Yanpu Yang, Qiyuan Zuo, Kai Zhang, Xinran Li, Wenfeng Yu, Lijing Ji
The product design process, fraught with uncertainties and ambiguities in its requirements and constraints, commonly traverses multiple stages, each emphasizing distinct design aspects. This engenders heterogeneity in decision-making criteria, rendering the effective integration of information from various stages of product design decision-making (PDDM) a pivotal task in identifying the optimal design solution. Surprisingly, limited research has attended to the challenge of consolidating such heterogeneous information across multiple PDDM stages. To bridge this gap, our study employs real numbers, interval numbers, and linguistic terms to capture the heterogeneous judgments of decision-makers. We fuse the Maximization Deviation Method with the analytic hierarchy process (AHP) for determining indicators’ weights, while decision-makers’ weights are derived through a dual consideration of uncertainty measure using fuzzy entropy and a distance-minimization model applied to the PDDM matrix for achieving consistency. Leveraging the advantage of axiomatic design, product design alternatives are evaluated based on their PDDM information content of PDDM matrices. Given the multistage nature of product design, stages’ weights are computed by assessing the information content and consistency degree of PDDM matrices at each stage. Ultimately, our approach achieves multistage heterogeneous decision-making fusion in product design through information axiom weighting. A case study involving the decision-making process for a specific numerical control machine design illustrates the efficacy of our method in integrating multistage heterogeneous PDDM data, yielding a comprehensive perspective on the viability of product design schemes. Results show that the ranking sequence of the product design schemes solidifies to x3 > x2 > x1 in stages 2 and 3 of PDDM, diverging from the initial order observed in stage 1 (x2 > x3 > x1), while the fused result from the multistage heterogeneous PDDM analysis aligns with the later stages’ rankings, indicating the credibility and persuasiveness are fortified. This methodology thus offers a robust framework for synthesizing and navigating the uncertainties and complexities inherent in multistage heterogeneous PDDM contexts.
{"title":"Research on Multistage Heterogeneous Information Fusion of Product Design Decision-Making Based on Axiomatic Design","authors":"Yanpu Yang, Qiyuan Zuo, Kai Zhang, Xinran Li, Wenfeng Yu, Lijing Ji","doi":"10.3390/systems12060222","DOIUrl":"https://doi.org/10.3390/systems12060222","url":null,"abstract":"The product design process, fraught with uncertainties and ambiguities in its requirements and constraints, commonly traverses multiple stages, each emphasizing distinct design aspects. This engenders heterogeneity in decision-making criteria, rendering the effective integration of information from various stages of product design decision-making (PDDM) a pivotal task in identifying the optimal design solution. Surprisingly, limited research has attended to the challenge of consolidating such heterogeneous information across multiple PDDM stages. To bridge this gap, our study employs real numbers, interval numbers, and linguistic terms to capture the heterogeneous judgments of decision-makers. We fuse the Maximization Deviation Method with the analytic hierarchy process (AHP) for determining indicators’ weights, while decision-makers’ weights are derived through a dual consideration of uncertainty measure using fuzzy entropy and a distance-minimization model applied to the PDDM matrix for achieving consistency. Leveraging the advantage of axiomatic design, product design alternatives are evaluated based on their PDDM information content of PDDM matrices. Given the multistage nature of product design, stages’ weights are computed by assessing the information content and consistency degree of PDDM matrices at each stage. Ultimately, our approach achieves multistage heterogeneous decision-making fusion in product design through information axiom weighting. A case study involving the decision-making process for a specific numerical control machine design illustrates the efficacy of our method in integrating multistage heterogeneous PDDM data, yielding a comprehensive perspective on the viability of product design schemes. Results show that the ranking sequence of the product design schemes solidifies to x3 > x2 > x1 in stages 2 and 3 of PDDM, diverging from the initial order observed in stage 1 (x2 > x3 > x1), while the fused result from the multistage heterogeneous PDDM analysis aligns with the later stages’ rankings, indicating the credibility and persuasiveness are fortified. This methodology thus offers a robust framework for synthesizing and navigating the uncertainties and complexities inherent in multistage heterogeneous PDDM contexts.