Real economy has always been a crucial component of China’s economic development, while fictitious economy has experienced rapid growth in past decades. As a result, the connection between the real and fictitious economy has become increasingly complex. This study utilized a hierarchical framework for classifying real economy and conducted a hidden causality test and EEMD method to explore a causal relationship between markets. Monthly data from July 2001 to September 2022 were analyzed using a TVP-SV-VAR model to investigate dynamic relationships among the manufacturing, construction, real estate, and financial industries as well as the mechanisms between the real and fictitious economies. The study outcomes demonstrated that the financial and real estate industries have only short-term positive effects on the manufacturing and construction industries, and in the later period of sample intervals, both industries had negative effects on the construction industry. The construction industry in the real economy has already shown a trend of moving “from Real to Virtual”, while the core manufacturing industry in the real economy has not yet exhibited this trend. To prevent the spread of this trend in the real economy, it is necessary to guide the fictitious economy to serve the real economy by regulating its development appropriately. This study offers a novel perspective for examining the real economy and the fictitious economy in China.
{"title":"Exploring the Dynamic Impact between the Industries in China: New Perspective Based on Pattern Causality and Time-Varying Effect","authors":"Hongming Li, Jiahui Li, Yuanying Jiang","doi":"10.3390/systems11070318","DOIUrl":"https://doi.org/10.3390/systems11070318","url":null,"abstract":"Real economy has always been a crucial component of China’s economic development, while fictitious economy has experienced rapid growth in past decades. As a result, the connection between the real and fictitious economy has become increasingly complex. This study utilized a hierarchical framework for classifying real economy and conducted a hidden causality test and EEMD method to explore a causal relationship between markets. Monthly data from July 2001 to September 2022 were analyzed using a TVP-SV-VAR model to investigate dynamic relationships among the manufacturing, construction, real estate, and financial industries as well as the mechanisms between the real and fictitious economies. The study outcomes demonstrated that the financial and real estate industries have only short-term positive effects on the manufacturing and construction industries, and in the later period of sample intervals, both industries had negative effects on the construction industry. The construction industry in the real economy has already shown a trend of moving “from Real to Virtual”, while the core manufacturing industry in the real economy has not yet exhibited this trend. To prevent the spread of this trend in the real economy, it is necessary to guide the fictitious economy to serve the real economy by regulating its development appropriately. This study offers a novel perspective for examining the real economy and the fictitious economy in China.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74444351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
China’s public cultural service system transitioned from a centrally controlled model to a more complex one due to the gradual introduction of market forces. This change brought new challenges and opportunities, making the role of market forces a practical concern. By analyzing data from 282 public and 153 private children’s libraries in China, this study investigates how market forces compensate for the government’s capacity limitations in constructing public cultural service systems. Results show that market factors within the scope of our study do not negatively impact the system but instead promote synergy between government and market entities to meet children’s cultural needs. It is essential not to sever the role of the market from its interdependent relationship with the government, as this stance is based on unrealistic assessments of how policies function in practice, potentially leading to inadequate public cultural services. This study provides novel empirical evidence from China by confirming the interdependent relationship between the market and the government in constructing public cultural service systems and highlights the significance of applying complexity thinking. Overall, understanding the complexity of the role of market forces is essential for the construction of a robust and inclusive public cultural service system.
