This study examines the effects of e-banking service quality on customer satisfaction in the Commercial Bank of Ethiopia(CBE)branches in Wolaita Sodo town.Using a causal research design,the study explored the cause-an...This study examines the effects of e-banking service quality on customer satisfaction in the Commercial Bank of Ethiopia(CBE)branches in Wolaita Sodo town.Using a causal research design,the study explored the cause-and-effect relationship between service quality dimensions and customer satisfaction.A sample of 385 customers was selected using convenience sampling,with 365 questionnaires returned.Data were collected through questionnaires and analyzed using SPSS V.21.The Cronbach’s alpha value of 0.72 from a pilot study confirmed reliability.Descriptive and inferential statistics,including multiple linear regression and one-way ANOVA,were employed.Results revealed that three service quality dimensions-responsiveness,reliability,and assurance-were statistically significant and positively influenced customer satisfaction,while two dimensions showed negative associations.The regression model’s coefficient of determination(R²)was 0.621,indicating a moderate explanatory power.Findings suggest that CBE managers and stakeholders should prioritize improving responsiveness,reliability,and assurance by providing prompt,dependable,and trustworthy services.Due to limitations in time and resources,this study was confined to CBE branches in Wolaita Sodo town;future research could expand to a national level or other service sectors.展开更多
This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn...This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn in the face of digital disruption.The research uses a quantitative approach focusing on empirical customer data to accurately predict buying trends and adapt marketing techniques.Demand forecasts in the health sector are important,as in every sector.In particular,the material forecast and stock forecasting of the purchasing unit of hospitals are among the areas that receive significant attention.Four classifiers(Random Forest,Logistic Regression,Gradient Boosting and XGBoost)are trained and evaluated using various performance indicators as part of a systematic approach involving Kaggle data collection,preparation and model selection.The results show excellent accuracy in predicting customer attrition,but there are limitations in precision and recall,indicating room for improvement.Confusion matrices provide information about the performance of each classifier,allowing for continuous improvement of predictive analytics techniques.Ethical concerns are rigorously addressed throughout the work process to guarantee appropriate data and machine learning methodologies.The proposals emphasize the proactive use of predictive analytics to identify at-risk customers and implement targeted retention strategies.Incorporating new data sources,improving customer experience,and utilizing collaborative churn management methods are recommended to increase forecast accuracy and business outcomes.Finally,this research provides important insights into the usefulness of predictive analytics for customer churn forecasting as well as practical recommendations for businesses seeking to increase customer retention and reduce churn risk.By leveraging empirical research findings and implementing ethical and rigorous churn control strategies,businesses can achieve long-term success in today’s changing market environment.展开更多
The jewelry industry faces intense competition,making customer loyalty essential for sustained success.This paper examines customer loyalty through the lens of the ABC attitude model,which encompasses cognitive,affect...The jewelry industry faces intense competition,making customer loyalty essential for sustained success.This paper examines customer loyalty through the lens of the ABC attitude model,which encompasses cognitive,affective,and behavioral dimensions.Cognitive factors,such as perceived quality and brand reputation,establish the foundation of trust,while affective factors,including emotional attachment and trust,strengthen customer relationships.Behavioral factors,such as repeat purchases and advocacy,reflect observable loyalty actions.The study proposes strategies to enhance loyalty,including delivering superior products and services,strengthening customer relationship management,and leveraging word-of-mouth and digital marketing.These approaches provide actionable insights for building long-term customer relationships in a competitive market.Future research could explore emerging technologies and cultural influences to further refine loyalty strategies.This research highlights the multidimensional nature of customer loyalty and offers practical recommendations for jewelry enterprises.展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
The purpose of this research is to examine the impact of artificial intelligence(AI)on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach,specifically ...The purpose of this research is to examine the impact of artificial intelligence(AI)on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach,specifically the partial least squares method,to test the hypotheses and explore the relationships between various variables.The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance.Additionally,the results of this study provide valuable insights for both academic and practical communities.This study highlights the importance of specific variables,such as organizational and customer agility,customer experience,customer relationship quality,and customer performance in AI assimilation.By exploring these variables,it contributes significantly to the academic,managerial,and social aspects of AI and its impact on customer performance.展开更多
