issue Circular Economy Systems: Design, Use, and Innovation)
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18 pages, 2711 KiB  
Article
Multidimensional Evolution and Driving Factors of Securities Firms’ Collaborative Bond Joint Underwriting Networks in China: A Comprehensive Analysis from 2011 to 2020
by Yuan Cao, Ying Yang, Hongkun Ma, Xiangyi Kong, Xueran Li, Yiran Du and Dou Chen
Systems 2023, 11(5), 253; https://doi.org/10.3390/systems11050253 - 16 May 2023
Viewed by 1485
Abstract
This study utilizes the joint bond joint underwriting data of China’s securities firms from 2011 to 2020 to systematically explore the evolutionary characteristics of China’s collaborative bond joint underwriting networks from temporal, topological, and spatial dimensions. By employing social network analysis, Ucinet, and [...] Read more.
This study utilizes the joint bond joint underwriting data of China’s securities firms from 2011 to 2020 to systematically explore the evolutionary characteristics of China’s collaborative bond joint underwriting networks from temporal, topological, and spatial dimensions. By employing social network analysis, Ucinet, and ArcGIS, we construct a longitudinal network panel data to quantitatively analyze the driving factors and their underlying mechanisms. The findings reveal that, in terms of topological structure, China’s bond joint underwriting networks exhibit increasingly mature, active, balanced, and accessible features, with domestic securities firms such as China Securities Co., Ltd. emerging as the backbone and foreign-backed firms gradually fading. In the spatial dimension, urban collaboration presents a transformation from triangular to butterfly-shaped, quadrilateral, and ultimately multicore networks. At the regional scale, inter-regional collaboration is most extensive between the eastern regions, followed by eastern–central regions, with eastern–western and central–western regions relatively less engaged. At the urban scale, the central positions of Beijing, Shenzhen, and Shanghai are gradually strengthening, and their external radiation scope is expanding annually. The underlying mechanism driving this evolution is the increasing opportunities for securities firms to establish and adjust their cooperative relationships due to the maturing and active bond joint underwriting networks in China. To compensate for the opportunity cost of bond joint underwriting and to maximize collaboration benefits, securities firms need to select potential partners with close geographical proximity, similar business domains, larger underwriting scales, “friends of friends,” and “network star” status, thereby promoting the continuous evolution of China’s bond joint underwriting syndicates. Full article
(This article belongs to the Section Systems Practice in Social Science)
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18 pages, 3666 KiB  
Article
Dynamics of Medical Screening: A Simulation Model of PSA Screening for Early Detection of Prostate Cancer
by Özge Karanfil
Systems 2023, 11(5), 252; https://doi.org/10.3390/systems11050252 - 16 May 2023
Cited by 2 | Viewed by 2033
Abstract
In this study, we present a novel simulation model and case study to explore the long-term dynamics of early detection of disease, also known as routine population screening. We introduce a realistic and portable modeling framework that can be used for most cases [...] Read more.
In this study, we present a novel simulation model and case study to explore the long-term dynamics of early detection of disease, also known as routine population screening. We introduce a realistic and portable modeling framework that can be used for most cases of cancer, including a natural disease history and a realistic yet generic structure that allows keeping track of critical stocks that have been generally overlooked in previous modeling studies. Our model is specific to prostate-specific antigen (PSA) screening for prostate cancer (PCa), including the natural progression of the disease, respective changes in population size and composition, clinical detection, adoption of the PSA screening test by medical professionals, and the dissemination of the screening test. The key outcome measures for the model are selected to show the fundamental tradeoff between the main harms and benefits of screening, with the main harms including (i) overdiagnosis, (ii) unnecessary biopsies, and (iii) false positives. The focus of this study is on building the most reliable and flexible model structure for medical screening and keeping track of its main harms and benefits. We show the importance of some metrics which are not readily measured or considered by existing medical literature and modeling studies. While the model is not primarily designed for making inferences about optimal screening policies or scenarios, we aim to inform modelers and policymakers about potential levers in the system and provide a reliable model structure for medical screening that may complement other modeling studies designed for cancer interventions. Our simulation model can offer a formal means to improve the development and implementation of evidence-based screening, and its future iterations can be employed to design policy recommendations to address important policy areas, such as the increasing pool of cancer survivors or healthcare spending in the U.S. Full article
(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)
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40 pages, 1693 KiB  
Article
Smart Elderly Care: An Intelligent e-Procurement System for Elderly Supplier Selecting
by Simeng Qin, Mingli Zhang, Haiju Hu and Yanan Wang
Systems 2023, 11(5), 251; https://doi.org/10.3390/systems11050251 - 15 May 2023
Cited by 5 | Viewed by 2785
Abstract
(1) Objective: to accelerate the digitalization of the elderly care service industry and the construction of the smart elderly care industry, this paper designs an intelligent e-procurement system for elderly suppliers selecting from the perspective of smart elderly care, which can enhance the [...] Read more.