{"title":"Understanding Complexity in the Role of Market Forces in the Construction of a Public Cultural Service System: Evidence from 435 Children's Libraries in China","authors":"Jinlong Lin, Zhengxin Zhao, Xiaoxiao Chen","doi":"10.3390/systems11070317","DOIUrl":"https://doi.org/10.3390/systems11070317","url":null,"abstract":"China’s public cultural service system transitioned from a centrally controlled model to a more complex one due to the gradual introduction of market forces. This change brought new challenges and opportunities, making the role of market forces a practical concern. By analyzing data from 282 public and 153 private children’s libraries in China, this study investigates how market forces compensate for the government’s capacity limitations in constructing public cultural service systems. Results show that market factors within the scope of our study do not negatively impact the system but instead promote synergy between government and market entities to meet children’s cultural needs. It is essential not to sever the role of the market from its interdependent relationship with the government, as this stance is based on unrealistic assessments of how policies function in practice, potentially leading to inadequate public cultural services. This study provides novel empirical evidence from China by confirming the interdependent relationship between the market and the government in constructing public cultural service systems and highlights the significance of applying complexity thinking. Overall, understanding the complexity of the role of market forces is essential for the construction of a robust and inclusive public cultural service system.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81290428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Logical and Activity Learning for Enhanced Thinking Skills (LAL) method is an educational approach that fosters the development of critical thinking, problem-solving, and decision-making abilities in students using practical, experiential learning activities. Although LAL has demonstrated favorable effects on children’s cognitive growth, it presents various obstacles, including the requirement for tailored instruction and the complexity of tracking advancement. The present study presents a model known as the Deep Neural Networks-based Logical and Activity Learning Model (DNN-LALM) as a potential solution to tackle the challenges above. The DNN-LALM employs sophisticated machine learning methodologies to offer tailored instruction and assessment tracking, and enhanced proficiency in cognitive and task-oriented activities. The model under consideration has been assessed using a dataset comprising cognitive assessments of children. The findings indicate noteworthy enhancements in accuracy, precision, and recall. The model above attained a 93% accuracy rate in detecting logical patterns and an 87% precision rate in forecasting activity outcomes. The findings of this study indicate that the implementation of DNN-LALM can augment the efficacy of LAL in fostering cognitive growth, thereby facilitating improved monitoring of children’s advancement by educators and parents. The model under consideration can transform the approach toward LAL in educational environments, facilitating more individualized and efficacious learning opportunities for children.
{"title":"Exploring the Computational Effects of Advanced Deep Neural Networks on Logical and Activity Learning for Enhanced Thinking Skills","authors":"Deming Li, Kellyt D. Ortegas, Marvin White","doi":"10.3390/systems11070319","DOIUrl":"https://doi.org/10.3390/systems11070319","url":null,"abstract":"The Logical and Activity Learning for Enhanced Thinking Skills (LAL) method is an educational approach that fosters the development of critical thinking, problem-solving, and decision-making abilities in students using practical, experiential learning activities. Although LAL has demonstrated favorable effects on children’s cognitive growth, it presents various obstacles, including the requirement for tailored instruction and the complexity of tracking advancement. The present study presents a model known as the Deep Neural Networks-based Logical and Activity Learning Model (DNN-LALM) as a potential solution to tackle the challenges above. The DNN-LALM employs sophisticated machine learning methodologies to offer tailored instruction and assessment tracking, and enhanced proficiency in cognitive and task-oriented activities. The model under consideration has been assessed using a dataset comprising cognitive assessments of children. The findings indicate noteworthy enhancements in accuracy, precision, and recall. The model above attained a 93% accuracy rate in detecting logical patterns and an 87% precision rate in forecasting activity outcomes. The findings of this study indicate that the implementation of DNN-LALM can augment the efficacy of LAL in fostering cognitive growth, thereby facilitating improved monitoring of children’s advancement by educators and parents. The model under consideration can transform the approach toward LAL in educational environments, facilitating more individualized and efficacious learning opportunities for children.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77777918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rima H. Binsaeed, Zahid Yousaf, A. Grigorescu, Alina Samoila, R. Chițescu, A. Nassani
In the current digital era, digital technologies develop and emerge rapidly, businesses, especially the electronic sector more connected to information technology, facing challenges in the terms of its technology infrastructure and tactical directions. That’s why most of them adopt the latest digital technology (DT) and design novel business strategies and models. The growing significance of AI in the transformation of manufacturing operations and the demand for a thorough knowledge of the variables affecting its adoption serve as the driving forces behind the study. Several researchers have presented that digital technology can lead toward AI adoption. Though, previous studies lack an efficient transformation pathway. Therefore, this study establishes an inventive approach and aims to investigate the direct link between digital technology and AI adoption, the mediating function of knowledge sharing (KS) between them, and explore the moderating impact of privacy and security that assist in the acceleration of AI adoption in electronics manufacturing enterprises through the antecedent of digital technology. This study is quantitative in nature, random sampling method and questionnaire is used as a survey tool. Depending on 298 questionnaire data from electronic firms of Saudi Arabia, this study performs multi-level correlation and regression analysis to evaluate study hypotheses. Findings confirm that digital technology has a positive influence on AI adoption. In addition, outcomes corroborate that knowledge sharing mediates in the linkage between digital technology and AI adoption. The results also proved that privacy and security have a positive moderation impact on the association between digital technology and AI adoption. This study enlighten that the adoption of this framework enables electronic manufacturing companies to strategically integrate digital-technologies to promote effective AI adoption, increase its operational efficiency, and sustain a competitive advantage in the constantly evolving manufacturing landscape. The outcomes as well supplement the previous study on the linkage between digital technology and AI adoption, expand application space and theoretical boundary from the perspective of knowledge sharing, privacy and security at the managerial level, and give reference for AI adoption in, as electronics manufacturing firms.