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141518023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In previous research on the development of the relationships between product attributes and customer satisfaction, the models did not adequately consider nonlinearity and the fuzzy emotions of customers in online reviews. Also, stable customer satisfaction was considered. However, customer satisfaction is changing with time rapidly, and a time-series analysis for customer satisfaction has not been conducted previously. To address these challenges, this study designed a novel methodology using adaptive neuro-fuzzy inference systems (ANFIS) in conjunction with Bi-objective particle swarm optimization (BOPSO) and sentiment analysis techniques. Sentiment analysis is employed to extract time-series customer satisfaction data from online reviews. Then, an ANFIS with the BOPSO method is proposed for the establishment of customer satisfaction models. In previous studies, ANFIS is an effective method to model customer satisfaction which can handle fuzziness and nonlinearity. However, when dealing with a large number of inputs, the modeling process may fail due to the complexity of the structure and the lengthy computational time required. Incorporating the BOPSO algorithm into ANFIS can identify the optimal inputs in ANFIS and effectively mitigate the inherent limitations of ANFIS. Using mobile phones as a case study, a comparison was performed between the proposed approach and another four approaches in modeling time-series customer satisfaction.
{"title":"An Intelligent Adaptive Neuro-Fuzzy Inference System for Modeling Time-Series Customer Satisfaction in Product Design","authors":"Huimin Jiang, Farzad Sabetzadeh, Chen Zhang","doi":"10.3390/systems12060224","DOIUrl":"https://doi.org/10.3390/systems12060224","url":null,"abstract":"In previous research on the development of the relationships between product attributes and customer satisfaction, the models did not adequately consider nonlinearity and the fuzzy emotions of customers in online reviews. Also, stable customer satisfaction was considered. However, customer satisfaction is changing with time rapidly, and a time-series analysis for customer satisfaction has not been conducted previously. To address these challenges, this study designed a novel methodology using adaptive neuro-fuzzy inference systems (ANFIS) in conjunction with Bi-objective particle swarm optimization (BOPSO) and sentiment analysis techniques. Sentiment analysis is employed to extract time-series customer satisfaction data from online reviews. Then, an ANFIS with the BOPSO method is proposed for the establishment of customer satisfaction models. In previous studies, ANFIS is an effective method to model customer satisfaction which can handle fuzziness and nonlinearity. However, when dealing with a large number of inputs, the modeling process may fail due to the complexity of the structure and the lengthy computational time required. Incorporating the BOPSO algorithm into ANFIS can identify the optimal inputs in ANFIS and effectively mitigate the inherent limitations of ANFIS. Using mobile phones as a case study, a comparison was performed between the proposed approach and another four approaches in modeling time-series customer satisfaction.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"230 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141518024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Global economic growth, marked by rising GDP and population, has spurred demand for essential goods including furniture. This study presents a comprehensive demand forecasting analysis for retail furniture sales in the U.S. for the next 36 months using Multiple Linear Regression (MLR) and Holt–Winters methods. Leveraging retail sales data from 2019 to 2023, alongside key influencing factors such as furniture imports, consumer sentiment, and housing starts, we developed two predictive models. The results indicated that retail furniture sales exhibited strong seasonality and a positive trend, with the lowest forecasted demand in April 2024 (USD 9118 million) and the highest in December 2026 (USD 13,577 million). The average annual demand for 2024, 2025, and 2026 is projected at USD 12,122.5 million, USD 12,522.67 million, and USD 12,922.17 million, respectively, based on MLR, while Holt–Winters results are slightly more conservative. The models were compared using the Mean Absolute Percentage Error (MAPE) metric, with the MLR model yielding a MAPE of 3.47% and the Holt–Winters model achieving a MAPE of 4.21%. The study’s findings align with global market projections and highlight the growing demand trajectory in the U.S. furniture industry, providing valuable insights for strategic decision-making and operations management.