(1) Objective: to accelerate the digitalization of the elderly care service industry and the construction of the smart elderly care industry, this paper designs an intelligent e-procurement system for elderly suppliers selecting from the perspective of smart elderly care, which can enhance the efficiency of elderly care supply chains and assist manufacturers of elderly products in choosing a reliable, high-quality supplier during trades. (2) Methods: the e-procurement system, including six modules, is built with an improved dynamic Markov Decision Process selection model combined with an Analytic Network Process, bringing dynamic evolution of both inventory cost and purchasing cost into long-term reward calculation, and taking into account 15 common indexes and 7 specific indexes when evaluating suppliers’ competitiveness. (3) Results: a real sample shows that when facing 50 suppliers with 50 different quotations, the e-procurement system selects a stable and reliable supplier that brings the best long-term profits for demand enterprises in ten purchase periods, and it makes the selecting process more efficient and more prompt. (4) Conclusions: the model can be used in the circumstance where an elderly product producer is forced to decide on a long-term strategy or reselect a new stable supplier since it is focused on choosing long-term and high-quality suppliers over numerous periods. Full article
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23 pages, 5582 KiB  
Article
A Change-Sensitive Complexity Measurement for Business Process Models Based on Control Structure
by Changhong Zhou, Dengliang Zhang, Deyan Chen and Cong Liu
Systems 2023, 11(5), 250; https://doi.org/10.3390/systems11050250 - 15 May 2023
Cited by 1 | Viewed by 1760
Abstract
The analysis of the process model complexity has significant implications for the operation, maintenance, and optimization of processes. As process models consist of control structures with specific repetitive patterns, the complexity of the control structures often determines the process model complexity. While the [...] Read more.
The analysis of the process model complexity has significant implications for the operation, maintenance, and optimization of processes. As process models consist of control structures with specific repetitive patterns, the complexity of the control structures often determines the process model complexity. While the existing methods for measuring the process model complexity consider most control structure complexity, some changes in branch structures cannot be reflected in the process model complexity. To address this issue, this paper considers the impact of the number and position of activities in branching structures on the process model complexity, distinguishes the connection forms between branch structures, and defines the complexity of the branching structures. We propose a new complexity measurement (CP) based on the control structures. The theoretical validity of CPs was confirmed using Weyuker’s properties, and the process structure variant model was used to experiment with its sensitivity. The findings indicate that the CP satisfies eight out of the nine properties proposed by Weyuker. Compared with the other complexity measurement methods of the process model, the CP is more sensitive to some structural changes in the process model. Therefore, when the structure of the process model changes, the CP reflects the changes in the process model complexity more accurately. Full article
(This article belongs to the Special Issue Business Intelligence as a Tool for Business Competitiveness)
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27 pages, 750 KiB  
Article
Can Companies Reduce Carbon Emission Intensity to Enhance Sustainability?
by Sisi Zheng and Shanyue Jin
Systems 2023, 11(5), 249; https://doi.org/10.3390/systems11050249 - 15 May 2023
Cited by 9 | Viewed by 2849
Abstract
With the rapid development of global industrialization and modernization, carbon emissions have brought about serious climate warming and environmental pollution problems. Chinese enterprises, as the major players in carbon emissions, are important in terms of promoting the green transformation of the economy. It [...] Read more.
