The surge in mobile shopping faces a challenge as not all potential consumers are comfortable with this mode. Retailers need a deeper understanding of factors influencing user experience to enhance marketing strategies. Despite extensive research, a gap remains in comprehending this aspect. Using a statistical PLS-SEM-ANN approach, this research aims to explore the psychological dimensions of expert and non-expert mobile shoppers for establishing better targeted marketing strategies in m-commerce settings. Analyzing experience levels in mobile commerce (m-commerce), key drivers like enjoyment, usefulness, subjective norms, and trust were scrutinized as interaction settings for consumers using mobile technologies. The findings reveal that, for less experienced m-shoppers, trust is the most significant driver of attitude and satisfaction, while, for experienced users, trust and usefulness are the primary antecedents. This research provides novel insights, aiding mobile marketers in refining targeting strategies based on consumer experience levels, emphasizing the importance of usefulness and trustworthiness for a seamless m-shopping experience.
{"title":"Does Experience Matter? Unraveling the Drivers of Expert and Non-Expert Mobile Consumers","authors":"Simona Vinerean, Dan-Cristian Dabija, Gandolfo Dominici","doi":"10.3390/jtaer19020050","DOIUrl":"https://doi.org/10.3390/jtaer19020050","url":null,"abstract":"The surge in mobile shopping faces a challenge as not all potential consumers are comfortable with this mode. Retailers need a deeper understanding of factors influencing user experience to enhance marketing strategies. Despite extensive research, a gap remains in comprehending this aspect. Using a statistical PLS-SEM-ANN approach, this research aims to explore the psychological dimensions of expert and non-expert mobile shoppers for establishing better targeted marketing strategies in m-commerce settings. Analyzing experience levels in mobile commerce (m-commerce), key drivers like enjoyment, usefulness, subjective norms, and trust were scrutinized as interaction settings for consumers using mobile technologies. The findings reveal that, for less experienced m-shoppers, trust is the most significant driver of attitude and satisfaction, while, for experienced users, trust and usefulness are the primary antecedents. This research provides novel insights, aiding mobile marketers in refining targeting strategies based on consumer experience levels, emphasizing the importance of usefulness and trustworthiness for a seamless m-shopping experience.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140634978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT.
{"title":"Role of Algorithm Awareness in Privacy Decision-Making Process: A Dual Calculus Lens","authors":"Sujun Tian, Bin Zhang, Hongyang He","doi":"10.3390/jtaer19020047","DOIUrl":"https://doi.org/10.3390/jtaer19020047","url":null,"abstract":"In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital products companies are increasingly adopting subscription pricing, but consumers who pay the subscription service may not be able to access all digital products sold on the sales platform. This paper explores the question of why some digital products are not in the subscription service of the sales platform. To address this problem, we develop an analytical model to examine two strategies of the firm. One is that the firm does not add the new product into the subscription service, the other is that the firm adds the new product into the subscription service. By comparing the profits under two sets, we find the condition under which the firm should add the new digital product into the subscription service. The results show that if the percentage of existing subscribers is below a certain threshold, and the subscription price is over a certain threshold, it is better for the firm to add the new product into the subscription service. We also analyze how the main variables affect the firm’s profit and the piracy rate. Our research provides useful insights for firms in choosing pricing schemes for the newly released product and offers advice for policymakers on controlling the piracy rate of digital products industry.