In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to...In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.展开更多
Even though people approach a new technological development with distance, they have to use these technologies in order to keep up with the requirements of the age. As a result of the rapidly developing artificial int...Even though people approach a new technological development with distance, they have to use these technologies in order to keep up with the requirements of the age. As a result of the rapidly developing artificial intelligence and technology sector, the concept of metaverse is a virtual world that can be used in the health, tourism, marketing, advertising, education and gaming industries. When the literature is reviewed, it is seen that there are many domestic and foreign sources on the subject, but most of these sources are generally related to developments in the gaming and entertainment sectors. But apart from this, studies are being carried out to make it possible to use it in many sectors, including its use in the health sector. With this study, it is aimed to briefly mention what the existing metaverse studies are and to prepare a detailed research result for its use in the health sector. This study consists of five chapters. In the first part, information on the definition of the metaverse concept and how it has developed is given, and the infrastructure requirements necessary for the formation of the metaverse universe are mentioned. In the second section, it is discussed what kind of hardware and software tools may be needed if metaverse technology is possible. In the third section, research on the areas in which metaverse technology can be used in the health sector and current studies and future studies are included. In the fourth section, the legal aspects of this situation when a metaverse-based health service is to be initiated with twin avatars are discussed. In the last section, the advantages and disadvantages of metaverse technology are discussed. It can be seen from the study that the positive aspects of this kind of activity in the field of health will be more positive. As a result, the study constitutes an important value that will be a source for similar studies to be conducted in the future.展开更多
The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends ...The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends on a deep understanding of customer behavior. This study proposes a theoretical framework for multi-dimensional customer behavior analysis, aiming to comprehensively capture customer behavioral characteristics in the digital environment. This framework integrates concepts of multi-source data including transaction history, browsing trajectories, social media interactions, and location information, constructing a theoretically more comprehensive customer profile. The research discusses the potential applications of this theoretical framework in precision marketing scenarios such as personalized recommendations, cross-selling, and customer churn prevention. Through analysis, the study points out that multi-dimensional analysis may significantly improve the targeting and theoretical conversion rates of marketing activities. However, the research also explores theoretical challenges that may be faced in the application process, such as data privacy and information overload, and proposes corresponding conceptual coping strategies. This study provides a new theoretical perspective on how businesses can optimize marketing decisions using big data thinking while respecting customer privacy, laying a foundation for future empirical research.展开更多
Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected o...Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.展开更多
To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred ...To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.展开更多
Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and,after that,end their connection with the bank.Therefore,customer retention is es...Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and,after that,end their connection with the bank.Therefore,customer retention is essential in today’s extremely competitive banking market.Additionally,having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele.These factors make reducing client attrition a crucial step that banks must pursue.In our research,we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers.We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics.In addition,we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis.Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.展开更多
To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers ...To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers to represent the importance of each demand.Then,the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator,yielding a dynamic vector of the subjective importance of the demand index.Finally,the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.展开更多
The Italian textile machinery sector,renowned for its technological excellence and innovative capacity,continues to navigate a complex global market with a strategic emphasis on digitalization,sustainability,and stron...The Italian textile machinery sector,renowned for its technological excellence and innovative capacity,continues to navigate a complex global market with a strategic emphasis on digitalization,sustainability,and strong customer partnerships.Marco Salvade’,President of ACIMIT,provided insights into the industry’s performance,key trends,and future directions.In the first quarter of 2025,Italian textile machinery exports saw a 6%decrease compared to the same period in 2024,totaling€363 million.This dip reflects ongoing geopolitical tensions and a cautious approach among global clients toward new investments.Despite these challenges,Italian manufacturers maintain a strong reputation for technological leadership and resilience.展开更多