With the rapid development of global industrialization and modernization, carbon emissions have brought about serious climate warming and environmental pollution problems. Chinese enterprises, as the major players in carbon emissions, are important in terms of promoting the green transformation of the economy. It is particularly important to investigate the relationship and mechanism of action between carbon emission reduction and corporate sustainable development in Chinese enterprises. This study aims to determine whether reducing the intensity of carbon emissions can make businesses more sustainable and to analyze the moderating influences of government environmental subsidies, media monitoring, and executives’ green opinions on the link between the two variables. The study sample consists of Shanghai and Shenzhen A-shares data from 2015 to 2020, and a fixed-effects model is employed for analysis. Data were obtained from the China Stock Market & Accounting Research database, the Financial News Database of Listed Companies, and enterprise financial statement notes, etc. Stata17.0 was used to clean and analyze the data. The results indicate that businesses can greatly improve their long-term viability by lowering their carbon emissions. Additionally, government environmental subsidies, media monitoring, and executives’ green perceptions all enhance the correlation between corporate sustainability and reduce carbon emission intensity. This study not only enriches the relationship between environmental governance and sustainable development from a theoretical perspective, but also further expands the stakeholder theory. It also finds the mechanism of the role of the government and media on corporate carbon emissions for sustainable development in practice, which provides effective guidance to accelerate the promotion of carbon emission reduction and, thus, the sustainable development of Chinese enterprises. Full article
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1 pages, 246 KiB  
Correction
Correction: Cabrera et al. Distinctions Organize Information in Mind and Nature: Empirical Findings of Identity-Other Distinctions (D) in Cognitive and Material Complexity. Systems 2022, 10, 41
by Derek Cabrera, Laura Cabrera and Elena Cabrera
Systems 2023, 11(5), 248; https://doi.org/10.3390/systems11050248 - 15 May 2023
Viewed by 879
Abstract
In the original publication [...] Full article
17 pages, 3380 KiB  
Article
Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice
by Abraham George, Padmanabhan Badrinath, Peter Lacey, Chris Harwood, Alex Gray, Paul Turner and Davinia Springer
Systems 2023, 11(5), 247; https://doi.org/10.3390/systems11050247 - 14 May 2023
Cited by 2 | Viewed by 3810
Abstract
In public health, the routine use of linear forecasting, which restricts our ability to understand the combined effects of different interventions, demographic changes and wider health determinants, and the lack of reliable estimates for intervention impacts have limited our ability to effectively model [...] Read more.
In public health, the routine use of linear forecasting, which restricts our ability to understand the combined effects of different interventions, demographic changes and wider health determinants, and the lack of reliable estimates for intervention impacts have limited our ability to effectively model population needs. Hence, we adopted system dynamics modelling to forecast health and care needs, assuming no change in population behaviour or determinants, then generated a “Better Health” scenario to simulate the combined impact of thirteen interventions across cohorts defined by age groups and diagnosable conditions, including “no conditions”. Risk factors for the incidence of single conditions, progression toward complex needs and levels of morbidity including frailty were used to create the dynamics of the model. Incidence, prevalence and mortality for each cohort were projected over 25 years with “do nothing” and “Better Health” scenarios. The size of the “no conditions” cohort increased, and the other cohorts decreased in size. The impact of the interventions on life expectancy at birth and healthy life expectancy is significant, adding 5.1 and 5.0 years, respectively. We demonstrate the feasibility, applicability and utility of using system dynamics modelling to develop a robust case for change to invest in prevention that is acceptable to wider partners. Full article
(This article belongs to the Special Issue System Dynamics Models for Public Health and Health Care Policy)
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21 pages, 6254 KiB  
Article
Complexity Review of NIMBY Conflict: Characteristics, Mechanism and Evolution Simulation
by Luxin Cui, Yu Chen, Xing Wang and Shiyu Liu
Systems 2023, 11(5), 246; https://doi.org/10.3390/systems11050246 - 14 May 2023
Cited by 3 | Viewed by 2707
Abstract
In the process of modernization and urbanization, some government projects or facilities with negative externalities have caused the psychology of residents nearby to “Not in My Backyard” (NIMBY). That is, adopting strong and resolute, sometimes highly emotional collective opposition or even resistance behavior. [...] Read more.