{"title":"Knowledge Sharing Key Issue for Digital Technology and Artificial Intelligence Adoption","authors":"Rima H. Binsaeed, Zahid Yousaf, A. Grigorescu, Alina Samoila, R. Chițescu, A. Nassani","doi":"10.3390/systems11070316","DOIUrl":"https://doi.org/10.3390/systems11070316","url":null,"abstract":"In the current digital era, digital technologies develop and emerge rapidly, businesses, especially the electronic sector more connected to information technology, facing challenges in the terms of its technology infrastructure and tactical directions. That’s why most of them adopt the latest digital technology (DT) and design novel business strategies and models. The growing significance of AI in the transformation of manufacturing operations and the demand for a thorough knowledge of the variables affecting its adoption serve as the driving forces behind the study. Several researchers have presented that digital technology can lead toward AI adoption. Though, previous studies lack an efficient transformation pathway. Therefore, this study establishes an inventive approach and aims to investigate the direct link between digital technology and AI adoption, the mediating function of knowledge sharing (KS) between them, and explore the moderating impact of privacy and security that assist in the acceleration of AI adoption in electronics manufacturing enterprises through the antecedent of digital technology. This study is quantitative in nature, random sampling method and questionnaire is used as a survey tool. Depending on 298 questionnaire data from electronic firms of Saudi Arabia, this study performs multi-level correlation and regression analysis to evaluate study hypotheses. Findings confirm that digital technology has a positive influence on AI adoption. In addition, outcomes corroborate that knowledge sharing mediates in the linkage between digital technology and AI adoption. The results also proved that privacy and security have a positive moderation impact on the association between digital technology and AI adoption. This study enlighten that the adoption of this framework enables electronic manufacturing companies to strategically integrate digital-technologies to promote effective AI adoption, increase its operational efficiency, and sustain a competitive advantage in the constantly evolving manufacturing landscape. The outcomes as well supplement the previous study on the linkage between digital technology and AI adoption, expand application space and theoretical boundary from the perspective of knowledge sharing, privacy and security at the managerial level, and give reference for AI adoption in, as electronics manufacturing firms.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76844676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hung-Chia Yang, Ling Jin, A. Lazar, Annika Todd-Blick, A. Sim, K. Wu, Qianmiao Chen, C. Spurlock
Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention.