The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health ...The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.展开更多
Starting from the issues related to the construction and management of customs supervision places for railway and water transportation,this paper systematically analyzes the problems and challenges in the facility lay...Starting from the issues related to the construction and management of customs supervision places for railway and water transportation,this paper systematically analyzes the problems and challenges in the facility layout of customs operation places,the construction of a public platform for customs and port logistics monitoring,the utilization of information and data,and the intensity of technological innovation.Based on in-depth research,this paper attempts to propose improvement strategies and suggestions in terms of scientifically planning the layout of customs supervision places for railway and water transportation,improving the operation of the logistics monitoring public platform,developing and utilizing the information and data system,building a smart customs,and innovating the supervision mode.This aims to further optimize the customs supervision process,improve supervision efficiency and accuracy,and provide a reference for the facilitation and safe development of international trade.展开更多
Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current so...Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current solutions face numerous challenges in continuously ensuring trustworthy routing,fulfilling diverse requirements,achieving reasonable resource allocation,and safeguarding against malicious behaviors of network operators.We propose CrowdRouting,a novel cross-domain routing scheme based on crowdsourcing,dedicated to establishing sustained trust in cross-domain routing,comprehensively considering and fulfilling various customized routing requirements,while ensuring reasonable resource allocation and effectively curbing malicious behavior of network operators.Concretely,CrowdRouting employs blockchain technology to verify the trustworthiness of border routers in different network domains,thereby establishing sustainable and trustworthy crossdomain routing based on sustained trust in these routers.In addition,CrowdRouting ingeniously integrates a crowdsourcing mechanism into the auction for routing,achieving fair and impartial allocation of routing rights by flexibly embedding various customized routing requirements into each auction phase.Moreover,CrowdRouting leverages incentive mechanisms and routing settlement to encourage network domains to actively participate in cross-domain routing,thereby promoting optimal resource allocation and efficient utilization.Furthermore,CrowdRouting introduces a supervisory agency(e.g.,undercover agent)to effectively suppress the malicious behavior of network operators through the game and interaction between the agent and the network operators.Through comprehensive experimental evaluations and comparisons with existing works,we demonstrate that CrowdRouting excels in providing trustworthy and fine-grained customized routing services,stimulating active participation in cross-domain routing,inhibiting malicious operator behavior,and maintaining reasonable resource allocation,all of which outperform baseline schemes.展开更多
1 The Inuit's remarkable ability to thrive(蓬勃发展)in one of Earth's toughest environments hasn't happened by chance.Their complex system of traditions,passed down through countless generations,represents...1 The Inuit's remarkable ability to thrive(蓬勃发展)in one of Earth's toughest environments hasn't happened by chance.Their complex system of traditions,passed down through countless generations,represents far more than mere survival strategies-it's a highly developed blueprint for living in harmony with the Arctic's unforgiving landscape.展开更多
Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptab...Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.展开更多
文摘This study examines the effects of e-banking service quality on customer satisfaction in the Commercial Bank of Ethiopia(CBE)branches in Wolaita Sodo town.Using a causal research design,the study explored the cause-and-effect relationship between service quality dimensions and customer satisfaction.A sample of 385 customers was selected using convenience sampling,with 365 questionnaires returned.Data were collected through questionnaires and analyzed using SPSS V.21.The Cronbach’s alpha value of 0.72 from a pilot study confirmed reliability.Descriptive and inferential statistics,including multiple linear regression and one-way ANOVA,were employed.Results revealed that three service quality dimensions-responsiveness,reliability,and assurance-were statistically significant and positively influenced customer satisfaction,while two dimensions showed negative associations.The regression model’s coefficient of determination(R²)was 0.621,indicating a moderate explanatory power.Findings suggest that CBE managers and stakeholders should prioritize improving responsiveness,reliability,and assurance by providing prompt,dependable,and trustworthy services.Due to limitations in time and resources,this study was confined to CBE branches in Wolaita Sodo town;future research could expand to a national level or other service sectors.