{"title":"Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models","authors":"Melike Nur İnce, Çağatay Taşdemir","doi":"10.3390/systems12060219","DOIUrl":"https://doi.org/10.3390/systems12060219","url":null,"abstract":"Global economic growth, marked by rising GDP and population, has spurred demand for essential goods including furniture. This study presents a comprehensive demand forecasting analysis for retail furniture sales in the U.S. for the next 36 months using Multiple Linear Regression (MLR) and Holt–Winters methods. Leveraging retail sales data from 2019 to 2023, alongside key influencing factors such as furniture imports, consumer sentiment, and housing starts, we developed two predictive models. The results indicated that retail furniture sales exhibited strong seasonality and a positive trend, with the lowest forecasted demand in April 2024 (USD 9118 million) and the highest in December 2026 (USD 13,577 million). The average annual demand for 2024, 2025, and 2026 is projected at USD 12,122.5 million, USD 12,522.67 million, and USD 12,922.17 million, respectively, based on MLR, while Holt–Winters results are slightly more conservative. The models were compared using the Mean Absolute Percentage Error (MAPE) metric, with the MLR model yielding a MAPE of 3.47% and the Holt–Winters model achieving a MAPE of 4.21%. The study’s findings align with global market projections and highlight the growing demand trajectory in the U.S. furniture industry, providing valuable insights for strategic decision-making and operations management.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yingjie Ju, Hanping Hou, Jianliang Yang, Yuheng Ren, Jimei Yang
This paper delves into optimizing the rotation of relief supplies within the relief supply chain system, concentrating on reserve quantity decisions for governments and humanitarian organizations involved in disaster response. By integrating a trade-in strategy with suppliers, it ensures a precise and timely response to the fluctuating demand for relief supplies post-disaster. Utilizing the newsvendor model, optimization theory, and supply chain coordination principles, we developed a comprehensive model that calculates optimal reserve quantities for pre-positioning demanders. It also outlines the expected profit function for suppliers and a robust supply chain coordination model. The findings highlight that optimal stockpiling decisions for relief supplies are heavily influenced by cost parameters, material characteristics, and the relationship between trade-in pricing and market resale values. Notably, higher trade-in prices generally reduce the government’s optimal reserve quantities, impacting strategic decisions within supply chain coordination. This research adds to disaster management literature by offering strategic insights into how coordination and pricing strategies can improve disaster preparedness and response efficiency and effectiveness.
{"title":"Integrating Trade-In Strategies for Optimal Pre-Positioning Decisions in Relief Supply-Chain Systems","authors":"Yingjie Ju, Hanping Hou, Jianliang Yang, Yuheng Ren, Jimei Yang","doi":"10.3390/systems12060216","DOIUrl":"https://doi.org/10.3390/systems12060216","url":null,"abstract":"This paper delves into optimizing the rotation of relief supplies within the relief supply chain system, concentrating on reserve quantity decisions for governments and humanitarian organizations involved in disaster response. By integrating a trade-in strategy with suppliers, it ensures a precise and timely response to the fluctuating demand for relief supplies post-disaster. Utilizing the newsvendor model, optimization theory, and supply chain coordination principles, we developed a comprehensive model that calculates optimal reserve quantities for pre-positioning demanders. It also outlines the expected profit function for suppliers and a robust supply chain coordination model. The findings highlight that optimal stockpiling decisions for relief supplies are heavily influenced by cost parameters, material characteristics, and the relationship between trade-in pricing and market resale values. Notably, higher trade-in prices generally reduce the government’s optimal reserve quantities, impacting strategic decisions within supply chain coordination. This research adds to disaster management literature by offering strategic insights into how coordination and pricing strategies can improve disaster preparedness and response efficiency and effectiveness.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"21 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141532771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtual try-on services, which significantly reduce return rates and enhance user shopping experiences, pose a crucial question: how can user willingness to use these services be increased? Additionally, “Generation Z” consumers, known for their unique traits and significant consumption potential, have been relatively understudied in this context. To address this theoretical gap, this study employs the Stimulus–Organism–Response (S-O-R) theory as its research framework, using online virtual shoe try-on services as a case study. Focusing on “Generation Z” consumers, this study utilizes literature review, user research, factor analysis, and linear regression to establish a user experience evaluation scale and behavior model. The user experience evaluation scale identifies positive elements such as convenience, price value, visual information acquisition, emotional value, and social interaction. The negative elements include technical limitations, personalized service deficiencies, and uncertainty. In the behavior model, all elements influence user attitudes. Notably, only the negative elements directly affect users’ willingness to use the service, except social interaction among the positive elements, which positively impacts usage intention. This study also reveals that “Generation Z” consumers have novel requirements for social interaction. This research effectively addresses existing theoretical gaps and provides a foundational theory for the development of related services and technologies.