{"title":"Gender Gaps in Mode Usage, Vehicle Ownership, and Spatial Mobility When Entering Parenthood: A Life Course Perspective","authors":"Hung-Chia Yang, Ling Jin, A. Lazar, Annika Todd-Blick, A. Sim, K. Wu, Qianmiao Chen, C. Spurlock","doi":"10.3390/systems11060314","DOIUrl":"https://doi.org/10.3390/systems11060314","url":null,"abstract":"Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78292238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Research suggests that unpleasant emotions induced by feedback may reduce its efficiency in enhancing students’ performance, which is a crucial issue to address in education. In the context of Chinese language instruction in higher education, this study sought to investigate how students regulate their emotions as a result of feedback through the lens of individuals’ feedback orientation. In light of the feedback orientation lens and its conceptual framework, we applied in-depth qualitative interviews to explore how students experienced feedback, the negative emotions they experienced, and the emotion regulation strategies they used. Eleven undergraduates across years one to five joined our in-depth interviews. Students reported negative emotions when they received feedback that did not live up to their expectations or was unrealistic for them to accept. However, students’ feedback orientation supported their emotion regulation techniques, which in turn supported students’ adaptive feedback processing to interpret and take action to use feedback for academic performance improvement. Students also actively sought further teacher feedback or peer support to deal with a wide range of negative emotions. These findings imply the significance of fostering in students a high level of feedback orientation and the necessity of additional empirical investigation into the relationships between feedback orientation and emotional well-being in higher education. By shedding light on how students regulate the emotions that external feedback causes in them, the study adds valuable qualitative findings to the existing literature on positive psychology research in terms of emotions and emotion regulation. It also emphasizes how crucial students’ personal feedback orientation is for improving emotional well-being in the context of feedback.
{"title":"Unpacking the Complexities of Emotional Responses to External Feedback, Internal Feedback Orientation and Emotion Regulation in Higher Education: A Qualitative Exploration","authors":"Lana T. Yang, Yiqi Wu, Yuan Liang, Min Yang","doi":"10.3390/systems11060315","DOIUrl":"https://doi.org/10.3390/systems11060315","url":null,"abstract":"Research suggests that unpleasant emotions induced by feedback may reduce its efficiency in enhancing students’ performance, which is a crucial issue to address in education. In the context of Chinese language instruction in higher education, this study sought to investigate how students regulate their emotions as a result of feedback through the lens of individuals’ feedback orientation. In light of the feedback orientation lens and its conceptual framework, we applied in-depth qualitative interviews to explore how students experienced feedback, the negative emotions they experienced, and the emotion regulation strategies they used. Eleven undergraduates across years one to five joined our in-depth interviews. Students reported negative emotions when they received feedback that did not live up to their expectations or was unrealistic for them to accept. However, students’ feedback orientation supported their emotion regulation techniques, which in turn supported students’ adaptive feedback processing to interpret and take action to use feedback for academic performance improvement. Students also actively sought further teacher feedback or peer support to deal with a wide range of negative emotions. These findings imply the significance of fostering in students a high level of feedback orientation and the necessity of additional empirical investigation into the relationships between feedback orientation and emotional well-being in higher education. By shedding light on how students regulate the emotions that external feedback causes in them, the study adds valuable qualitative findings to the existing literature on positive psychology research in terms of emotions and emotion regulation. It also emphasizes how crucial students’ personal feedback orientation is for improving emotional well-being in the context of feedback.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87863467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of the internet economy, many manufacturers have opened online direct sales channels and built multi-channel distribution systems. Meanwhile, both consumers and companies are paying more attention to extended warranty services. Considering a dual-channel supply chain with a manufacturer and a retailer, we assume the manufacturer provides an extended warranty in the online direct channel and investigates the decision making of the supply chain players. We develop three game models to study this problem, and they are the basic model without extended warranty (Model B), the decentralized decision model with the manufacturer’s extended warranty (Model M), and the centralized decision model with the manufacturer’s extended warranty (Model C). The Stackelberg game method is used to solve the established model, the influence of relevant parameters on the solution result is analyzed, and different models are compared. Compared with Model B, we find that the whole supply chain always be better, but the retailer would be worse in Model M. Compared with Model M, we find that the entire supply chain always performs better in Model C. Finally, we do some sensitivity analysis.