{"title":"Whether to Add a Digital Product into Subscription Service?","authors":"Linlan Zhang, Yu Zhang","doi":"10.3390/jtaer19020048","DOIUrl":"https://doi.org/10.3390/jtaer19020048","url":null,"abstract":"Digital products companies are increasingly adopting subscription pricing, but consumers who pay the subscription service may not be able to access all digital products sold on the sales platform. This paper explores the question of why some digital products are not in the subscription service of the sales platform. To address this problem, we develop an analytical model to examine two strategies of the firm. One is that the firm does not add the new product into the subscription service, the other is that the firm adds the new product into the subscription service. By comparing the profits under two sets, we find the condition under which the firm should add the new digital product into the subscription service. The results show that if the percentage of existing subscribers is below a certain threshold, and the subscription price is over a certain threshold, it is better for the firm to add the new product into the subscription service. We also analyze how the main variables affect the firm’s profit and the piracy rate. Our research provides useful insights for firms in choosing pricing schemes for the newly released product and offers advice for policymakers on controlling the piracy rate of digital products industry.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xueyu Liu, Jie Lin, Xiaoyan Jiang, Tingzhen Chang, Haowen Lin
The growing number of online users commenting on review platforms has fueled the development of electronic word–of–mouth (eWOM). At the same time, merchants have improved their requirements for the length and frequency of online reviews. However, few studies have examined the updating mechanism of online reviews length and frequency from the perspective of businesses. This study explores the relationship between online commenting platform users and eWOM and examines how eWOM information richness affects online user review behavior. We used media richness theory (MRT) to quantify the information richness of eWOM content (linguistic, textual, and photographical) to build an empirical framework. For the research data, we used advanced big data analytics to retrieve and analyze TripAdvisor data on restaurant services in nine major tourist destinations, the United States, Mexico, and mainland Europe (including UK, Spain, Netherlands, etc.), over a long period of time. Based on >10 million eWOM, this study used multiple regression to examine the impact of eWOM information richness on online user review behavior, considering the moderating effect of information ambiguity. Our research results show that content information richness positively affects online user review behavior, increasing their frequency and length. Information ambiguity play a moderating role that strengthens this relationship. This supports our theoretical hypothesis. Finally, for greater applicability and reliability, we conducted a comparative study on the degree of differences in the relationship between eWOM and users based on different cultural backgrounds across countries.
{"title":"eWOM Information Richness and Online User Review Behavior: Evidence from TripAdvisor","authors":"Xueyu Liu, Jie Lin, Xiaoyan Jiang, Tingzhen Chang, Haowen Lin","doi":"10.3390/jtaer19020046","DOIUrl":"https://doi.org/10.3390/jtaer19020046","url":null,"abstract":"The growing number of online users commenting on review platforms has fueled the development of electronic word–of–mouth (eWOM). At the same time, merchants have improved their requirements for the length and frequency of online reviews. However, few studies have examined the updating mechanism of online reviews length and frequency from the perspective of businesses. This study explores the relationship between online commenting platform users and eWOM and examines how eWOM information richness affects online user review behavior. We used media richness theory (MRT) to quantify the information richness of eWOM content (linguistic, textual, and photographical) to build an empirical framework. For the research data, we used advanced big data analytics to retrieve and analyze TripAdvisor data on restaurant services in nine major tourist destinations, the United States, Mexico, and mainland Europe (including UK, Spain, Netherlands, etc.), over a long period of time. Based on >10 million eWOM, this study used multiple regression to examine the impact of eWOM information richness on online user review behavior, considering the moderating effect of information ambiguity. Our research results show that content information richness positively affects online user review behavior, increasing their frequency and length. Information ambiguity play a moderating role that strengthens this relationship. This supports our theoretical hypothesis. Finally, for greater applicability and reliability, we conducted a comparative study on the degree of differences in the relationship between eWOM and users based on different cultural backgrounds across countries.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research delves into the factors influencing the adoption of ChatGPT, a sophisticated AI-based chatbot, among Generation Z members in Croatia. Employing an extended UTAUT2 model, the impact of various factors on the behavioral intention to use ChatGPT is explored. The study included 694 Generation Z participants, and data were collected through an online survey featuring self-reporting questions. The analysis utilized statistical software packages for performing both confirmatory and exploratory factor analyses, in addition to hierarchical linear regression. Key findings reveal that performance expectancy, social influence, hedonic motivation, habit, and personal innovativeness significantly influence the behavioral intention to use ChatGPT. However, effort expectancy, facilitating conditions, and price value do not exhibit a significant impact. Notably, the study excludes the use behavior factor due to multicollinearity issues with behavioral intention. While the research does not focus on moderating factors, it reports that the adapted UTAUT2 model explains 65% of the variance in the adoption of ChatGPT by Generation Z users.