In the process of modernization and urbanization, some government projects or facilities with negative externalities have caused the psychology of residents nearby to “Not in My Backyard” (NIMBY). That is, adopting strong and resolute, sometimes highly emotional collective opposition or even resistance behavior. This triggered a NIMBY conflict. From the perspective of Complexity, this study re-examines the characteristics and evolution mechanism of NIMBY conflict and draws the following conclusions: (1) NIMBY conflict is a complex system that interacts between multiple subjects and the environment; (2) Adaptability is the driving force for the evolution of NIMBY conflict. Through detectors, regularizers, and effectors, NIMBY subjects can be encouraged to gradually adapt to changes in the external environment and maximize their own interests; (3) In NIMBY conflict, the government conflict response method is more important than the intervention time. Residents’ communication efficiency and connection probability will affect residents’ behavior choices. The lower the residents’ communication efficiency, the less likely it is to form a NIMBY conflict. Full article
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17 pages, 1093 KiB  
Article
Bidding Evaluation and Contractor Selection Using Balance Index Model and Comprehensive Input Efficiency Based on Data Envelopment Analysis
by Xun Liu, Siyu Chen, Zhenhan Ding and Bixiao Xu
Systems 2023, 11(5), 245; https://doi.org/10.3390/systems11050245 - 14 May 2023
Cited by 4 | Viewed by 2427
Abstract
In order to ensure a smooth construction project, it is necessary to select an appropriate contractor. However, traditional bid evaluation methods are highly subjective in determining weights. Data envelopment analysis (DEA), a comprehensive bid evaluation method that considers multiple factors, was introduced to [...] Read more.
In order to ensure a smooth construction project, it is necessary to select an appropriate contractor. However, traditional bid evaluation methods are highly subjective in determining weights. Data envelopment analysis (DEA), a comprehensive bid evaluation method that considers multiple factors, was introduced to reduce subjectivity and provide a simple yet comprehensive method for evaluating bids. Based on the existing cross-evaluation and balance index models, this research proposed a new DEA ranking model—the comprehensive input efficiency model, as well as its specific application steps. Additionally, a case study on selecting contractors for a water engineering project was presented to demonstrate the effectiveness of this model. The results indicated that the comprehensive input efficiency model could achieve the same ranking function as the balance index and was suitable for assessing bidders’ relative efficiency. Moreover, the comprehensive input efficiency model proposed in this research is more simplified. Thus, this research compensates for the drawbacks of the existing comprehensive evaluation models in that the bid evaluation process is cumbersome, thereby extending the research on DEA methods in bid evaluation. Additionally, the model provides tenderers with a more efficient and effective bid evaluation method to select the most appropriate contractor. In the future, research may be conducted to apply DEA to other types of projects, including service projects, real estate, and consulting services. Full article
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23 pages, 3012 KiB  
Systematic Review
Exploring the Benefits and Drawbacks of AR and VR Technologies for Learners of Mathematics: Recent Developments
by Mustafa Cevikbas, Neslihan Bulut and Gabriele Kaiser
Systems 2023, 11(5), 244; https://doi.org/10.3390/systems11050244 - 14 May 2023
Cited by 19 | Viewed by 13547
Abstract
Despite the growing interest in the field, the overall impact of augmented reality (AR) and virtual reality (VR) on mathematics learning remains unclear, with previous studies reporting mixed results. Moreover, to date, no systematic review has evaluated the potential of AR/VR in mathematics [...] Read more.