{"title":"Pricing Decision of the Dual-Channel Supply Chain with the Manufacturer's Extended Warranty","authors":"Chenbo Zhu, Jiwei Liang, Yaqian Liu","doi":"10.3390/systems11060313","DOIUrl":"https://doi.org/10.3390/systems11060313","url":null,"abstract":"With the rapid development of the internet economy, many manufacturers have opened online direct sales channels and built multi-channel distribution systems. Meanwhile, both consumers and companies are paying more attention to extended warranty services. Considering a dual-channel supply chain with a manufacturer and a retailer, we assume the manufacturer provides an extended warranty in the online direct channel and investigates the decision making of the supply chain players. We develop three game models to study this problem, and they are the basic model without extended warranty (Model B), the decentralized decision model with the manufacturer’s extended warranty (Model M), and the centralized decision model with the manufacturer’s extended warranty (Model C). The Stackelberg game method is used to solve the established model, the influence of relevant parameters on the solution result is analyzed, and different models are compared. Compared with Model B, we find that the whole supply chain always be better, but the retailer would be worse in Model M. Compared with Model M, we find that the entire supply chain always performs better in Model C. Finally, we do some sensitivity analysis.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89077710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanjun Chen, Hongwei Liu, Zhanming Wen, Weizhen Lin
This study proposes a random forest model to address the limited explanation of consumer purchase behavior in search advertising, considering the influence of anchoring effects on rational consumer behavior. The model comprises two components: prediction and explanation. The prediction part employs various algorithms, including logistic regression (LR), adaptive boosting (ADA), extreme gradient boosting (XGB), multilayer perceptron (MLP), naive bayes (NB), and random forest (RF), for optimal prediction. The explanation part utilizes the SHAP explainable framework to identify significant indicators and reveal key factors influencing consumer purchase behavior and their relative importance. Our results show that (1) the explainable machine learning model based on the random forest algorithm performed optimally (F1 = 0.8586), making it suitable for analyzing and predicting consumer purchase behavior. (2) The dimension of product information is the most crucial attribute influencing consumer purchase behavior, with features such as sales level, display priority, granularity, and price significantly influencing consumer perceptions. These attributes can be considered by merchants to develop appropriate tactics for improving the user experience. (3) Consumers’ purchase intentions vary based on the presented anchor point. Specifically, high anchor information related to product quality ratings increases the likelihood of purchase, while price anchors prompted consumers to compare similar products and opt for the most economical option. Our findings provide guidance for optimizing marketing strategies and improving user experience while also contributing to a deeper understanding of the decision−making mechanisms and pathways in online consumer purchase behavior.
{"title":"How Explainable Machine Learning Enhances Intelligence in Explaining Consumer Purchase Behavior: A Random Forest Model with Anchoring Effects","authors":"Yanjun Chen, Hongwei Liu, Zhanming Wen, Weizhen Lin","doi":"10.3390/systems11060312","DOIUrl":"https://doi.org/10.3390/systems11060312","url":null,"abstract":"This study proposes a random forest model to address the limited explanation of consumer purchase behavior in search advertising, considering the influence of anchoring effects on rational consumer behavior. The model comprises two components: prediction and explanation. The prediction part employs various algorithms, including logistic regression (LR), adaptive boosting (ADA), extreme gradient boosting (XGB), multilayer perceptron (MLP), naive bayes (NB), and random forest (RF), for optimal prediction. The explanation part utilizes the SHAP explainable framework to identify significant indicators and reveal key factors influencing consumer purchase behavior and their relative importance. Our results show that (1) the explainable machine learning model based on the random forest algorithm performed optimally (F1 = 0.8586), making it suitable for analyzing and predicting consumer purchase behavior. (2) The dimension of product information is the most crucial attribute influencing consumer purchase behavior, with features such as sales level, display priority, granularity, and price significantly influencing consumer perceptions. These attributes can be considered by merchants to develop appropriate tactics for improving the user experience. (3) Consumers’ purchase intentions vary based on the presented anchor point. Specifically, high anchor information related to product quality ratings increases the likelihood of purchase, while price anchors prompted consumers to compare similar products and opt for the most economical option. Our findings provide guidance for optimizing marketing strategies and improving user experience while also contributing to a deeper understanding of the decision−making mechanisms and pathways in online consumer purchase behavior.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85978443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daifeng Li, Xin Li, Fengyun Gu, Ziyang Pan, Dingquan Chen, Andrew D. Madden
Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error).