本研究探讨了影响克罗地亚 Z 世代成员采用 ChatGPT（一种基于人工智能的复杂聊天机器人）的因素。研究采用扩展的UTAUT2 模型，探讨了各种因素对使用 ChatGPT 的行为意向的影响。该研究包括 694 名 Z 世代参与者，数据通过在线调查收集，其中包括自我报告问题。除了分层线性回归外，分析还利用统计软件包进行了确认性和探索性因素分析。主要研究结果表明，绩效预期、社会影响、享乐动机、习惯和个人创新能力对使用 ChatGPT 的行为意向有显著影响。然而，努力期望、便利条件和价格价值并没有表现出明显的影响。值得注意的是，由于与行为意向存在多重共线性问题，本研究排除了使用行为因素。虽然该研究没有关注调节因素，但它报告说，改编后的 UTAUT2 模型解释了 Z 世代用户采用 ChatGPT 的 65% 的变异。
{"title":"Understanding the Adoption Dynamics of ChatGPT among Generation Z: Insights from a Modified UTAUT2 Model","authors":"Antun Biloš, Bruno Budimir","doi":"10.3390/jtaer19020045","DOIUrl":"https://doi.org/10.3390/jtaer19020045","url":null,"abstract":"This research delves into the factors influencing the adoption of ChatGPT, a sophisticated AI-based chatbot, among Generation Z members in Croatia. Employing an extended UTAUT2 model, the impact of various factors on the behavioral intention to use ChatGPT is explored. The study included 694 Generation Z participants, and data were collected through an online survey featuring self-reporting questions. The analysis utilized statistical software packages for performing both confirmatory and exploratory factor analyses, in addition to hierarchical linear regression. Key findings reveal that performance expectancy, social influence, hedonic motivation, habit, and personal innovativeness significantly influence the behavioral intention to use ChatGPT. However, effort expectancy, facilitating conditions, and price value do not exhibit a significant impact. Notably, the study excludes the use behavior factor due to multicollinearity issues with behavioral intention. While the research does not focus on moderating factors, it reports that the adapted UTAUT2 model explains 65% of the variance in the adoption of ChatGPT by Generation Z users.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenbo Li, Yajie Zhang, Bin Dan, Xumei Zhang, Ronghua Sui
Service-oriented third-party e-commerce platforms have emerged as a new trend in the manufacturing industry. This paper aims to investigate the platforms’ value-added service (VAS) and charging strategies with a dynamic evolution analysis. Considering the change in the user numbers and characteristics of the e-commerce industry, this paper proposes a system dynamics model composed of multi-value chains and a third-party e-commerce platform. The simulation results indicate that the platform should reduce VAS investment and appropriately increase the VAS fee in the early development period. After the number of users stabilizes, the platform should appropriately increase its VAS investment and reduce the VAS fee. When the VAS fee is low, the platform profit first increases and then decreases as the VAS level increases. Differently, the platform profit will first decrease, then increase, and finally decrease as the VAS level improves when the VAS fee is low. This paper further finds that the strong cross-network effect of manufacturers is not always beneficial to the platform.
{"title":"Simulation Modeling and Analysis on the Value-Added Service of the Third-Party E-Commerce Platform Supporting Multi-Value Chain Collaboration","authors":"Wenbo Li, Yajie Zhang, Bin Dan, Xumei Zhang, Ronghua Sui","doi":"10.3390/jtaer19020044","DOIUrl":"https://doi.org/10.3390/jtaer19020044","url":null,"abstract":"Service-oriented third-party e-commerce platforms have emerged as a new trend in the manufacturing industry. This paper aims to investigate the platforms’ value-added service (VAS) and charging strategies with a dynamic evolution analysis. Considering the change in the user numbers and characteristics of the e-commerce industry, this paper proposes a system dynamics model composed of multi-value chains and a third-party e-commerce platform. The simulation results indicate that the platform should reduce VAS investment and appropriately increase the VAS fee in the early development period. After the number of users stabilizes, the platform should appropriately increase its VAS investment and reduce the VAS fee. When the VAS fee is low, the platform profit first increases and then decreases as the VAS level increases. Differently, the platform profit will first decrease, then increase, and finally decrease as the VAS level improves when the VAS fee is low. This paper further finds that the strong cross-network effect of manufacturers is not always beneficial to the platform.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the direct and indirect influences of behavioral quality, social support, perceived system, emotional perception, and public expectation on user favorability regarding government chatbots in both government service and policy consultation contexts. The findings reveal that while behavioral quality, social support, and perceived system directly affect user favorability in both scenarios, public expectation uniquely impacts user favorability in policy consultation settings, but not in government service scenarios. Furthermore, the analysis indicates that social support, emotional perception, and public expectation all indirectly influence user favorability through their mediating effect on behavioral quality in both contexts. Notably, the significant distinction between the two scenarios is the presence of an indirect impact of perceived system on user favorability within policy consultation scenarios, which is absent in government service scenarios. This study sheds light on the intricate interplay of factors shaping user favorability with government chatbots, and provides valuable insights for improving user experiences and user favorability in different governmental service contexts.