Despite the growing interest in the field, the overall impact of augmented reality (AR) and virtual reality (VR) on mathematics learning remains unclear, with previous studies reporting mixed results. Moreover, to date, no systematic review has evaluated the potential of AR/VR in mathematics education, including its benefits and drawbacks for learners. To address this gap, the present systematic literature review aims to identify research trends, determine characteristics and methodologies, and explore the potential benefits and drawbacks of AR/VR technologies in mathematics learning based on existing empirical studies. In accordance with the PRISMA guidelines, we analyzed 59 peer-reviewed journal articles published in English that focused on AR/VR implementation in mathematics education. The review determined that geometry was the most widely studied topic of mathematics, with several studies focusing on the use of AR/VR to assist students with learning disabilities. The present review offers evidence for the potential of AR/VR potential in consolidating learners’ socio-emotional, cognitive/meta-cognitive, and pedagogical development in mathematics learning. Nevertheless, a few issues, including technological glitches, cost, start-up effort, health issues, and unfamiliarity with AR/VR, pose challenges to the successful application of AR/VR in the classroom. This systematic review contributes to the existing body of knowledge in the field and recommends avenues for future research. Full article
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25 pages, 2097 KiB  
Article
Input Efficiency Measurement and Improvement Strategies of New Infrastructure under High-Quality Development
by Sai Wang, Xiumei Sun, Xuhui Cong and Yongkun Gao
Systems 2023, 11(5), 243; https://doi.org/10.3390/systems11050243 - 13 May 2023
Cited by 6 | Viewed by 1851
Abstract
As a result of implementing new development concepts and absorbing new technical revolutions in the Intelligent Economy Age, new infrastructure is defined as a new driving force for high-quality development. However, as new infrastructure is constructed, there are problems such as the small [...] Read more.
As a result of implementing new development concepts and absorbing new technical revolutions in the Intelligent Economy Age, new infrastructure is defined as a new driving force for high-quality development. However, as new infrastructure is constructed, there are problems such as the small scale of high-tech industries, weak economic support and human capital, and difficulty in carrying out new infrastructure construction projects, so it has become crucial to find solutions to these problems. Using the slacks-based measure model and Moran index, this study compares and analyzes the input efficiency of new infrastructure in 30 provinces of China from 2011 to 2020, alongside the analysis of temporal and spatial differences. China’s new infrastructure input generally shows a stable development trend in terms of efficiency, while the regional coordination still needs to be strengthened. Eastern China maintains a leading trend, Central China is developing rapidly, and the western region and Northeastern China do not form high-value agglomeration areas. This study puts forward relevant policy recommendations from four dimensions—optimizing the industrial structure, giving scope to government function, focusing on key areas, and compensating for weak links—to supply a powerful impetus for the development of new infrastructure. Full article
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16 pages, 2572 KiB  
Article
Identifying the Mutual Correlations and Evaluating the Weights of Factors and Consequences of Mobile Application Insecurity
by Elena Zaitseva, Tetiana Hovorushchenko, Olga Pavlova and Yurii Voichur
Systems 2023, 11(5), 242; https://doi.org/10.3390/systems11050242 - 12 May 2023
Cited by 4 | Viewed by 1610
Abstract
Currently, there is a contradiction between the growing number of mobile applications in use and the responsibility that is placed on them, on the one hand, and the imperfection of the methods and tools for ensuring the security of mobile applications, on the [...] Read more.
Currently, there is a contradiction between the growing number of mobile applications in use and the responsibility that is placed on them, on the one hand, and the imperfection of the methods and tools for ensuring the security of mobile applications, on the other hand. Therefore, ensuring the security of mobile applications by developing effective methods and tools is a challenging task today. This study aims to evaluate the mutual correlations and weights of factors and consequences of mobile application insecurity. We have developed a method of evaluating the weights of factors of mobile application insecurity, which, taking into account the mutual correlations of mobile application insecurity consequences from these factors, determines the weights of the factors and allows us to conclude which factors are necessary to identify and accurately determine (evaluate) to ensure an appropriate level of reliability of forecasting and assess the security of mobile applications. The experimental results of our research are the evaluation of the weights of ten OWASP mobile application insecurity factors the identification of the mutual correlations of the consequences of mobile applications’ insecurity from these factors, and the identification of common factors on which more than one consequence depends. Full article
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15 pages, 1546 KiB  
Article
Organizational Paradoxes and Metamorphosis in Collective Action
by Alberto F. De Toni, Giuseppe Zollo and Alberto De Zan
Systems 2023, 11(5), 241; https://doi.org/10.3390/systems11050241 - 12 May 2023
Cited by 1 | Viewed by 1703
Abstract
This paper addresses the subject of organizational paradoxes through the lens of complexity theory. The first part of the study focuses on the formalization of the key elements in order to better understand the concept of organizational tension through the presentation of related [...] Read more.