{"title":"A Study on the Willingness of “Generation Z” Consumers to Use Online Virtual Try-On Shopping Services Based on the S-O-R Framework","authors":"Zhicheng Wang, Qianling Jiang","doi":"10.3390/systems12060217","DOIUrl":"https://doi.org/10.3390/systems12060217","url":null,"abstract":"Virtual try-on services, which significantly reduce return rates and enhance user shopping experiences, pose a crucial question: how can user willingness to use these services be increased? Additionally, “Generation Z” consumers, known for their unique traits and significant consumption potential, have been relatively understudied in this context. To address this theoretical gap, this study employs the Stimulus–Organism–Response (S-O-R) theory as its research framework, using online virtual shoe try-on services as a case study. Focusing on “Generation Z” consumers, this study utilizes literature review, user research, factor analysis, and linear regression to establish a user experience evaluation scale and behavior model. The user experience evaluation scale identifies positive elements such as convenience, price value, visual information acquisition, emotional value, and social interaction. The negative elements include technical limitations, personalized service deficiencies, and uncertainty. In the behavior model, all elements influence user attitudes. Notably, only the negative elements directly affect users’ willingness to use the service, except social interaction among the positive elements, which positively impacts usage intention. This study also reveals that “Generation Z” consumers have novel requirements for social interaction. This research effectively addresses existing theoretical gaps and provides a foundational theory for the development of related services and technologies.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them.
{"title":"Modeling a Multimodal Routing Problem with Flexible Time Window in a Multi-Uncertainty Environment","authors":"Yan Ge, Yan Sun, Chen Zhang","doi":"10.3390/systems12060212","DOIUrl":"https://doi.org/10.3390/systems12060212","url":null,"abstract":"In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"8 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Grassroots communities in the 21st century take on the role of social innovators and contribute to addressing market failures and system failures through innovative action. The aim of this empirical study is to evaluate the various modes in which social innovations (products and services) arise in the conditions of community-led grassroots initiatives, to compare the patterns of social and economic value creation through these innovations and to elaborate the possibilities of their commercial exploitation. Drawing from data on 63 innovative products and service of 106 grassroots, taking the optics of grounded theory and adopting the approach of comparative analysis, this study sheds a light on the emergence of “pure”, “bi-focal”, and “market-exposed SI” products and services. Furthermore, the results suggest that it is possible to conceptualize the demand for community-led products and services. The majority of identified innovative products and services of grassroots generate a mix of social and economic outcomes that address both the social needs within one’s own community and needs of various stakeholders, or marginalized groups. The differentiation of SI into “pure”, “bi-focal”, and “market-exposed” was found to be relevant. Also, we identified a rationale for further elaboration of the evolutionary patterns of SI development, as we found that some “pure” SIs have the potential to be introduced to market within the later stages of the SI life-cycle. Some of the investigated products and service had to be commercially exploited due to a paradox—some social, public beneficial solutions need to be commercially exploited to be provided sustainably in the long term.