文摘This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn in the face of digital disruption.The research uses a quantitative approach focusing on empirical customer data to accurately predict buying trends and adapt marketing techniques.Demand forecasts in the health sector are important,as in every sector.In particular,the material forecast and stock forecasting of the purchasing unit of hospitals are among the areas that receive significant attention.Four classifiers(Random Forest,Logistic Regression,Gradient Boosting and XGBoost)are trained and evaluated using various performance indicators as part of a systematic approach involving Kaggle data collection,preparation and model selection.The results show excellent accuracy in predicting customer attrition,but there are limitations in precision and recall,indicating room for improvement.Confusion matrices provide information about the performance of each classifier,allowing for continuous improvement of predictive analytics techniques.Ethical concerns are rigorously addressed throughout the work process to guarantee appropriate data and machine learning methodologies.The proposals emphasize the proactive use of predictive analytics to identify at-risk customers and implement targeted retention strategies.Incorporating new data sources,improving customer experience,and utilizing collaborative churn management methods are recommended to increase forecast accuracy and business outcomes.Finally,this research provides important insights into the usefulness of predictive analytics for customer churn forecasting as well as practical recommendations for businesses seeking to increase customer retention and reduce churn risk.By leveraging empirical research findings and implementing ethical and rigorous churn control strategies,businesses can achieve long-term success in today’s changing market environment.
文摘The jewelry industry faces intense competition,making customer loyalty essential for sustained success.This paper examines customer loyalty through the lens of the ABC attitude model,which encompasses cognitive,affective,and behavioral dimensions.Cognitive factors,such as perceived quality and brand reputation,establish the foundation of trust,while affective factors,including emotional attachment and trust,strengthen customer relationships.Behavioral factors,such as repeat purchases and advocacy,reflect observable loyalty actions.The study proposes strategies to enhance loyalty,including delivering superior products and services,strengthening customer relationship management,and leveraging word-of-mouth and digital marketing.These approaches provide actionable insights for building long-term customer relationships in a competitive market.Future research could explore emerging technologies and cultural influences to further refine loyalty strategies.This research highlights the multidimensional nature of customer loyalty and offers practical recommendations for jewelry enterprises.
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
文摘The purpose of this research is to examine the impact of artificial intelligence(AI)on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach,specifically the partial least squares method,to test the hypotheses and explore the relationships between various variables.The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance.Additionally,the results of this study provide valuable insights for both academic and practical communities.This study highlights the importance of specific variables,such as organizational and customer agility,customer experience,customer relationship quality,and customer performance in AI assimilation.By exploring these variables,it contributes significantly to the academic,managerial,and social aspects of AI and its impact on customer performance.
基金the National Social Science Foundation of China(NSSFC)“Study on the Digital Transition of China’s Retail Business”(Grant No.18BJY176).
文摘In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.
文摘Even though people approach a new technological development with distance, they have to use these technologies in order to keep up with the requirements of the age. As a result of the rapidly developing artificial intelligence and technology sector, the concept of metaverse is a virtual world that can be used in the health, tourism, marketing, advertising, education and gaming industries. When the literature is reviewed, it is seen that there are many domestic and foreign sources on the subject, but most of these sources are generally related to developments in the gaming and entertainment sectors. But apart from this, studies are being carried out to make it possible to use it in many sectors, including its use in the health sector. With this study, it is aimed to briefly mention what the existing metaverse studies are and to prepare a detailed research result for its use in the health sector. This study consists of five chapters. In the first part, information on the definition of the metaverse concept and how it has developed is given, and the infrastructure requirements necessary for the formation of the metaverse universe are mentioned. In the second section, it is discussed what kind of hardware and software tools may be needed if metaverse technology is possible. In the third section, research on the areas in which metaverse technology can be used in the health sector and current studies and future studies are included. In the fourth section, the legal aspects of this situation when a metaverse-based health service is to be initiated with twin avatars are discussed. In the last section, the advantages and disadvantages of metaverse technology are discussed. It can be seen from the study that the positive aspects of this kind of activity in the field of health will be more positive. As a result, the study constitutes an important value that will be a source for similar studies to be conducted in the future.
文摘The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends on a deep understanding of customer behavior. This study proposes a theoretical framework for multi-dimensional customer behavior analysis, aiming to comprehensively capture customer behavioral characteristics in the digital environment. This framework integrates concepts of multi-source data including transaction history, browsing trajectories, social media interactions, and location information, constructing a theoretically more comprehensive customer profile. The research discusses the potential applications of this theoretical framework in precision marketing scenarios such as personalized recommendations, cross-selling, and customer churn prevention. Through analysis, the study points out that multi-dimensional analysis may significantly improve the targeting and theoretical conversion rates of marketing activities. However, the research also explores theoretical challenges that may be faced in the application process, such as data privacy and information overload, and proposes corresponding conceptual coping strategies. This study provides a new theoretical perspective on how businesses can optimize marketing decisions using big data thinking while respecting customer privacy, laying a foundation for future empirical research.