This paper addresses the subject of organizational paradoxes through the lens of complexity theory. The first part of the study focuses on the formalization of the key elements in order to better understand the concept of organizational tension through the presentation of related constructs, i.e., dilemmas, dialectics and paradoxes. The second part of the paper introduces the key to interpreting complexity theory, highlighting how the characteristic of emergence in complex systems makes it possible to identify two different levels: that of organizational elements and that of organizational forms, both of which are impacted by tension. That reflection leads the authors to postulate that metamorphosis is the process by which organizations, constantly crossed by tension, regenerate the organizational forms’ level on the basis of evolving tensions between organizational elements. Full article
(This article belongs to the Special Issue Managing Complexity: A Practitioner's Guide)
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23 pages, 11698 KiB  
Article
Spatial Correlation Network Analysis of Industrial Green Technology Innovation Efficiency in China
by Decheng Fan and Xiaolin Wu
Systems 2023, 11(5), 240; https://doi.org/10.3390/systems11050240 - 12 May 2023
Viewed by 1785
Abstract
Exploring the spatial correlation network and its structural characteristics of China’s industrial green technology innovation efficiency is significant for promoting the coordinated development of inter-regional industrial green transformation. Based on the innovation value chain, this paper divides China’s industrial green technology innovation system [...] Read more.
Exploring the spatial correlation network and its structural characteristics of China’s industrial green technology innovation efficiency is significant for promoting the coordinated development of inter-regional industrial green transformation. Based on the innovation value chain, this paper divides China’s industrial green technology innovation system into three interrelated sub-stages: technology research and development, achievement transformation, and commercialization. The NSBM model is used to measure the efficiency of industrial green technology innovation in 30 provinces and cities in mainland China from 2011 to 2020. The modified gravity model and social network analysis method are introduced to explore its spatial correlation network’s structural characteristics and evolution rules. The results show that the spatial network correlation intensity of the three stages of green technology innovation efficiency in regional industry has gradually strengthened. There is no strict hierarchical structure, and the spatial network tends to be stable. The network shows an apparent “core–edge” distribution in all three stages, with the eastern coastal and central more developed regions at the network’s core. Meanwhile, the northeastern and western remote areas are at the network’s edge and less connected with other regions’ provinces and cities. The distribution of network blocks in the three stages of green technology innovation efficiency is similar. The net benefit block mainly includes the eastern coastal and surrounding developed areas. The net spillover block mainly consists of the economically backward northwest region. The broker block is primarily distributed in the surrounding provinces and cities of the Bohai Rim. The bidirectional spillover block is mainly located in the southwest region. Finally, some suggestions are put forward to promote the coordinated improvement of regional industrial green technology innovation efficiency from the perspective of integrity, individuality, and agglomeration. Full article
(This article belongs to the Section Systems Practice in Social Science)
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30 pages, 979 KiB  
Article
Organizational Routines and Digital Transformation: An Analysis of How Organizational Routines Impact Digital Transformation Transition in a Saudi University
by Ibrahim Almatrodi and Dimitra Skoumpopoulou
Systems 2023, 11(5), 239; https://doi.org/10.3390/systems11050239 - 9 May 2023
Cited by 8 | Viewed by 3744
Abstract
This study was undertaken in response to the current lack of research identifying organizational routine influences that are exerted on organizations, including in relation to digital transition. Digital transformation refers to the integration of digital technologies, such as data analytics and automation, into [...] Read more.