{"title":"Factors Influencing User Favorability of Government Chatbots on Digital Government Interaction Platforms across Different Scenarios","authors":"Yuanyuan Guo, Peng Dong","doi":"10.3390/jtaer19020043","DOIUrl":"https://doi.org/10.3390/jtaer19020043","url":null,"abstract":"This study investigates the direct and indirect influences of behavioral quality, social support, perceived system, emotional perception, and public expectation on user favorability regarding government chatbots in both government service and policy consultation contexts. The findings reveal that while behavioral quality, social support, and perceived system directly affect user favorability in both scenarios, public expectation uniquely impacts user favorability in policy consultation settings, but not in government service scenarios. Furthermore, the analysis indicates that social support, emotional perception, and public expectation all indirectly influence user favorability through their mediating effect on behavioral quality in both contexts. Notably, the significant distinction between the two scenarios is the presence of an indirect impact of perceived system on user favorability within policy consultation scenarios, which is absent in government service scenarios. This study sheds light on the intricate interplay of factors shaping user favorability with government chatbots, and provides valuable insights for improving user experiences and user favorability in different governmental service contexts.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Iulia Diana Nagy, Dan-Cristian Dabija, Romana Emilia Cramarenco, Monica Ioana Burcă-Voicu
This article aims to highlight the influencing factors on omni-channel consumer attitudes towards virtual shopping channels, providing the literature with a new conceptual model that studies the use of technology by omni-channel consumers. The research hypotheses were established based on the literature review, and a conceptual model was defined. Quantitative research was carried out on an emerging market through the survey technique to verify the relations between the investigated concepts. In total, 307 responses from Millennials and Generation Z members were analyzed using structural equations modeling in SmartPLS. The results show that both channel and consumer characteristics, alongside their media contexts, influence the attitude and willingness to access and use retail channels. To keep up with constantly changing consumer needs, companies are advised to continually analyze the target market and implement any necessary measures. The paper expands the studies investigating the behavior of technology users, enhancing the UTAUT2 model-based literature.
本文旨在强调全渠道消费者对虚拟购物渠道态度的影响因素，为研究全渠道消费者使用技术的文献提供一个新的概念模型。在文献综述的基础上提出了研究假设，并定义了概念模型。通过调查技术对新兴市场进行了定量研究，以验证所研究概念之间的关系。利用 SmartPLS 中的结构方程模型分析了千禧一代和 Z 世代成员的 307 份回复。结果表明，渠道和消费者的特征以及他们的媒体背景都会影响他们获取和使用零售渠道的态度和意愿。为了跟上不断变化的消费者需求，建议企业持续分析目标市场并采取必要的措施。本文扩展了对技术用户行为的研究，加强了基于UTAUT2 模型的文献。
{"title":"The Use of Digital Channels in Omni-Channel Retail—An Empirical Study","authors":"Iulia Diana Nagy, Dan-Cristian Dabija, Romana Emilia Cramarenco, Monica Ioana Burcă-Voicu","doi":"10.3390/jtaer19020042","DOIUrl":"https://doi.org/10.3390/jtaer19020042","url":null,"abstract":"This article aims to highlight the influencing factors on omni-channel consumer attitudes towards virtual shopping channels, providing the literature with a new conceptual model that studies the use of technology by omni-channel consumers. The research hypotheses were established based on the literature review, and a conceptual model was defined. Quantitative research was carried out on an emerging market through the survey technique to verify the relations between the investigated concepts. In total, 307 responses from Millennials and Generation Z members were analyzed using structural equations modeling in SmartPLS. The results show that both channel and consumer characteristics, alongside their media contexts, influence the attitude and willingness to access and use retail channels. To keep up with constantly changing consumer needs, companies are advised to continually analyze the target market and implement any necessary measures. The paper expands the studies investigating the behavior of technology users, enhancing the UTAUT2 model-based literature.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.
{"title":"Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode","authors":"Fuqiang Lu, Runxue Jiang, Hualing Bi, Zhiyuan Gao","doi":"10.3390/jtaer19020041","DOIUrl":"https://doi.org/10.3390/jtaer19020041","url":null,"abstract":"Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction.
{"title":"Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility","authors":"Dian Puteri Ramadhani, Andry Alamsyah, Mochamad Yudha Febrianta, Lusiana Zulfa Amelia Damayanti","doi":"10.3390/jtaer19020040","DOIUrl":"https://doi.org/10.3390/jtaer19020040","url":null,"abstract":"The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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