文摘Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.
文摘To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.
文摘Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and,after that,end their connection with the bank.Therefore,customer retention is essential in today’s extremely competitive banking market.Additionally,having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele.These factors make reducing client attrition a crucial step that banks must pursue.In our research,we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers.We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics.In addition,we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis.Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.
文摘To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers to represent the importance of each demand.Then,the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator,yielding a dynamic vector of the subjective importance of the demand index.Finally,the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.
文摘The Italian textile machinery sector,renowned for its technological excellence and innovative capacity,continues to navigate a complex global market with a strategic emphasis on digitalization,sustainability,and strong customer partnerships.Marco Salvade’,President of ACIMIT,provided insights into the industry’s performance,key trends,and future directions.In the first quarter of 2025,Italian textile machinery exports saw a 6%decrease compared to the same period in 2024,totaling€363 million.This dip reflects ongoing geopolitical tensions and a cautious approach among global clients toward new investments.Despite these challenges,Italian manufacturers maintain a strong reputation for technological leadership and resilience.
文摘The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.
文摘Starting from the issues related to the construction and management of customs supervision places for railway and water transportation,this paper systematically analyzes the problems and challenges in the facility layout of customs operation places,the construction of a public platform for customs and port logistics monitoring,the utilization of information and data,and the intensity of technological innovation.Based on in-depth research,this paper attempts to propose improvement strategies and suggestions in terms of scientifically planning the layout of customs supervision places for railway and water transportation,improving the operation of the logistics monitoring public platform,developing and utilizing the information and data system,building a smart customs,and innovating the supervision mode.This aims to further optimize the customs supervision process,improve supervision efficiency and accuracy,and provide a reference for the facilitation and safe development of international trade.
基金supported in part by the National Natural Science Foundation of China under Grant U23A20300 and 62072351in part by the Key Research Project of Shaanxi Natural Science Foundation under Grant 2023-JC-ZD-35+1 种基金in part by the Concept Verification Funding of Hangzhou Institute of Technology of Xidian University under Grant GNYZ2024XX007in part by the 111 Project under Grant B16037.
文摘Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current solutions face numerous challenges in continuously ensuring trustworthy routing,fulfilling diverse requirements,achieving reasonable resource allocation,and safeguarding against malicious behaviors of network operators.We propose CrowdRouting,a novel cross-domain routing scheme based on crowdsourcing,dedicated to establishing sustained trust in cross-domain routing,comprehensively considering and fulfilling various customized routing requirements,while ensuring reasonable resource allocation and effectively curbing malicious behavior of network operators.Concretely,CrowdRouting employs blockchain technology to verify the trustworthiness of border routers in different network domains,thereby establishing sustainable and trustworthy crossdomain routing based on sustained trust in these routers.In addition,CrowdRouting ingeniously integrates a crowdsourcing mechanism into the auction for routing,achieving fair and impartial allocation of routing rights by flexibly embedding various customized routing requirements into each auction phase.Moreover,CrowdRouting leverages incentive mechanisms and routing settlement to encourage network domains to actively participate in cross-domain routing,thereby promoting optimal resource allocation and efficient utilization.Furthermore,CrowdRouting introduces a supervisory agency(e.g.,undercover agent)to effectively suppress the malicious behavior of network operators through the game and interaction between the agent and the network operators.Through comprehensive experimental evaluations and comparisons with existing works,we demonstrate that CrowdRouting excels in providing trustworthy and fine-grained customized routing services,stimulating active participation in cross-domain routing,inhibiting malicious operator behavior,and maintaining reasonable resource allocation,all of which outperform baseline schemes.
文摘1 The Inuit's remarkable ability to thrive(蓬勃发展)in one of Earth's toughest environments hasn't happened by chance.Their complex system of traditions,passed down through countless generations,represents far more than mere survival strategies-it's a highly developed blueprint for living in harmony with the Arctic's unforgiving landscape.
文摘Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.