{"title":"The Evolution of Pure, Bi-Focal and Market-Exposed Social Innovations within Community-Based Systems","authors":"Michal Hrivnák, Peter Moritz","doi":"10.3390/systems12060196","DOIUrl":"https://doi.org/10.3390/systems12060196","url":null,"abstract":"Grassroots communities in the 21st century take on the role of social innovators and contribute to addressing market failures and system failures through innovative action. The aim of this empirical study is to evaluate the various modes in which social innovations (products and services) arise in the conditions of community-led grassroots initiatives, to compare the patterns of social and economic value creation through these innovations and to elaborate the possibilities of their commercial exploitation. Drawing from data on 63 innovative products and service of 106 grassroots, taking the optics of grounded theory and adopting the approach of comparative analysis, this study sheds a light on the emergence of “pure”, “bi-focal”, and “market-exposed SI” products and services. Furthermore, the results suggest that it is possible to conceptualize the demand for community-led products and services. The majority of identified innovative products and services of grassroots generate a mix of social and economic outcomes that address both the social needs within one’s own community and needs of various stakeholders, or marginalized groups. The differentiation of SI into “pure”, “bi-focal”, and “market-exposed” was found to be relevant. Also, we identified a rationale for further elaboration of the evolutionary patterns of SI development, as we found that some “pure” SIs have the potential to be introduced to market within the later stages of the SI life-cycle. Some of the investigated products and service had to be commercially exploited due to a paradox—some social, public beneficial solutions need to be commercially exploited to be provided sustainably in the long term.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"67 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In an era where technology increasingly blurs the boundaries between humans and machines, artifacts have become crucial mediums for critically examining the technological, social, and ethical dimensions of Human–Computer Interaction (HCI). This study explores artifacts as a key yet underutilized medium for speculation in the evolving field of HCI from a systemic perspective. While artifacts increasingly enable HCI to move beyond optimizing user experiences towards critically and collaboratively envisioning futures, perspectives comprehensively examining artifacts across the speculative design process and their impacts remain limited. Through a literature review of 53 speculative artifacts within the scope of HCI, this research elucidates the roles of artifacts across intention, making, and impact. Four categories of speculative artifacts emerged—Reflective, Exploratory, Interventional, and Heuristic—demonstrating how artifacts employ material, ambiguous, functional, and provocative forms to shape experiences, behaviors, and social norms. This study highlights the need for HCI to increasingly recognize the capacity of artifacts to support critical, sustained, participatory speculation by providing tangible representations of alternative futures. Speculative artifacts thus serve as powerful mediums to engage in societal discourse around the ethics and values of emerging technologies and to envision and enact responsible innovation. The materialization of alternative futures through artifacts allows researchers to reimagine socio-technological relationships, pushing design into inclusive, controversial spaces where diverse stakeholders can collaboratively shape desired and undesired futures.