This study was undertaken in response to the current lack of research identifying organizational routine influences that are exerted on organizations, including in relation to digital transition. Digital transformation refers to the integration of digital technologies, such as data analytics and automation, into an organization, engendering changes in its work routines, processes, structure, and culture. However, digital transition is a strategic process involving significant structural and procedural changes in the shift from one technology to another. Therefore, understanding the effect of organizational routines is essential for understanding how digital transformation impacts an organization, and how best to manage this transition. This study explores the impact of organizational routines on digital transition, in order to understand how they can facilitate a successful digital transformation. It employs a single case study of a university that recently implemented digital technologies, including big data analytics and automation, in some of its managerial services for its employees. It marked a significant technological shift for this public university, and the study specifically explores how the organizational routines affected this digital transition, particularly in terms of managerial and administrative issues. In modern times, many universities worldwide have undergone significant changes, and it is therefore essential to document the impact of organizational routines on digital transition, especially in developing countries where universities play a crucial societal role. The complexity of universities as organizations, and the interaction between organizational routines and digital transition highlight the importance of a case study approach for understanding this complexity. The university with which this study is concerned is a leading public university that holds considerable influence and a leadership role within the higher education sector, and which has adopted various technologies and information systems. The success of the digital transformation at this university may have a significant impact on other universities in the region and encourage them to adopt similar approaches to digital transition and digital transformation in the future, if they understand the impact of organizational routines in such transitions. The results show that organizational routines play a leading role in digital transformation transition; moreover, some aspects can explain the ways in which these routines influence digital transformation transition, such as inherited status, the adaptation of technology and changes to current organizational settings, and power. This study can contribute toward the successful implementation of digital transformation and influence the strategies adopted for the transitions required by digital technologies. Full article
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19 pages, 778 KiB  
Article
A Study of the Impact of Executive Power and Employee Stock Ownership Plans on Corporate Cost Stickiness: Evidence from China A-Share Non-Financial Listed Companies
by Dongxue Zhai, Xuefeng Zhao, Yanfei Bai and Delin Wu
Systems 2023, 11(5), 238; https://doi.org/10.3390/systems11050238 - 9 May 2023
Cited by 4 | Viewed by 1988
Abstract
It is of great value to study the stickiness of enterprise cost for reducing enterprise cost and improving enterprise performance. This paper selected all A-share non-financial listed companies from 2014 to 2019 to study the impact of executive power and employee stock ownership [...] Read more.
It is of great value to study the stickiness of enterprise cost for reducing enterprise cost and improving enterprise performance. This paper selected all A-share non-financial listed companies from 2014 to 2019 to study the impact of executive power and employee stock ownership plans on cost stickiness. The study found that the higher the executive power, the stronger the cost stickiness of the enterprise. By reducing the adjustment costs and optimistic expectations of management and improving the performance sensitivity of executive compensation and quality of information disclosure, an employee stock ownership plan plays a role in suppressing the cost-stickiness effect of executive power. The larger the scale and the more times the employee stock ownership plan is implemented, the stronger the inhibition effect is. An employee stock ownership plan has a stronger inhibiting effect on the cost-stickiness effect of executive power in enterprises with a large proportion of state-owned and institutional shares and high employee status. Combining the research themes of management accounting and financial accounting, this study discusses the economic consequences of ESOP from the perspective of enterprise cost control, which is helpful for internal and external stakeholders of enterprises to understand the characteristics and effects of ESOP in the new era, and also provides new evidence for enterprise cost control while enlightening policy makers and listed companies to explore the feasible mechanism of enterprise cost control from the staff level. It is of great value to study the stickiness of enterprise cost for reducing enterprise cost and improving enterprise performance. This paper selected all A-share non-financial listed companies from 2014 to 2019 to study the impact of executive power and an employee stock ownership plan on cost stickiness. It is found that the higher the executive power, the stronger the cost stickiness. An employee stock ownership plan has a stronger inhibiting effect on the cost-stickiness effect of executive power in enterprises with a large proportion of state-owned and institutional shares and high employee status. This study provides new evidence for corporate cost control. Full article
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20 pages, 4178 KiB  
Article
Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies
by Qais Ibrahim Ahmed, Hani Attar, Ayman Amer, Mohanad A. Deif and Ahmed A. A. Solyman
Systems 2023, 11(5), 237; https://doi.org/10.3390/systems11050237 - 8 May 2023
Cited by 27 | Viewed by 2541
Abstract
Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper [...] Read more.
Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault). Full article
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30 pages, 16145 KiB  
Article
Modeling the Potential for Rural Tourism Development via GWR and MGWR in the Context of the Analysis of the Rural Lodging Supply in Extremadura, Spain
by José Manuel Sánchez-Martín, Ana María Hernández-Carretero, Juan Ignacio Rengifo-Gallego, María José García-Berzosa and Luz María Martín-Delgado
Systems 2023, 11(5), 236; https://doi.org/10.3390/systems11050236 - 8 May 2023
Cited by 5 | Viewed by 2217
Abstract
The harmonious development of tourism activity in rural areas must be based on effective tourism plans adapted to the territory. To achieve this, it is necessary that the tourist potential of the area be taken into consideration. However, the tourist attraction capacity is [...] Read more.
The harmonious development of tourism activity in rural areas must be based on effective tourism plans adapted to the territory. To achieve this, it is necessary that the tourist potential of the area be taken into consideration. However, the tourist attraction capacity is not always considered, which has led to a significant increase in the number of rural lodgings. This has caused strong imbalances in Extremadura, Spain. On the basis of this premise, in this research study, we aim to determine whether there is an adjustment between the main factors that attract rural tourists to the study area. To determine this, we make use of different geostatistical procedures based on spatially weighted regression models (GWR and MGWR). A comparative study is conducted using these models, on the basis of which it is deduced that one type of regression offers advantages over the other. However, the results show that neither regression models can explain the presence of rural accommodation in places that do not meet the requirements demanded by tourists. This fact shows that the increase in the supply of rural accommodation follows unsuitable patterns in some cases, which translates into numerous problems, such as low occupancy levels. In this study, it is concluded that there is no strong relationship between the attractiveness of a territory and its volume of supply, highlighting the need to rethink tourism plans in order to adjust them relative to reality. Full article
(This article belongs to the Section Systems Practice in Social Science)
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34 pages, 3674 KiB  
Article
A Learning-Based Optimal Decision Scenario for an Inventory Problem under a Price Discount Policy
by Alaa Fouad Momena, Mostafijur Rahaman, Rakibul Haque, Shariful Alam and Sankar Prasad Mondal
Systems 2023, 11(5), 235; https://doi.org/10.3390/systems11050235 - 8 May 2023
Cited by 8 | Viewed by 1627
Abstract
This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased. The demand can be [...] Read more.
This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased. The demand can be boosted by lowering the selling price. However, lowering the selling price may not always give the best profit. Impreciseness plays a vital role during such decision-making. The decision-making and managerial activities may be imprecise due to some decision variables. For instance, the selling price may not be deterministic. A vague selling price will make the retail decision imprecise. To achieve this goal, the retailer must minimize impreciseness as much as possible. Learning through repetition may be a practical approach in this regard. This paper investigates the impact of fuzzy impreciseness and triangular dense fuzzy setting, which dilutes the impreciseness involved with managerial decisions. Based on the mentioned objectives, this article considers an inventory model with price-dependent demand and time and a purchasing cost-dependent holding cost in an uncertain phenomenon. This paper incorporates the all-units discount policy into the unit purchase cost according to the order quantity. In this paper, the sense of learning is accounted for using a dense fuzzy set by considering the unit selling price as a triangular dense fuzzy number to lessen the impreciseness in the model. Four fuzzy optimization methods are used to obtain the usual extreme profit when searching for the optimal purchasing cost and sale price. It is perceived from the numerical outcomes that a dense fuzzy environment contributes the best results compared to a crisp and general fuzzy environment. Managerial insights from this paper are that learning from repeated dealing activities contributes to enhancing profitability by diluting impreciseness about the selling price and demand rate and taking the best opportunity from the discount facility while purchasing. Full article
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