销售预测是时间序列预测的一个非常实际的应用。它是用来帮助企业识别和利用信息，以降低成本和利润最大化。例如，在众多的制造企业中，销售预测是库存优化的关键指标，直接影响到成本节约的水平。然而，现有的研究方法主要集中在多变量时间序列(MTS)的序列和局部相关性检测上，很少考虑多变量时间序列中不同时间序列之间的差异性信息建模。销售时间序列的预测精度受到动态和复杂环境的显著影响，因此识别销售MTS中不同时间序列之间的差异性信号就显得尤为重要。为了从销售序列中提取更多有价值的信息，提高销售预测的准确性，我们设计了一个可以预测未来多步销售的通用区分机制(UDM)框架。普遍性是指在相似情境下销售序列的本能特征和相关模式。区别对应于由于复杂或未观察到的影响因素而导致的特定时间序列的波动。在机制上，提出了一种基于查询稀疏度度量(query-sparsity measurement, QSM)的注意力计算方法，以提高所提模型处理大规模销售MTS的效率。此外，为了改善库存优化的具体决策场景，保证多步预测的稳定精度，我们使用了Pin-DTW (Pinball loss and Dynamic Time Warping)联合损失函数。通过在菜鸟公共数据集上的实验，以及我们与格兰仕的合作，我们能够证明该模型的有效性和实用价值。与最佳基线相比，格兰仕数据集的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和均方根误差(RMSE)分别提高了57.27%、50.68%和35.26%，菜鸟数据集的平均绝对误差(MAPE)和均方根误差(RMSE)分别提高了16.58%、6.07%和5.27%。
{"title":"A Universality-Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization","authors":"Daifeng Li, Xin Li, Fengyun Gu, Ziyang Pan, Dingquan Chen, Andrew D. Madden","doi":"10.3390/systems11060311","DOIUrl":"https://doi.org/10.3390/systems11060311","url":null,"abstract":"Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error).","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78685095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advent of the era of big data, the application of big data analytics in entrepreneurial activities has become increasingly prevalent. However, research on the relationship between big data analytic capabilities and entrepreneurial activities is still in its infancy, and the mechanism by which the two interact remains unclear. Drawing on resource-based theory and entrepreneurial process theory, this research examines the impact mechanism of big data analytic capabilities on the growth performance of start-up enterprises and explores the mediating role of entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation. Empirical analysis reveals that big data analytic capabilities have a significant positive impact on the growth performance of start-up enterprises; entrepreneurial opportunity exploitation plays a mediating role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises, but entrepreneurial opportunity recognition does not show a significant mediating effect between the two; and entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation play a chain-mediated role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises. These research findings enrich the study of digital entrepreneurship and provide valuable references for the entrepreneurial practice of start-up enterprises.
{"title":"Research on the Mechanism of the Role of Big Data Analytic Capabilities on the Growth Performance of Start-Up Enterprises: The Mediating Role of Entrepreneurial Opportunity Recognition and Exploitation","authors":"Xinqiang Chen, Weijun Chen, Jiangjie Chen","doi":"10.3390/systems11060310","DOIUrl":"https://doi.org/10.3390/systems11060310","url":null,"abstract":"With the advent of the era of big data, the application of big data analytics in entrepreneurial activities has become increasingly prevalent. However, research on the relationship between big data analytic capabilities and entrepreneurial activities is still in its infancy, and the mechanism by which the two interact remains unclear. Drawing on resource-based theory and entrepreneurial process theory, this research examines the impact mechanism of big data analytic capabilities on the growth performance of start-up enterprises and explores the mediating role of entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation. Empirical analysis reveals that big data analytic capabilities have a significant positive impact on the growth performance of start-up enterprises; entrepreneurial opportunity exploitation plays a mediating role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises, but entrepreneurial opportunity recognition does not show a significant mediating effect between the two; and entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation play a chain-mediated role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises. These research findings enrich the study of digital entrepreneurship and provide valuable references for the entrepreneurial practice of start-up enterprises.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74449363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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