{"title":"Exploring the Roles of Artifacts in Speculative Futures: Perspectives in HCI","authors":"Lin Zhu, Jiayue Wang, Jiawei Li","doi":"10.3390/systems12060194","DOIUrl":"https://doi.org/10.3390/systems12060194","url":null,"abstract":"In an era where technology increasingly blurs the boundaries between humans and machines, artifacts have become crucial mediums for critically examining the technological, social, and ethical dimensions of Human–Computer Interaction (HCI). This study explores artifacts as a key yet underutilized medium for speculation in the evolving field of HCI from a systemic perspective. While artifacts increasingly enable HCI to move beyond optimizing user experiences towards critically and collaboratively envisioning futures, perspectives comprehensively examining artifacts across the speculative design process and their impacts remain limited. Through a literature review of 53 speculative artifacts within the scope of HCI, this research elucidates the roles of artifacts across intention, making, and impact. Four categories of speculative artifacts emerged—Reflective, Exploratory, Interventional, and Heuristic—demonstrating how artifacts employ material, ambiguous, functional, and provocative forms to shape experiences, behaviors, and social norms. This study highlights the need for HCI to increasingly recognize the capacity of artifacts to support critical, sustained, participatory speculation by providing tangible representations of alternative futures. Speculative artifacts thus serve as powerful mediums to engage in societal discourse around the ethics and values of emerging technologies and to envision and enact responsible innovation. The materialization of alternative futures through artifacts allows researchers to reimagine socio-technological relationships, pushing design into inclusive, controversial spaces where diverse stakeholders can collaboratively shape desired and undesired futures.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"105 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fashion sector, recognized for its resource-intensive methods, is currently encountering pressing sustainability issues due to its substantial dependence on natural resources, extensive utilization of chemicals, and exploitation of labor within its supply chain, thus giving rise to notable environmental and ethical apprehensions. In the Industry 4.0 era, which emphasizes the integration of new production technologies to enhance working conditions, productivity, and production facility quality, the fashion sector has discovered opportunities to tackle sustainability challenges by adopting technology for transitioning to circular, greener, and digital systems with reduced environmental impact. Despite promising prospects, the opportunities provided by this paradigm are yet to be fully realized. In this context, design is crucial in enhancing digitally driven production processes for fashion companies within this framework. To explore this, the study suggests an iterative approach to recognizing challenges and opportunities, concentrating on the current alignment with technological advancements. A design-focused strategy aims to devise a comprehensive approach to fashion sustainability and circular economy (CE) principles within the Industry 4.0 framework. The aim is to establish an innovative laboratory model that aids fashion companies in effectively managing the sustainable and digital transition. The study offers insights into potential research opportunities to accelerate the Industry 4.0 transformation in the fashion sector. It envisages a more positive, sustainable, and responsible future by establishing a Circular Fashion-Tech Lab, integrating innovative technologies for sustainable and circular practices in the fashion industry.
{"title":"Fostering Directions for Digital Technology Adoption in Sustainable and Circular Fashion: Toward the Circular Fashion-Tech Lab","authors":"Daria Casciani, Erminia D’Itria","doi":"10.3390/systems12060190","DOIUrl":"https://doi.org/10.3390/systems12060190","url":null,"abstract":"The fashion sector, recognized for its resource-intensive methods, is currently encountering pressing sustainability issues due to its substantial dependence on natural resources, extensive utilization of chemicals, and exploitation of labor within its supply chain, thus giving rise to notable environmental and ethical apprehensions. In the Industry 4.0 era, which emphasizes the integration of new production technologies to enhance working conditions, productivity, and production facility quality, the fashion sector has discovered opportunities to tackle sustainability challenges by adopting technology for transitioning to circular, greener, and digital systems with reduced environmental impact. Despite promising prospects, the opportunities provided by this paradigm are yet to be fully realized. In this context, design is crucial in enhancing digitally driven production processes for fashion companies within this framework. To explore this, the study suggests an iterative approach to recognizing challenges and opportunities, concentrating on the current alignment with technological advancements. A design-focused strategy aims to devise a comprehensive approach to fashion sustainability and circular economy (CE) principles within the Industry 4.0 framework. The aim is to establish an innovative laboratory model that aids fashion companies in effectively managing the sustainable and digital transition. The study offers insights into potential research opportunities to accelerate the Industry 4.0 transformation in the fashion sector. It envisages a more positive, sustainable, and responsible future by establishing a Circular Fashion-Tech Lab, integrating innovative technologies for sustainable and circular practices in the fashion industry.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"83 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.
{"title":"Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced","authors":"Bixi Wang, Wenfeng Zheng, Ruiyang Wang, Siyu Lu, Lirong Yin, Lei Wang, Zhengtong Yin, Xinbing Chen","doi":"10.3390/systems12060188","DOIUrl":"https://doi.org/10.3390/systems12060188","url":null,"abstract":"With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"47 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141152932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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